Leveraging Geographic Information System (GIS) for proactive campus security: A spatiotemporal analysis of crime hotspots at Obafemi Awolowo University, Nigeria

 Corresponding author: John Adeyemi Eyinade. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Leveraging Geographic Information System (GIS) for proactive campus security: A spatiotemporal analysis of crime hotspots at Obafemi Awolowo University, Nigeria Caleb Olutayo OLUWADARE and John Adeyemi EYINADE * Department of Surveying and Geoinformatics, Faculty of Environmental Design and Management, Obafemi Awolowo University, Ile-Ife, Nigeria. World Journal of Advanced Research and Reviews, 2025, 27(01), 448-461 Publication history: Received on 27 May 2025; revised on 01 July 2025; accepted on 04 July 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.1.2555 Abstract Ensuring campus security is paramount for higher education institutions, particularly in regions like Nigeria where pervasive insecurity necessitates a shift from reactive to proactive safety measures. This study addresses the critical gap in data-driven campus security by employing Geographic Information System (GIS) methodologies to analyze crime patterns at Obafemi Awolowo University (OAU) from 2017 to 2022. Reported crime data, acquired from the OAU security department, revealed annual crime rates per 1,000 inhabitants of 3.62 (2017), 3.94 (2018), 5.11 (2019), 1.4 (2020), 3.23 (2021), and 4.62 (2022). Theft constituted the majority of reported incidents, predominantly in academic areas and halls of residence. Spatial autocorrelation analysis using Global Moran's I confirmed significant positive clustering of crime incidents across the campus (p < 0.05 for all distance bands), indicating that crime is not random. Hotspot analysis (Getis-Ord Gi*) identified critical high-crime areas including Fajuyi Hall, Awolowo Hall, Post Graduate Hall, Oduduwa Lecture Theater, Admin Extension, Sport Center, and the Banking Area, while the Maintenance area was identified as a cool spot. These findings underscore the effectiveness of GIS in pinpointing specific vulnerabilities, enabling university management to implement targeted, evidence-based interventions for enhanced security. The study advocates for a proactive, spatially-informed approach to campus security, offering a replicable framework for similar institutions facing evolving security challenge Keywords: Geographic Information System (GIS); Crime Hotspots; Campus Security; Spatial Analysis; Crime Prevention; University Safety 1. Introduction Ensuring a secure and conducive environment is paramount for the effective functioning and long-term sustainability of higher education institutions globally [1, 2, 3]. Beyond merely facilitating academic pursuits, campus safety is intrinsically linked to student recruitment and retention, faculty productivity, research integrity, and the overall reputation of the university within the global academic landscape [4]. Universities, characterized by their open access, high population density, and diverse socio-economic profiles, present unique security challenges [5, 6]. These range from common opportunistic crimes, such as theft and assault, to more organized criminal activities and even the pervasive threat of external security breaches, including terrorism and abductions, which can have catastrophic consequences [5]. In Nigeria, the imperative for robust campus security is particularly pronounced, amplified by a broader national landscape grappling with multifaceted security challenges [7, 8, 9]. Recent years have underscored the severe vulnerabilities within the educational sector, exemplified by incidents such as the suspension of academic operations at Veritas University due to credible terror threats, and the tragic kidnapping of over 200 schoolgirls from the Government World Journal of Advanced Research and Reviews, 2025, 27(01), 448-460 449 Girls Secondary School in Chibok, Borno State [10, 11, 7]. These events not only highlight the susceptibility of educational institutions to external aggressions but also underscore the systemic inadequacy of existing security protocols. Obafemi Awolowo University (OAU), a leading institution in Nigeria, has historically and presently faced its share of crime and instability since its establishment. This includes serious historical incidents and persistent challenges such as the recurrent theft of personal items from student residential halls and academic areas, alongside various instances of physical violence [12, 13, 14]. A critical examination of existing campus security measures, both at OAU and across many Nigerian universities, reveals a predominantly reactive paradigm [13]. Security interventions, such as the deployment of temporary checkpoints at the entrance of affected halls, are frequently initiated only after a crime has been reported. While seemingly addressing immediate concerns, these reactive strategies are inherently limited and often ineffective; they are temporary, lack sustained presence, and rarely facilitate the apprehension of offenders or proactively deter future incidents [14]. This reactive stance, as evidenced by the broader response to security breaches in the country, signifies a profound systemic weakness. Crucially, this highlights a significant research gap: the absence of systematic, evidence-based, and spatiallyinformed