Spatial Pattern Analysis of Lecture Halls in the University of Calabar, Nigeria: A GIS and Nearest Neighbour Statistics Approach

Spatial Pattern Analysis of Lecture Halls in the University of Calabar, Nigeria: A GIS and Nearest Neighbour Statistics Approach | 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 Article Spatial Pattern Analysis of Lecture Halls in the University of Calabar, Nigeria: A GIS and Nearest Neighbour Statistics Approach David Mkpanam Nyong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7948719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The strategic location of educational infrastructure plays an important role in efficient academic administration and effective distribution of learning facilities on a university campus. This research work utilizes Geographic Information System (GIS) tools and methodologies in the analysis of the distribution pattern of lectures on the University of Calabar’s campus. The main focus of this research was to identify the randomness or otherwise of the distribution of the learning facilities. The data collected included the geographical locations of all forty-eight (48) identified lectures. The data was collected using a Garmin 625 GPS device. The data was analyzed by merging it with the University of Calabar’s google earth image. The methodology involved the use of the Nearest Neighborhood Analysis (NNA) to determine the hypothesis of randomness or otherwise of the distribution. The findings showed a Nearest Neighborhood Ratio (Rn) of 0.01, the observed mean was 62.79 meters, the average mean distance was calculated to be 94.97 meters, while the highly significant Z score was − 4.49 (p < .001). The analysis shows clear evidence of not following the criteria of proper distribution of learning facilities outlined in the Central Place Theory. The research work concludes that Geographic Information System tools and methodologies offer useful platforms in campus planning. The tools offer valuable guides in development planning on campus. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Physical sciences/Mathematics and computing Physical sciences/Physics Spatial Pattern Geographic Information Systems (GIS) Nearest Neighbour Analysis Lecture Halls University of Calabar Campus Planning Figures Figure 1 Figure 2 1. INTRODUCTION The location of facilities in an institutional setting is an important, though frequently ignored, aspect of the efficiency of the institution. The rational allocation of facilities, which is apt in complicated physical and social entities that university settings represent, reduces commuting costs, facilitates interactions among discipline members, and provides uniform access by university members (Castells, 1994 ). The pattern of location of facilities, which could be concentrated, random, or uniform, can seriously affect pedestrian movement, timetableness, or overall university experience (Oluwadare & Julius, 2011 ). The relevance of this information, given the rapid expansion of the university system in Nigeria, cannot be overstated (Jaja, 2013 ; Otonko, 2012 ). Theoretical foundation, upon which service distribution can be analyzed, frequently begins with Christaller’s ( 1966 ) Central Place Theory (CPT). Although it was first conceptualized in the context of urban settlements, concepts of threshold, range, and hierarchy of services can be remotely adapted in the context of a university campus. The campus can be considered as a microcosm of an urban setting, where classrooms can be considered as ‘centers’ delivering ‘services’ of education. The most efficient distribution pattern, according to CPT, would be one which best serves the university population by minimizing distances, thus preventing both concentrations as well as inequities of distribution. At the global level, there has been an increased interest in the use of Geographic Information Systems (GIS) and Geospatial Statistics in the planning of educational institutions. Various authors have used the aforementioned tools in mapping the distribution pattern of various levels of schools, including tertiary schools. For example, Aliyu, Shahidah, & Aliyu ( 2013 ) created a map of post-secondary institutions in Yola, Nigeria, which showed an unequal distribution of schools. Similar usage of GIS was showcased by Tao, Baofeng, & Xiaojia ( 2017 ), which showed the effectiveness of GIS in planning the distribution of rural schools in China. Regarding university infrastructure planning, Chukwu, Ezenwenyi, & Mebude ( 2018 ) demonstrated the use of GIS in mapping the distribution of Nigerian universities offering forestry education, which showed imbalances. In Nigeria, there had been research on the distribution of urban amenities like healthcare facilities (Adewoyin et al. 2018, Fadahunsi et al. 2017 ), banks (Abdullahi et al. 2011 ), ATMs (Efiong, Efiong & Ogba, 2016 ), and filling stations (Mohammed, Musa & Jeb, 2014 ). Evidently, there was a relative lack of research efforts directly focusing on the internal layout of university campuses through sophisticated spatial approaches. Even though there was research by Olubadewo, Abdulkarim, and Ahmed ( 2013 ), which championed GIS as an Educational Decision Support System in the context of primary schools, there was less emphasis from this direction on University campuses. Many universities are substantial landowners. Even more, university planning decisions often create lasting impacts. Hence, this research attempts to fill this gap by carrying out a detailed spatial pattern analysis of the University of Calabar’s lecture halls. The research has three specific objectives: 1.Identify the locations of all the lecture halls in the University of Calabar using GPS/GIS. Identify how the locations of the University of Calabar’s lecture halls are distributed using the NNAnalysis. Present the meaning of the locations of the University of Calabar’s lecture halls. The hypothesis stated for this research work were: H₀: The distribution of the University of Calabar’s lecture halls is random. H₁: The distribution of University of Calabar’s lecture halls is not random. This research help provide an important evidence base on how the physical planning unit in the University of Calabar can make use of the application of spatial science in the management of educational infrastructure. 2. RESULTS The data collected and analyzed for this project are presented below in an attempt to show the distribution pattern of the lecture theaters in the University of Calabar. 