Geospatial Multi-Criteria Decision Analysis for Hotel Site Suitability Assessment in Minna Metropolis, Nigeria: Integrating Remote Sensing and GIS

Geospatial Multi-Criteria Decision Analysis for Hotel Site Suitability Assessment in Minna Metropolis, Nigeria: Integrating Remote Sensing and GIS | 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 Geospatial Multi-Criteria Decision Analysis for Hotel Site Suitability Assessment in Minna Metropolis, Nigeria: Integrating Remote Sensing and GIS Ekundayo A. Adesina, S. Chukwu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6370819/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Applied Geomatics → Version 1 posted 9 You are reading this latest preprint version Abstract The hotel industry's substantial contribution to national economies, particularly in tourism-driven regions, underlines its role in employment generation. However, spatial disparities in hotel distribution, prevalent in many African nations, like Nigeria, necessitate strategic planning for new developments. This study employed a geospatial multi-criteria decision analysis (MCDA) framework, integrating Geographic Information Systems (GIS) and Remote Sensing, to assess hotel site suitability within Minna Metropolis. Utilizing the Analytical Hierarchy Process (AHP), five critical factors, Land Use/Land Cover (LULC), slope, elevation, existing hotel distribution, and road network accessibility, were weighted and integrated via a weighted overlay in ArcGIS 10.8. The resulting suitability map categorized the metropolis into four classes: highly suitable, moderately suitable, less suitable, and not suitable. Findings revealed that over 50% (approximately 76 km²) of Minna Metropolis exhibits moderate to high suitability for hotel development, with LULC, existing hotel density, and road network accessibility identified as the primary influencing factors. This research provides a robust spatial decision support tool for hotel industry professionals and urban planners, offering valuable insights for optimized hotel site selection in similar urbanizing contexts. Analytical Hierarchy Process (AHP) Geographic Information Systems (GIS) Geospatial Analysis Hotel Site Selection Multi-Criteria Decision Analysis (MCDA) Remote Sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction The global hospitality sector, particularly the hotel industry, is a pivotal economic catalyst, especially in regions experiencing burgeoning tourism and dynamic business travel patterns (Kasim, 2021 ). The hotel industry significantly contributes to economic growth by stimulating local economies through job creation, increased tax revenues, and the development of ancillary services that support the broader tourism ecosystem (Dwyer et al., 2019 ). Africa's hotel market is undergoing significant expansion, driven by accelerating urbanization, a growing middle class, and increased foreign direct investment, a key strategic player in this landscape (UNWTO, 2023). Recent industry reports indicate Nigeria's ascent to the second position in Africa's 2024 hotel development rankings, as measured by room numbers, reflecting substantial growth in investment and the country's enhanced attractiveness to leading international hotel chains (THISDAYLIVE, 2024 ). This trajectory highlights the critical importance of strategic hotel site selection, as location directly and profoundly influences profitability, market penetration, long-term operational sustainability, and the overall competitiveness of hotel enterprises (European Journal of Tourism Hospitality and Recreation, 2020 ; Kim and Jogaratnam, 2010 ). However, the equitable spatial distribution of hotel developments presents a significant and multifaceted challenge in many rapidly urbanizing African centers. The confluence of accelerated urbanization, escalating hospitality demand, and often constrained urban planning capacities frequently result in haphazard development patterns and suboptimal site selection outcomes (Aribigbola, 2018). In Nigeria, particularly within developing urban centers such as Minna Metropolis, identifying optimal hotel locations is paramount for driving sustainable economic development, enhancing regional tourism potential, and ensuring balanced and equitable urban growth trajectories. The increasing influx of investments from international hotel chains further accentuates the critical need for informed and data-driven site selection methodologies, highlighting the potential risks associated with uncoordinated and haphazard development patterns, as evidenced by industry reports from Amadeus Hospitality (2024). Without systematic spatial planning frameworks, these investments may yield suboptimal financial returns, contribute to unsustainable urban sprawl, and exacerbate existing infrastructural deficits and challenges (Kim and Jogaratnam, 2010 ). Moreover, the absence of strategic spatial planning can lead to the concentration of hotel developments in already congested urban areas, neglecting the developmental potential of peripheral locations and consequently hindering equitable regional development outcomes (Adeleke and Adewole, 2019 ). Despite the availability of advanced geospatial technologies, including Remote Sensing (RS) and Geographical Information Systems (GIS), for spatial analysis applications, their comprehensive and integrated application in hotel site suitability assessments within rapidly urbanizing African contexts such as Minna Metropolis remains notably limited (Oyinloye and Adeyemo, 2022 ). Existing studies often lack an integrated analytical approach that effectively combines these powerful geospatial technologies with Multi-Criteria Decision Analysis (MCDA) frameworks, such as the Analytical Hierarchy Process (AHP), which enables the systematic evaluation of multiple, often conflicting, decision criteria (Malczewski, 2006 ). Furthermore, a significant knowledge gap exists in the effective integration of recent and dynamic spatial data, including high-resolution satellite imagery, open-source geospatial datasets, and real-time urban development indicators, with advanced MCDA methodologies to develop robust and data-driven spatial decision support systems for hotel development within such dynamic and rapidly evolving urban environments (Feizizadeh and Blaschke, 2013 ). Specifically, many contemporary studies fail to consider the dynamic nature of urban growth processes, the potential impact of climate change on site suitability assessments, and the comprehensive integration of socioeconomic factors within the decision-making framework (Aribigbola, 2018). This research aims to address these critical knowledge gaps by developing and implementing a geospatial MCDA framework for the comprehensive assessment of hotel site suitability within Minna Metropolis, Niger State, Nigeria. By integrating recent and dynamic spatial data, including high-resolution remote sensing imagery, open-source GIS datasets, and relevant local socioeconomic indicators, and by applying rigorous analytical methods such as the Analytical Hierarchy Process (AHP), this study contributes to the development of sustainable urban planning strategies provides robust support for the regional hospitality sector. The findings generated from this research provide valuable and actionable insights for hotel industry stakeholders, urban planners, and policymakers, facilitating informed and data-driven decisions regarding hotel development in similar urbanizing contexts across the African continent. Incorporating change-advanced geospatial techniques with MCDA methodologies offers a robust, transparent, and replicable analytical framework in other regions facing similar urban development challenges. Moreover, this study directly addresses the urgent need for data-driven spatial decision support systems that comprehensively consider the dynamic nature of urban growth and the long-term sustainability of hotel development, thereby promoting balanced, equitable, and environmentally sustainable urban expansion. By providing a comprehensive and spatially explicit analysis, this research endeavours to mitigate the risks associated with haphazard development patterns and ensure that hotel investments contribute positively and sustainably to the socioeconomic development of Minna Metropolis and other comparable urban centers. Study Area This study focuses on Minna, the capital city of Niger State, located in the North-Central geopolitical zone of Nigeria, experiencing rapid urbanization driven by natural population growth and rural-urban migration, consistent with trends observed in many Nigerian state capitals (National Bureau of Statistics, 2023 ); according to recent demographic projections, Minna's population has seen a significant increase over the past decade, placing heightened demands on urban infrastructure and services (Niger State Urban Development Report, 2024); geographically, Minna is situated between latitudes 9°31'20"N and 9°41'27"N and longitudes 6°24'59"E and 6°37'42"E, as illustrated in Fig. 1 , its strategic location making it a key administrative and commercial hub within the region, and notably, Minna possesses a diverse landscape and is proximate to several tourist attractions, including the Gurara Waterfalls, which have the potential to stimulate the local hospitality industry (Niger State Tourism Board, 2023), however, the city's current hotel infrastructure is struggling to keep pace with the increasing demands of business travelers and tourists, highlighting the need for strategic spatial planning; furthermore, recent economic reports indicate that Minna is attracting increased attention from domestic and international investors, particularly in sectors related to agriculture and infrastructure development (Nigerian Investment Promotion Commission, 2024 ), which is expected to further drive urbanization and increase the demand for quality hotel accommodations, and the spatial analysis of hotel site suitability within Minna, therefore, provides a timely and relevant contribution to the city's urban development planning. 3. Materials and Methods This study employed a systematic methodological approach involving data acquisition, geospatial data processing, application of the Analytical Hierarchy Process (AHP), and spatial suitability analysis, designed to integrate diverse geospatial datasets and expert knowledge to determine optimal hotel site locations in Minna Metropolis; the systematic integration of geospatial data processing, AHP, and spatial suitability analysis aligns with a widely adopted methodology in spatial planning and site selection studies across various global contexts, where multi-criteria decision-making frameworks are commonly employed to address complex spatial problems (Malczewski, 2006 ; Eastman et al., 1995 ); the use of AHP, in particular, is consistent with its established role in facilitating the integration of expert knowledge and quantitative data in spatial decision-making, as demonstrated in numerous studies focusing on land use planning and urban development (Saaty, 1980 ; Jankowski, 1995 ); furthermore, the application of GIS-based spatial suitability analysis, incorporating diverse geospatial datasets, reflects a standard practice in contemporary urban planning research, where the spatial distribution of relevant factors is crucial for informed decision-making (Carver, 1991 ; Stewart et al., 2018 ), and this methodological approach is consistent with the increasing reliance on geospatial technologies to optimize land use allocation and promote sustainable urban development in various international settings. 3.1 Data Acquisition and Sources The study utilised a combination of primary and secondary data sources to ensure comprehensive spatial analysis, with primary data collection involving the use of handheld Global Positioning System (GPS) devices to record the precise coordinates of existing hotel locations within Minna Metropolis, and field surveys conducted to validate and characterize current land cover types; secondary data sources included: (1) the administrative boundary map of Minna Metropolis, obtained from reputable geospatial data repositories (Regional Geospatial Data Infrastructure, 2023 ), (2) Landsat 8 Operational Land Imager (OLI) satellite imagery with a 30 m spatial resolution, sourced from the United States Geological Survey (USGS) Earth Explorer platform (USGS Earth Explorer, 2024), (3) Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data with a 30 m resolution, also obtained from USGS Earth Explorer (USGS Earth Explorer, 2024), and (4) high-resolution imagery from Google Earth Pro, used for visual interpretation and validation; all datasets were chosen for their relevance, accuracy, and currency, ensuring the robustness of the spatial analysis; the integration of primary GPS data for hotel location mapping and secondary data from reputable sources like USGS and Google Earth Pro reflects a common methodological approach in spatial analysis studies globally, where the combination of field data and satellite imagery is employed to enhance accuracy and comprehensiveness (Longley et al., 2015 ; Fisher and Unwin, 2005); the use of freely available datasets like Landsat 8 and SRTM DEM is consistent with the increasing trend in spatial research to utilise open-source data, promoting accessibility and reproducibility (Wulder et al., 2012 ; Farr et al., 2007 ), and the validation of data using high-resolution imagery from Google Earth Pro aligns with standard practices in remote sensing and GIS analysis, ensuring the reliability of the spatial datasets used in the study (Campbell and Wynne, 2011 ; Lillesand et al., 2015 ). 3.2 Influencing Factors for Hotel Site Suitability A comprehensive, multi-criteria approach, informed by a rigorous review of pertinent literature and expert consultation, was employed to evaluate hotel site suitability, identifying five key spatial determinants: Land Use/Land Cover (LULC), slope, elevation, existing hotel distribution, and road network accessibility, selected based on their established influence on hotel viability and accessibility, reflecting the complex interplay between environmental and infrastructural considerations crucial for optimal site selection; this multi-criteria approach, utilising LULC, slope, elevation, hotel distribution, and accessibility, aligns with methodologies used in similar studies across diverse geographical contexts, where these factors are consistently identified as critical determinants for hotel site selection (Wang et al., 2018 ; Chen et al., 2020 ), and the integration of environmental and infrastructural considerations reflects a global trend in spatial planning, emphasizing the need for a holistic approach to site suitability analysis (Geneletti, 2012 ; Malczewski, 2006 ); the utilisation of a multi-criteria decision analysis (MCDA) framework, encompassing LULC, topographic factors (slope and elevation), market dynamics (hotel distribution), and accessibility, is a prevalent strategy in international spatial planning research, reflecting a consensus on the importance of integrating diverse spatial datasets to optimize site selection (Feizizadeh et al., 2017 ; Dadashpoor et al., 2019 ); this approach is consistent with the increasing adoption of GIS-based MCDA techniques in urban development studies, where the incorporation of environmental and infrastructural variables is recognised as crucial for sustainable and economically viable development, aligning with global efforts to promote informed and integrated land use planning. 