Land suitability assessment for urban growth analysis using geospatial techniques and integrated ensemble models

Land suitability assessment for urban growth analysis using geospatial techniques and integrated ensemble models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Land suitability assessment for urban growth analysis using geospatial techniques and integrated ensemble models Somnath Rudra, Pritiranjan Das, SURAJ KUMAR MALLICK, Biswajit Maity, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2389227/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2025 Read the published version in Environment, Development and Sustainability → Version 1 posted You are reading this latest preprint version Abstract In order to achieve the socio-environmental sustainability, it is important to identify the suitable zone for urban built-up area development in highly populous countries in the global south. Therefore, this study has been conducted in a medium class city in eastern India based on some significant influential factors and their sub-criteria. Different geophysical data, official data, and open street data were used to assess the land suitability for future urban growth by integrating MCDM techniques and different geo-statistical models namely Analytical Hierarchy Process, Frequency Ratio, Weights of Evidence, and Evidential Believe Function. Based on the land suitability assessment (LSA) outcomes, four different zones were identified such as very high suitability zone, high suitability zone, moderately suitable zone and low suitable zone. The findings of this study revealed that the central part of Midnapore municipality has been observed low suitability while > 50% area is under moderate suitability zone. Moreover, the peripheral regions, especially in western, northern, and eastern parts are observed to be high to very high suitable zones for urban expansion. The outcome of the selected models has been validated (> 80%) by the receiver operating characteristics (ROC) curve. Therefore, proper planning is required for western and eastern surroundings, as these areas are predominately forest cover and agricultural land respectively. Hopefully, the identification of proper site suitability will helpful for urban planner to reshaping the urban landscape as sustainable urban future. Urban built-up land Frequency ratio Weights of evidence Evidential believe functions Land suitability assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Urbanisation is the process of transforming natural landscape to cultural landscape in terms of dense and high-rise built-up area, resemblance with increasing population and economic importance. According to census of India, urban population has been increased about 17 times during the period of 1901–2021. Since 1980, the urban population is booming in rapid pace due to industrialised urban economy, in turn, rural exodus (Kundu, 2011 ). Consequently, Indian cities has been facing haphazard, unplanned, and unsystematic urban expansion related problems (Kumar et al., 2018 ; Maity et al., 2020 ). Hence, it is necessary to examine the future urban built-up area growth potential surface for sustainable urban planning (Shikary and Rudra, 2022 ). Land suitability assessment (LSA) for urban built-up area development is reflected as a powerful and effective method for finding suitable locations for built-up area development. This method was developed by combining several categories of measures and weights (Alexander et al., 2012 ). The LSA of urban body indicates the best sites for urban growth using the suitability assessment method over any particular area (Joerin et al., 2001). GIS performance is one of the essential tools for governing and controlling the land suitability of any urban areas (Rosli, 2014 ). By considering environmental sustainability, LSA using GIS techniques has the potential to find suitable urban built-up area development sites (Park, 2018; Nath et al., 2021 ). The rate of urban population in Midnapore town between 2001 and 2011 was 12.92 percent that seemed to resemble the growth rate of West Bengal and India (Census 2011). This surge of urbanization and high population increase has created a variety of issues. In the increasingly urbanizing world, the Midnapore town has similarly seen quick urban growth which unavoidably has implications on the environment and society. The patterns of urban growth have not been exclusively homogeneous at spatiotemporal extent. The growth of urban structures did not follow a proper plan. The distribution of urban facilities is not equal, clustered into city centre which does not ensure the fulfilment of need of the all residents, and sustainable development. Site suitability analysis using multicriteria decision making approach (MCDM) helps in identifying suitable sites. This analytical approach reduced time and effort otherwise manually search the record, processing data and adequate field survey require was very difficult. Searching for new urban development site is being challenging particularly in urban central area and also the outside area with stringent environmental standards or regulations. The result of the suitability analysis produces a spatiotemporal variation of detailed display. Kumar and Shaikh ( 2013 ) used integrated suitability index, analytic hierarchy process (AHP), and MCE methods for LSA in Mussoorie municipal area, India. However, the AHP, established by Saaty is an effective and well-known method for creating an adequacy map (Saaty, 1980 ). The pairwise comparison method was introduced by Fechner in 1860 and developed by Thurstone in 1927. However, weighted overlay analysis (WOA), the multi-influence factor (MIF) method (Arivazhagan et al., 2016), land suitability (Marull et al., 2007 ), and multi-criteria evaluation (MCE) (Eastman et al., 1993 ; Collins et al., 2001) are the most useful methods for LSA. Additionally, we used frequency ratio (FR), Bayesian weights of evidence (BWOE), and evidential belief function (EBF) models. BWOE is an important predictive model and it is directly related to the FR model. FR is the standardized method for determining the weights according to the relater significant pairwise comparison of various factors (Lee and Pradhan, 2007 ; Yilmaz, 2009 ; Pradhan and Lee, 2010 ; Yalcin et al., 2011 ). It is important because this method uses training data for weight calculation. So, there is no chance for bias. In contrast to FR and BWOE, EBF provides adequate evidence to clarify the plausibility and uncertainty functions, as well as the belief and disbelief functions. Nevertheless, FR and BWOE have the demerit of setting up an interrelationship between conditioning factors and sample site occurrences. So, sample sites should have an issue of ground validation. This is how we can check the error of setting an interrelationship between the condition factors and built sample points. Moreover, the suitable area for future built-up area development depends on various geophysical and anthropogenic factors. The models evolved in this study are highly influenced by those factors. Hence, FR, BWOE, and EBF models have been used in this study. In order to create a final built-up area suitability map based on factors that drive urban expansion and the importance of each criterion have evaluated by experts. This research will combine GIS techniques with the AHP, FR, BWOE, and EBF models. The FR, BWOE, and EBF models have been generally employed in landslide susceptibility analysis (Park 2011 ; Althuwaynee et al. 2012 ; Mohammady et al. 2012 ; Pourghasemi et al. 2013 ), groundwater recharge potentiality analysis (Maity et al., 2022 ) etc., whereas for built-up suitability mapping, which is still relatively new. Furthermore, the current study is the going to discover the data-driven MCDM techniques and geo-statistical ensemble methods to generate built-up growth suitability in the Midnapore municipality area. 2. Database And Methodology 2.1 Study area: Midnapore is one of the oldest towns of West Bengal which was established in the year 1865. It is situated within the Midnapore Kharagpur development authority’s belt with an average elevation of 47 metres above msl. Midnapore town is the administrative headquarters of the Paschim Medinipur district in West Bengal. It is made up of 25 wards and has an area of 18.65 km 2 . It stretches from 87°17' 17.21" to 87°20' 52.43" East latitude and from 22°23' 44.91" to 22°26′ 38.61" North latitude (Fig. 1 ). The town was chosen because of its fast urbanisation during the previous three decades. Midnapore is also a historical urban hub in eastern India. It is located on the Kangsabati River's bank. Midnapore is situated in the Paschim Medinipur district's central region. The town has a hot tropical monsoon climate. Soils around the Kangsabati River are alluvial with varying degrees of clay or sand, whilst soils closer to Rangamati are generally lateritic. The environment is similar to that of South Bengal, with the large eucalyptus and sal forests on the northwest side of town which are part of the Dalma Bengal-Jharkhand Range. Arabari, the forest range where India's first joint forest management plan was developed, is about 30 kilometres away. Apart from Kharagpur, it is the only significant town in the Paschim Medinipur districts. According to the Census of India, the population was 169,127, with a decadal growth rate of 12.92 percent from 2001 to 2011. Midnapore is well-connected to nearby towns by highways and railway lines, and it is also urbanised with adequate infrastructural amenities (District Human Development Report 2011). As the town is the district headquarter, many administrative offices, educational institutions, and other amenities have attracted a large number of people. Furthermore, the population of this town is continually growing. As a result, the town is always expanding in a new direction. The built-up area is expanding outside the authority limit, and curiously, it is spreading in certain directions with many informal settlements and other structures. 2.2 Data collection and processing The present study is the result of amalgamation of GIS application and extensive field work by collecting multiple thematic layers using satellite images, open street maps, and some secondary data to create a site suitability map. Landsat 8 imagery is used to create land use and land cover maps. The Open Street Map is used to obtain maps of roads and railways. A GPS survey to determine the location of various service facilities. The 2021 land use map has been used to extract built-up areas and generate training and testing point data (Fig. 1 ). All the thematic layers have been generated using ArcGIS 10.4 software. 2.3 GIS based Analytical hierarchy process For the analysis of built-up suitability map six factors have been considered depending of their importance in Midnapore town namely Distance from the road (A), Distance from the urban centre (B), Distance from the railway station (C), Land Price (D), Distance from the service centre (E) and Land use/ land cover (F). In multicriteria decision making (MCDM) approach, Analytical Hierarchy Process (AHP) model is calculated by assessing the weighted value allocated for each factor. The AHP is the methodical procedure of situating public infrastructure (Banai-Kashani, 1989 ). This method also involves pair wise comparisons of different decision variables. In construction of pairwise matrix, each factor is given a value between 1 to 9. Experts produced pairwise comparison matrices under the condition that judgements are assessed to determine appropriate alternatives to estimate related absolute numbers ranging from 1 to 9, the fundamental scales of the AHP (Saaty, 2007 ), as shown in Table 1 . Using AHP in the appropriate site selection of a built-up region, four different scenarios were generated. All the methods evolved in this study are briefly introduced in methodical flow diagram (Fig. 3 ) Table 1 Fundamental Saaty’s scale for pair wise comparison matrix (Saaty, 1980 ) Numerical values Verbal term Explanation 1 Equally important Two elements have equal important 3 Moderately more important Experience or judgement slightly favours one element 5 Strongly more important Experience or judgement strongly favours one element 7 Very strongly more important Dominance of one element proved in practice 9 Extremely more important The highest order dominance of one element over another 2,4,6,8 Important intermediate values Compromise is needed Then the pair-wise comparison matrix (Table 3 ) has been normalized. The weight can be obtained depending on the relative importance of the various factors from the pair wish comparison matrix (Table 2 ). Table 2 Pairwise comparison matrix for AHP method Factors A B C D E F Distance from the road (A) 1.00 5.00 7.00 6.00 5.00 0.17 Distance from the urban centre (B) 0.20 1.00 