2.1 Geo-DB of Lecture Hall A field survey was used to identify and map all the forty-eight (48) lecture theaters on the University of Calabar campus. The names, as well as the Eastings and Northings of all forty-eight (48) identified points, are all indicated in table 4.1 below. The data provides the first institutional foundation on which all other analyses will rely. It can be deduced from the data that all the identified points fall within a particular boundary on the university campus. The Eastings vary from 8.3399 to 8.3541, while the Eastings vary from 4.9340–4.9546. Table 1 Geographic Coordinates of Lecture Halls in the University of Calabar LECTURE HALLS EASTINGS NORTHINGS 1 NAT 8.339916667 4.952813889 2 Crop science/soil science 8.340194444 4.953111111 3 Agric Economics 8.340527778 4.953 4 Institute of Education 8.341166667 4.952472222 5 Curriculum & Teaching 8.340555556 4.953527778 6 Social Science 8.34075 4.954111111 7 Open pav1 8.340972222 4.954305556 8 Open Pav2 8.341722222 4.954611111 9 Food science & technology 8.3425 4.954472222 10 Home Economics 8.342444444 4.952583333 11 pav 1A &B 8.346888889 4.951583333 12 medical Lab science 8.347666667 4.951861111 13 College of Medical sci 8.34825 4.951222222 14 Math/Computer/engeer/statistics 8.350027778 4.951 15 Pure & Applied Chemistry 8.350888889 4.950388889 16 Physics 8.351583333 4.950027778 17 Biological science 8.352277778 4.94975 18 Pav D 8.352277778 4.949361111 19 Pav E 8.352527778 4.949194444 20 Pav F 8.352277778 4.948972222 21 I C T lab 8.353638889 4.949722222 22 Pharmacy 8.354083333 4.934027778 23 Medicine 8.353388889 4.951305556 24 Chemistry Auditorium 8.351138889 4.951166667 25 Dentistry 8.351222222 4.951666667 26 Dental Lab 8.350916667 4.951555556 27 Nslt5 8.349638889 4.950111111 28 Nslt1,2,3,4 8.350333333 4.949888889 29 Law room 8.350638889 4.949222222 30 CES 8.351888889 4.948444444 31 Language lab 8.349861111 4.948944444 32 Radiography 8.349777778 4.948638889 33 Forestry and wildlife 8.349472222 4.948861111 34 Modern language 8.349444444 4.948722222 35 History & international studies 8.349138889 4.949083333 36 Linguistic 8.349 4.948972222 37 Pav3A&B 8.348555556 4.949555556 38 Pav2A&B 8.348472222 4.949083333 39 PGDM 8.347916667 4.949666667 40 MBA11 8.347527778 4.949722222 41 Management science 8.348611111 4.9485 42 Theatres & media 8.349138889 4.948194444 43 Oceanography 8.348694444 4.94775 44 Education 8.3495 4.946861111 45 moot Court 8.347583333 4.947333333 46 Mass comm/music/fine art 8.34975 4.94575 47 Entrepreneurship 8.34775 4.947194444 48 Chemistry 8.351694444 4.951194444 Source : Researcher’s Fieldwork, (2025) The result showed a Nearest Neighbour Ratio (Rn) of 0.01. As indicated by the NNA principle, an Rn measure of 0 represents an ideal cluster distribution, an Rn measure of 1 represents a random distribution, while an Rn measure of 2.15 represents an ideal uniform distribution. The resulting measure of 0.01 is almost ideal as it approaches an Rn measure of 0. The statistical significance of this pattern is confirmed by the Z-score of -4.49 and the p-value of < 0.001. A Z-score less than − 2.58 signifies a statistically significant clustered pattern at the 99% confidence level. The highly negative Z-score and the infinitesimally small p-value allow for the definitive rejection of the null hypothesis (H₀). Therefore, we conclude that the lecture halls in the University of Calabar are not randomly distributed; their distribution is significantly clustered. The expression of this clustering in space can be seen by mapping the data points. The lecturer rooms do not occupy the entire space of the campus uniformly. Instead, they can be found in particular regions, particularly the centers of departments like the College of Social Sciences, Biological Sciences, and the College of Medical Sciences. At the same time, there are very large regions on the campus landscape devoid of lecturer rooms. Table 2 Results of Nearest Neighbour Analysis for Lecture Halls Parameter Value Nearest Neighbour Ratio (Rn) 0.01 Z-Score -4.490866 P-Value 0.000007 Observed Mean Distance 62.7928 meters Expected Mean Distance 94.9719 meters Number of Lecture Halls (n) 48 Method Manhattan Distance 3. DISCUSSION 3.1 Interpretation of the Clustered Pattern The result of this research clearly presents the significance of the cluster pattern of the location of the lecture halls on the University of Calabar. The measure of the degree of this cluster pattern from the nearest neighbor ratio (Rn), which is very small (Rn = 0.01), as well as its significance from the Z-score value, reveals that the distribution of facilities on this university involves the development of the aforementioned locations in a cluster pattern near faculty boundaries. There are various interpretations of this phenomenon. To begin with, this could be attributed to the mode of development of faculties and their accompanying lecture theaters built side by side with existing ones, thus reaping the benefits of agglomeration economies. This "development by accretion" is prevalent in many legacy institutions where facilities are allocated in fixed plots of land. The second interpretation could be that this phenomenon represents a functional layout where an attempt is made to create interfaces among disciplines in related disciplines—for example, grouping science labs and corresponding lecture theaters. The more extreme manifestation of this phenomenon, as identified in this research, could thus indicate inefficiencies in traffic in particular zones of the campus or commuting across different regions in case of time constraints. 3.2 Comparison with Existing Literature The result obtained from the study showed a cluster distribution, which is supported by various researches on the distribution of facilities in other Nigerian urban centers, though different from the ones considered in this study. For instance, Efiong, Efiong, and Ogba ( 2016 ) revealed that the distribution of ATMs in Calabar Metropolis was highly significant in a cluster distribution, thus attributed by the authors to commercial viability and highly densely trafficked locations. There are also researches on healthcare facilities (Fadahunsi et al., 2017 ; Sanni, 2010 ) and filling station facilities (Mohammed et al., 2014 ) in other urban centers of Nigeria that showed a similar distribution. Also, this finding can be attested by Aschale ( 2017 ), with regard to the effectiveness of GIS technology, which was stressed by Aschale ( 2017 ), in his research among Debre Markos Town, Ethiopia, concerning the role of GIS technology in making known location-disparities in the distribution of schools. Also, this finding is an endorsement of the research by Olubadewo, Abdulkarim, & Ahmed ( 2013 ), which emphasized the significant role of GIS technology in educational planning. This research serves as an endorsement on the micro-level in the context of the university environment. However, it is clear from the observation that the pattern does not conform to the conventional framework of Central Place Theory (Christaller, 1966 ). The Central Place Theory states that service centers follow a rational and hierarchical layout. From an organizational standpoint, it could be considered that a more diffuse pattern of lecture theaters could be more appropriate in a university setting, as it would ensure that the longest possible travel time of members of the university from one class location to another is reduced. The level of clustering observed in this research implies that this optimal pattern has been overridden by another factor. 