3.2.1 Land use/land cover (LULC) distribution LULC distribution is a fundamental factor influencing hotel site suitability, reflecting the current state of urban development and environmental characteristics; in this study, LULC classification was performed using Landsat 8 OLI imagery from 2021, processed in ENVI 5.1, employing the Maximum Likelihood Classification (MLC) algorithm, a widely recognised supervised classification technique, to categorize the LULC into six distinct classes: built-up areas, grassland, forest, bare soil, wetlands, and water bodies, with classification accuracy assessed using standard remote sensing validation techniques, ensuring the reliability of the LULC data; while recent advances in remote sensing classification, including deep learning algorithms, were considered, the MLC algorithm was chosen for its proven effectiveness and computational efficiency in this context (Congalton and Green, 2019 ); Table 1 presents the LULC classes and their corresponding descriptions; the use of Landsat 8 OLI imagery and the MLC algorithm for LULC classification is consistent with methodologies employed in numerous spatial planning and site suitability studies globally, where satellite imagery is extensively utilised for its synoptic coverage and cost-effectiveness (Liu et al., 2014 ; Seto et al., 2011 ), and the selection of the MLC algorithm, despite advancements in deep learning, reflects a pragmatic approach balancing accuracy with computational efficiency, a common consideration in studies with limited computational resources, aligning with methodological choices in various international contexts (Jensen, 2016 ; Richards and Jia, 2006 ). Table 1 LULC classes in the study area LULC classes Interpretation Built-Up Areas consisting of residential, commercial, transportation, and facilities. Water Body The area consists of streams, tributes, rivers, and lakes Vegetations This consists of leaves (broad and scanty), and also includes any form of green plantations that cannot be associated with food or economic crops. Agricultural Land This consists of regions where crops are grown for food or economic purposes. These locations are identified by their closeness to built-up regions, having a depiction of ridges in them, etc. Bare soil Involves mineral properties and bare soils where reefs dominate the ecosystem 3.2.2 Elevation map Elevation is a critical factor influencing hotel site suitability, impacting development costs and potential flood risks; the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) with a 30 m resolution, obtained from the United States Geological Survey (USGS) Earth Explorer platform (USGS Earth Explorer, 2024), was utilised for elevation analysis, this open-source dataset being widely recognised for its accuracy and global coverage (Farr et al., 2007 ); in ArcGIS 10.8, the DEM was processed to derive elevation classes relevant to hotel development, with areas of lower elevations assessed for potential flood risks, and higher elevations evaluated for accessibility and transportation costs, and the DEM was also integrated with other spatial datasets to determine proximity to key features such as highways and water bodies, crucial for hotel site selection; the utilisation of SRTM DEM data for elevation analysis is a common practice in global spatial planning and site suitability studies, reflecting the dataset's reliability and accessibility (Rodriguez et al., 2005 ; Jarvis et al., 2008 ), and the integration of elevation data with other spatial layers, such as road networks and water bodies, is consistent with methodologies employed in diverse geographical contexts, where the combined analysis of topographic and infrastructural factors is essential for informed site selection (Dewan et al., 2011 ; Ouma et al., 2016 ); the assessment of flood risks at lower elevations and accessibility at higher elevations aligns with established practices in urban development planning, demonstrating a global consensus on the importance of considering topographic influences in land use allocation (Tarboton, 1997 ; Montgomery and Dietrich, 1994 ). 3.2.3 Slope map Slope is another essential factor influencing the feasibility and cost of hotel construction, with the SRTM DEM used to generate a slope map of Minna Metropolis, classified into five categories representing varying degrees of steepness: 0–2% (nearly level), 2–6% (very gently sloping), 6–12% (moderately sloping), 12–20% (moderately steep), and > 20% (steep), classifications consistent with standard geotechnical engineering practices (Abrahart and White, 2001 ); the slope map, formatted using ArcGIS 10.8, was integrated into the Analytical Hierarchy Process (AHP) to assess its influence on hotel site suitability; the utilisation of SRTM DEM data for slope analysis and the classification of slope into standardized categories reflect common practices in global spatial planning and geotechnical engineering studies, where accurate slope representation is crucial for infrastructure development and land use allocation (Van Westen et al., 2008 ; Lin et al., 2013 ); the integration of slope data into the AHP framework is consistent with methodologies employed in various international contexts, where topographic factors are incorporated into multi-criteria decision-making processes to optimize site suitability assessments (Feizizadeh et al., 2017 ; Dadashpoor et al., 2019 ); the use of ArcGIS 10.8 for slope map generation and data integration aligns with the widespread adoption of GIS software in spatial analysis research, demonstrating a standardized approach to processing and analysing topographic data across diverse geographical settings. 3.2.4 Kernel density analysis of existing hotels and road network analysis Kernel density analysis is a robust spatial technique for assessing the distribution and clustering of point or line features, and in this study, the kernel density tool in ArcGIS 10.8 was used to map the spatial density of existing hotels, providing insights into the current market distribution and potential competitive areas, a technique widely applied in urban spatial analysis (Silverman, 1986 ); to evaluate accessibility, a critical factor for hotel viability, road network analysis was performed, and a line density algorithm in ArcGIS 10.8 was used to assess the density of the road network, reflecting the ease of access to potential hotel sites, an analysis crucial for understanding the impact of transportation infrastructure on hotel site selection; the application of kernel density analysis for assessing hotel clustering and road network density analysis for evaluating accessibility aligns with methodologies employed in various international urban spatial studies, where these techniques are utilized to understand spatial patterns and accessibility issues (Okabe and Sugihara, 2012 ; Geurs and van Wee, 2004 ); the use of ArcGIS 10.8 for these analyses is consistent with the global adoption of GIS software in spatial research, demonstrating a standardized approach to spatial data processing and analysis (Longley et al., 2015 ; Fisher and Unwin, 2005); the integration of kernel density analysis for market analysis and road network density for accessibility evaluation reflects a holistic approach to site suitability assessment, mirroring the methodologies applied in numerous studies across diverse geographical contexts, where both market dynamics and infrastructural factors are considered crucial for optimal site selection (Law et al., 2009 ; Handy and Niemeier, 1997 ). 3.2.5 Multi-criteria evaluation A GIS-based Multi-Criteria Decision Analysis (MCDA) framework was employed to integrate the various influencing factors, involving defining objectives, identifying and standardizing criteria, assigning weights, aggregating criteria, and validating the results, with the Analytical Hierarchy Process (AHP), a well-established MCDA technique, used to derive the weights for each factor, recognized for its ability to incorporate expert knowledge and facilitate pairwise comparisons, ensuring robust and consistent weighting (Saaty, 1980 ), and expert consultations and a review of relevant literature conducted to ensure the reliability of the assigned weights; the use of a GIS-based MCDA framework, particularly the AHP, is a widely adopted methodology in spatial planning and site suitability studies globally, reflecting the technique's effectiveness in integrating diverse spatial criteria and expert knowledge (Malczewski, 2006 ; Eastman et al., 1995 ); the AHP's capacity to facilitate pairwise comparisons and derive consistent weights is consistent with its application in numerous studies across diverse geographical contexts, where its robustness in handling complex spatial decision problems is recognized (Jankowski, 1995 ; Geneletti, 2012 ); the integration of expert consultations and literature reviews to validate the assigned weights aligns with best practices in MCDA, ensuring the reliability and applicability of the results, a common approach in international spatial planning research (Carver, 1991 ; Stewart et al., 2018 ). 3.3 Weighted Overlay The weighted overlay technique, a standard method in GIS-based MCDA, was used to combine the standardized factor maps, with a pairwise comparison matrix, developed through AHP, used to assign standardized relative weights to each factor, and the weighted linear combination method then used to integrate these weighted factors, resulting in a hotel site suitability map, with Table 2 presenting the influencing factors and their corresponding AHP weights; the utilization of the weighted overlay technique in conjunction with AHP for spatial suitability analysis is a widely recognized and applied methodology in global spatial planning research, reflecting a consensus on its effectiveness in integrating diverse spatial criteria (Malczewski, 2006 ; Eastman et al., 1995 ); the use of pairwise comparison matrices and weighted linear combination methods aligns with established practices in MCDA, ensuring a robust and transparent approach to spatial decision-making, as demonstrated in numerous studies across diverse geographical contexts (Jankowski, 1995 ; Carver, 1991 ); the integration of AHP-derived weights into the weighted overlay process is consistent with the increasing adoption of hybrid MCDA techniques in urban development planning, where the combination of quantitative and qualitative data is crucial for informed site selection (Stewart et al., 2018 ; Geneletti, 2012 ). Table 2 Influencing factors considered in this study and their calculated AHP weights Factors Weight Land Use Land Cover 46.88 The density of existing hotels 25.81 Road network 11.94 Slope map 7.86 Elevation map 7.51 Obtained consistency ratio (CR): 0.09 3.4 Hotel Suitability Map of Minna Metropolis Prior to the application of the weighted overlay technique, each influencing factor map was reclassified to reflect its relative contribution to hotel site suitability, involving assigning suitability scores to each class within the factor maps; for instance, in the slope map, classes representing steeper slopes were assigned lower suitability scores, reflecting their reduced suitability for hotel development due to increased construction costs and potential accessibility issues; the reclassified factor maps, along with their assigned weights derived from the Analytical Hierarchy Process (AHP), were then integrated using the weighted overlay tool in ArcGIS 10.8, generating a composite hotel site suitability map, visually representing the spatial distribution of suitability across Minna Metropolis; to facilitate interpretation and communication of the results, the continuous suitability scores were reclassified into four discrete categories: 1 = Highly Suitable, 2 = Suitable, 3 = Less Suitable, and 4 = Not Suitable, allowing for a clear and concise representation of hotel site suitability, aligning with standard cartographic practices for suitability mapping (Malczewski, 2006 ); the reclassification of factor maps and the use of the weighted overlay technique are consistent with methodologies employed in numerous spatial suitability studies globally, where the standardization of input data is crucial for integrated analysis (Eastman et al., 1995 ; Carver, 1991 ); the application of AHP-derived weights and the subsequent reclassification of suitability scores into discrete categories reflect established practices in multi-criteria decision analysis, ensuring the robustness and interpretability of the results (Jankowski, 1995 ; Stewart et al., 2018 ); the utilization of ArcGIS 10.8 for these processes aligns with the widespread adoption of GIS software in spatial analysis research, demonstrating a standardized approach to data processing and visualization across diverse geographical contexts (Longley et al., 2015 ; Fisher and Unwin, 2005). 4. Results and Discussion 4.1 Land Use/Land Cover Distribution Analysis of Minna Metropolis The Land Use/Land Cover (LULC) map of Minna Metropolis, derived from the 2021 Landsat 8 OLI imagery using the Maximum Likelihood Classification (MLC) algorithm, is presented in Fig. 2, with the percentage distribution of each LULC class illustrated in Fig. 3 ; as a rapidly urbanising metropolitan area, Minna is characterised by a significant proportion of bare ground, accounting for approximately 59.8% of the total land area, reflecting ongoing urban expansion and infrastructure development activities (National Population Commission, 2018 ), and built-up areas, representing the existing urban fabric, constitute 34.44% of the land area, highlighting the spatial extent of urban development within the metropolis; vegetation cover, including grassland and forested areas, accounts for approximately 4% of the land area, indicating the presence of green spaces within the urban matrix, agricultural land, representing areas used for farming activities, comprises 1.9% of the study area, and water bodies, including rivers and reservoirs, constitute the smallest LULC category, occupying only 0.061% of the total area; the LULC distribution provides critical insights into the spatial characteristics of Minna Metropolis, influencing hotel site suitability through factors such as accessibility, environmental quality, and potential land use conflicts, and the predominance of bare ground and built-up areas stresses the need for strategic urban planning to ensure sustainable hotel development, balancing economic growth with environmental considerations, which aligns with similar observations in rapidly urbanizing regions globally where land use changes significantly impact urban development patterns (Seto et al., 2011 ; Liu et al., 2014 ); recent studies have highlighted the impact of LULC changes on urban microclimate and environmental sustainability, emphasizing the importance of integrating LULC data into urban development planning, a trend observed in diverse geographical contexts (Oke, 1987 ; Foley et al., 2005 ), where the expansion of bare ground and built-up areas often leads to urban heat island effects and altered hydrological processes, necessitating careful consideration in urban planning and hotel site selection. 