6.00 7.00 8.00 0.33 Distance from the railway station (C) 0.14 0.17 1.00 7.00 9.00 0.20 Land Price (D) 0.17 0.14 0.14 1.00 7.00 0.20 Distance from the service centre (E) 0.20 0.11 0.11 0.14 1.00 9.00 LULC (F) 6.00 3.00 0.11 5.00 0.11 1.00 The consistency ratio (CR) is a consistency statistic that is used to show the probability judgement of the matrix (Saaty, 1980 ). $$CR=\frac{CI}{RI} \left(1\right)$$ Where the random index (RI) gives the average of the resulting consistency index (CI) depending on the order of the matrix given by Saaty ( 1980 ), and CI is expressed as $$CI=({{\lambda }}_{\text{max}}- n)/\left(n-1\right) \left(2\right)$$ Where, \({{\lambda }}_{\text{m}\text{a}\text{x}}\) is the largest or principal Eigen value of the matrix and n is the order of the matrix. Saaty also considered that CR \(\le\) 0.01 is a reasonable level of consistency. Table 3 Normalized pairwise comparison matrix Factors A B C D E F Weight λ Distance from the road (A) 0.13 0.53 0.36 0.23 0.17 0.02 0.24 5.98 Distance from the urban centre (B) 0.03 0.11 0.31 0.27 0.27 0.03 0.17 5.93 Distance from the railway station (C) 0.02 0.02 0.05 0.27 0.30 0.02 0.11 6.12 Land Price (D) 0.02 0.02 0.01 0.04 0.23 0.02 0.06 6.66 Distance from the service centres (E) 0.03 0.01 0.01 0.01 0.03 0.83 0.15 6.44 LULC (F) 0.78 0.32 0.01 0.19 0.00 0.05 0.27 6.15 \({\lambda }_{max}\) = 6.21; CI = 0.04; CR = 0.0343 Built-up suitability index (BSI) is calculated by adding each factor multiplied by a weight (Table 4 ), as shown in Eq. 3. $$\text{B}\text{S}\text{I} =\sum AHP \left(\text{w}\text{e}\text{i}\text{g}\text{h}\text{t} :\text{e}\text{a}\text{c}\text{h} \text{f}\text{a}\text{c}\text{t}\text{o}\text{r}\right) \left(3\right)$$ Table 4 Influencing factors and subclasses and their perspective weight driven through Analytical hierarchy process analysis Factors Class Potentiality for Built-up Weights Rank Overall weight Distance from the road 0-100 Very High 24 7 168 100–300 High 5 120 300–600 Moderate 3 72 600–1500 Low 1 24 Distance from the urban centre 0-1500 Very High 17 7 119 1500–2600 High 5 85 2600–3600 Moderate 3 51 3600–5100 Low 2 34 Distance from the railway station 3600–5400 Very High 11 7 77 2200–3600 High 5 55 1100–2200 Moderate 3 33 0-1100 Low 1 11 Land Price 7.5–10 Very High 6 5 30 5.5–7.5 High 7 42 3.5–7.5 Moderate 7 42 2-3.5 Low 3 18 Distance from the service centres 0-1200 Very High 15 7 105 1200–2100 High 5 75 2100–3200 Moderate 3 45 3200–4600 Low 1 15 LULC Water Body Very Low 27 0 0 Vegetation Cover Low 1 27 Agricultural Land Moderate 3 81 Open Land High 6 162 Built-up Very High 1 27 2.4 Frequency Ratio model Frequency ratio (FR) model, a bivariate statistical method was used to analyse the spatial effect of the suitable location of built-up point in each class of classified urban built-up suitable influencing factors (Maity et al., 2022 ; Mallick et al., 2023b ). In this process, real built-up point data are used for simulating and validating to reflect real built-up expansion behaviours and their dynamics in study area. The FR of built-up suitability is the ratio of the probability of built-up occurrence to the probability of a non-occurrence of given attributes. To produce built-up suitability map, the FR models was applied using the GIS technique, which spatially represent the information. FR was computed for all classes of each conditioning factor. The FR distribution maps were then summed up to derive the built-up suitability using Eqs. 4 and 5. FR values of more than 1 indicates a high relationship with built-up suitability, whereas values of less than 1 mean a low relationship with built-up suitability. The value of 1 reflects an average condition: $${FR}_{ij }=\frac{N(L\cap {C}_{ij})/N\left(L\right)}{N{(C}_{ij})/N(C)} \left(4\right)$$ Where \(N\left(L\right)\) is the total number of built-up pixels \(N\left(C\right)\) is the total number of pixels in the entire study area, \({C}_{ij}\) is the \({j}^{th}\) class attribute of urban built-up suitable conditioning factors, C = \({C}_{i}\) , \(\left(i=\text{1,2},3,\dots \dots ..n\right)\) , \(N{(C}_{ij})\) is the total number of pixels in the class \({C}_{ij}\) and \(N(L\cap {C}_{ij})\) is the quantity of built-up pixels in \({C}_{ij}\) . BSI is calculated by adding each factor multiplied by a weight (Table 5 ), as shown in Eq. 5. $$\text{B}\text{S}\text{I}=\sum FR \left(\text{F}\text{R}:\text{r}\text{a}\text{t}\text{i}\text{n}\text{g} \text{o}\text{f} \text{e}\text{a}\text{c}\text{h} \text{f}\text{a}\text{c}\text{t}\text{o}\text{r}\text{s} \text{t}\text{y}\text{p}\text{e} \text{o}\text{r} \text{r}\text{a}\text{n}\text{g}\text{e}\right) \left(5\right)$$ Table 5 Influencing factors and subclasses and their perspective weight driven through frequency ratio analysis Factors Class Potentiality for Built-up Number of pixels in a class % of pixel in a class Number of pixels of Built-up point % of pixel of Built-up point FR Distance from the road 0-100 Very High 19850 40.45 805 74.61 1.84 100–300 High 12980 26.45 172 15.94 0.6 300–600 Moderate 7004 14.27 45 4.17 0.29 600–1500 Low 9242 18.83 57 5.28 0.28 Distance from the urban centre 0-1500 Very High 8500 17.32 400 37.07 2.14 1500–2600 High 13521 27.55 433 40.13 1.46 2600–3600 Moderate 16015 32.63 195 18.07 0.55 3600–5100 Low 11040 22.5 51 4.73 0.21 Distance from the railway station 0-1100 Very High 14997 30.56 518 48.01 1.57 1100–2200 High 14584 29.72 357 33.09 1.11 2200–3600 Moderate 11737 23.92 149 13.81 0.58 3600–5400 Low 7758 15.81 55 5.1 0.32 Land Price 7.5–10 Very High 6162 9.53 303 28.08 2.95 5.5–7.5 High 15836 24.49 371 34.38 1.4 3.5–7.5 Moderate 24466 37.83 291 26.97 0.71 2-3.5 Low 18206 28.15 114 10.57 0.38 Distance from the service centres 0-1200 Very High 13609 27.73 571 52.92 1.91 1200–2100 High 14755 30.07 304 28.17 0.94 2100–3200 Moderate 13035 26.56 139 12.88 0.49 3200–4600 Low 7677 15.64 65 6.02 0.39 LULC Water Body Very Low 2766 4.28 0 0 0 Vegetation Cover Low 19283 29.82 20 1.85 0.06 Agricultural Land Moderate 26377 40.79 34 3.15 0.08 Open Land High 2755 4.26 46 4.26 1 Built-up Very High 13489 20.86 979 90.73 4.35 2.5 Bayesian Weights of Evidence (BWOE): The FR process is actually the initial step of Bayesian Weights of Evidence (BWOE) and this model is evaluating the non-occurrence of built-up in the similar class of evidence. Then, the natural logarithm of both values (occurrence and non-occurrence) was considered. These values are the weights to support the occurrence and non-occurrence of built-up point with respect to each class of evidence (Maity et al., 2022 ; Mallick et al., 2023a ). The subtraction value of those weights represents the spatial association of each built-up pixel and each class of evidence. The variance of both weights and standard deviation are calculated. Finally, the standardized value representing the significance of the spatial association and measurement the comparative certainty of the posterior probability was calculated. The estimated probability value of each conditioning factors with respect to evidence should be present in a raster-based map format. Hence, the classified factor layers were reclassified with the new value obtains from the BWOE model. Finally, by integrating and summing up all reclassified evidence for built-up suitability map. Based on relative weightage of the conditioning factors, BWOE model is calculated on a study area. BWOE can be expressed as follows: $${W}^{+}=Ln\frac{P\left(\frac{B}{A}\right)}{P\left(\frac{B{\prime }}{A{\prime }}\right)} \left(14\right)$$ $${W}^{-}=Ln\frac{P\left(\frac{B}{A}\right)}{P\left(\frac{B{\prime }}{A{\prime }}\right)} \left(15\right)$$ where, \({W}^{+}\) and \({W}^{-}\) denote the positive and negative weights of the conditioning factors; \(P\) is the probability, \(B\) stands for conditioning factor, \(B’\) stands for absence of conditioning factors, \(A\) stands for built-up data and \(A’\) stands for absence of built-up data. Then, the standard deviation (SD) of the contrast value can be calculated as follows: $${SD}_{c}= \sqrt{\left(\left({S}^{2}\left({W}^{+}\right)+ {S}^{2}\left({W}^{-}\right)\right)\right)} \left(16\right)$$ Where \({S}^{2}\) denotes the influencing weight of the conditioning factors (Mallick et al., 2023a ). The standardised contrast \(\tau\) is considered as measure of confidence that can be calculated as follows: $$\tau =\left(\frac{C}{{SD}_{c}}\right) \left(17\right)$$ After assigning all the conditioning parameters with the built-up data, the value of \({W}^{+}\) , \({W}^{-}\) , \({SD}_{c }\) , and \(\tau\) has been calculated (Table 6 ). Table 6 Influencing factors and subclasses and their perspective weight driven through Bayesian weights of evidence analysis Factors Class W+ W- C S 2 (W +) S 2 (W -) S (C) τ Distance from the road 0-100 -0.612 0.852 1.465 0.013 0.039 0.23 6.375 100–300 0.506 -0.134 -0.64 0.063 0.012 0.273 -2.343 300–600 1.23 -0.111 -1.342 0.24 0.01 0.5 -2.682 600–1500 1.272 -0.154 -1.426 0.189 0.011 0.447 -3.189 Distance from the urban centre 0-1500 -0.761 0.273 1.034 0.027 0.016 0.207 4.994 1500–2600 -0.376 0.191 0.567 0.025 0.017 0.204 2.778 2600–3600 0.591 -0.196 -0.787 0.055 0.012 0.26 -3.027 3600–5100 1.56 -0.206 -1.766 0.211 0.01 0.471 -3.749 Distance from the railway station 0-1100 -0.452 0.289 0.741 0.021 0.019 0.2 3.703 1100–2200 -0.107 0.049 0.157 0.03 0.015 0.213 0.737 2200–3600 0.549 -0.125 -0.674 0.072 0.012 0.29 -2.326 3600–5400 1.131 -0.12 -1.251 0.196 0.011 0.455 -2.753 Land Price 7.5–10 -1.081 0.229 1.31 0.036 0.014 0.223 5.887 5.5–7.5 -0.339 0.14 0.48 0.029 0.015 0.211 2.278 3.5–7.5 0.338 -0.161 -0.499 0.037 0.014 0.225 -2.216 2-3.5 0.98 -0.219 -1.198 0.095 0.011 0.325 -3.685 Distance from the service centres 0-1200 -0.646 0.429 1.075 0.019 0.021 0.2 5.365 1200–2100 0.065 -0.027 -0.092 0.035 0.014 0.222 -0.414 2100–3200 0.724 -0.171 -0.895 0.078 0.011 0.299 -2.997 3200–4600 0.955 -0.108 -1.063 0.166 0.011 0.42 -2.528 LULC Water Body 0 -0.044 -0.044 0 0.01 0.1 -0.437 Vegetation Cover 2.78 -0.335 -3.115 0.541 0.01 0.742 -4.198 Agricultural Land 2.561 -0.492 -3.053 0.317 0.01 0.573 -5.333 Open Land -0.001 0 0.001 0.235 0.01 0.495 0.001 Built-up -1.47 2.144 3.614 0.011 0.108 0.345 10.482 2.6 Evidential Belief Function (Dempster-Shafer) model This theory was first proposed by Dempster ( 1967 ) as a spatial integration model with mathematically developed by Shafer (1976). Dempster-Shafer theory of evidence refers to the generalization of Bayes theorem of probability. This theory combines the belief from several sources of evidence and the relative elasticity to accept uncertainty. The theory predicts how closely the evidence shows the certainty of a hypothesis rather than predicting the possible accuracy of a hypothesis. Dempster-Shafer theory has been applied effectively using GIS in many applications. The theory demonstrated four basic Evidential Belief Function models namely, Bel (degree of belief), Dis (degree of disbelief), Pls (degree of plausibility), and Unc (degree of uncertainty). Dis denotes the belief of false suggestion based on the given evidence. Bel and Pls provide the upper and lower boundary respectively of the probability for the suggestion. Unc means ignorance that is the difference between plausibility and belief (Fig. 2 ). In this study, built-up suitability conditioning factors are considered as independent variable and built-up point data is considered as dependent variable. The Belief and Disbelief weight value of each conditioning factor class can be calculated as follow: $$Bel=\frac{{W}_{i}}{{\varnothing }_{i}}=\frac{\text{p}\text{e}\text{r}\text{c}\text{e}\text{n}\text{t} \text{o}\text{f} \text{b}\text{u}\text{i}\text{l}\text{t}-\text{u}\text{p} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n} }{\text{p}\text{e}\text{r}\text{c}\text{e}\text{n}\text{t} \text{o}\text{f} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n}} \left(6\right)$$ $$Dis =\frac{{W{\prime }}_{i}}{{\varnothing {\prime }}_{i}}=\frac{\text{p}\text{e}\text{r}\text{c}\text{e}\text{n}\text{t} \text{o}\text{f} \text{n}\text{o}\text{n} \text{b}\text{u}\text{i}\text{l}\text{t}-\text{u}\text{p} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n} }{\text{p}\text{e}\text{r}\text{c}\text{e}\text{n}\text{t} \text{o}\text{f} \text{n}\text{o}\text{n} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n}} \left(7\right)$$ $$Unc=1-\left(Bel+Dis\right) \left(8\right)$$ $$Pls=1-Dis \left(9\right)$$ Where \({W}_{i}\) , \({\varnothing }_{i}\) , \({W{\prime }}_{i}\) and \({\varnothing {\prime }}_{i}\) could be calculated form the following equation: $${W}_{i}=\frac{\text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{b}\text{u}\text{i}\text{l}\text{t}-\text{u}\text{p} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n} }{\text{t}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{b}\text{u}\text{i}\text{l}\text{t}-\text{u}\text{p} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n}} \left(10\right)$$ $${\varnothing }_{i}=\frac{\text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n} }{\text{t}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n}} \left(11\right)$$ $${W{\prime }}_{i}=\frac{\text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{n}\text{o}\text{n} \text{b}\text{u}\text{i}\text{l}\text{t}-\text{u}\text{p} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n} }{\text{t}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{b}\text{u}\text{i}\text{l}\text{t}-\text{u}\text{p} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n}} \left(12\right)$$ $${\varnothing {\prime }}_{i}=\frac{\text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{n}\text{o}\text{n} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n} }{\text{t}\text{o}\text{t}\text{a}\text{l} \text{n}\text{u}\text{m}\text{b}\text{e}\text{r} \text{o}\text{f} \text{p}\text{i}\text{x}\text{e}\text{l} \text{i}\text{n} \text{t}\text{h}\text{e} \text{d}\text{o}\text{m}\text{a}\text{i}\text{n}} \left(13\right)$$ Therefore, the influencing factors were sequentially integrated using Eq. 10–13, summed up Eq. 6 to obtain future built-up suitability map of the study area and also used Eq. 7–9 consequently to obtain disbelief, uncertainty and plausibility maps of built-up suitability (Table 7 ). Table 7 Influencing factors and subclasses and their perspective weight driven through EBF analysis Factors Class Potentiality for Built-up Number of pixels in a class Number of pixels of Built-up point Evidence Belief Function (Dempster-Shafer Model) Bel Dis Unc Pls Distance from the road 0-100 Very High 19850 805 0.611 0.111 0.279 0.889 100–300 High 12980 172 0.2 0.297 0.504 0.703 300–600 Moderate 7004 45 0.097 0.29 0.613 0.71 600–1500 Low 9242 57 0.093 0.303 0.604 0.697 Distance from the urban centre 0-1500 Very High 8500 400 0.491 0.189 0.32 0.811 1500–2600 High 13521 433 0.334 0.205 0.461 0.795 2600–3600 Moderate 16015 195 0.127 0.302 0.571 0.698 3600–5100 Low 11040 51 0.048 0.305 0.647 0.695 Distance from the railway 0-1100 Very High 14997 518 0.438 0.189 0.373 0.811 1100–2200 High 14584 357 0.311 0.24 0.449 0.76 2200–3600 Moderate 11737 149 0.161 0.286 0.553 0.714 3600–5400 Low 7758 55 0.09 0.285 0.625 0.715 Land Price 7.5–10 Very High 6162 303 0.542 0.195 0.264 0.805 5.5–7.5 High 15836 371 0.258 0.213 0.529 0.787 3.5–7.5 Moderate 24466 291 0.131 0.288 0.581 0.712 2-3.5 Low 18206 114 0.069 0.305 0.626 0.695 Distance from the service centres 0-1200 Very High 13609 571 0.514 0.164 0.323 0.836 1200–2100 High 14755 304 0.252 0.258 0.49 0.742 2100–3200 Moderate 13035 139 0.131 0.298 0.571 0.702 3200–4600 Low 7677 65 0.104 0.28 0.616 0.72 LULC Water Body Very Low 2766 0 0 0.201 0.799 0.799 Vegetation Cover Low 19283 20 0.011 0.269 0.72 0.731 Agricultural Land Moderate 26377 34 0.014 0.315 0.671 0.685 Open Land High 2755 46 0.182 0.192 0.625 0.808 Built-up Very High 13489 979 0.792 0.023 0.185 0.977 3. Result 3.1 Identification of influencing factors Identifying appropriate factors for urban development is a critical foundational task in urban planning. The six important criteria influencing land suitability of Midnapore towns have been identified such as distance from the road, distance from the urban centre, distance from the railway station, land price, distance from the service centre and land use/ land cover (LULC) (Fig. 4 ). The sum of these criteria defines the degree of appropriateness and also aids in identifying the land's constraints for urban development. The numerous map layers were created for this purpose. Distance from the road Road is also an important criterion in built-up area site suitability because of the need to transport raw materials and finish products (Ding et al., 2017 ; Bui et al., 2012 ). So, effort is made to suitably locate the site nearer to any existing road in order to find out better accessibility and their strength of important for road proximity criterion. Thus, the maximum possibility of built-up area suitability is found close to the road or minimum distance rather farthest distance of the major road. So, the maximum weight is given to the proximal distance and comparatively lesser weight is given to the farthest situation. Distance from the urban centre Most of the cases, urban centre has an important role in business purpose. But based on residential facility all time it has not equal important (Aburas et al. 2015 ) because land price and overcrowding condition are barrier. In this study urban centre of Midnapore municipality has a significance importance on built-up area suitability analysis. So, closest to the urban centre has given maximum weightages and less weightage has given for maximum distance from the urban centre to develop the suitable built-up sites. Distance from the railway station Railway is an important factor for the study of built-up area suitability (Aburas et al. 2017 ; Pradhan et al. 2010 ) and the most important factor is the distance from the railway station. People are generally preferring to settle down at areas short distance from the rail station but not exactly to near to the rail line. In Midnapore town only one railway station is located that is Midnapore. Therefore, maximum importance is given to the moderate distance and comparatively, lesser importance is given to the high and furthest distance. Land value The land price at a specific point of time is based on its location that referred as land value (Aburas et al., 2017 ). Land value depends on the availability of urban services, the accessibility by roads and railways, physical condition of the land etc. So, the land values are higher in the town centre than they are on the periphery. Accessibility has an influence on property value within a community, and land parcels near roads have high prices. The land revenue department of the Paschim Medinipur district determines the cost along the route in Midnapore municipality. The approximate cost of land within 0–500 metre from the major road was Rs. 5000 per m 2 , 500–1000 m was Rs. 3750 per m 2 , and above 1000 m was Rs. 2500 per m 2 at the time of this study, indicating that the cost is reducing as one moves away from the road. Because of the cheap investment cost, low-value land provides the greatest possibility for a suitable location. Land with a lower rating has been allocated a higher level of relevance in this study. Thus, the maximum weight has been given to higher to moderate land values near to the city centre and minimum weight has been considered for very high and low land values of Midnapore municipality area. Distance from the service centre Different service centres are used as an influencing factor for this study; those are educational centres, market facilities, and healthcare facilities. These service facilities play an important role in deciding whether build or not to build a house and have a direct impact on land prices (Aburas et al., 2017 ). As usual, closest to the service centre has given maximum weightages and less weightage has given for maximum distance from the service centre to develop the suitable built-up sites. LULC Land use and land cover assessment is an important measure to execute any built-up area development planning over an extent (Aburas et al., 2015 ; Pradhan and Lee, 2010 ; Tahmassebipoor et al., 2016 ). LULC map of the Midnapore municipal area has been classified as built-up, open land, agricultural land, vegetation cover, and water body. In this study, built up land is not ideal for future development since once a building is established, it remains for minimum of 50–75 years. Open land has most potential for the development of future built-up areas and carried maximum weightage followed by agriculture land and vegetation cover. However, built-up area and water body consider as most unsuitable areas for the further built-up area development. 3.2 Assessment of suitable built-up sites using EBF, AHP, FR, and BWOE models The EBF model is used to produce the built-up area suitable zone and correlate it with the influencing factors. A comparatively high Bel value indicates a higher probability of built-up occurrence, while a low Bel value implies a lower probability of occurrence. In the EBF model, belief map (Fig. 4 A) is compared to the disbelief map (Fig. 4 B) which indicates that the Bel values are low for areas where Dis values are high and vice versa. Analysis of these map demonstrates that high urban built-up probability is for the areas of high degree of Bel and low degree of Dis for the urban built-up point occurrences. The uncertainty map (Fig. 4 C) indicates lack of information to provide a real prove for built-up occurrences. According to EBF analysis, the high uncertainty values are belonging to the areas where belief values are low. Moreover, the plausibility (Fig. 4 D) shows high values for the area where uncertainty value relatively is low. According to this model, most of the areas (49.68%) of Midnapore municipality are under moderately suitable for future built-up area development and 16.64% of area has been observed as very high suitable zone for built-up area development in the western part (Table 8 ). The built-up area suitable zone also identified using on AHP, FR, and BWOE models based on the influencing factors such as distance from the road plays a key influence, as does distance from the urban centre, resulting in a high potential for built-up area adaptability. Therefore, the built-up area growth possibility is high in the nearby residential areas, but the vegetation area and the open land have a low built-up possibility. For the distance to diverse service centres, railways, land values, and highways, it demonstrates that built-up growth mostly occurs along them, and the built-up area's growth possibilities drop fast as the distance rises. All the models are similar kind of results for future built-up area selection (Table 8 ). The AHP is used to compare with six thematic layer to calculate normalised weights. The result shows that the calculated highest weight is attributed to distance from the road near 100 m (168), and the lowest weights is land use classes as water body (0). The built-up area suitability map (Fig. 4 A) has been generated by overlying the thematic maps based on AHP weights. The built-up area suitability map obtained using AHP model specified four suitability zones like, very high, covering 10.70% of the total Midnapore urban area, moderate zone 53.46% and low zone covering 17.97% area (Table 8 ). The built-up area suitability map (Fig. 5 B) obtains using FR model is a specified four built-up area suitability zones like, very high zone, with 16.90%, area, high zone (19.02%), moderate zone (57.80%) and low zone (6.29%). The built-up area suitability zone conducted by EBF model shows that moderate zone (54.63%) is greater in percentage than other zones. For BWOE model, the quantile method was also adopted to classify the urban built-up area suitability map into four classes: low, medium, high and very high. According to the final built-up area suitability map (Fig. 5 D), the very high zone found nearer to the road. The moderate zone covered slightly higher area (54.63%) than the high and very high zone (Table 8 ). Table 8 Spatial distribution of built-up suitability zone in km 2 AHP (km 2 ) % FR (km 2 ) % EBF (km 2 ) % BWOE (km 2 ) % Low 8.92 17.97 3.12 6.29 9.34 18.82 5.21 10.50 Moderate 26.54 53.46 28.69 57.80 24.66 49.68 27.12 54.63 High 8.87 17.87 9.44 19.02 7.38 14.87 8.69 17.51 Very high 5.31 10.70 8.39 16.90 8.26 16.64 8.62 17.37 3.3 Validation The validity of the built-up area suitability map prepared in this study is only achievable when a new built-up area is constructed in future. Thus, a measure of validity and predictive ability should be provided. A general way of validation is by using the built-up area that was not used in the modelling procedure. As these built-up areas are independent of the model, they were treated as the "future" built-up areas in this study. As it was indicated earlier, 70% of the built-up area in the study area was picked randomly for modelling, and the remaining 30 percent was split for validation. The resultant prediction rate curve reveals that the AHP, FR, BWOE, and EBF models have good predictive ability for future build-up areas (Fig. 6 ). To quantify how well the modelling worked, the area under the curve (AUC) was utilized to establish a quantitative comparison between the four models. The model with the greatest AUC is deemed the best. The map produced by the FR model displayed the best result for built-up area suitability mapping in the research region. The findings of the AUC evaluation indicated that the values of the AHP, FR, BWOE, and EBF models were 83.6%, 84.5%, 82.2%, and 81.5%, respectively. 