3.3 Implications for Campus Planning and Management The identified cluster pattern has important implications for next planning and management with in the University of Calabar: Pedestrian Mobility and Congestion : Clustering of the lecture theaters could contribute to congestion during peak hours in particular locations, thus putting pressure on walkways and potentially posing safety concerns. Moreover, it implies that students with schedules involving multiple clusters will experience more difficult walks between classes. Resource Allocation : The clustering could reveal an issue of imbalanced allocation of resources. Certain departments could be situated in resource-rich clusters, while others, which could be newer departments, could be situated on the peripherals or not allocated proper dedicated space. Expansion Planning : However, in planning for expansion, it is best that the physical planning unit of the university identifies an approach of either supporting existing concentrations or establishing a decentralized pattern. Ad-hoc expansion of the existing concentrations will make the existing pattern worse. 3.4 Recommendations From the findings, the following recommendations can be proposed: The University’s Physical Planning and Works Department should embrace GIS technology as one of its fundamental tools in its master planning of the campus. Ambitions in building academic facilities in the future will focus on diversifying the distribution of the locations of the lecture halls from the current concentrated facilities. A supplementary research will be undertaken involving the use of network analysis with the intention of evaluating the connectivity of the aforementioned lecture theaters from student hostels and from main transit routes. 4.5. Study Limitations and Future Research The research relies on the geometric pattern of distribution. The data on the capacity, quality, or schedule of the lecture rooms is not used in this research. However, this information could help develop an adequate multi-criteria assessment of the functional effectiveness of the lecture rooms. Moreover, the application of the Manhattan distance concept seems appropriate in the grid-structured environment of an urban setting. However, this concept could be explored by using the network distance concept by mapping actual data of roads within the campus. 4. MATERIALS AND METHODS 5.1 Study Area The research was carried out within the University of Calabar’s main campus. The University of Calabar is located in Calabar, which is the capital of Cross River State. The university is estimated to be 8 kilometers from the city center. The university’s main campus occupies a substantial amount of land. The ground cover has varied topographic features, including academic buildings, administrative offices, student hostels, employees’ residential locations, as well as vacant spaces. The main campus of the university serves as the unit of investigation. The Map of the University and lecture halls distribution is shown on Figs. 1 and 2 . 5.2 Data Sources and Preparation The research could use both primary and secondary geospatial data. Primary Data: The data collected was in the form of geographical location coordinates (latitude and longitude) of all the identified lecture theaters within the boundary of the research. The data was collected through field observation by using a handheld GPS device. Secondary Data: The image of the University of Calabar satellite of higher resolution was downloaded from Google Earth Pro. The image was then used as a base map. 5.3 Instrumentation and Data Collection The data collection was executed in a systematic, multi-stage process to ensure completeness and accuracy: Reconnaissance Survey: An initial visit was made to acquaint oneself with the layout of the campus and locations of all possible lecture hall structures. This helped in planning an efficient route. GPS Data Collection: The geographic position of each of the lecture halls was determined using a Garmin GPSMAP 625 handheld GPS receiver. The positional accuracy of this GPS receiver is estimated to be in the range of 3–5 meters. The GPS receiver was fixed in its position until the position error was estimated to be below a threshold, after which the position was recorded. The name of the corresponding lecture hall was also recorded. Preparation of the base map of the campus was done by exporting the image from Google Earth and georeferencing it in ArcGIS 10.5 by using ground control points (GCPs), which resulted in the base map of the campus conforming to a real-world coordinate system (WGS 1984 UTM Zone 32N). Data Integration: The GPS data recorded was then used to create a point feature class in ArcGIS by importing the recorded GPS coordinates. The data was then joined with the names of the corresponding lecture theaters in an attribute table. 5.4 Data Analysis Techniques The core of the analysis involved spatial pattern detection using the Nearest Neighbour Analysis (NNA). Nearest Neighbour Analysis (NNA): This method estimates the level of geographical clustering or dispersal by comparing the average of the observed distances from each data point to its nearest-neighbour data point with the average distance that would be expected if the data points were randomly distributed. The points vector of the lecture rooms, which represents the points of interest. The total area of the campus measured in square kilometers. The total area was computed from the digital boundary of the campus. For this task, the Manhattan distance measure was considered. The measure represents the distance of moving in a grid layout of roads, which is more realistic than the Euclidean distance measure. The reproducibility of this approach is very high, given that it uses algorithms common in commercial GIS packages that all take defined parameters. Declarations Ethical Approval Not applicable. Consent to Participate Not applicable. Consent to Publish Not applicable. 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. Author Contribution The author contributed to the study conception and design. All material preparation, data collection, and analysis were performed by **David Mkpanam Nyong** . Acknowledgements The author gratefully acknowledges the support of the Google Earth pro for providing access satellite image used in this study. Data Availability The datasets generated during and/or analyzed during the current study, including the point shapefile of lecture hall locations and the associated attribute table, are available from the corresponding author on reasonable request. References Abdullahi, I. B., Ijaiya, M. A., Abdulraheem, A., Abdukadir, R. I. & Ibrahim, R. Spatial distribution of commercial banks in Ilorin metropolis, Kwara State, Nigeria. Conflu. J. Environ. 