4.2 Topography 4.2.1 Elevation map The topographic characteristics of Minna Metropolis, particularly elevation, play a significant role in determining hotel site suitability, as illustrated in Figs. 4 and 5 , which present the numerical elevation values and the reclassified elevation suitability map, respectively; the elevation within Minna Metropolis ranges from 195 meters to 441 meters above sea level, with the southern region exhibiting the highest elevations, reflecting its underlying geological structure and landform characteristics (Geological Survey of Nigeria, 2023), creating diverse landscape features influencing both accessibility and scenic attractiveness; the reclassified elevation suitability map (Fig. 5 ) indicates a spatial trend where hotel site selection becomes less favorable towards the northern region of the city, suggesting that lower elevations may present challenges related to accessibility, drainage, and potential flood risks, while the southern region, with its higher elevations, demonstrates a greater proportion of highly suitable areas for hotel development; the results highlight that a substantial portion of the study area falls within the highly suitable elevation range, particularly in the southern region, attributed to factors such as enhanced scenic views, improved drainage, and reduced flood risk, aligning with observations in other regions where higher elevations often possess greater suitability for scenic attractiveness, a critical factor for hotels targeting tourism and leisure markets (Dewan et al., 2011 ); furthermore, recent studies on urban topography have emphasized the importance of considering elevation in infrastructure development and land use planning, highlighting its impact on accessibility and environmental sustainability (Tarboton, 1997 ), a consideration consistent with global trends in urban planning where topographic data is integral for informed site selection and development strategies, particularly in regions with varied elevation profiles (Ouma et al., 2016 ). 4.2.2 Slope map The slope characteristics of Minna Metropolis, reflecting its underlying landform patterns, are critical for assessing hotel site suitability, as presented in Fig. 6, which shows the slope map of the study area expressed as a percentage gradient, and Fig. 7 , which reclassifies the slope classes into five textual categories: 'Level,' 'Very Gently Sloping,' 'Moderately Sloping,' 'Moderately Steep Sloping,' and 'Steep Sloping'; unlike the elevation map, the spatial distribution of slope classes in Minna Metropolis exhibits a more heterogeneous pattern, reflecting the complex geomorphology of the region, yet the study area is predominantly characterized by 'Level' terrain, indicating a relatively flat topography overall; nevertheless, 'Steep Sloping' areas are observed in the northern and eastern regions, as indicated by the red areas in Fig. 7 , posing potential challenges for hotel development due to increased construction costs and accessibility constraints; the heterogeneous slope distribution in Minna, with its level terrains and steep sloping areas, is consistent with patterns observed in other rapidly developing urban areas globally, where varying slope conditions influence infrastructure development and land use planning (Lin et al., 2013 ; Van Westen et al., 2008 ); recent studies have highlighted the importance of considering slope stability and erosion potential in urban development, emphasizing the need for detailed slope analysis (Montgomery and Dietrich, 1994 ), a critical consideration in diverse climatic and geological contexts where slope characteristics significantly impact urban development viability and sustainability. 4.3 Distribution and Kernel Density Map of Existing Hotels The spatial distribution of existing hotels within Minna Metropolis provides valuable insights into the current market structure and potential competitive areas, as illustrated in Fig. 8 , which presents the point distribution of hotels represented by red points, serving as the basis for generating a kernel density map highlighting areas of hotel clustering; the kernel density map (Fig. 9) reveals a concentration of hotels in the central region of the study area, indicated by the red and yellow areas, suggesting a preference for central locations due to their accessibility and proximity to commercial and administrative centers, while the northern and peripheral regions exhibit a sparse distribution of hotels, indicating lower market penetration; this spatial pattern is consistent with observations in other urban areas, where hotel development tends to concentrate in central locations (Law et al., 2009 ), and the kernel density analysis provides a quantitative measure of hotel clustering, facilitating the identification of potential market gaps and opportunities; this central clustering phenomenon is observed in numerous urban environments globally, where accessibility and proximity to key commercial and administrative areas drive hotel location choices (Prideaux, 2000 ; McKercher, 2005 ), and the identification of peripheral market gaps aligns with broader tourism geography studies that emphasize the importance of spatial analysis in understanding and predicting hospitality industry trends. 4.4 Road Network Analysis and Hotel Accessibility Map Accessibility is a critical factor influencing the viability of hotel locations, particularly in urban environments, aligning with the observation by Rodrigue et al. ( 2017 ) that accessibility deficiencies are a significant challenge in the global hotel industry, necessitating a comprehensive analysis of road networks and hotel accessibility for informed site selection; Fig. 10 presents the road network density and accessibility map of Minna Metropolis, illustrating four levels of traffic density as an indicator of accessibility, revealing a discernible decrease in road network efficiency and suitability from the central region towards the periphery, highlighting the challenges of accessing peripheral areas, which may deter hotel development; the results indicate that a majority of the roads within the central region experience heavy traffic congestion, a common occurrence in urban areas with high economic activity, underscoring the need for strategic transportation planning to improve accessibility and support hotel development; this pattern of decreasing accessibility towards urban peripheries is consistent with findings in numerous global urban settings where central areas boast superior infrastructure and connectivity (Handy and Niemeier, 1997 ; Rodrigue et al., 2017 ), and recent advancements in transportation network analysis have emphasized the use of accessibility indices and network centrality measures to assess the impact of transportation infrastructure on urban development (Geurs and van Wee, 2004 ), reflecting a worldwide trend in utilizing sophisticated analytical methods to optimize urban accessibility and development. 4.5 AHP Results To determine the relative importance of the influencing factors for hotel site suitability in Minna Metropolis, the Analytical Hierarchy Process (AHP) was employed, considering five key factors: Land Use/Land Cover (LULC), existing hotel density, road accessibility, slope, and elevation, with Table 2 presenting the calculated weights for each factor derived from the AHP algorithm; the results indicate that LULC is the most influential factor, accounting for approximately 46% of the overall weight, underscoring the critical role of land use patterns in hotel site selection, reflecting the importance of accessibility, environmental quality, and potential land use conflicts, while existing hotel density and road accessibility also emerged as significant factors, highlighting the importance of market competition and transportation infrastructure, and elevation, while still relevant, was found to be the least influential factor, contributing approximately 8% to the overall weight; the AHP results provide a robust and transparent basis for the subsequent suitability mapping, reflecting the consensus of expert opinion and relevant literature (Saaty, 1980 ); the dominance of LULC in site suitability assessments aligns with global trends where land use patterns significantly influence development decisions in the hospitality sector (Miller and Morckel, 2012 ; Wong and Fesenmaier, 2007 ), and the application of AHP for multi-criteria decision-making is consistent with its widespread use in spatial planning and site selection studies across diverse geographical contexts (Malczewski, 2006 ; Joerin et al., 2001 ), demonstrating a universal approach to integrating diverse factors in suitability analyses. 4.6 Hotel Site Suitability Map The integrated hotel site suitability map for Minna Metropolis, generated using the weighted overlay technique and the AHP-derived weights, is presented in Fig. 11 , delineating four suitability classes: 'Highly Suitable,' 'Suitable,' 'Less Suitable,' and 'Not Suitable'; the map reveals that a significant portion of the central region and the far north of the study area are classified as 'Highly Suitable' for hotel development, encompassing approximately 54.24 square kilometers, characterized by favorable LULC patterns, high road accessibility, and proximity to existing hotel clusters, while 'Suitable' areas, covering approximately 22.60 square kilometers, are predominantly located in the southern and northern regions, indicating moderate suitability for hotel development; 'Less Suitable' areas, represented in orange, cover 17.98 square kilometers and are concentrated in the central region, presenting challenges related to LULC constraints or accessibility, and 'Not Suitable' areas, depicted in red, extend over 46.89 square kilometers and are predominantly found in the central region, constrained by dense urban development or environmental factors; overall, the suitability map indicates that over 50% of Minna Metropolis is at least moderately suitable for hotel development, highlighting the potential for further hotel development in the city, particularly in the central and northern regions, and the spatial distribution of suitability classes provides valuable insights for hotel developers and urban planners, facilitating informed decisions regarding hotel site selection and urban development strategies; the application of weighted overlay and AHP for spatial suitability analysis aligns with global best practices in urban planning and site selection, where multi-criteria decision-making is essential (Eastman et al., 1995 ; Carver, 1991 ), and the identification of optimal development locations through spatial suitability mapping is consistent with numerous studies across diverse geographical contexts (Malczewski, 2006 ; Jankowski, 1995 ), demonstrating the broad applicability of these techniques in urban development planning. 5. Conclusion This study successfully applied a geospatial multi-criteria decision analysis (MCDA) framework, integrating the Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) methodologies, to identify optimal hotel site locations in Minna Metropolis, Niger State, Nigeria, addressing the pressing need for strategic accommodation planning in response to the city's increasing tourism and immigration, which are driving demand for hospitality services; the study's findings reveal a critical spatial pattern: the central urban core of Minna Metropolis, characterized by high hotel density and urban development, is identified as the least suitable area for new hotel construction, while the peripheral northern and southern regions are classified as 'Most Suitable' and 'Moderately Suitable,' respectively, encompassing over 54% of the city's land area, underscoring the importance of considering urban development patterns and market saturation in hotel site selection; the AHP analysis highlighted Land Use/Land Cover (LULC) as the most influential factor in determining hotel site suitability, reflecting its significant impact on accessibility, environmental quality, and development costs, and existing hotel density also emerged as a critical factor, emphasizing the importance of market analysis and competitive positioning in hotel development, findings that align with recent studies emphasizing the integration of LULC and market analysis in urban land use planning (Stewart et al., 2018 ); the implications of this research are significant for hotel industry stakeholders and urban planners, as by prioritizing LULC and existing hotel density, developers can optimize hotel site selection to maximize economic benefits and minimize environmental impacts, and the spatial suitability map generated in this study provides a robust decision support tool for urban development planning, facilitating the sustainable expansion of the hospitality sector in Minna Metropolis; future research should explore the integration of socio-economic factors and detailed environmental assessments to further refine hotel site suitability models, ensuring long-term sustainability and economic viability (Geneletti, 2012 ). Declarations Ethical Approval This paper has not been previously published and is not currently under review for publication in any other journal. Consent to Participate The authors were involved in developing and submitting this paper for publication in Applied Geomatics. Consent to Publish The authors consent to the publication of this manuscript in Applied Geomatics, confirming it is original and not under consideration elsewhere. Competing Interests The authors have no relevant financial or non-financial interests to disclose Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Data Availability Data will be made available on request. Author Contributions The authors contributed to the study's conception and design. EAA and SC handled material preparation, data collection, and analysis, with EAA supervising the study. EAA and SC wrote the first draft of the manuscript, and the authors commented on previous versions. The authors read and approved the final manuscript. References Abrahart, RJ, & White, M (2001) Neural network-based rainfall-runoff modelling using a differential split-sample approach. Journal of Hydrology, 252(1-4), 216-232. Adeleke, OO, & Adewole, AO (2019) Spatial distribution of hospitality facilities in urban centers. Journal of Geography and Regional Planning, 12(4), 89-98. Amadeus Hospitality (2024) Hospitality Group and Business Performance Index - Q2 2024. Retrieved from https://www.amadeus-hospitality.com/resources/hospitality-group-business-performance-index-q2-2024/ Aribigbola, OA (2008) Improving Urban Land Use Planning and Management in Nigeria: The Case of Akure. Theoretical and Empirical Researches in Urban Management, 3(9), 1-14. Campbell, JB, & Wynne, RH (2011) Introduction to remote sensing. Guilford Press. Carver, S (1991) Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information System, 5(3), 321-339. Chen, Y, Li, X, & Liu, X (2020) Identifying urban functional zones based