4. Discussion The enormous amount of population increase creates unplanned and unsystematic urban expansion related problems in Indian cities (Shikary and Rudra 2020 ; Mallick et al. 2023b ). Hence, suitable site identification is required for urban expansion in small and medium class town along with the metro cities. In this study, four geo-statistical models have been evolved to finding suitable sites for managing future cityscape of Midnapore municipality. The models envisaged four built-up area development zones i.e., very high suitable zone, highly suitable zone, moderate suitable zone, and low suitable zone. These four models show that the suitable urban expansion sites can be over the western lateritic surface and eastern agricultural land for continuous expansion, while ribbon shaped development will be happened in the northern part of the city. This study has conducted in a medium class city in India. Hence, the open space within the down town provide high weightage to be the high to very high suitable zone for built-up area development, in turn, infill development. Recently, the haphazard and rapid built-up area expansion has been observed in the south-western part nearer to Kangsabati River, Boisakhipalli, north-western part, and eastern part (Dharma) of Midnapore municipality (Fig. 7 ). Specifically, western and south-western sides are the part of forest cover and river bank area, in turn, ecologically sensitive zone. This area cannot be considered as suitable sites for built-up growth. Although, the peripheral urban growth is likely occupied the open surface or forest cover (Mallick, 2021 ). But the problem is that, we can not accept these lands as suitable for the urban growth. Because, growth of built-up in the pasture surface, water body or a forest cover destroyed ecological balance and carrying capacity of the land. Suitable site should be the fallow land or open land where ecological valuation is less. In case of Midnapore, Khairullachak, Abash, Golapichak, Keranichoti, and Iswarpally will be most suitable sites for built-up area development. Peripheral urbanization and built-up growth are very common in the populous countries (Maity et al., 2020 ; Mallick 2022 ), and the study also depicted similar outcomes. All these regions in the Midnapore have got the momentum and expanded in faster rate due to comparatively less land price, quick accessibility with the railway and roadways. 5. Conclusions In this work, AHP, FR, Dempster-Shafer EBF and BWOE models were employed for built-up growth suitability mapping, and these were evaluated by means of their validations. The validation technique was undertaken by overlaying the built-up growth suitability map with the reserved validation set. The generated prediction rate curve was utilized for comparison using the AUC value. The AUC reveals that the success rates of the AHP, FR, EBF and BWOE models were 83.6%, 84.5%, 81.5%, and 82.2% respectively. It can be explicated that all the validated models have greater accuracies and successfully check the errors. According to these geo-spatial models, Midnapore municipality has several opportunities for built-up area development such as infill development within the city and outgrowth in the eastern and north-western part of the city. However, the results are absolutely essential to carry out the future built-up area development planning in and around the Midnapore municipality area. Despite this, each model develops a region-specific suitability mapping that is distinguishable from one suitability map to another. This sort of regional planning work is useful for planners, policy makers, and stakeholders. Additionally, all of these models can help a variety of urban planners and organisations create appropriate plans for citizen participation in decision-making process for the long-term urban development of the Midnapore municipality and its surroundings. Declarations Declaration of conflict of interest The authors declare that there is no conflict of interest regarding the results and data. Acknowledgment The authors are thankful to the DST-FIST sponsored Department of Geography, Vidyasagar University, Midnapore for conducting this research. 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Tahmassebipoor, N., Rahmati, O., Noormohamdi, F., & Lee, S. (2016). Spatial analysis of groundwater potential using weights-of-evidential and evidence belief function models and remote sensing. Arabian Journal of Geoscience , 9 (1), 79. Yalcin, A., Reis, S., Aydinoglu, A. C., & &Yomralioglu, T. (2011). A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena , 85 (3), 274–287. Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Computers & Geosciences , 35 (6), 1125–1138. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2389227","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":161365422,"identity":"357cf4ce-6444-4631-a68e-6ea5dce95667","order_by":0,"name":"Somnath Rudra","email":"","orcid":"","institution":"Vidyasagar University","correspondingAuthor":false,"prefix":"","firstName":"Somnath","middleName":"","lastName":"Rudra","suffix":""},{"id":161365423,"identity":"636ff80a-01b3-462a-b283-6e91f78e678c","order_by":1,"name":"Pritiranjan Das","email":"","orcid":"","institution":"University of Delhi Delhi School of Economics","correspondingAuthor":false,"prefix":"","firstName":"Pritiranjan","middleName":"","lastName":"Das","suffix":""},{"id":161365424,"identity":"01ed63ef-5561-4273-a7ba-7b82d8338e6e","order_by":2,"name":"SURAJ KUMAR MALLICK","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0994-086X","institution":"Vidyasagar University","correspondingAuthor":true,"prefix":"","firstName":"SURAJ","middleName":"KUMAR","lastName":"MALLICK","suffix":""},{"id":161365425,"identity":"fc74f742-be6f-440e-8958-404d55e82d72","order_by":3,"name":"Biswajit Maity","email":"","orcid":"","institution":"Vidyasagar University","correspondingAuthor":false,"prefix":"","firstName":"Biswajit","middleName":"","lastName":"Maity","suffix":""},{"id":161365426,"identity":"c47d7449-5c91-4cb3-a22e-ff5577e25a06","order_by":4,"name":"Netrananda Sahu","email":"","orcid":"","institution":"University of Delhi Delhi School of Economics","correspondingAuthor":false,"prefix":"","firstName":"Netrananda","middleName":"","lastName":"Sahu","suffix":""},{"id":161365427,"identity":"5e99ebf1-e5a6-4776-b480-9dc22a1bff1a","order_by":5,"name":"Ulfat Khan","email":"","orcid":"","institution":"Bankura Sammilani College","correspondingAuthor":false,"prefix":"","firstName":"Ulfat","middleName":"","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2022-12-17 19:48:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2389227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2389227/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10668-024-05928-0","type":"published","date":"2025-01-05T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":30548140,"identity":"fa39c4b5-c997-4730-958b-61222e927d39","added_by":"auto","created_at":"2022-12-20 05:20:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2705251,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area with distribution of training and testing data points\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2389227/v1/7199cd1d84d2369bf465cf4d.png"},{"id":30549041,"identity":"d7ac0bc9-fa86-4fe9-b652-db48e99d2825","added_by":"auto","created_at":"2022-12-20 05:44:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":416028,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated result of EBF model: Belief (A), Disbelief (B), Uncertainty (C) and Plausibility (D)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2389227/v1/ab8158a7c98f698a928a2791.jpeg"},{"id":30548139,"identity":"dde22aad-7853-4537-b5cc-3804b1e09b58","added_by":"auto","created_at":"2022-12-20 05:20:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":609312,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological flow diagram for land suitability assessment for urban growth analysis of Midnapore municipality\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2389227/v1/9e2dbc22fc5366ec1291dec2.png"},{"id":30548817,"identity":"ff07b43a-ac10-47ac-9d8d-120a5b777650","added_by":"auto","created_at":"2022-12-20 05:36:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":327974,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent influencing factors: Distance from the road (A), Land value (B), Distance from the railway (C), Distance from the different services centres (D), Distance from the urban centre (E) and LULC (F)\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2389227/v1/79efd0a444271445c6a61f47.jpeg"},{"id":30548145,"identity":"6d40dc86-50ba-432d-8619-acd729d100d6","added_by":"auto","created_at":"2022-12-20 05:20:52","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":605994,"visible":true,"origin":"","legend":"\u003cp\u003eBuilt-up area suitability maps through AHP (A), FR (B), EBF (C) and BWOE (D)\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2389227/v1/252a0992b7bbdb43097a4aa8.jpeg"},{"id":30548142,"identity":"16ebab42-f17f-4520-833c-8f98effccdfa","added_by":"auto","created_at":"2022-12-20 05:20:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":13567,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the built-up area suitable zones\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2389227/v1/4b4f2f4932040a87a3fe571f.png"},{"id":30548549,"identity":"69924a42-01f2-433a-a417-34732264f675","added_by":"auto","created_at":"2022-12-20 05:28:51","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":491201,"visible":true,"origin":"","legend":"\u003cp\u003ePeripheral urban growth in Midnapore town during 2001-2021. A. Rangamati; B. Abash; C. Dharma; D. Kansaipar.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2389227/v1/5f670db9a4dbed3c2a6b065f.jpeg"},{"id":73094315,"identity":"0d02bac1-80c1-4ae7-9553-33256e596ee2","added_by":"auto","created_at":"2025-01-06 16:23:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6381269,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2389227/v1/0ede8260-7c65-42ea-a834-c1166aaa9fdd.pdf"}],"financialInterests":"","formattedTitle":"Land suitability assessment for urban growth analysis using geospatial techniques and integrated ensemble models","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrbanisation is the process of transforming natural landscape to cultural landscape in terms of dense and high-rise built-up area, resemblance with increasing population and economic importance. According to census of India, urban population has been increased about 17 times during the period of 1901\u0026ndash;2021. Since 1980, the urban population is booming in rapid pace due to industrialised urban economy, in turn, rural exodus (Kundu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consequently, Indian cities has been facing haphazard, unplanned, and unsystematic urban expansion related problems (Kumar et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Maity et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, it is necessary to examine the future urban built-up area growth potential surface for sustainable urban planning (Shikary and Rudra, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Land suitability assessment (LSA) for urban built-up area development is reflected as a powerful and effective method for finding suitable locations for built-up area development. This method was developed by combining several categories of measures and weights (Alexander et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The LSA of urban body indicates the best sites for urban growth using the suitability assessment method over any particular area (Joerin et al., 2001). GIS performance is one of the essential tools for governing and controlling the land suitability of any urban areas (Rosli, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). By considering environmental sustainability, LSA using GIS techniques has the potential to find suitable urban built-up area development sites (Park, 2018; Nath et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rate of urban population in Midnapore town between 2001 and 2011 was 12.92 percent that seemed to resemble the growth rate of West Bengal and India (Census 2011). This surge of urbanization and high population increase has created a variety of issues. In the increasingly urbanizing world, the Midnapore town has similarly seen quick urban growth which unavoidably has implications on the environment and society. The patterns of urban growth have not been exclusively homogeneous at spatiotemporal extent. The growth of urban structures did not follow a proper plan. The distribution of urban facilities is not equal, clustered into city centre which does not ensure the fulfilment of need of the all residents, and sustainable development.