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Technol. 7 (7), 527–533. https://doi.org/10.18178/ijiet.2017.7.7.929 (2017). Taylor, P. J. Distance transformation and distance decay functions. Geographical Anal. 3 (3), 221–238. https://doi.org/10.1111/j.1538-4632.1971.tb00370.x (1971). Umar, J. U. & Bolanle, W. Locational distribution of health care facilities in rural area of Ondo State. Br. J. Educ. Soc. Behav. Sci. 11 (2), 1–8. https://doi.org/10.9734/BJESBS/2015/19253 (2015). Wazzan, K. The distribution of the basic education schools in Lattakia city (Syria). J. Educational Social Res. 7 (1), 71–79. https://doi.org/10.5901/jesr.2017.v7n1p71 (2017). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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2","display":"","copyAsset":false,"role":"figure","size":923837,"visible":true,"origin":"","legend":"\u003cp\u003eUniversity of Calabar lecture halls on satellite image\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7948719/v1/0aa484d53a79827ba8c94841.png"},{"id":102171118,"identity":"7923f718-85aa-4ea7-9592-5d2d4e08f428","added_by":"auto","created_at":"2026-02-09 04:25:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1636143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7948719/v1/784e8a68-3c67-4295-a46d-6fbf2fbae3b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Pattern Analysis of Lecture Halls in the University of Calabar, Nigeria: A GIS and Nearest Neighbour Statistics Approach","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe location of facilities in an institutional setting is an important, though frequently ignored, aspect of the efficiency of the institution. The rational allocation of facilities, which is apt in complicated physical and social entities that university settings represent, reduces commuting costs, facilitates interactions among discipline members, and provides uniform access by university members (Castells, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The pattern of location of facilities, which could be concentrated, random, or uniform, can seriously affect pedestrian movement, timetableness, or overall university experience (Oluwadare \u0026amp; Julius, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The relevance of this information, given the rapid expansion of the university system in Nigeria, cannot be overstated (Jaja, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Otonko, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTheoretical foundation, upon which service distribution can be analyzed, frequently begins with Christaller\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) Central Place Theory (CPT). Although it was first conceptualized in the context of urban settlements, concepts of threshold, range, and hierarchy of services can be remotely adapted in the context of a university campus. The campus can be considered as a microcosm of an urban setting, where classrooms can be considered as \u0026lsquo;centers\u0026rsquo; delivering \u0026lsquo;services\u0026rsquo; of education. The most efficient distribution pattern, according to CPT, would be one which best serves the university population by minimizing distances, thus preventing both concentrations as well as inequities of distribution.\u003c/p\u003e \u003cp\u003eAt the global level, there has been an increased interest in the use of Geographic Information Systems (GIS) and Geospatial Statistics in the planning of educational institutions. Various authors have used the aforementioned tools in mapping the distribution pattern of various levels of schools, including tertiary schools. For example, Aliyu, Shahidah, \u0026amp; Aliyu (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) created a map of post-secondary institutions in Yola, Nigeria, which showed an unequal distribution of schools. Similar usage of GIS was showcased by Tao, Baofeng, \u0026amp; Xiaojia (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which showed the effectiveness of GIS in planning the distribution of rural schools in China. Regarding university infrastructure planning, Chukwu, Ezenwenyi, \u0026amp; Mebude (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated the use of GIS in mapping the distribution of Nigerian universities offering forestry education, which showed imbalances.\u003c/p\u003e \u003cp\u003eIn Nigeria, there had been research on the distribution of urban amenities like healthcare facilities (Adewoyin et al. 2018, Fadahunsi et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), banks (Abdullahi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), ATMs (Efiong, Efiong \u0026amp; Ogba, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and filling stations (Mohammed, Musa \u0026amp; Jeb, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Evidently, there was a relative lack of research efforts directly focusing on the internal layout of university campuses through sophisticated spatial approaches. Even though there was research by Olubadewo, Abdulkarim, and Ahmed (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which championed GIS as an Educational Decision Support System in the context of primary schools, there was less emphasis from this direction on University campuses. Many universities are substantial landowners. Even more, university planning decisions often create lasting impacts.\u003c/p\u003e \u003cp\u003eHence, this research attempts to fill this gap by carrying out a detailed spatial pattern analysis of the University of Calabar\u0026rsquo;s lecture halls. The research has three specific objectives:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e1.Identify the locations of all the lecture halls in the University of Calabar using GPS/GIS.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIdentify how the locations of the University of Calabar\u0026rsquo;s lecture halls are distributed using the NNAnalysis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePresent the meaning of the locations of the University of Calabar\u0026rsquo;s lecture halls.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe hypothesis stated for this research work were:\u003c/p\u003e \u003cp\u003eH₀: The distribution of the University of Calabar\u0026rsquo;s lecture halls is random.\u003c/p\u003e \u003cp\u003eH₁: The distribution of University of Calabar\u0026rsquo;s lecture halls is not random.\u003c/p\u003e \u003cp\u003eThis research help provide an important evidence base on how the physical planning unit in the University of Calabar can make use of the application of spatial science in the management of educational infrastructure.\u003c/p\u003e"},{"header":"2. RESULTS","content":"\u003cp\u003eThe data collected and analyzed for this project are presented below in an attempt to show the distribution pattern of the lecture theaters in the University of Calabar.