on semantic embedding of POI data. Remote Sensing, 12(1), 151. Congalton, RG, & Green, K (2019) Assessing the accuracy of remotely sensed data: principles and practices. CRC Press. Dadashpoor, H, Azizi, P, & Moghadam, HS (2019) Factors affecting urban sprawl in Iran: A review. Journal of Urban Planning and Development, 145(1), 04018059. Dewan, AM, Yamaguchi, Y, & Rahman, M (2011) Evaluating flood hazard and inundation damage in the southwestern coastal region of Bangladesh using DEMs, remote sensing and GIS techniques. Progress in Physical Geography, 35(2), 163-187. Dwyer, L, Chen, N (Chris) & Lee, J (Jiyeon) (2019) The role of place attachment in tourism research. Journal of Travel & Tourism Marketing, 36(5), 645-652. Eastman, JR, Kyem, PAK, Toledano, J, & Jin, W (1995) GIS and decision making. UNITAR Explorations in Geographic Information Systems Technology, 4, 1-60. European Journal of Tourism Hospitality and Recreation (2020) Competition in Hotel Industry: Theory, Evidence and Business Practice. European Journal of Tourism Hospitality and Recreation, 10(3), 200-202. DOI: 10.2478/ejthr-2020-0017 Farr, TG, Rosen, PA, Caro, E, Crippen, R, Duren, R, Hensley, S, & Alsdorf, DE (2007) The shuttle radar topography mission. Reviews of geophysics, 45(2). Feizizadeh, B, & Blaschke, T (2013) GIS-MCDA for urban sustainability. Environmental Monitoring and Assessment, 185(11), 9123-9141. Feizizadeh, B, Blaschke, T, Nazemosadat, MJ, & Karkhaneh, SP (2017) A GIS-based fuzzy-AHP approach for flood susceptibility mapping in the Taleghan watershed, Iran. Science of the total environment, 615, 667-680. Fisher, P, & Unwin, D (2005) Re-presenting GIS. John Wiley & Sons. Foley, JA, DeFries, R, Asner, GP, Barford, C, Bonan, G, Carpenter, SR, & Snyder, PK (2005) Global consequences of land use. Science, 309(5734), 570-574. Geneletti, D (2012) Integrating ecosystem services into strategic environmental assessment: A methodological framework and an application to spatial planning. Environmental impact assessment review, 36, 21-33. Geological Survey of Nigeria (2023) Geurs, KT, & van Wee, B (2004) Accessibility evaluation of land-use and transport strategies: review and research directions. Journal of transport geography, 12(2), 127-140. Handy, SL, & Niemeier, DA (1997) Measuring accessibility: an exploration of issues and alternatives. Environment and planning A, 29(7), 1175-1194. Jankowski, P (1995) Integrating geographical information systems and multiple criteria decision-making methods. International Journal of Geographical Information Systems, 9(3), 251-273. Jarvis, A, Reuter, HI, Nelson, A, & Guevara, E (2008) Hole-filled SRTM for the globe version 4. International centre for tropical agriculture (CIAT). Jensen, JR (2016) Remote sensing of the environment: an earth resource perspective. Pearson Education Limited. Joerin, F, Thériault, M, & Musy, A (2001) Using GIS and outranking multi-criteria analysis for land-use suitability assessment. Journal of environmental management, 61(2), 153-169. Kasim, M (2021) The role of local communities in developing potential areas for marine tourism. Journal of Sustainable Tourism, 29(7), 1101-1118. Kim, K, & Jogaratnam, G (2010) Effects of Individual and Organizational Factors on Job Satisfaction and Intent to Stay in the Hotel and Restaurant Industry. Journal of Human Resources in Hospitality & Tourism, 9(3), 324-339. Law, R, Leung, R, & Lo, A (2009) The impact of hotel clustering on the length of stay of inbound tourists. Journal of travel research, 48(2), 195-202. Lillesand, TM, Kiefer, RW, & Chipman, JW (2015) Remote sensing and image interpretation. John Wiley & Sons. Lin, CY, Chen, SC, & Wu, CC (2013) Integrating multi-criteria decision making and GIS for slope hazard assessment. Natural hazards, 65(1), 45-63. Liu, X, Hu, G, Chen, Y, Huang, Y, & Cao, X (2014) Extracting urban built-up areas from Landsat images using the normalized difference built-up index. Landscape and urban planning, 121, 95-101. Longley, PA, Goodchild, MF, Maguire, DJ, & Rhind, DW (2015) Geographic information systems and science. John Wiley & Sons. Malczewski, J (2006) GIS-based multicriteria decision analysis. John Wiley & Sons. McKercher, B (2005) Tourism: a discipline of disciplines. Channel View Publications. Miller, NG, & Morckel, C (2012) Hotel location and performance. Journal of hospitality & tourism research, 36(1), 3-26. Montgomery, DR, & Dietrich, WE (1994) A physically based model for the topographic control on shallow landsliding. Water resources research, 30(5), 1153-1171. National Bureau of Statistics (2023) National Population Commission. (2018) Niger State Tourism Board (2023) Niger State Urban Development Report. (2024) Nigerian Investment Promotion Commission. (2024) Okabe, A, & Sugihara, K (2012) Spatial analysis along networks: statistical tools and applications. John Wiley & Sons. Oke, TR (1987) Boundary layer climates. Routledge. Ouma, YO, Tateishi, R, & Johnson, BA (2016) Urban flood vulnerability and risk mapping using integrated multi-parametric flood index (IMFI). Water, 8(7), 244. Oyinloye, MA, & Adeyemo, AA (2022) Application of GIS in urban land use analysis. Remote Sensing Applications: Society and Environment, 27, 100790. Prideaux, B (2000) The role of tourist attractions in the development of sustainable tourism in regional areas. Sustainable tourism in island destinations: issues and approaches, 118-142. Regional Geospatial Data Infrastructure (2023) Richards, JA., & Jia, X (2006) Remote sensing digital image analysis: an introduction. Springer Science & Business Media. Rodrigue, JP, Comtois, C, & Slack, B (2017) The geography of transport systems. Routledge. Rodriguez, E, Morris, CS & Belz, JE (2005) A global assessment of the height accuracy of the Shuttle Radar Topography Mission. Photogrammetric engineering and remote sensing, 71(6), 689-695. Saaty, TL (1980) The analytic hierarchy processes. McGraw-Hill International. Seto, KC, Guneralp, B, & Hutyra, LR (2011) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the national academy of sciences, 109(40), 16083-16088. Silverman, BW (1986) Density estimation for statistics and data analysis. CRC press. Stewart, TJ, Figueira, J, & Greco, S (2018) Multiple criteria decision analysis: State of the art surveys. International Series in Operations Research & Management Science, 270, 1-32. Tarboton, DG (1997) A new method for determining flow directions and upslope areas in grid digital elevation models. Water resources research, 33(3), 309-319. THISDAYLIVE (2024) Transcorp Hotels Named Hospitality Company of 2024. Retrieved from https://www.thisdaylive.com/index.php/2025/03/24/transcorp-hotels-named-hospitality-company-of-2024/ United Nations World Tourism Organization (2023) United States Geological Survey Earth Explorer (2024) Van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data requirements for landslide hazard and risk assessment: a review. Landslides, 5(3), 261-273. Wang, Y., Li, X., & Liu, X. (2018). Inferring urban land use from POI and road data using random forests. Remote Sensing, 10(6), 808. Wong, K. K., & Fesenmaier, D. R. (2007). Defining the spatial extent of tourism regions. Journal of travel research, 46(2), 143-152. Wulder, M. A., White, J. C., Nelson, R. F. & Roy, D. P. (2012). Opening the archive: How free satellite data has fostered an unprecedented period of global mapping. Remote Sensing of Environment , 123 , 2-11. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Applied Geomatics → Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 08 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 03 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6370819","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494050018,"identity":"52cbf6d5-e8c7-4926-b80f-8de16c891467","order_by":0,"name":"Ekundayo A. Adesina","email":"data:image/png;base64,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","orcid":"","institution":"Federal University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ekundayo","middleName":"A.","lastName":"Adesina","suffix":""},{"id":494050020,"identity":"43901d0d-f572-492e-9854-da81621f3e76","order_by":1,"name":"S. Chukwu","email":"","orcid":"","institution":"Federal University of Technology","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"","lastName":"Chukwu","suffix":""}],"badges":[],"createdAt":"2025-04-03 15:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6370819/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6370819/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12518-025-00685-9","type":"published","date":"2026-02-03T15:58:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88239525,"identity":"234d4a1c-abf1-4dc7-a957-822d80226bb1","added_by":"auto","created_at":"2025-08-04 10:59:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":263565,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area Map\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/72b6f96e5db2209e48e93699.png"},{"id":88239526,"identity":"e469fde1-9f28-48b6-bf33-f747c55376f2","added_by":"auto","created_at":"2025-08-04 10:59:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":720494,"visible":true,"origin":"","legend":"\u003cp\u003eLand Use Land Cover map of Minna\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/b58f3ba4c276da14eea4b402.png"},{"id":88239924,"identity":"02f3e64a-311c-493b-b241-43eac7976b72","added_by":"auto","created_at":"2025-08-04 11:07:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126725,"visible":true,"origin":"","legend":"\u003cp\u003eLULC distribution chart\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/2897237873da1ebdedcc6e01.png"},{"id":88241018,"identity":"7a325c0c-dcfd-44f6-8b4b-a5f6636046a0","added_by":"auto","created_at":"2025-08-04 11:15:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":352539,"visible":true,"origin":"","legend":"\u003cp\u003eElevation distribution map of the study area\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/be6428691e919926fb3e4a17.png"},{"id":88239528,"identity":"d8e45ec5-eb6c-4335-984f-38e92dd54d76","added_by":"auto","created_at":"2025-08-04 10:59:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":188968,"visible":true,"origin":"","legend":"\u003cp\u003eElevation suitability map for hotel sitting\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/a75576d27670ec90c300b5d0.png"},{"id":88239530,"identity":"ef4ce000-69c7-42f6-a78d-ad8041bb912e","added_by":"auto","created_at":"2025-08-04 10:59:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":504152,"visible":true,"origin":"","legend":"\u003cp\u003eSlope map of the study area\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/508799a85f3330948a44b262.png"},{"id":88241020,"identity":"779c49e0-c227-401c-bb4a-baa0ededcd88","added_by":"auto","created_at":"2025-08-04 11:15:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":514502,"visible":true,"origin":"","legend":"\u003cp\u003eReclassified slope map\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/7fa964eaa74460a83282ff47.png"},{"id":88239920,"identity":"1bd59089-ef75-457a-aeeb-40619f6742f1","added_by":"auto","created_at":"2025-08-04 11:07:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":365355,"visible":true,"origin":"","legend":"\u003cp\u003eExisting hotels in Minna\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/a0fb657800eb64e5ae4586fa.png"},{"id":88239548,"identity":"269adee3-9327-4ea2-a284-25805a6431c7","added_by":"auto","created_at":"2025-08-04 10:59:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":196791,"visible":true,"origin":"","legend":"\u003cp\u003eKernel density map of existing hotels\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/c928a8d6dc3fa08282f59520.png"},{"id":88239541,"identity":"1c4191bc-8e08-4c39-8c7a-24d8fee561f8","added_by":"auto","created_at":"2025-08-04 10:59:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":203561,"visible":true,"origin":"","legend":"\u003cp\u003eRoad Network Analysis\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/112438c7fc6a208283756050.png"},{"id":88239545,"identity":"8e33722f-238e-46fd-b090-c815fafefd8c","added_by":"auto","created_at":"2025-08-04 10:59:17","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":388885,"visible":true,"origin":"","legend":"\u003cp\u003eHotel Site Suitability Map\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/9342b46b82622c3b10aab43b.png"},{"id":102235132,"identity":"09e25a50-78e4-4aa3-a566-60917034b520","added_by":"auto","created_at":"2026-02-09 16:15:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4409312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6370819/v1/aadf10ed-45be-4903-bfff-5dd066984eb8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial Multi-Criteria Decision Analysis for Hotel Site Suitability Assessment in Minna Metropolis, Nigeria: Integrating Remote Sensing and GIS","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global hospitality sector, particularly the hotel industry, is a pivotal economic catalyst, especially in regions experiencing burgeoning tourism and dynamic business travel patterns (Kasim, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The hotel industry significantly contributes to economic growth by stimulating local economies through job creation, increased tax revenues, and the development of ancillary services that support the broader tourism ecosystem (Dwyer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Africa's hotel market is undergoing significant expansion, driven by accelerating urbanization, a growing middle class, and increased foreign direct investment, a key strategic player in this landscape (UNWTO, 2023). Recent industry reports indicate Nigeria's ascent to the second position in Africa's 2024 hotel development rankings, as measured by room numbers, reflecting substantial growth in investment and the country's enhanced attractiveness to leading international hotel chains (THISDAYLIVE, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This trajectory highlights the critical importance of strategic hotel site selection, as location directly and profoundly influences profitability, market penetration, long-term operational sustainability, and the overall competitiveness of hotel enterprises (European Journal of Tourism Hospitality and Recreation, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim and Jogaratnam, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the equitable spatial distribution of hotel developments presents a significant and multifaceted challenge in many rapidly urbanizing African centers. The confluence of accelerated urbanization, escalating hospitality demand, and often constrained urban planning capacities frequently result in haphazard development patterns and suboptimal site selection outcomes (Aribigbola, 2018). In Nigeria, particularly within developing urban centers such as Minna Metropolis, identifying optimal hotel locations is paramount for driving sustainable economic development, enhancing regional tourism potential, and ensuring balanced and equitable urban growth trajectories. The increasing influx of investments from international hotel chains further accentuates the critical need for informed and data-driven site selection methodologies, highlighting the potential risks associated with