\u003c/p\u003e \u003cp\u003eSite suitability analysis using multicriteria decision making approach (MCDM) helps in identifying suitable sites. This analytical approach reduced time and effort otherwise manually search the record, processing data and adequate field survey require was very difficult. Searching for new urban development site is being challenging particularly in urban central area and also the outside area with stringent environmental standards or regulations. The result of the suitability analysis produces a spatiotemporal variation of detailed display. Kumar and Shaikh (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) used integrated suitability index, analytic hierarchy process (AHP), and MCE methods for LSA in Mussoorie municipal area, India. However, the AHP, established by Saaty is an effective and well-known method for creating an adequacy map (Saaty, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). The pairwise comparison method was introduced by Fechner in 1860 and developed by Thurstone in 1927. However, weighted overlay analysis (WOA), the multi-influence factor (MIF) method (Arivazhagan et al., 2016), land suitability (Marull et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and multi-criteria evaluation (MCE) (Eastman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Collins et al., 2001) are the most useful methods for LSA. Additionally, we used frequency ratio (FR), Bayesian weights of evidence (BWOE), and evidential belief function (EBF) models. BWOE is an important predictive model and it is directly related to the FR model. FR is the standardized method for determining the weights according to the relater significant pairwise comparison of various factors (Lee and Pradhan, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yilmaz, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pradhan and Lee, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yalcin et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is important because this method uses training data for weight calculation. So, there is no chance for bias. In contrast to FR and BWOE, EBF provides adequate evidence to clarify the plausibility and uncertainty functions, as well as the belief and disbelief functions. Nevertheless, FR and BWOE have the demerit of setting up an interrelationship between conditioning factors and sample site occurrences. So, sample sites should have an issue of ground validation. This is how we can check the error of setting an interrelationship between the condition factors and built sample points. Moreover, the suitable area for future built-up area development depends on various geophysical and anthropogenic factors. The models evolved in this study are highly influenced by those factors. Hence, FR, BWOE, and EBF models have been used in this study.\u003c/p\u003e \u003cp\u003eIn order to create a final built-up area suitability map based on factors that drive urban expansion and the importance of each criterion have evaluated by experts. This research will combine GIS techniques with the AHP, FR, BWOE, and EBF models. The FR, BWOE, and EBF models have been generally employed in landslide susceptibility analysis (Park \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Althuwaynee et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mohammady et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pourghasemi et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), groundwater recharge potentiality analysis (Maity et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) etc., whereas for built-up suitability mapping, which is still relatively new. Furthermore, the current study is the going to discover the data-driven MCDM techniques and geo-statistical ensemble methods to generate built-up growth suitability in the Midnapore municipality area.\u003c/p\u003e"},{"header":"2. Database And Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area:\u003c/h2\u003e \u003cp\u003eMidnapore is one of the oldest towns of West Bengal which was established in the year 1865. It is situated within the Midnapore Kharagpur development authority\u0026rsquo;s belt with an average elevation of 47 metres above msl. Midnapore town is the administrative headquarters of the Paschim Medinipur district in West Bengal. It is made up of 25 wards and has an area of 18.65 km\u003csup\u003e2\u003c/sup\u003e. It stretches from 87\u0026deg;17' 17.21\" to 87\u0026deg;20' 52.43\" East latitude and from 22\u0026deg;23' 44.91\" to 22\u0026deg;26\u0026prime; 38.61\" North latitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The town was chosen because of its fast urbanisation during the previous three decades. Midnapore is also a historical urban hub in eastern India.\u003c/p\u003e \u003cp\u003eIt is located on the Kangsabati River's bank. Midnapore is situated in the Paschim Medinipur district's central region. The town has a hot tropical monsoon climate. Soils around the Kangsabati River are alluvial with varying degrees of clay or sand, whilst soils closer to Rangamati are generally lateritic. The environment is similar to that of South Bengal, with the large eucalyptus and \u003cem\u003esal\u003c/em\u003e forests on the northwest side of town which are part of the Dalma Bengal-Jharkhand Range. Arabari, the forest range where India's first joint forest management plan was developed, is about 30 kilometres away.\u003c/p\u003e \u003cp\u003eApart from Kharagpur, it is the only significant town in the Paschim Medinipur districts. According to the Census of India, the population was 169,127, with a decadal growth rate of 12.92 percent from 2001 to 2011. Midnapore is well-connected to nearby towns by highways and railway lines, and it is also urbanised with adequate infrastructural amenities (District Human Development Report 2011).\u003c/p\u003e \u003cp\u003eAs the town is the district headquarter, many administrative offices, educational institutions, and other amenities have attracted a large number of people. Furthermore, the population of this town is continually growing. As a result, the town is always expanding in a new direction. The built-up area is expanding outside the authority limit, and curiously, it is spreading in certain directions with many informal settlements and other structures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection and processing\u003c/h2\u003e \u003cp\u003eThe present study is the result of amalgamation of GIS application and extensive field work by collecting multiple thematic layers using satellite images, open street maps, and some secondary data to create a site suitability map. Landsat 8 imagery is used to create land use and land cover maps. The Open Street Map is used to obtain maps of roads and railways. A GPS survey to determine the location of various service facilities. The 2021 land use map has been used to extract built-up areas and generate training and testing point data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All the thematic layers have been generated using ArcGIS 10.4 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 GIS based Analytical hierarchy process\u003c/h2\u003e \u003cp\u003eFor the analysis of built-up suitability map six factors have been considered depending of their importance in Midnapore town namely Distance from the road (A), Distance from the urban centre (B), Distance from the railway station (C), Land Price (D), Distance from the service centre (E) and Land use/ land cover (F). In multicriteria decision making (MCDM) approach, Analytical Hierarchy Process (AHP) model is calculated by assessing the weighted value allocated for each factor. The AHP is the methodical procedure of situating public infrastructure (Banai-Kashani, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). This method also involves pair wise comparisons of different decision variables. In construction of pairwise matrix, each factor is given a value between 1 to 9. Experts produced pairwise comparison matrices under the condition that judgements are assessed to determine appropriate alternatives to estimate related absolute numbers ranging from 1 to 9, the fundamental scales of the AHP (Saaty, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Using AHP in the appropriate site selection of a built-up region, four different scenarios were generated. All the methods evolved in this study are briefly introduced in methodical flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFundamental Saaty\u0026rsquo;s scale for pair wise comparison matrix (Saaty, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1980\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumerical values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVerbal term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExplanation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEqually important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo elements have equal important\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately more important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperience or judgement slightly favours one element\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly more important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperience or judgement strongly favours one element\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery strongly more important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDominance of one element proved in practice\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely more important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe highest order dominance of one element over another\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2,4,6,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImportant intermediate values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompromise is needed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThen the pair-wise comparison matrix (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) has been normalized. The weight can be obtained depending on the relative importance of the various factors from the pair wish comparison matrix (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise comparison matrix for AHP method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the road (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the urban centre (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the railway station (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Price (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the service centre (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC (F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe consistency ratio (CR) is a consistency statistic that is used to show the probability judgement of the matrix (Saaty, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1980\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$CR=\\frac{CI}{RI} \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere the random index (RI) gives the average of the resulting consistency index (CI) depending on the order of the matrix given by Saaty (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), and CI is expressed as\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$CI=({{\\lambda }}_{\\text{max}}- n)/\\left(n-1\\right) \\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\lambda }}_{\\text{m}\\text{a}\\text{x}}\\)\u003c/span\u003e\u003c/span\u003eis the largest or principal Eigen value of the matrix and \u003cem\u003en\u003c/em\u003e is the order of the matrix. Saaty also considered that CR \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le\\)\u003c/span\u003e\u003c/span\u003e 0.01 is a reasonable level of consistency.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNormalized pairwise comparison matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eλ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the road (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the urban centre (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the railway station (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Price (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the service centres (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC (F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\lambda }_{max}\\)\u003c/span\u003e\u003c/span\u003e= 6.21; CI\u0026thinsp;=\u0026thinsp;0.04; CR\u0026thinsp;=\u0026thinsp;0.0343\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBuilt-up suitability index (BSI) is calculated by adding each factor multiplied by a weight (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), as shown in Eq.\u0026nbsp;3.