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Geo-DB of Lecture Hall\u003c/h2\u003e \u003cp\u003eA field survey was used to identify and map all the forty-eight (48) lecture theaters on the University of Calabar campus. The names, as well as the Eastings and Northings of all forty-eight (48) identified points, are all indicated in table 4.1 below. The data provides the first institutional foundation on which all other analyses will rely. It can be deduced from the data that all the identified points fall within a particular boundary on the university campus. The Eastings vary from 8.3399 to 8.3541, while the Eastings vary from 4.9340\u0026ndash;4.9546.\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\u003eGeographic Coordinates of Lecture Halls in the University of Calabar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLECTURE HALLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEASTINGS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNORTHINGS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.339916667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.952813889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCrop science/soil science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.340194444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.953111111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAgric Economics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.340527778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInstitute of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.341166667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.952472222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCurriculum \u0026amp; Teaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.340555556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.953527778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSocial Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.34075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.954111111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOpen pav1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.340972222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.954305556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOpen Pav2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.341722222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.954611111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFood science \u0026amp; technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.954472222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHome Economics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.342444444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.952583333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003epav 1A \u0026amp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.346888889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951583333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003emedical Lab science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.347666667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951861111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCollege of Medical sci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.34825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951222222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMath/Computer/engeer/statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.350027778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePure \u0026amp; Applied Chemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.350888889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.950388889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePhysics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.351583333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.950027778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBiological science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.352277778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.94975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePav D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.352277778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949361111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePav E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.352527778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949194444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePav F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.352277778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.948972222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eI C T lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.353638889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949722222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.354083333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.934027778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMedicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.353388889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951305556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eChemistry Auditorium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.351138889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951166667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDentistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.351222222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDental Lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.350916667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951555556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNslt5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.349638889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.950111111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNslt1,2,3,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.350333333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949888889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLaw room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.350638889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949222222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.351888889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.948444444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLanguage lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.349861111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.948944444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRadiography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.349777778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.948638889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eForestry and wildlife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.349472222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.948861111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModern language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.349444444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.948722222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHistory \u0026amp; international studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.349138889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949083333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLinguistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.948972222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePav3A\u0026amp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.348555556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949555556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePav2A\u0026amp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.348472222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949083333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.347916667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949666667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMBA11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.347527778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.949722222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eManagement science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.348611111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTheatres \u0026amp; media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.349138889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.948194444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOceanography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.348694444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.94775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.3495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.946861111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emoot Court\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.347583333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.947333333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMass comm/music/fine art\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.34975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.94575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntrepreneurship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.34775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.947194444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.351694444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.951194444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: \u003cem\u003eResearcher\u0026rsquo;s Fieldwork, (2025)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe result showed a Nearest Neighbour Ratio (Rn) of 0.01. As indicated by the NNA principle, an Rn measure of 0 represents an ideal cluster distribution, an Rn measure of 1 represents a random distribution, while an Rn measure of 2.15 represents an ideal uniform distribution. The resulting measure of 0.01 is almost ideal as it approaches an Rn measure of 0.\u003c/p\u003e \u003cp\u003eThe statistical significance of this pattern is confirmed by the Z-score of -4.49 and the p-value of \u0026lt;\u0026thinsp;0.001. A Z-score less than \u0026minus;\u0026thinsp;2.58 signifies a statistically significant clustered pattern at the 99% confidence level. The highly negative Z-score and the infinitesimally small p-value allow for the definitive rejection of the null hypothesis (H₀). Therefore, we conclude that the lecture halls in the University of Calabar are not randomly distributed; their distribution is significantly clustered.\u003c/p\u003e \u003cp\u003eThe expression of this clustering in space can be seen by mapping the data points. The lecturer rooms do not occupy the entire space of the campus uniformly. Instead, they can be found in particular regions, particularly the centers of departments like the College of Social Sciences, Biological Sciences, and the College of Medical Sciences. At the same time, there are very large regions on the campus landscape devoid of lecturer rooms.\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\u003eResults of Nearest Neighbour Analysis for Lecture Halls\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\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNearest Neighbour Ratio (Rn)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZ-Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-4.490866\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP-Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.000007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserved Mean Distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.7928 meters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpected Mean Distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.9719 meters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Lecture Halls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManhattan Distance\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"},{"header":"3. DISCUSSION","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Interpretation of the Clustered Pattern\u003c/h2\u003e \u003cp\u003eThe result of this research clearly presents the significance of the cluster pattern of the location of the lecture halls on the University of Calabar. The measure of the degree of this cluster pattern from the nearest neighbor ratio (Rn), which is very small (Rn\u0026thinsp;=\u0026thinsp;0.01), as well as its significance from the Z-score value, reveals that the distribution of facilities on this university involves the development of the aforementioned locations in a cluster pattern near faculty boundaries.\u003c/p\u003e \u003cp\u003eThere are various interpretations of this phenomenon. To begin with, this could be attributed to the mode of development of faculties and their accompanying lecture theaters built side by side with existing ones, thus reaping the benefits of agglomeration economies. This \"development by accretion\" is prevalent in many legacy institutions where facilities are allocated in fixed plots of land. The second interpretation could be that this phenomenon represents a functional layout where an attempt is made to create interfaces among disciplines in related disciplines\u0026mdash;for example, grouping science labs and corresponding lecture theaters. The more extreme manifestation of this phenomenon, as identified in this research, could thus indicate inefficiencies in traffic in particular zones of the campus or commuting across different regions in case of time constraints.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison with Existing Literature\u003c/h2\u003e \u003cp\u003eThe result obtained from the study showed a cluster distribution, which is supported by various researches on the distribution of facilities in other Nigerian urban centers, though different from the ones considered in this study. For instance, Efiong, Efiong, and Ogba (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) revealed that the distribution of ATMs in Calabar Metropolis was highly significant in a cluster distribution, thus attributed by the authors to commercial viability and highly densely trafficked locations. There are also researches on healthcare facilities (Fadahunsi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sanni, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and filling station facilities (Mohammed et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in other urban centers of Nigeria that showed a similar distribution.