uncoordinated and haphazard development patterns, as evidenced by industry reports from Amadeus Hospitality (2024). Without systematic spatial planning frameworks, these investments may yield suboptimal financial returns, contribute to unsustainable urban sprawl, and exacerbate existing infrastructural deficits and challenges (Kim and Jogaratnam, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Moreover, the absence of strategic spatial planning can lead to the concentration of hotel developments in already congested urban areas, neglecting the developmental potential of peripheral locations and consequently hindering equitable regional development outcomes (Adeleke and Adewole, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the availability of advanced geospatial technologies, including Remote Sensing (RS) and Geographical Information Systems (GIS), for spatial analysis applications, their comprehensive and integrated application in hotel site suitability assessments within rapidly urbanizing African contexts such as Minna Metropolis remains notably limited (Oyinloye and Adeyemo, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Existing studies often lack an integrated analytical approach that effectively combines these powerful geospatial technologies with Multi-Criteria Decision Analysis (MCDA) frameworks, such as the Analytical Hierarchy Process (AHP), which enables the systematic evaluation of multiple, often conflicting, decision criteria (Malczewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, a significant knowledge gap exists in the effective integration of recent and dynamic spatial data, including high-resolution satellite imagery, open-source geospatial datasets, and real-time urban development indicators, with advanced MCDA methodologies to develop robust and data-driven spatial decision support systems for hotel development within such dynamic and rapidly evolving urban environments (Feizizadeh and Blaschke, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Specifically, many contemporary studies fail to consider the dynamic nature of urban growth processes, the potential impact of climate change on site suitability assessments, and the comprehensive integration of socioeconomic factors within the decision-making framework (Aribigbola, 2018).\u003c/p\u003e\u003cp\u003eThis research aims to address these critical knowledge gaps by developing and implementing a geospatial MCDA framework for the comprehensive assessment of hotel site suitability within Minna Metropolis, Niger State, Nigeria. By integrating recent and dynamic spatial data, including high-resolution remote sensing imagery, open-source GIS datasets, and relevant local socioeconomic indicators, and by applying rigorous analytical methods such as the Analytical Hierarchy Process (AHP), this study contributes to the development of sustainable urban planning strategies provides robust support for the regional hospitality sector. The findings generated from this research provide valuable and actionable insights for hotel industry stakeholders, urban planners, and policymakers, facilitating informed and data-driven decisions regarding hotel development in similar urbanizing contexts across the African continent. Incorporating change-advanced geospatial techniques with MCDA methodologies offers a robust, transparent, and replicable analytical framework in other regions facing similar urban development challenges. Moreover, this study directly addresses the urgent need for data-driven spatial decision support systems that comprehensively consider the dynamic nature of urban growth and the long-term sustainability of hotel development, thereby promoting balanced, equitable, and environmentally sustainable urban expansion. By providing a comprehensive and spatially explicit analysis, this research endeavours to mitigate the risks associated with haphazard development patterns and ensure that hotel investments contribute positively and sustainably to the socioeconomic development of Minna Metropolis and other comparable urban centers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study focuses on Minna, the capital city of Niger State, located in the North-Central geopolitical zone of Nigeria, experiencing rapid urbanization driven by natural population growth and rural-urban migration, consistent with trends observed in many Nigerian state capitals (National Bureau of Statistics, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); according to recent demographic projections, Minna's population has seen a significant increase over the past decade, placing heightened demands on urban infrastructure and services (Niger State Urban Development Report, 2024); geographically, Minna is situated between latitudes 9\u0026deg;31'20\"N and 9\u0026deg;41'27\"N and longitudes 6\u0026deg;24'59\"E and 6\u0026deg;37'42\"E, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, its strategic location making it a key administrative and commercial hub within the region, and notably, Minna possesses a diverse landscape and is proximate to several tourist attractions, including the Gurara Waterfalls, which have the potential to stimulate the local hospitality industry (Niger State Tourism Board, 2023), however, the city's current hotel infrastructure is struggling to keep pace with the increasing demands of business travelers and tourists, highlighting the need for strategic spatial planning; furthermore, recent economic reports indicate that Minna is attracting increased attention from domestic and international investors, particularly in sectors related to agriculture and infrastructure development (Nigerian Investment Promotion Commission, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which is expected to further drive urbanization and increase the demand for quality hotel accommodations, and the spatial analysis of hotel site suitability within Minna, therefore, provides a timely and relevant contribution to the city's urban development planning.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Materials and Methods","content":"\u003cp\u003eThis study employed a systematic methodological approach involving data acquisition, geospatial data processing, application of the Analytical Hierarchy Process (AHP), and spatial suitability analysis, designed to integrate diverse geospatial datasets and expert knowledge to determine optimal hotel site locations in Minna Metropolis; the systematic integration of geospatial data processing, AHP, and spatial suitability analysis aligns with a widely adopted methodology in spatial planning and site selection studies across various global contexts, where multi-criteria decision-making frameworks are commonly employed to address complex spatial problems (Malczewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Eastman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); the use of AHP, in particular, is consistent with its established role in facilitating the integration of expert knowledge and quantitative data in spatial decision-making, as demonstrated in numerous studies focusing on land use planning and urban development (Saaty, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Jankowski, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); furthermore, the application of GIS-based spatial suitability analysis, incorporating diverse geospatial datasets, reflects a standard practice in contemporary urban planning research, where the spatial distribution of relevant factors is crucial for informed decision-making (Carver, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Stewart et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and this methodological approach is consistent with the increasing reliance on geospatial technologies to optimize land use allocation and promote sustainable urban development in various international settings.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data Acquisition and Sources\u003c/h2\u003e\u003cp\u003eThe study utilised a combination of primary and secondary data sources to ensure comprehensive spatial analysis, with primary data collection involving the use of handheld Global Positioning System (GPS) devices to record the precise coordinates of existing hotel locations within Minna Metropolis, and field surveys conducted to validate and characterize current land cover types; secondary data sources included: (1) the administrative boundary map of Minna Metropolis, obtained from reputable geospatial data repositories (Regional Geospatial Data Infrastructure, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), (2) Landsat 8 Operational Land Imager (OLI) satellite imagery with a 30 m spatial resolution, sourced from the United States Geological Survey (USGS) Earth Explorer platform (USGS Earth Explorer, 2024), (3) Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data with a 30 m resolution, also obtained from USGS Earth Explorer (USGS Earth Explorer, 2024), and (4) high-resolution imagery from Google Earth Pro, used for visual interpretation and validation; all datasets were chosen for their relevance, accuracy, and currency, ensuring the robustness of the spatial analysis; the integration of primary GPS data for hotel location mapping and secondary data from reputable sources like USGS and Google Earth Pro reflects a common methodological approach in spatial analysis studies globally, where the combination of field data and satellite imagery is employed to enhance accuracy and comprehensiveness (Longley et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fisher and Unwin, 2005); the use of freely available datasets like Landsat 8 and SRTM DEM is consistent with the increasing trend in spatial research to utilise open-source data, promoting accessibility and reproducibility (Wulder et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Farr et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and the validation of data using high-resolution imagery from Google Earth Pro aligns with standard practices in remote sensing and GIS analysis, ensuring the reliability of the spatial datasets used in the study (Campbell and Wynne, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lillesand et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Influencing Factors for Hotel Site Suitability\u003c/h2\u003e\u003cp\u003eA comprehensive, multi-criteria approach, informed by a rigorous review of pertinent literature and expert consultation, was employed to evaluate hotel site suitability, identifying five key spatial determinants: Land Use/Land Cover (LULC), slope, elevation, existing hotel distribution, and road network accessibility, selected based on their established influence on hotel viability and accessibility, reflecting the complex interplay between environmental and infrastructural considerations crucial for optimal site selection; this multi-criteria approach, utilising LULC, slope, elevation, hotel distribution, and accessibility, aligns with methodologies used in similar studies across diverse geographical contexts, where these factors are consistently identified as critical determinants for hotel site selection (Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the integration of environmental and infrastructural considerations reflects a global trend in spatial planning, emphasizing the need for a holistic approach to site suitability analysis (Geneletti, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Malczewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); the utilisation of a multi-criteria decision analysis (MCDA) framework, encompassing LULC, topographic factors (slope and elevation), market dynamics (hotel distribution), and accessibility, is a prevalent strategy in international spatial planning research, reflecting a consensus on the importance of integrating diverse spatial datasets to optimize site selection (Feizizadeh et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dadashpoor et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); this approach is consistent with the increasing adoption of GIS-based MCDA techniques in urban development studies, where the incorporation of environmental and infrastructural variables is recognised as crucial for sustainable and economically viable development, aligning with global efforts to promote informed and integrated land use planning.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Land use/land cover (LULC) distribution\u003c/h2\u003e\u003cp\u003eLULC distribution is a fundamental factor influencing hotel site suitability, reflecting the current state of urban development and environmental characteristics; in this study, LULC classification was performed using Landsat 8 OLI imagery from 2021, processed in ENVI 5.1, employing the Maximum Likelihood Classification (MLC) algorithm, a widely recognised supervised classification technique, to categorize the LULC into six distinct classes: built-up areas, grassland, forest, bare soil, wetlands, and water bodies, with classification accuracy assessed using standard remote sensing validation techniques, ensuring the reliability of the LULC data; while recent advances in remote sensing classification, including deep learning algorithms, were considered, the MLC algorithm was chosen for its proven effectiveness and computational efficiency in this context (Congalton and Green, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the LULC classes and their corresponding descriptions; the use of Landsat 8 OLI imagery and the MLC algorithm for LULC classification is consistent with methodologies employed in numerous spatial planning and site suitability studies globally, where satellite imagery is extensively utilised for its synoptic coverage and cost-effectiveness (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Seto et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the selection of the MLC algorithm, despite advancements in deep learning, reflects a pragmatic approach balancing accuracy with computational efficiency, a common consideration in studies with limited computational resources, aligning with methodological choices in various international contexts (Jensen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Richards and Jia, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\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\u003eLULC classes in the study area\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULC classes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAreas consisting of residential, commercial, transportation, and facilities.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater Body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe area consists of streams, tributes, rivers, and lakes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis consists of leaves (broad and scanty), and\u0026nbsp;also includes any form of green plantations that cannot be associated with food or economic crops.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural Land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis consists of regions where crops are grown for food or economic purposes. These locations are identified by their closeness to built-up regions, having a\u0026nbsp;depiction of ridges in them, etc.