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{B}\\text{S}\\text{I} =\\sum AHP \\left(\\text{w}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t} :\\text{e}\\text{a}\\text{c}\\text{h} \\text{f}\\text{a}\\text{c}\\text{t}\\text{o}\\text{r}\\right) \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfluencing factors and subclasses and their perspective weight driven through Analytical hierarchy process analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotentiality for Built-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeights\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverall weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the urban centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;2600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2600\u0026ndash;3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u0026ndash;5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the railway station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u0026ndash;5400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2200\u0026ndash;3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1100\u0026ndash;2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Price\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the service centres\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1200\u0026ndash;2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2100\u0026ndash;3200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3200\u0026ndash;4600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Frequency Ratio model\u003c/h2\u003e \u003cp\u003eFrequency ratio (FR) model, a bivariate statistical method was used to analyse the spatial effect of the suitable location of built-up point in each class of classified urban built-up suitable influencing factors (Maity et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mallick et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). In this process, real built-up point data are used for simulating and validating to reflect real built-up expansion behaviours and their dynamics in study area. The FR of built-up suitability is the ratio of the probability of built-up occurrence to the probability of a non-occurrence of given attributes. To produce built-up suitability map, the FR models was applied using the GIS technique, which spatially represent the information. FR was computed for all classes of each conditioning factor. The FR distribution maps were then summed up to derive the built-up suitability using Eqs.\u0026nbsp;4 and 5.\u003c/p\u003e \u003cp\u003eFR values of more than 1 indicates a high relationship with built-up suitability, whereas values of less than 1 mean a low relationship with built-up suitability. The value of 1 reflects an average condition:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${FR}_{ij }=\\frac{N(L\\cap {C}_{ij})/N\\left(L\\right)}{N{(C}_{ij})/N(C)} \\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(N\\left(L\\right)\\)\u003c/span\u003e\u003c/span\u003e is the total number of built-up pixels \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(N\\left(C\\right)\\)\u003c/span\u003e\u003c/span\u003e is the total number of pixels in the entire study area, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({j}^{th}\\)\u003c/span\u003e\u003c/span\u003e class attribute of urban built-up suitable conditioning factors, C = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(i=\\text{1,2},3,\\dots \\dots ..n\\right)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(N{(C}_{ij})\\)\u003c/span\u003e\u003c/span\u003e is the total number of pixels in the class \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{ij}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(N(L\\cap {C}_{ij})\\)\u003c/span\u003e\u003c/span\u003e is the quantity of built-up pixels in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{ij}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBSI is calculated by adding each factor multiplied by a weight (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), as shown in Eq.\u0026nbsp;5.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\text{B}\\text{S}\\text{I}=\\sum FR \\left(\\text{F}\\text{R}:\\text{r}\\text{a}\\text{t}\\text{i}\\text{n}\\text{g} \\text{o}\\text{f} \\text{e}\\text{a}\\text{c}\\text{h} \\text{f}\\text{a}\\text{c}\\text{t}\\text{o}\\text{r}\\text{s} \\text{t}\\text{y}\\text{p}\\text{e} \\text{o}\\text{r} \\text{r}\\text{a}\\text{n}\\text{g}\\text{e}\\right) \\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfluencing factors and subclasses and their perspective weight driven through frequency ratio analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotentiality for Built-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of pixels in a class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% of pixel in a class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of pixels of Built-up point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% of pixel of Built-up point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the urban centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;2600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2600\u0026ndash;3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u0026ndash;5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the railway station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1100\u0026ndash;2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2200\u0026ndash;3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u0026ndash;5400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Price\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the service centres\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1200\u0026ndash;2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2100\u0026ndash;3200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3200\u0026ndash;4600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Bayesian Weights of Evidence (BWOE):\u003c/h2\u003e \u003cp\u003eThe FR process is actually the initial step of Bayesian Weights of Evidence (BWOE) and this model is evaluating the non-occurrence of built-up in the similar class of evidence. Then, the natural logarithm of both values (occurrence and non-occurrence) was considered. These values are the weights to support the occurrence and non-occurrence of built-up point with respect to each class of evidence (Maity et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mallick et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). The subtraction value of those weights represents the spatial association of each built-up pixel and each class of evidence. The variance of both weights and standard deviation are calculated. Finally, the standardized value representing the significance of the spatial association and measurement the comparative certainty of the posterior probability was calculated.\u003c/p\u003e \u003cp\u003eThe estimated probability value of each conditioning factors with respect to evidence should be present in a raster-based map format. Hence, the classified factor layers were reclassified with the new value obtains from the BWOE model. Finally, by integrating and summing up all reclassified evidence for built-up suitability map. Based on relative weightage of the conditioning factors, BWOE model is calculated on a study area. BWOE can be expressed as follows:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$${W}^{+}=Ln\\frac{P\\left(\\frac{B}{A}\\right)}{P\\left(\\frac{B{\\prime }}{A{\\prime }}\\right)} \\left(14\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$${W}^{-}=Ln\\frac{P\\left(\\frac{B}{A}\\right)}{P\\left(\\frac{B{\\prime }}{A{\\prime }}\\right)} \\left(15\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}^{+}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}^{-}\\)\u003c/span\u003e\u003c/span\u003e denote the positive and negative weights of the conditioning factors; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\)\u003c/span\u003e\u003c/span\u003e is the probability, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(B\\)\u003c/span\u003e\u003c/span\u003estands for conditioning factor, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(B\u0026rsquo;\\)\u003c/span\u003e\u003c/span\u003e stands for absence of conditioning factors, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(A\\)\u003c/span\u003e\u003c/span\u003e stands for built-up data and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(A\u0026rsquo;\\)\u003c/span\u003e\u003c/span\u003e stands for absence of built-up data.\u003c/p\u003e \u003cp\u003eThen, the standard deviation (SD) of the contrast value can be calculated as follows:\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$${SD}_{c}= \\sqrt{\\left(\\left({S}^{2}\\left({W}^{+}\\right)+ {S}^{2}\\left({W}^{-}\\right)\\right)\\right)} \\left(16\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}^{2}\\)\u003c/span\u003e\u003c/span\u003e denotes the influencing weight of the conditioning factors (Mallick et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe standardised contrast \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\tau\\)\u003c/span\u003e\u003c/span\u003e is considered as measure of confidence that can be calculated as follows:\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\tau =\\left(\\frac{C}{{SD}_{c}}\\right) \\left(17\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAfter assigning all the conditioning parameters with the built-up data, the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}^{+}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}^{-}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({SD}_{c }\\)\u003c/span\u003e\u003c/span\u003e, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\tau\\)\u003c/span\u003e\u003c/span\u003e has been calculated (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfluencing factors and subclasses and their perspective weight driven through Bayesian weights of evidence analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;(W\u0026nbsp;+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;(W\u0026nbsp;-)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS\u0026nbsp;(C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eτ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-3.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the urban centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;2600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2600\u0026ndash;3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-3.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u0026ndash;5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-3.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the railway station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1100\u0026ndash;2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2200\u0026ndash;3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u0026ndash;5400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Price\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-3.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the service centres\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1200\u0026ndash;2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2100\u0026ndash;3200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3200\u0026ndash;4600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-4.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-5.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Evidential Belief Function (Dempster-Shafer) model\u003c/h2\u003e \u003cp\u003eThis theory was first proposed by Dempster (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) as a spatial integration model with mathematically developed by Shafer (1976). Dempster-Shafer theory of evidence refers to the generalization of Bayes theorem of probability. This theory combines the belief from several sources of evidence and the relative elasticity to accept uncertainty. The theory predicts how closely the evidence shows the certainty of a hypothesis rather than predicting the possible accuracy of a hypothesis. Dempster-Shafer theory has been applied effectively using GIS in many applications.