approaches to crime analysis that can transition campus security from a reactive to a truly preventive model [15, 10]. Traditional crime reporting often lacks the granular spatial detail necessary to understand crime patterns, cluster formations, and underlying environmental factors, thus hindering the development of targeted and effective interventions [8, 9]. To bridge this gap, a deeper understanding of crime dynamics rooted in criminological theories is essential [16]. Security, in its fundamental essence, refers to the deliberate actions undertaken to safeguard individuals, physical structures, or even sovereign nations from various forms of harm, threats, or criminal activities [17]. Crime itself, broadly defined by [18] as behavior involving the obtaining of resources from others via force, fraud, or stealth, is universally considered a public wrong. Understanding the spatial and temporal distribution of crime is central to developing effective prevention strategies, which is where environmental criminology offers crucial insights [19, 17]. Two influential theories from environmental criminology are particularly pertinent to understanding the spatial dynamics of campus crime. Routine Activity Theory, for instance, posits that a criminal event arises from the convergence of a motivated offender, a suitable target, and the absence of a capable guardian in time and space [20, 21]. On a university campus, this framework elucidates the prevalence of opportunistic crimes [22], such as theft, where valuable student possessions represent available targets in areas potentially lacking overt security presence [19]. Complementing this, Crime Pattern Theory further illuminates how criminal activity is not randomly dispersed but rather follows predictable patterns influenced by the daily routines and activity spaces of both offenders and victims [20]. This theory highlights the significance of "awareness spaces," where offenders operate within familiar nodes (e.g., academic buildings, residential halls), paths (common routes), and edges (transitional zones) within the campus environment [20, 22]. Consequently, these areas can become predictable loci for criminal acts, leading to the formation of crime "hotspots." Together, these theoretical frameworks provide a robust foundation for comprehending why certain areas within a university campus might disproportionately experience higher crime rates or specific types of offenses, thereby moving beyond mere incident reporting to a deeper, environmentally informed analysis of crime causation [23]. The effective visualization and analytical interpretation of geospatial information are thus paramount for modern crime prevention and intervention [24]. Geographic Information Systems (GIS) serve as highly sophisticated computational tools indispensable for the creation, rigorous analysis, and precise identification of patterns embedded within geographic data [25, 24]. By enabling the comprehensive examination and analysis of diverse data layers within a mapped context [26], GIS profoundly enhances our understanding of complex phenomena and facilitates more informed decision-making [24, 27]. Its inherent power lies in its capacity to clarify the precise spatial relationships between various elements, simplify otherwise complex analyses, and reveal clear, actionable insights, given that information cannot be fully comprehended unless it is spatially contextualized [28]. In the specialized domain of criminology, GIS has become an indispensable asset. Its applications range from fundamental crime mapping [28, 24, 29], which visually represents individual crime incidents and their spatial distribution, to advanced hotspot analysis [24]. Techniques such as Global Moran's I and Getis-Ord Gi* are employed within GIS environments to statistically identify and validate clusters of criminal activity (hotspots) and areas of significantly lower crime (cool spots) [30,26]. This statistical validation is critical, ensuring that identified spatial concentrations are not merely random occurrences but statistically significant patterns. The ability to precisely delineate these concentrations of criminal activity is vital for the strategic allocation of security resources and the implementation of targeted policing efforts [31, 32]. This study, therefore, aims to directly address the critical gap in proactive campus security by leveraging Geographic Information System (GIS) technology to revolutionize the understanding and management of campus security at Obafemi Awolowo University. Utilizing reported crime data from 2017 to 2022, this research will systematically World Journal of Advanced Research and Reviews, 2025, 27(01), 448-460 450 determine annual crime rates, analyze the spatiotemporal distribution of various crime types, and rigorously identify persistent crime hotspots and cool spots across the campus environment. By transforming raw crime data into actionable geospatial intelligence, this study will offer precise, evidence-based insights that can inform the strategic allocation of security resources, guide the implementation of targeted preventive measures, and ultimately foster a safer and more conducive learning and living environment for the entire university community. The findings will not only be instrumental for OAU's specific context but will also provide a replicable methodology and valuable lessons for other higher education institutions grappling with similar security challenges in Nigeria and beyond. 