\u003c/p\u003e \u003cp\u003eAlso, this finding can be attested by Aschale (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), with regard to the effectiveness of GIS technology, which was stressed by Aschale (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), in his research among Debre Markos Town, Ethiopia, concerning the role of GIS technology in making known location-disparities in the distribution of schools. Also, this finding is an endorsement of the research by Olubadewo, Abdulkarim, \u0026amp; Ahmed (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which emphasized the significant role of GIS technology in educational planning. This research serves as an endorsement on the micro-level in the context of the university environment.\u003c/p\u003e \u003cp\u003eHowever, it is clear from the observation that the pattern does not conform to the conventional framework of Central Place Theory (Christaller, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1966\u003c/span\u003e). The Central Place Theory states that service centers follow a rational and hierarchical layout. From an organizational standpoint, it could be considered that a more diffuse pattern of lecture theaters could be more appropriate in a university setting, as it would ensure that the longest possible travel time of members of the university from one class location to another is reduced. The level of clustering observed in this research implies that this optimal pattern has been overridden by another factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Implications for Campus Planning and Management\u003c/h2\u003e \u003cp\u003eThe identified cluster pattern has important implications for next planning and management with in the University of Calabar:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePedestrian Mobility and Congestion\u003c/b\u003e: Clustering of the lecture theaters could contribute to congestion during peak hours in particular locations, thus putting pressure on walkways and potentially posing safety concerns. Moreover, it implies that students with schedules involving multiple clusters will experience more difficult walks between classes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eResource Allocation\u003c/b\u003e: The clustering could reveal an issue of imbalanced allocation of resources. Certain departments could be situated in resource-rich clusters, while others, which could be newer departments, could be situated on the peripherals or not allocated proper dedicated space.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExpansion Planning\u003c/b\u003e: However, in planning for expansion, it is best that the physical planning unit of the university identifies an approach of either supporting existing concentrations or establishing a decentralized pattern. Ad-hoc expansion of the existing concentrations will make the existing pattern worse.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Recommendations\u003c/h2\u003e \u003cp\u003eFrom the findings, the following recommendations can be proposed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe University\u0026rsquo;s Physical Planning and Works Department should embrace GIS technology as one of its fundamental tools in its master planning of the campus.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAmbitions in building academic facilities in the future will focus on diversifying the distribution of the locations of the lecture halls from the current concentrated facilities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA supplementary research will be undertaken involving the use of network analysis with the intention of evaluating the connectivity of the aforementioned lecture theaters from student hostels and from main transit routes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Study Limitations and Future Research\u003c/h2\u003e \u003cp\u003eThe research relies on the geometric pattern of distribution. The data on the capacity, quality, or schedule of the lecture rooms is not used in this research. However, this information could help develop an adequate multi-criteria assessment of the functional effectiveness of the lecture rooms. Moreover, the application of the Manhattan distance concept seems appropriate in the grid-structured environment of an urban setting. However, this concept could be explored by using the network distance concept by mapping actual data of roads within the campus.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Study Area\u003c/h2\u003e \u003cp\u003eThe research was carried out within the University of Calabar\u0026rsquo;s main campus. The University of Calabar is located in Calabar, which is the capital of Cross River State. The university is estimated to be 8 kilometers from the city center. The university\u0026rsquo;s main campus occupies a substantial amount of land. The ground cover has varied topographic features, including academic buildings, administrative offices, student hostels, employees\u0026rsquo; residential locations, as well as vacant spaces. The main campus of the university serves as the unit of investigation. The Map of the University and lecture halls distribution is shown on Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Data Sources and Preparation\u003c/h2\u003e \u003cp\u003eThe research could use both primary and secondary geospatial data.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePrimary Data: The data collected was in the form of geographical location coordinates (latitude and longitude) of all the identified lecture theaters within the boundary of the research. The data was collected through field observation by using a handheld GPS device.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSecondary Data: The image of the University of Calabar satellite of higher resolution was downloaded from Google Earth Pro. The image was then used as a base map.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Instrumentation and Data Collection\u003c/h2\u003e \u003cp\u003eThe data collection was executed in a systematic, multi-stage process to ensure completeness and accuracy:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eReconnaissance Survey: An initial visit was made to acquaint oneself with the layout of the campus and locations of all possible lecture hall structures. This helped in planning an efficient route.