\u003c/p\u003e \u003cp\u003eThe theory demonstrated four basic Evidential Belief Function models namely, Bel (degree of belief), Dis (degree of disbelief), Pls (degree of plausibility), and Unc (degree of uncertainty). Dis denotes the belief of false suggestion based on the given evidence. Bel and Pls provide the upper and lower boundary respectively of the probability for the suggestion. Unc means ignorance that is the difference between plausibility and belief (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, built-up suitability conditioning factors are considered as independent variable and built-up point data is considered as dependent variable. The Belief and Disbelief weight value of each conditioning factor class can be calculated as follow:\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$Bel=\\frac{{W}_{i}}{{\\varnothing }_{i}}=\\frac{\\text{p}\\text{e}\\text{r}\\text{c}\\text{e}\\text{n}\\text{t} \\text{o}\\text{f} \\text{b}\\text{u}\\text{i}\\text{l}\\text{t}-\\text{u}\\text{p} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n} }{\\text{p}\\text{e}\\text{r}\\text{c}\\text{e}\\text{n}\\text{t} \\text{o}\\text{f} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n}} \\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equk\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e\n$$Dis =\\frac{{W{\\prime }}_{i}}{{\\varnothing {\\prime }}_{i}}=\\frac{\\text{p}\\text{e}\\text{r}\\text{c}\\text{e}\\text{n}\\text{t} \\text{o}\\text{f} \\text{n}\\text{o}\\text{n} \\text{b}\\text{u}\\text{i}\\text{l}\\text{t}-\\text{u}\\text{p} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n} }{\\text{p}\\text{e}\\text{r}\\text{c}\\text{e}\\text{n}\\text{t} \\text{o}\\text{f} \\text{n}\\text{o}\\text{n} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n}} \\left(7\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equl\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equl\" name=\"EquationSource\"\u003e\n$$Unc=1-\\left(Bel+Dis\\right) \\left(8\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equm\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equm\" name=\"EquationSource\"\u003e\n$$Pls=1-Dis \\left(9\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{i}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varnothing }_{i}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W{\\prime }}_{i}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varnothing {\\prime }}_{i}\\)\u003c/span\u003e\u003c/span\u003ecould be calculated form the following equation:\u003cdiv id=\"Equn\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equn\" name=\"EquationSource\"\u003e\n$${W}_{i}=\\frac{\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{b}\\text{u}\\text{i}\\text{l}\\text{t}-\\text{u}\\text{p} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n} }{\\text{t}\\text{o}\\text{t}\\text{a}\\text{l} \\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{b}\\text{u}\\text{i}\\text{l}\\text{t}-\\text{u}\\text{p} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n}} \\left(10\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equo\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equo\" name=\"EquationSource\"\u003e\n$${\\varnothing }_{i}=\\frac{\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n} }{\\text{t}\\text{o}\\text{t}\\text{a}\\text{l} \\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n}} \\left(11\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equp\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equp\" name=\"EquationSource\"\u003e\n$${W{\\prime }}_{i}=\\frac{\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{n}\\text{o}\\text{n} \\text{b}\\text{u}\\text{i}\\text{l}\\text{t}-\\text{u}\\text{p} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n} }{\\text{t}\\text{o}\\text{t}\\text{a}\\text{l} \\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{b}\\text{u}\\text{i}\\text{l}\\text{t}-\\text{u}\\text{p} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n}} \\left(12\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equq\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equq\" name=\"EquationSource\"\u003e\n$${\\varnothing {\\prime }}_{i}=\\frac{\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{n}\\text{o}\\text{n} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n} }{\\text{t}\\text{o}\\text{t}\\text{a}\\text{l} \\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r} \\text{o}\\text{f} \\text{p}\\text{i}\\text{x}\\text{e}\\text{l} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{d}\\text{o}\\text{m}\\text{a}\\text{i}\\text{n}} \\left(13\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTherefore, the influencing factors were sequentially integrated using Eq.\u0026nbsp;10\u0026ndash;13, summed up Eq.\u0026nbsp;6 to obtain future built-up suitability map of the study area and also used Eq.\u0026nbsp;7\u0026ndash;9 consequently to obtain disbelief, uncertainty and plausibility maps of built-up suitability (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfluencing factors and subclasses and their perspective weight driven through EBF analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePotentiality for Built-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of pixels in a class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of pixels of Built-up point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eEvidence Belief Function (Dempster-Shafer Model)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUnc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the urban centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;2600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2600\u0026ndash;3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u0026ndash;5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the railway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1100\u0026ndash;2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2200\u0026ndash;3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600\u0026ndash;5400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Price\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from the service centres\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1200\u0026ndash;2100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2100\u0026ndash;3200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3200\u0026ndash;4600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of influencing factors\u003c/h2\u003e \u003cp\u003eIdentifying appropriate factors for urban development is a critical foundational task in urban planning. The six important criteria influencing land suitability of Midnapore towns have been identified such as distance from the road, distance from the urban centre, distance from the railway station, land price, distance from the service centre and land use/ land cover (LULC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The sum of these criteria defines the degree of appropriateness and also aids in identifying the land's constraints for urban development. The numerous map layers were created for this purpose.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDistance from the road\u003c/span\u003e \u003c/p\u003e \u003cp\u003eRoad is also an important criterion in built-up area site suitability because of the need to transport raw materials and finish products (Ding et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bui et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). So, effort is made to suitably locate the site nearer to any existing road in order to find out better accessibility and their strength of important for road proximity criterion. Thus, the maximum possibility of built-up area suitability is found close to the road or minimum distance rather farthest distance of the major road. So, the maximum weight is given to the proximal distance and comparatively lesser weight is given to the farthest situation.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDistance from the urban centre\u003c/span\u003e \u003c/p\u003e \u003cp\u003eMost of the cases, urban centre has an important role in business purpose. But based on residential facility all time it has not equal important (Aburas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) because land price and overcrowding condition are barrier. In this study urban centre of Midnapore municipality has a significance importance on built-up area suitability analysis. So, closest to the urban centre has given maximum weightages and less weightage has given for maximum distance from the urban centre to develop the suitable built-up sites.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDistance from the railway station\u003c/span\u003e \u003c/p\u003e \u003cp\u003eRailway is an important factor for the study of built-up area suitability (Aburas et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pradhan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and the most important factor is the distance from the railway station. People are generally preferring to settle down at areas short distance from the rail station but not exactly to near to the rail line. In Midnapore town only one railway station is located that is Midnapore. Therefore, maximum importance is given to the moderate distance and comparatively, lesser importance is given to the high and furthest distance.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLand value\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe land price at a specific point of time is based on its location that referred as land value (Aburas et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Land value depends on the availability of urban services, the accessibility by roads and railways, physical condition of the land etc. So, the land values are higher in the town centre than they are on the periphery. Accessibility has an influence on property value within a community, and land parcels near roads have high prices. The land revenue department of the Paschim Medinipur district determines the cost along the route in Midnapore municipality. The approximate cost of land within 0\u0026ndash;500 metre from the major road was Rs. 5000 per m\u003csup\u003e2\u003c/sup\u003e, 500\u0026ndash;1000 m was Rs. 3750 per m\u003csup\u003e2\u003c/sup\u003e, and above 1000 m was Rs. 2500 per m\u003csup\u003e2\u003c/sup\u003e at the time of this study, indicating that the cost is reducing as one moves away from the road. Because of the cheap investment cost, low-value land provides the greatest possibility for a suitable location. Land with a lower rating has been allocated a higher level of relevance in this study. Thus, the maximum weight has been given to higher to moderate land values near to the city centre and minimum weight has been considered for very high and low land values of Midnapore municipality area.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDistance from the service centre\u003c/span\u003e \u003c/p\u003e \u003cp\u003eDifferent service centres are used as an influencing factor for this study; those are educational centres, market facilities, and healthcare facilities. These service facilities play an important role in deciding whether build or not to build a house and have a direct impact on land prices (Aburas et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As usual, closest to the service centre has given maximum weightages and less weightage has given for maximum distance from the service centre to develop the suitable built-up sites.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLULC\u003c/span\u003e \u003c/p\u003e \u003cp\u003eLand use and land cover assessment is an important measure to execute any built-up area development planning over an extent (Aburas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pradhan and Lee, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tahmassebipoor et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). LULC map of the Midnapore municipal area has been classified as built-up, open land, agricultural land, vegetation cover, and water body. In this study, built up land is not ideal for future development since once a building is established, it remains for minimum of 50\u0026ndash;75 years. Open land has most potential for the development of future built-up areas and carried maximum weightage followed by agriculture land and vegetation cover. However, built-up area and water body consider as most unsuitable areas for the further built-up area development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Assessment of suitable built-up sites using EBF, AHP, FR, and BWOE models\u003c/h2\u003e \u003cp\u003eThe EBF model is used to produce the built-up area suitable zone and correlate it with the influencing factors. A comparatively high Bel value indicates a higher probability of built-up occurrence, while a low Bel value implies a lower probability of occurrence. In the EBF model, belief map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) is compared to the disbelief map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) which indicates that the Bel values are low for areas where Dis values are high and vice versa. Analysis of these map demonstrates that high urban built-up probability is for the areas of high degree of Bel and low degree of Dis for the urban built-up point occurrences. The uncertainty map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) indicates lack of information to provide a real prove for built-up occurrences. According to EBF analysis, the high uncertainty values are belonging to the areas where belief values are low. Moreover, the plausibility (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) shows high values for the area where uncertainty value relatively is low. According to this model, most of the areas (49.68%) of Midnapore municipality are under moderately suitable for future built-up area development and 16.64% of area has been observed as very high suitable zone for