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBare soil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvolves mineral properties and bare soils where reefs dominate the ecosystem\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=\"Section3\"\u003e\u003ch2\u003e3.2.2 Elevation map\u003c/h2\u003e\u003cp\u003eElevation is a critical factor influencing hotel site suitability, impacting development costs and potential flood risks; the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) with a 30 m resolution, obtained from the United States Geological Survey (USGS) Earth Explorer platform (USGS Earth Explorer, 2024), was utilised for elevation analysis, this open-source dataset being widely recognised for its accuracy and global coverage (Farr et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e); in ArcGIS 10.8, the DEM was processed to derive elevation classes relevant to hotel development, with areas of lower elevations assessed for potential flood risks, and higher elevations evaluated for accessibility and transportation costs, and the DEM was also integrated with other spatial datasets to determine proximity to key features such as highways and water bodies, crucial for hotel site selection; the utilisation of SRTM DEM data for elevation analysis is a common practice in global spatial planning and site suitability studies, reflecting the dataset's reliability and accessibility (Rodriguez et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jarvis et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and the integration of elevation data with other spatial layers, such as road networks and water bodies, is consistent with methodologies employed in diverse geographical contexts, where the combined analysis of topographic and infrastructural factors is essential for informed site selection (Dewan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ouma et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); the assessment of flood risks at lower elevations and accessibility at higher elevations aligns with established practices in urban development planning, demonstrating a global consensus on the importance of considering topographic influences in land use allocation (Tarboton, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Montgomery and Dietrich, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Slope map\u003c/h2\u003e\u003cp\u003eSlope is another essential factor influencing the feasibility and cost of hotel construction, with the SRTM DEM used to generate a slope map of Minna Metropolis, classified into five categories representing varying degrees of steepness: 0\u0026ndash;2% (nearly level), 2\u0026ndash;6% (very gently sloping), 6\u0026ndash;12% (moderately sloping), 12\u0026ndash;20% (moderately steep), and \u0026gt;\u0026thinsp;20% (steep), classifications consistent with standard geotechnical engineering practices (Abrahart and White, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); the slope map, formatted using ArcGIS 10.8, was integrated into the Analytical Hierarchy Process (AHP) to assess its influence on hotel site suitability; the utilisation of SRTM DEM data for slope analysis and the classification of slope into standardized categories reflect common practices in global spatial planning and geotechnical engineering studies, where accurate slope representation is crucial for infrastructure development and land use allocation (Van Westen et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); the integration of slope data into the AHP framework is consistent with methodologies employed in various international contexts, where topographic factors are incorporated into multi-criteria decision-making processes to optimize site suitability assessments (Feizizadeh et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dadashpoor et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); the use of ArcGIS 10.8 for slope map generation and data integration aligns with the widespread adoption of GIS software in spatial analysis research, demonstrating a standardized approach to processing and analysing topographic data across diverse geographical settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4 Kernel density analysis of existing hotels and road network analysis\u003c/h2\u003e\u003cp\u003eKernel density analysis is a robust spatial technique for assessing the distribution and clustering of point or line features, and in this study, the kernel density tool in ArcGIS 10.8 was used to map the spatial density of existing hotels, providing insights into the current market distribution and potential competitive areas, a technique widely applied in urban spatial analysis (Silverman, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1986\u003c/span\u003e); to evaluate accessibility, a critical factor for hotel viability, road network analysis was performed, and a line density algorithm in ArcGIS 10.8 was used to assess the density of the road network, reflecting the ease of access to potential hotel sites, an analysis crucial for understanding the impact of transportation infrastructure on hotel site selection; the application of kernel density analysis for assessing hotel clustering and road network density analysis for evaluating accessibility aligns with methodologies employed in various international urban spatial studies, where these techniques are utilized to understand spatial patterns and accessibility issues (Okabe and Sugihara, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Geurs and van Wee, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); the use of ArcGIS 10.8 for these analyses is consistent with the global adoption of GIS software in spatial research, demonstrating a standardized approach to spatial data processing and analysis (Longley et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fisher and Unwin, 2005); the integration of kernel density analysis for market analysis and road network density for accessibility evaluation reflects a holistic approach to site suitability assessment, mirroring the methodologies applied in numerous studies across diverse geographical contexts, where both market dynamics and infrastructural factors are considered crucial for optimal site selection (Law et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Handy and Niemeier, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.2.5 Multi-criteria evaluation\u003c/h2\u003e\u003cp\u003eA GIS-based Multi-Criteria Decision Analysis (MCDA) framework was employed to integrate the various influencing factors, involving defining objectives, identifying and standardizing criteria, assigning weights, aggregating criteria, and validating the results, with the Analytical Hierarchy Process (AHP), a well-established MCDA technique, used to derive the weights for each factor, recognized for its ability to incorporate expert knowledge and facilitate pairwise comparisons, ensuring robust and consistent weighting (Saaty, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), and expert consultations and a review of relevant literature conducted to ensure the reliability of the assigned weights; the use of a GIS-based MCDA framework, particularly the AHP, is a widely adopted methodology in spatial planning and site suitability studies globally, reflecting the technique's effectiveness in integrating diverse spatial criteria and expert knowledge (Malczewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Eastman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); the AHP's capacity to facilitate pairwise comparisons and derive consistent weights is consistent with its application in numerous studies across diverse geographical contexts, where its robustness in handling complex spatial decision problems is recognized (Jankowski, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Geneletti, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); the integration of expert consultations and literature reviews to validate the assigned weights aligns with best practices in MCDA, ensuring the reliability and applicability of the results, a common approach in international spatial planning research (Carver, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Stewart et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Weighted Overlay\u003c/h2\u003e\u003cp\u003eThe weighted overlay technique, a standard method in GIS-based MCDA, was used to combine the standardized factor maps, with a pairwise comparison matrix, developed through AHP, used to assign standardized relative weights to each factor, and the weighted linear combination method then used to integrate these weighted factors, resulting in a hotel site suitability map, with Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presenting the influencing factors and their corresponding AHP weights; the utilization of the weighted overlay technique in conjunction with AHP for spatial suitability analysis is a widely recognized and applied methodology in global spatial planning research, reflecting a consensus on its effectiveness in integrating diverse spatial criteria (Malczewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Eastman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); the use of pairwise comparison matrices and weighted linear combination methods aligns with established practices in MCDA, ensuring a robust and transparent approach to spatial decision-making, as demonstrated in numerous studies across diverse geographical contexts (Jankowski, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Carver, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e); the integration of AHP-derived weights into the weighted overlay process is consistent with the increasing adoption of hybrid MCDA techniques in urban development planning, where the combination of quantitative and qualitative data is crucial for informed site selection (Stewart et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Geneletti, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\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\u003eInfluencing factors considered in this study and their calculated AHP weights\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\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\u003eWeight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Use Land Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe density of existing hotels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoad network\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope map\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation map\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eObtained consistency ratio (CR): 0.09\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Hotel Suitability Map of Minna Metropolis\u003c/h2\u003e\u003cp\u003ePrior to the application of the weighted overlay technique, each influencing factor map was reclassified to reflect its relative contribution to hotel site suitability, involving assigning suitability scores to each class within the factor maps; for instance, in the slope map, classes representing steeper slopes were assigned lower suitability scores, reflecting their reduced suitability for hotel development due to increased construction costs and potential accessibility issues; the reclassified factor maps, along with their assigned weights derived from the Analytical Hierarchy Process (AHP), were then integrated using the weighted overlay tool in ArcGIS 10.8, generating a composite hotel site suitability map, visually representing the spatial distribution of suitability across Minna Metropolis; to facilitate interpretation and communication of the results, the continuous suitability scores were reclassified into four discrete categories: 1\u0026thinsp;=\u0026thinsp;Highly Suitable, 2\u0026thinsp;=\u0026thinsp;Suitable, 3\u0026thinsp;=\u0026thinsp;Less Suitable, and 4\u0026thinsp;=\u0026thinsp;Not Suitable, allowing for a clear and concise representation of hotel site suitability, aligning with standard cartographic practices for suitability mapping (Malczewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); the reclassification of factor maps and the use of the weighted overlay technique are consistent with methodologies employed in numerous spatial suitability studies globally, where the standardization of input data is crucial for integrated analysis (Eastman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Carver, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e); the application of AHP-derived weights and the subsequent reclassification of suitability scores into discrete categories reflect established practices in multi-criteria decision analysis, ensuring the robustness and interpretability of the results (Jankowski, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Stewart et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); the utilization of ArcGIS 10.8 for these processes aligns with the widespread adoption of GIS software in spatial analysis research, demonstrating a standardized approach to data processing and visualization across diverse geographical contexts (Longley et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fisher and Unwin, 2005).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Land Use/Land Cover Distribution Analysis of Minna Metropolis\u003c/h2\u003e\u003cp\u003eThe Land Use/Land Cover (LULC) map of Minna Metropolis, derived from the 2021 Landsat 8 OLI imagery using the Maximum Likelihood Classification (MLC) algorithm, is presented in Fig.\u0026nbsp;2, with the percentage distribution of each LULC class illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e; as a rapidly urbanising metropolitan area, Minna is characterised by a significant proportion of bare ground, accounting for approximately 59.8% of the total land area, reflecting ongoing urban expansion and infrastructure development activities (National Population Commission, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and built-up areas, representing the existing urban fabric, constitute 34.44% of the land area, highlighting the spatial extent of urban development within the metropolis; vegetation cover, including grassland and forested areas, accounts for approximately 4% of the land area, indicating the presence of green spaces within the urban matrix, agricultural land, representing areas used for farming activities, comprises 1.9% of the study area, and water bodies, including rivers and reservoirs, constitute the smallest LULC category, occupying only 0.061% of the total area; the LULC distribution provides critical insights into the spatial characteristics of Minna Metropolis, influencing hotel site suitability through factors such as accessibility, environmental quality, and potential land use conflicts, and the predominance of bare ground and built-up areas stresses the need for strategic urban planning to ensure sustainable hotel development, balancing economic growth with environmental considerations, which aligns with similar observations in rapidly urbanizing regions globally where land use changes significantly impact urban development patterns (Seto et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); recent studies have highlighted the impact of LULC changes on urban microclimate and environmental sustainability, emphasizing the importance of integrating LULC data into urban development planning, a trend observed in diverse geographical contexts (Oke, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Foley et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), where the expansion of bare ground and built-up areas often leads to urban heat island effects and altered hydrological processes, necessitating careful consideration in urban planning and hotel site selection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Topography\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Elevation map\u003c/h2\u003e\u003cp\u003eThe topographic characteristics of Minna Metropolis, particularly elevation, play a significant role in determining hotel site suitability, as illustrated in Figs.