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGPS Data Collection: The geographic position of each of the lecture halls was determined using a Garmin GPSMAP 625 handheld GPS receiver. The positional accuracy of this GPS receiver is estimated to be in the range of 3\u0026ndash;5 meters. The GPS receiver was fixed in its position until the position error was estimated to be below a threshold, after which the position was recorded. The name of the corresponding lecture hall was also recorded.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePreparation of the base map of the campus was done by exporting the image from Google Earth and georeferencing it in ArcGIS 10.5 by using ground control points (GCPs), which resulted in the base map of the campus conforming to a real-world coordinate system (WGS 1984 UTM Zone 32N).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eData Integration: The GPS data recorded was then used to create a point feature class in ArcGIS by importing the recorded GPS coordinates. The data was then joined with the names of the corresponding lecture theaters in an attribute table.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Data Analysis Techniques\u003c/h2\u003e \u003cp\u003eThe core of the analysis involved spatial pattern detection using the Nearest Neighbour Analysis (NNA).\u003c/p\u003e \u003cp\u003eNearest Neighbour Analysis (NNA): This method estimates the level of geographical clustering or dispersal by comparing the average of the observed distances from each data point to its nearest-neighbour data point with the average distance that would be expected if the data points were randomly distributed.\u003c/p\u003e \u003cp\u003eThe points vector of the lecture rooms, which represents the points of interest. The total area of the campus measured in square kilometers. The total area was computed from the digital boundary of the campus. For this task, the Manhattan distance measure was considered. The measure represents the distance of moving in a grid layout of roads, which is more realistic than the Euclidean distance measure. The reproducibility of this approach is very high, given that it uses algorithms common in commercial GIS packages that all take defined parameters.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author contributed to the study conception and design. All material preparation, data collection, and analysis were performed by **David Mkpanam Nyong** .\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author gratefully acknowledges the support of the Google Earth pro for providing access satellite image used in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study, including the point shapefile of lecture hall locations and the associated attribute table, are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullahi, I. B., Ijaiya, M. A., Abdulraheem, A., Abdukadir, R. I. \u0026amp; Ibrahim, R. Spatial distribution of commercial banks in Ilorin metropolis, Kwara State, Nigeria. \u003cem\u003eConflu. J. Environ. Stud.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (1), 1\u0026ndash;5 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAliyu, A., Shahidah, M. A. \u0026amp; Aliyu, R. M. Mapping and spatial distribution of post secondary schools in Yola North Local Government Area of Adamawa State, Nigeria. \u003cem\u003eInt. J. Sci. Technol.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (5), 405\u0026ndash;422 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAschale, T. M. Assessment of schools spatial distribution and identifying suitable areas by GIS technology: In case of Debre Markos Town North Western Ethiopia. \u003cem\u003eJ. Resour. Dev. Manage.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 8\u0026ndash;20 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastells, M. The university system: Engine of development in the new economy. In Revitalizing higher education. Pergamon, Oxford. 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Educational Social Res.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (1), 71\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5901/jesr.2017.v7n1p71\u003c/span\u003e\u003cspan address=\"10.5901/jesr.2017.v7n1p71\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spatial Pattern, Geographic Information Systems (GIS), Nearest Neighbour Analysis, Lecture Halls, University of Calabar, Campus Planning","lastPublishedDoi":"10.21203/rs.3.rs-7948719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7948719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe strategic location of educational infrastructure plays an important role in efficient academic administration and effective distribution of learning facilities on a university campus. This research work utilizes Geographic Information System (GIS) tools and methodologies in the analysis of the distribution pattern of lectures on the University of Calabar\u0026rsquo;s campus. The main focus of this research was to identify the randomness or otherwise of the distribution of the learning facilities. The data collected included the geographical locations of all forty-eight (48) identified lectures. The data was collected using a Garmin 625 GPS device. The data was analyzed by merging it with the University of Calabar\u0026rsquo;s google earth image. The methodology involved the use of the Nearest Neighborhood Analysis (NNA) to determine the hypothesis of randomness or otherwise of the distribution. The findings showed a Nearest Neighborhood Ratio (Rn) of 0.01, the observed mean was 62.79 meters, the average mean distance was calculated to be 94.97 meters, while the highly significant Z score was \u0026minus;\u0026thinsp;4.49 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The analysis shows clear evidence of not following the criteria of proper distribution of learning facilities outlined in the Central Place Theory. The research work concludes that Geographic Information System tools and methodologies offer useful platforms in campus planning. The tools offer valuable guides in development planning on campus.\u003c/p\u003e","manuscriptTitle":"Spatial Pattern Analysis of Lecture Halls in the University of Calabar, Nigeria: A GIS and Nearest Neighbour Statistics Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:53:24","doi":"10.21203/rs.3.rs-7948719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54cb9c5c-9dc9-4c9c-9068-760dcd4b6a49","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59722592,"name":"Physical sciences/Engineering"},{"id":59722593,"name":"Earth and environmental sciences/Environmental sciences"},{"id":59722594,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":59722595,"name":"Physical sciences/Mathematics and computing"},{"id":59722596,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-02-09T04:24:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 08:53:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7948719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7948719","identity":"rs-7948719","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}