2. Methods This section delineates the comprehensive methodological framework employed to achieve the objectives of this study. It outlines the research design, describes the geographical characteristics of the study area, details the processes of data acquisition and preparation, and elaborates on the spatial analytical techniques utilized to identify crime patterns and hotspots on the Obafemi Awolowo University campus. The approach adopted is primarily quantitative, leveraging Geographic Information System (GIS) tools for spatial data processing and analysis. 2.1. Study Area This study was conducted within the central campus and student residential areas of Obafemi Awolowo University (OAU), a prominent federal university located in Ile-Ife, Osun State, Nigeria. OAU is a large, open-access institution with a significant population of students, faculty, and administrative staff, making its security profile particularly relevant for understanding campus safety dynamics in the Nigerian context. The defined study area specifically encompasses the core academic zone, comprising various lecture theaters, faculty offices, and administrative facilities, alongside the student residential zone, which includes nine distinct halls of residence. These areas represent the primary loci of daily activities for the majority of the university community, thereby making them critical for crime analysis. The geographical representation of the study area was established by digitizing satellite imagery acquired from Google Earth Pro. This digitized map data was subsequently exported in Keyhole Markup Language (KML) format and then seamlessly converted into usable layers within the ArcGIS 10.7.1 software application, serving as the foundational spatial dataset for the research. The geographical context of Obafemi Awolowo University and the specific boundaries of the study area are visually represented in Figure 1. Figure 1 Map of the Study Area (Obafemi Awolowo University Campus) World Journal of Advanced Research and Reviews, 2025, 27(01), 448-460 451 2.2. Data Acquisition The primary data for this study comprised reported crime incidents on the Obafemi Awolowo University campus for a six-year period, spanning from 2017 to 2022. This comprehensive crime data, detailing various types of offenses committed and their precise locations, was obtained directly from the security department of OAU. According to information provided by the security department, the collected data was deemed reliable and of high quality for the purpose of this analysis. The specific breakdown of crime types and their occurrences within both the academic area and halls of residence for each year from 2017 to 2022 is presented in Tables 1 through 6. For the calculation of crime rates, the total population of the campus was assumed to be 35,000 inhabitants. Table 1 Crime data in OAU Campus for year 2017 2017 CRIME RECORD Campus Area Theft Assault Threat to Life Breaking and entering Rape/Sexual Assault Car breakings Wilful Damage Academic Area 40 8 2 2 0 2 1 Halls of Residence 55 10 3 3 1 0 0 Table 2 Crime data in OAU Campus for year 2018 2018 CRIME RECORD Campus Area Theft Assault Threat to Life Breaking and entering Rape/Sexual Assault Car breakings Wilful Damage Academic Area 72 6 0 3 1 0 1 Halls of Residence 38 12 2 3 0 0 0 Table 3 Crime data in OAU Campus for year 2019 2019 CRIME RECORD Campus Area Theft Assaul t Threat to Life Breaking and entering Rape/Sexua l Assault Car breakings Wilful Damage Academic Area 113 3 4 2 2 0 0 Halls of Residence 49 5 0 0 0 0 1 Table 4 Crime data in OAU Campus for year 2020 2020 CRIME RECORD Campus Area Theft Assault Threat to Life Breaking and entering Rape/Sexual Assault Car breakings Wilful Damage Academic Area 30 1 0 1 0 0 0 Halls of Residence 12 4 0 0 0 0 1 World Journal of Advanced Research and Reviews, 2025, 27(01), 448-460 452 Table 5 Crime data in OAU Campus for year 2021 2021 CRIME RECORD Campus Area Thef t Assau lt Threat to Life Breaking and entering Rape/Sexual Assault Car breakings Wilful Damage Academic Area 64 9 1 4 1 0 0 Halls of Residence 30 0 0 2 0 0 2 Table 6 Crime data in OAU Campus for year 2022 2022 CRIME RECORD Campus Area Theft Assault Threat to Life Breaking and entering Rape/Sexual Assault Car breakings Wilful Damage Academic Area 101 14 4 2 0 1 2 Halls of Residence 27 8 1 2 0 0 0 2.3. Data Analysis The collected crime data was subjected to rigorous spatial analysis using Geographic Information System (GIS) methodologies, primarily within the ArcGIS 10.7.1 software environment. The analytical workflow commenced with the preparation of the crime incident data. Initially, the data, received in an Excel spreadsheet format, was saved as a Comma Separated Values (CSV) file. This CSV file was then imported into ArcMap and converted into a georeferenced shapefile. To ensure accurate spatial measurements and analysis, the coordinate system of the crime data was transformed from a geographic coordinate system to a Universal Transverse Mercator (UTM) coordinate system by using the projection tool in the ArcGIS ArcToolbox. Two principal spatial statistical techniques were employed to achieve the study's objectives 2.3.1. Spatial Autocorrelation (Global Moran's I) This statistical