\u0026nbsp;4 and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which present the numerical elevation values and the reclassified elevation suitability map, respectively; the elevation within Minna Metropolis ranges from 195 meters to 441 meters above sea level, with the southern region exhibiting the highest elevations, reflecting its underlying geological structure and landform characteristics (Geological Survey of Nigeria, 2023), creating diverse landscape features influencing both accessibility and scenic attractiveness; the reclassified elevation suitability map (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e) indicates a spatial trend where hotel site selection becomes less favorable towards the northern region of the city, suggesting that lower elevations may present challenges related to accessibility, drainage, and potential flood risks, while the southern region, with its higher elevations, demonstrates a greater proportion of highly suitable areas for hotel development; the results highlight that a substantial portion of the study area falls within the highly suitable elevation range, particularly in the southern region, attributed to factors such as enhanced scenic views, improved drainage, and reduced flood risk, aligning with observations in other regions where higher elevations often possess greater suitability for scenic attractiveness, a critical factor for hotels targeting tourism and leisure markets (Dewan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); furthermore, recent studies on urban topography have emphasized the importance of considering elevation in infrastructure development and land use planning, highlighting its impact on accessibility and environmental sustainability (Tarboton, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), a consideration consistent with global trends in urban planning where topographic data is integral for informed site selection and development strategies, particularly in regions with varied elevation profiles (Ouma et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Slope map\u003c/h2\u003e\u003cp\u003eThe slope characteristics of Minna Metropolis, reflecting its underlying landform patterns, are critical for assessing hotel site suitability, as presented in Fig.\u0026nbsp;6, which shows the slope map of the study area expressed as a percentage gradient, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e, which reclassifies the slope classes into five textual categories: 'Level,' 'Very Gently Sloping,' 'Moderately Sloping,' 'Moderately Steep Sloping,' and 'Steep Sloping'; unlike the elevation map, the spatial distribution of slope classes in Minna Metropolis exhibits a more heterogeneous pattern, reflecting the complex geomorphology of the region, yet the study area is predominantly characterized by 'Level' terrain, indicating a relatively flat topography overall; nevertheless, 'Steep Sloping' areas are observed in the northern and eastern regions, as indicated by the red areas in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e, posing potential challenges for hotel development due to increased construction costs and accessibility constraints; the heterogeneous slope distribution in Minna, with its level terrains and steep sloping areas, is consistent with patterns observed in other rapidly developing urban areas globally, where varying slope conditions influence infrastructure development and land use planning (Lin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Van Westen et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); recent studies have highlighted the importance of considering slope stability and erosion potential in urban development, emphasizing the need for detailed slope analysis (Montgomery and Dietrich, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), a critical consideration in diverse climatic and geological contexts where slope characteristics significantly impact urban development viability and sustainability.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Distribution and Kernel Density Map of Existing Hotels\u003c/h2\u003e\u003cp\u003eThe spatial distribution of existing hotels within Minna Metropolis provides valuable insights into the current market structure and potential competitive areas, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e8\u003c/span\u003e, which presents the point distribution of hotels represented by red points, serving as the basis for generating a kernel density map highlighting areas of hotel clustering; the kernel density map (Fig.\u0026nbsp;9) reveals a concentration of hotels in the central region of the study area, indicated by the red and yellow areas, suggesting a preference for central locations due to their accessibility and proximity to commercial and administrative centers, while the northern and peripheral regions exhibit a sparse distribution of hotels, indicating lower market penetration; this spatial pattern is consistent with observations in other urban areas, where hotel development tends to concentrate in central locations (Law et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and the kernel density analysis provides a quantitative measure of hotel clustering, facilitating the identification of potential market gaps and opportunities; this central clustering phenomenon is observed in numerous urban environments globally, where accessibility and proximity to key commercial and administrative areas drive hotel location choices (Prideaux, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; McKercher, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and the identification of peripheral market gaps aligns with broader tourism geography studies that emphasize the importance of spatial analysis in understanding and predicting hospitality industry trends.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Road Network Analysis and Hotel Accessibility Map\u003c/h2\u003e\u003cp\u003eAccessibility is a critical factor influencing the viability of hotel locations, particularly in urban environments, aligning with the observation by Rodrigue et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that accessibility deficiencies are a significant challenge in the global hotel industry, necessitating a comprehensive analysis of road networks and hotel accessibility for informed site selection; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents the road network density and accessibility map of Minna Metropolis, illustrating four levels of traffic density as an indicator of accessibility, revealing a discernible decrease in road network efficiency and suitability from the central region towards the periphery, highlighting the challenges of accessing peripheral areas, which may deter hotel development; the results indicate that a majority of the roads within the central region experience heavy traffic congestion, a common occurrence in urban areas with high economic activity, underscoring the need for strategic transportation planning to improve accessibility and support hotel development; this pattern of decreasing accessibility towards urban peripheries is consistent with findings in numerous global urban settings where central areas boast superior infrastructure and connectivity (Handy and Niemeier, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Rodrigue et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and recent advancements in transportation network analysis have emphasized the use of accessibility indices and network centrality measures to assess the impact of transportation infrastructure on urban development (Geurs and van Wee, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), reflecting a worldwide trend in utilizing sophisticated analytical methods to optimize urban accessibility and development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.5 AHP Results\u003c/h2\u003e\u003cp\u003eTo determine the relative importance of the influencing factors for hotel site suitability in Minna Metropolis, the Analytical Hierarchy Process (AHP) was employed, considering five key factors: Land Use/Land Cover (LULC), existing hotel density, road accessibility, slope, and elevation, with Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presenting the calculated weights for each factor derived from the AHP algorithm; the results indicate that LULC is the most influential factor, accounting for approximately 46% of the overall weight, underscoring the critical role of land use patterns in hotel site selection, reflecting the importance of accessibility, environmental quality, and potential land use conflicts, while existing hotel density and road accessibility also emerged as significant factors, highlighting the importance of market competition and transportation infrastructure, and elevation, while still relevant, was found to be the least influential factor, contributing approximately 8% to the overall weight; the AHP results provide a robust and transparent basis for the subsequent suitability mapping, reflecting the consensus of expert opinion and relevant literature (Saaty, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1980\u003c/span\u003e); the dominance of LULC in site suitability assessments aligns with global trends where land use patterns significantly influence development decisions in the hospitality sector (Miller and Morckel, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wong and Fesenmaier, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and the application of AHP for multi-criteria decision-making is consistent with its widespread use in spatial planning and site selection studies across diverse geographical contexts (Malczewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Joerin et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), demonstrating a universal approach to integrating diverse factors in suitability analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Hotel Site Suitability Map\u003c/h2\u003e\u003cp\u003eThe integrated hotel site suitability map for Minna Metropolis, generated using the weighted overlay technique and the AHP-derived weights, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e11\u003c/span\u003e, delineating four suitability classes: 'Highly Suitable,' 'Suitable,' 'Less Suitable,' and 'Not Suitable'; the map reveals that a significant portion of the central region and the far north of the study area are classified as 'Highly Suitable' for hotel development, encompassing approximately 54.24 square kilometers, characterized by favorable LULC patterns, high road accessibility, and proximity to existing hotel clusters, while 'Suitable' areas, covering approximately 22.60 square kilometers, are predominantly located in the southern and northern regions, indicating moderate suitability for hotel development; 'Less Suitable' areas, represented in orange, cover 17.98 square kilometers and are concentrated in the central region, presenting challenges related to LULC constraints or accessibility, and 'Not Suitable' areas, depicted in red, extend over 46.89 square kilometers and are predominantly found in the central region, constrained by dense urban development or environmental factors; overall, the suitability map indicates that over 50% of Minna Metropolis is at least moderately suitable for hotel development, highlighting the potential for further hotel development in the city, particularly in the central and northern regions, and the spatial distribution of suitability classes provides valuable insights for hotel developers and urban planners, facilitating informed decisions regarding hotel site selection and urban development strategies; the application of weighted overlay and AHP for spatial suitability analysis aligns with global best practices in urban planning and site selection, where multi-criteria decision-making is essential (Eastman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Carver, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), and the identification of optimal development locations through spatial suitability mapping is consistent with numerous studies across diverse geographical contexts (Malczewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Jankowski, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), demonstrating the broad applicability of these techniques in urban development planning.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study successfully applied a geospatial multi-criteria decision analysis (MCDA) framework, integrating the Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) methodologies, to identify optimal hotel site locations in Minna Metropolis, Niger State, Nigeria, addressing the pressing need for strategic accommodation planning in response to the city's increasing tourism and immigration, which are driving demand for hospitality services; the study's findings reveal a critical spatial pattern: the central urban core of Minna Metropolis, characterized by high hotel density and urban development, is identified as the least suitable area for new hotel construction, while the peripheral northern and southern regions are classified as 'Most Suitable' and 'Moderately Suitable,' respectively, encompassing over 54% of the city's land area, underscoring the importance of considering urban development patterns and market saturation in hotel site selection; the AHP analysis highlighted Land Use/Land Cover (LULC) as the most influential factor in determining hotel site suitability, reflecting its significant impact on accessibility, environmental quality, and development costs, and existing hotel density also emerged as a critical factor, emphasizing the importance of market analysis and competitive positioning in hotel development, findings that align with recent studies emphasizing the integration of LULC and market analysis in urban land use planning (Stewart et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); the implications of this research are significant for hotel industry stakeholders and urban planners, as by prioritizing LULC and existing hotel density, developers can optimize hotel site selection to maximize economic benefits and minimize environmental impacts, and the spatial suitability map generated in this study provides a robust decision support tool for urban development planning, facilitating the sustainable expansion of the hospitality sector in Minna Metropolis; future research should explore the integration of socio-economic factors and detailed environmental assessments to further refine hotel site suitability models, ensuring long-term sustainability and economic viability (Geneletti, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper has not been previously published and is not currently under review for publication in any other journal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors were involved in developing and submitting this paper for publication in Applied Geomatics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors consent to the publication of this manuscript in Applied Geomatics, confirming it is original and not under consideration elsewhere.