built-up area development in the western part (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe built-up area suitable zone also identified using on AHP, FR, and BWOE models based on the influencing factors such as distance from the road plays a key influence, as does distance from the urban centre, resulting in a high potential for built-up area adaptability. Therefore, the built-up area growth possibility is high in the nearby residential areas, but the vegetation area and the open land have a low built-up possibility. For the distance to diverse service centres, railways, land values, and highways, it demonstrates that built-up growth mostly occurs along them, and the built-up area's growth possibilities drop fast as the distance rises. All the models are similar kind of results for future built-up area selection (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe AHP is used to compare with six thematic layer to calculate normalised weights. The result shows that the calculated highest weight is attributed to distance from the road near 100 m (168), and the lowest weights is land use classes as water body (0). The built-up area suitability map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) has been generated by overlying the thematic maps based on AHP weights. The built-up area suitability map obtained using AHP model specified four suitability zones like, very high, covering 10.70% of the total Midnapore urban area, moderate zone 53.46% and low zone covering 17.97% area (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The built-up area suitability map (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) obtains using FR model is a specified four built-up area suitability zones like, very high zone, with 16.90%, area, high zone (19.02%), moderate zone (57.80%) and low zone (6.29%). The built-up area suitability zone conducted by EBF model shows that moderate zone (54.63%) is greater in percentage than other zones. For BWOE model, the quantile method was also adopted to classify the urban built-up area suitability map into four classes: low, medium, high and very high. According to the final built-up area suitability map (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), the very high zone found nearer to the road. The moderate zone covered slightly higher area (54.63%) than the high and very high zone (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial distribution of built-up suitability zone in km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAHP (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFR (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEBF (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBWOE (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e54.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation\u003c/h2\u003e \u003cp\u003eThe validity of the built-up area suitability map prepared in this study is only achievable when a new built-up area is constructed in future. Thus, a measure of validity and predictive ability should be provided. A general way of validation is by using the built-up area that was not used in the modelling procedure. As these built-up areas are independent of the model, they were treated as the \"future\" built-up areas in this study. As it was indicated earlier, 70% of the built-up area in the study area was picked randomly for modelling, and the remaining 30 percent was split for validation. The resultant prediction rate curve reveals that the AHP, FR, BWOE, and EBF models have good predictive ability for future build-up areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). To quantify how well the modelling worked, the area under the curve (AUC) was utilized to establish a quantitative comparison between the four models. The model with the greatest AUC is deemed the best. The map produced by the FR model displayed the best result for built-up area suitability mapping in the research region. The findings of the AUC evaluation indicated that the values of the AHP, FR, BWOE, and EBF models were 83.6%, 84.5%, 82.2%, and 81.5%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe enormous amount of population increase creates unplanned and unsystematic urban expansion related problems in Indian cities (Shikary and Rudra \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mallick et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Hence, suitable site identification is required for urban expansion in small and medium class town along with the metro cities. In this study, four geo-statistical models have been evolved to finding suitable sites for managing future cityscape of Midnapore municipality. The models envisaged four built-up area development zones i.e., very high suitable zone, highly suitable zone, moderate suitable zone, and low suitable zone. These four models show that the suitable urban expansion sites can be over the western lateritic surface and eastern agricultural land for continuous expansion, while ribbon shaped development will be happened in the northern part of the city. This study has conducted in a medium class city in India. Hence, the open space within the down town provide high weightage to be the high to very high suitable zone for built-up area development, in turn, infill development.\u003c/p\u003e \u003cp\u003eRecently, the haphazard and rapid built-up area expansion has been observed in the south-western part nearer to Kangsabati River, Boisakhipalli, north-western part, and eastern part (Dharma) of Midnapore municipality (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Specifically, western and south-western sides are the part of forest cover and river bank area, in turn, ecologically sensitive zone. This area cannot be considered as suitable sites for built-up growth. Although, the peripheral urban growth is likely occupied the open surface or forest cover (Mallick, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). But the problem is that, we can not accept these lands as suitable for the urban growth. Because, growth of built-up in the pasture surface, water body or a forest cover destroyed ecological balance and carrying capacity of the land. Suitable site should be the fallow land or open land where ecological valuation is less. In case of Midnapore, Khairullachak, Abash, Golapichak, Keranichoti, and Iswarpally will be most suitable sites for built-up area development. Peripheral urbanization and built-up growth are very common in the populous countries (Maity et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mallick \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the study also depicted similar outcomes. All these regions in the Midnapore have got the momentum and expanded in faster rate due to comparatively less land price, quick accessibility with the railway and roadways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this work, AHP, FR, Dempster-Shafer EBF and BWOE models were employed for built-up growth suitability mapping, and these were evaluated by means of their validations. The validation technique was undertaken by overlaying the built-up growth suitability map with the reserved validation set. The generated prediction rate curve was utilized for comparison using the AUC value. The AUC reveals that the success rates of the AHP, FR, EBF and BWOE models were 83.6%, 84.5%, 81.5%, and 82.2% respectively. It can be explicated that all the validated models have greater accuracies and successfully check the errors. According to these geo-spatial models, Midnapore municipality has several opportunities for built-up area development such as infill development within the city and outgrowth in the eastern and north-western part of the city.\u003c/p\u003e \u003cp\u003eHowever, the results are absolutely essential to carry out the future built-up area development planning in and around the Midnapore municipality area. Despite this, each model develops a region-specific suitability mapping that is distinguishable from one suitability map to another. This sort of regional planning work is useful for planners, policy makers, and stakeholders. Additionally, all of these models can help a variety of urban planners and organisations create appropriate plans for citizen participation in decision-making process for the long-term urban development of the Midnapore municipality and its surroundings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of conflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the results and data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the DST-FIST sponsored Department of Geography, Vidyasagar University, Midnapore for conducting this research. Das, P., and Maity, B. are also thankful to the University Grants Commission of India for providing Research Fellowship during this research work.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAburas, M. M., Abullah, S. H., Ramli, M. F., \u0026amp; Ash\u0026rsquo;aari, Z. H. (2015). Areview of land suitability analysis for urban growth by using the GIS-based analytical hierarchy process.Asian Journal of Applied Science, 3(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAburas, M. M., Abdullah, S. H., Ramil, M. F., \u0026amp; Asha\u0026rsquo;ari, Z. H. (2017). Land suitability analysis of urban growth in Seremban Malaysia, using GIS based analytical hierarchy process. \u003cem\u003eProcedia engineering\u003c/em\u003e, \u003cem\u003e198\u003c/em\u003e, 1128\u0026ndash;1136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlthuwaynee, O. F., Pradhan, B., \u0026amp; Lee, S. (2012). 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Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat\u0026mdash;Turkey). \u003cem\u003eComputers \u0026amp; Geosciences\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(6), 1125\u0026ndash;1138.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urban built-up land, Frequency ratio, Weights of evidence, Evidential believe functions, Land suitability assessment","lastPublishedDoi":"10.21203/rs.3.rs-2389227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2389227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to achieve the socio-environmental sustainability, it is important to identify the suitable zone for urban built-up area development in highly populous countries in the global south. Therefore, this study has been conducted in a medium class city in eastern India based on some significant influential factors and their sub-criteria. Different geophysical data, official data, and open street data were used to assess the land suitability for future urban growth by integrating MCDM techniques and different geo-statistical models namely Analytical Hierarchy Process, Frequency Ratio, Weights of Evidence, and Evidential Believe Function. Based on the land suitability assessment (LSA) outcomes, four different zones were identified such as very high suitability zone, high suitability zone, moderately suitable zone and low suitable zone. The findings of this study revealed that the central part of Midnapore municipality has been observed low suitability while\u0026thinsp;\u0026gt;\u0026thinsp;50% area is under moderate suitability zone. Moreover, the peripheral regions, especially in western, northern, and eastern parts are observed to be high to very high suitable zones for urban expansion. The outcome of the selected models has been validated (\u0026gt;\u0026thinsp;80%) by the receiver operating characteristics (ROC) curve. Therefore, proper planning is required for western and eastern surroundings, as these areas are predominately forest cover and agricultural land respectively. Hopefully, the identification of proper site suitability will helpful for urban planner to reshaping the urban landscape as sustainable urban future.\u003c/p\u003e","manuscriptTitle":"Land suitability assessment for urban growth analysis using geospatial techniques and integrated ensemble models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-20 05:20:46","doi":"10.21203/rs.3.rs-2389227/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78bc4224-dfa7-43d8-82e3-413172ff7c10","owner":[],"postedDate":"December 20th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T16:21:46+00:00","versionOfRecord":{"articleIdentity":"rs-2389227","link":"https://doi.org/10.1007/s10668-024-05928-0","journal":{"identity":"environment-development-and-sustainability","isVorOnly":false,"title":"Environment, Development and Sustainability"},"publishedOn":"2025-01-05 15:57:47","publishedOnDateReadable":"January 5th, 2025"},"versionCreatedAt":"2022-12-20 05:20:46","video":"","vorDoi":"10.1007/s10668-024-05928-0","vorDoiUrl":"https://doi.org/10.1007/s10668-024-05928-0","workflowStages":[]},"version":"v1","identity":"rs-2389227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2389227","identity":"rs-2389227","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}