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors contributed to the study's conception and design. EAA and SC handled material preparation, data collection, and analysis, with EAA supervising the study. EAA and SC wrote the first draft of the manuscript, and the authors commented on previous versions. The authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbrahart, RJ, \u0026amp; White, M (2001) Neural network-based rainfall-runoff modelling using a differential split-sample approach. Journal of Hydrology, 252(1-4), 216-232. \u003c/li\u003e\n\u003cli\u003eAdeleke, OO, \u0026amp; Adewole, AO (2019) Spatial distribution of hospitality facilities in urban centers. Journal of Geography and Regional Planning, 12(4), 89-98.\u003c/li\u003e\n\u003cli\u003eAmadeus Hospitality (2024) Hospitality Group and Business Performance Index - Q2 2024. Retrieved from https://www.amadeus-hospitality.com/resources/hospitality-group-business-performance-index-q2-2024/ \u003c/li\u003e\n\u003cli\u003eAribigbola, OA (2008) Improving Urban Land Use Planning and Management in Nigeria: The Case of Akure. Theoretical and Empirical Researches in Urban Management, 3(9), 1-14. \u003c/li\u003e\n\u003cli\u003eCampbell, JB, \u0026amp; Wynne, RH (2011) Introduction to remote sensing. Guilford Press. \u003c/li\u003e\n\u003cli\u003eCarver, S (1991) Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information System, 5(3), 321-339. \u003c/li\u003e\n\u003cli\u003eChen, Y, Li, X, \u0026amp; Liu, X (2020) Identifying urban functional zones based on semantic embedding of POI data. Remote Sensing, 12(1), 151. \u003c/li\u003e\n\u003cli\u003eCongalton, RG, \u0026amp; Green, K (2019) Assessing the accuracy of remotely sensed data: principles and practices. CRC Press.\u003c/li\u003e\n\u003cli\u003eDadashpoor, H, Azizi, P, \u0026amp; Moghadam, HS (2019) Factors affecting urban sprawl in Iran: A review. Journal of Urban Planning and Development, 145(1), 04018059. \u003c/li\u003e\n\u003cli\u003eDewan, AM, Yamaguchi, Y, \u0026amp; Rahman, M (2011) Evaluating flood hazard and inundation damage in the southwestern coastal region of Bangladesh using DEMs, remote sensing and GIS techniques. Progress in Physical Geography, 35(2), 163-187. \u003c/li\u003e\n\u003cli\u003eDwyer, L, Chen, N (Chris) \u0026amp; Lee, J (Jiyeon) (2019) The role of place attachment in tourism research. Journal of Travel \u0026amp; Tourism Marketing, 36(5), 645-652. \u003c/li\u003e\n\u003cli\u003eEastman, JR, Kyem, PAK, Toledano, J, \u0026amp; Jin, W (1995) GIS and decision making. UNITAR Explorations in Geographic Information Systems Technology, 4, 1-60. \u003c/li\u003e\n\u003cli\u003eEuropean Journal of Tourism Hospitality and Recreation (2020) Competition in Hotel Industry: Theory, Evidence and Business Practice. European Journal of Tourism Hospitality and Recreation, 10(3), 200-202. DOI: 10.2478/ejthr-2020-0017 \u003c/li\u003e\n\u003cli\u003eFarr, TG, Rosen, PA, Caro, E, Crippen, R, Duren, R, Hensley, S, \u0026amp; Alsdorf, DE (2007) The shuttle radar topography mission. Reviews of geophysics, 45(2). \u003c/li\u003e\n\u003cli\u003eFeizizadeh, B, \u0026amp; Blaschke, T (2013) GIS-MCDA for urban sustainability. Environmental Monitoring and Assessment, 185(11), 9123-9141. \u003c/li\u003e\n\u003cli\u003eFeizizadeh, B, Blaschke, T, Nazemosadat, MJ, \u0026amp; Karkhaneh, SP (2017) A GIS-based fuzzy-AHP approach for flood susceptibility mapping in the Taleghan watershed, Iran. Science of the total environment, 615, 667-680. \u003c/li\u003e\n\u003cli\u003eFisher, P, \u0026amp; Unwin, D (2005) Re-presenting GIS. John Wiley \u0026amp; Sons. \u003c/li\u003e\n\u003cli\u003eFoley, JA, DeFries, R, Asner, GP, Barford, C, Bonan, G, Carpenter, SR, \u0026amp; Snyder, PK (2005) Global consequences of land use. Science, 309(5734), 570-574. \u003c/li\u003e\n\u003cli\u003eGeneletti, D (2012) Integrating ecosystem services into strategic environmental assessment: A methodological framework and an application to spatial planning. Environmental impact assessment review, 36, 21-33. \u003c/li\u003e\n\u003cli\u003eGeological Survey of Nigeria (2023)\u003c/li\u003e\n\u003cli\u003eGeurs, KT, \u0026amp; van Wee, B (2004) Accessibility evaluation of land-use and transport strategies: review and research directions. Journal of transport geography, 12(2), 127-140. \u003c/li\u003e\n\u003cli\u003eHandy, SL, \u0026amp; Niemeier, DA (1997) Measuring accessibility: an exploration of issues and alternatives. Environment and planning A, 29(7), 1175-1194. \u003c/li\u003e\n\u003cli\u003eJankowski, P (1995) Integrating geographical information systems and multiple criteria decision-making methods. International Journal of Geographical Information Systems, 9(3), 251-273. \u003c/li\u003e\n\u003cli\u003eJarvis, A, Reuter, HI, Nelson, A, \u0026amp; Guevara, E (2008) Hole-filled SRTM for the globe version 4. International centre for tropical agriculture (CIAT). \u003c/li\u003e\n\u003cli\u003eJensen, JR (2016) Remote sensing of the environment: an earth resource perspective. Pearson Education Limited. \u003c/li\u003e\n\u003cli\u003eJoerin, F, Th\u0026eacute;riault, M, \u0026amp; Musy, A (2001) Using GIS and outranking multi-criteria analysis for land-use suitability assessment. Journal of environmental management, 61(2), 153-169. \u003c/li\u003e\n\u003cli\u003eKasim, M (2021) The role of local communities in developing potential areas for marine tourism. Journal of Sustainable Tourism, 29(7), 1101-1118. \u003c/li\u003e\n\u003cli\u003eKim, K, \u0026amp; Jogaratnam, G (2010) Effects of Individual and Organizational Factors on Job Satisfaction and Intent to Stay in the Hotel and Restaurant Industry. Journal of Human Resources in Hospitality \u0026amp; Tourism, 9(3), 324-339. \u003c/li\u003e\n\u003cli\u003eLaw, R, Leung, R, \u0026amp; Lo, A (2009) The impact of hotel clustering on the length of stay of inbound tourists. Journal of travel research, 48(2), 195-202. \u003c/li\u003e\n\u003cli\u003eLillesand, TM, Kiefer, RW, \u0026amp; Chipman, JW (2015) Remote sensing and image interpretation. John Wiley \u0026amp; Sons. \u003c/li\u003e\n\u003cli\u003eLin, CY, Chen, SC, \u0026amp; Wu, CC (2013) Integrating multi-criteria decision making and GIS for slope hazard assessment. Natural hazards, 65(1), 45-63. \u003c/li\u003e\n\u003cli\u003eLiu, X, Hu, G, Chen, Y, Huang, Y, \u0026amp; Cao, X (2014) Extracting urban built-up areas from Landsat images using the normalized difference built-up index. Landscape and urban planning, 121, 95-101. \u003c/li\u003e\n\u003cli\u003eLongley, PA, Goodchild, MF, Maguire, DJ, \u0026amp; Rhind, DW (2015) Geographic information systems and science. John Wiley \u0026amp; Sons. \u003c/li\u003e\n\u003cli\u003eMalczewski, J (2006) GIS-based multicriteria decision analysis. John Wiley \u0026amp; Sons. \u003c/li\u003e\n\u003cli\u003eMcKercher, B (2005) Tourism: a discipline of disciplines. Channel View Publications. \u003c/li\u003e\n\u003cli\u003eMiller, NG, \u0026amp; Morckel, C (2012) Hotel location and performance. Journal of hospitality \u0026amp; tourism research, 36(1), 3-26. \u003c/li\u003e\n\u003cli\u003eMontgomery, DR, \u0026amp; Dietrich, WE (1994) A physically based model for the topographic control on shallow landsliding. Water resources research, 30(5), 1153-1171. \u003c/li\u003e\n\u003cli\u003eNational Bureau of Statistics (2023)\u003c/li\u003e\n\u003cli\u003eNational Population Commission. (2018)\u003c/li\u003e\n\u003cli\u003eNiger State Tourism Board (2023)\u003c/li\u003e\n\u003cli\u003eNiger State Urban Development Report. (2024) \u003c/li\u003e\n\u003cli\u003eNigerian Investment Promotion Commission. (2024)\u003c/li\u003e\n\u003cli\u003eOkabe, A, \u0026amp; Sugihara, K (2012) Spatial analysis along networks: statistical tools and applications. John Wiley \u0026amp; Sons. \u003c/li\u003e\n\u003cli\u003eOke, TR (1987) Boundary layer climates. Routledge. \u003c/li\u003e\n\u003cli\u003eOuma, YO, Tateishi, R, \u0026amp; Johnson, BA (2016) Urban flood vulnerability and risk mapping using integrated multi-parametric flood index (IMFI). Water, 8(7), 244. \u003c/li\u003e\n\u003cli\u003eOyinloye, MA, \u0026amp; Adeyemo, AA (2022) Application of GIS in urban land use analysis. Remote Sensing Applications: Society and Environment, 27, 100790. \u003c/li\u003e\n\u003cli\u003ePrideaux, B (2000) The role of tourist attractions in the development of sustainable tourism in regional areas. Sustainable tourism in island destinations: issues and approaches, 118-142. \u003c/li\u003e\n\u003cli\u003eRegional Geospatial Data Infrastructure (2023) \u003c/li\u003e\n\u003cli\u003eRichards, JA., \u0026amp; Jia, X (2006) Remote sensing digital image analysis: an introduction. Springer Science \u0026amp; Business Media. \u003c/li\u003e\n\u003cli\u003eRodrigue, JP, Comtois, C, \u0026amp; Slack, B (2017) The geography of transport systems. Routledge. \u003c/li\u003e\n\u003cli\u003eRodriguez, E, Morris, CS \u0026amp; Belz, JE (2005) A global assessment of the height accuracy of the Shuttle Radar Topography Mission. Photogrammetric engineering and remote sensing, 71(6), 689-695. \u003c/li\u003e\n\u003cli\u003eSaaty, TL (1980) The analytic hierarchy processes. McGraw-Hill International. \u003c/li\u003e\n\u003cli\u003eSeto, KC, Guneralp, B, \u0026amp; Hutyra, LR (2011) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the national academy of sciences, 109(40), 16083-16088. \u003c/li\u003e\n\u003cli\u003eSilverman, BW (1986) Density estimation for statistics and data analysis. CRC press. \u003c/li\u003e\n\u003cli\u003eStewart, TJ, Figueira, J, \u0026amp; Greco, S (2018) Multiple criteria decision analysis: State of the art surveys. International Series in Operations Research \u0026amp; Management Science, 270, 1-32. \u003c/li\u003e\n\u003cli\u003eTarboton, DG (1997) A new method for determining flow directions and upslope areas in grid digital elevation models. Water resources research, 33(3), 309-319. \u003c/li\u003e\n\u003cli\u003eTHISDAYLIVE (2024) Transcorp Hotels Named Hospitality Company of 2024. Retrieved from https://www.thisdaylive.com/index.php/2025/03/24/transcorp-hotels-named-hospitality-company-of-2024/ \u003c/li\u003e\n\u003cli\u003eUnited Nations World Tourism Organization (2023) \u003c/li\u003e\n\u003cli\u003eUnited States Geological Survey Earth Explorer (2024)\u003c/li\u003e\n\u003cli\u003eVan Westen, C. J., Castellanos, E., \u0026amp; Kuriakose, S. L. (2008). Spatial data requirements for landslide hazard and risk assessment: a review. Landslides, 5(3), 261-273. \u003c/li\u003e\n\u003cli\u003eWang, Y., Li, X., \u0026amp; Liu, X. (2018). Inferring urban land use from POI and road data using random forests. Remote Sensing, 10(6), 808. \u003c/li\u003e\n\u003cli\u003eWong, K. K., \u0026amp; Fesenmaier, D. R. (2007). Defining the spatial extent of tourism regions. Journal of travel research, 46(2), 143-152. \u003c/li\u003e\n\u003cli\u003eWulder, M. A., White, J. C., Nelson, R. F. \u0026amp; Roy, D. P. (2012). Opening the archive: How free satellite data has fostered an unprecedented period of global mapping. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, \u003cem\u003e123\u003c/em\u003e, 2-11.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Analytical Hierarchy Process (AHP), Geographic Information Systems (GIS), Geospatial Analysis, Hotel Site Selection, Multi-Criteria Decision Analysis (MCDA), Remote Sensing","lastPublishedDoi":"10.21203/rs.3.rs-6370819/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6370819/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe hotel industry's substantial contribution to national economies, particularly in tourism-driven regions, underlines its role in employment generation. However, spatial disparities in hotel distribution, prevalent in many African nations, like Nigeria, necessitate strategic planning for new developments. This study employed a geospatial multi-criteria decision analysis (MCDA) framework, integrating Geographic Information Systems (GIS) and Remote Sensing, to assess hotel site suitability within Minna Metropolis. Utilizing the Analytical Hierarchy Process (AHP), five critical factors, Land Use/Land Cover (LULC), slope, elevation, existing hotel distribution, and road network accessibility, were weighted and integrated via a weighted overlay in ArcGIS 10.8. The resulting suitability map categorized the metropolis into four classes: highly suitable, moderately suitable, less suitable, and not suitable. Findings revealed that over 50% (approximately 76 km\u0026sup2;) of Minna Metropolis exhibits moderate to high suitability for hotel development, with LULC, existing hotel density, and road network accessibility identified as the primary influencing factors. This research provides a robust spatial decision support tool for hotel industry professionals and urban planners, offering valuable insights for optimized hotel site selection in similar urbanizing contexts.\u003c/p\u003e","manuscriptTitle":"Geospatial Multi-Criteria Decision Analysis for Hotel Site Suitability Assessment in Minna Metropolis, Nigeria: Integrating Remote Sensing and GIS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 10:59:11","doi":"10.21203/rs.3.rs-6370819/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-18T14:11:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T14:00:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T10:26:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71379374469854663027876880253832840040","date":"2025-08-22T11:47:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299806102143562090688892861365724095126","date":"2025-07-30T06:50:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T20:48:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-08T17:30:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-08T17:30:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Geomatics","date":"2025-04-03T15:41:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"692ef7d5-027b-44c0-a636-8c01a5314f39","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:12:05+00:00","versionOfRecord":{"articleIdentity":"rs-6370819","link":"https://doi.org/10.1007/s12518-025-00685-9","journal":{"identity":"applied-geomatics","isVorOnly":false,"title":"Applied Geomatics"},"publishedOn":"2026-02-03 15:58:58","publishedOnDateReadable":"February 3rd, 2026"},"versionCreatedAt":"2025-08-04 10:59:11","video":"","vorDoi":"10.1007/s12518-025-00685-9","vorDoiUrl":"https://doi.org/10.1007/s12518-025-00685-9","workflowStages":[]},"version":"v1","identity":"rs-6370819","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6370819","identity":"rs-6370819","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}