measure was utilized to assess the degree to which the values of a variable at one location are related to the values of the same variable at nearby locations. Global Moran's I quantifies the overall spatial autocorrelation within the dataset, indicating whether crime incidents are clustered (positive spatial autocorrelation), dispersed (negative spatial autocorrelation), or randomly distributed (zero spatial autocorrelation). The index value ranges from -1 to 1, with values closer to 1 indicating stronger clustering and a value of 0 indicating no spatial autocorrelation. The Global Moran's I tool in ArcGIS was used to calculate the Moran's I index value, along with a z-score and p-value, to determine the statistical significance of the observed spatial patterns across various distance bands. The results of the Global Moran's I statistical analysis are detailed in Table 7. 2.3.2. Hotspot Analysis (Getis-Ord Gi*) To identify statistically significant spatial clusters of high crime occurrences (hotspots) and low crime occurrences (cool spots), the Getis-Ord Gi* tool was employed. This method goes beyond simply mapping crime density by statistically determining whether the clustering of high or low values is more pronounced than would be expected by random chance. The output of this analysis clearly delineates "danger zones" (hotspots) requiring critical security attention and "peaceful zones" (cool spots) within the study area. The crime event map, generated using the "collect event tool," provided an initial visualization of crime concentration, followed by the Hotspot and Cold spot map analysis, depicted in Figure 5. World Journal of Advanced Research and Reviews, 2025, 27(01), 448-460 453 3. Results This section presents the findings from the spatial and temporal analysis of crime data collected from Obafemi Awolowo University (OAU) between 2017 and 2022. The results encompass crime frequencies by type and location, overall campus crime rates, and the identification of spatial crime patterns, including hotspots and cool spots. 3.1. Crime Frequencies and Trends The distribution of different crime types across the academic area and student residential halls on the OAU campus is illustrated in Figures 2 and 3, respectively. A consistent observation across both campus zones is that theft constitutes the highest proportion of reported incidents. Furthermore, the academic area generally recorded a higher overall frequency of crimes when compared to the halls of residence throughout the six-year study period. Figure 2 Annual Crime Frequency by Type in Academic Area (2017-2022) Figure 3 Annual Crime Frequency by Type in Halls of Residence (2017-2022) The overall crime rate for the OAU campus was determined using the standardized formula Crime Rate = (Number of reported crimes/ Total population) ×1000 World Journal of Advanced Research and Reviews, 2025, 27(01), 448-460 454 With the estimated campus population being 35,000 inhabitants, the calculated crime rates per 1,000 inhabitants for the years 2017 to 2022 are as follows: 3.62 (2017), 3.94 (2018), 5.11 (2019), 1.4 (2020), 3.23 (2021), and 4.62 (2022). The temporal trend of these annual crime rates across the study period is visualized in Figure 4. The year 2019 recorded the highest crime rate within the six-year span, whereas 2020 exhibited a notable decrease, registering the lowest crime rate. This significant reduction in 2020 is directly attributable to the prolonged shutdown of the university campus due to the global COVID-19 pandemic, which resulted in a substantial decrease in the on-campus population and, consequently, reduced opportunities for crime. Figure 4 Crime Rate Trend in OAU Campus (2017-2022) 3.2. Spatial Patterns of Crime The spatial distribution and concentration of crime events across the OAU campus are depicted in Figure 5, the crime event map. This visualization reveals that a majority of reported crimes consist of theft of phones in student residential halls and specific lecture theaters within the academic area. Furthermore, a considerable number of incidents, particularly forceful entry into parked cars and theft of money from vehicles, were reported in the banking area region. Incidents involving physical violence were frequently reported in the Student Union Building (SUB) area and the central market near ETF hall of residence. Figure 5 Crime Event Distribution (Integrative Analysis) Map of the Study Area World Journal of Advanced Research and Reviews, 2025, 27(01), 448-460 455 The Global Moran's I analysis was conducted to assess the overall spatial autocorrelation of crime incidents across the campus, with detailed statistical results presented in Table 7. The analysis indicated positive spatial autocorrelation across all examined distance bands, with all p-values being less than 0.05. This statistically significant finding confirms that the clustering of crime hotspots and cool spots is not attributable to random chance. The results also suggest that crime hotspots and cool spots frequently coexist in close proximity. Specifically, the clustering of crime hotspots was more pronounced at shorter distances, while the clustering of cool spots became more evident at longer distances. Table 7 Global Moran’s I Hotspot Analysis statistical result Distance Moran’s index Expected index variance z-score P value 445.00 0.159371 -0.006993 0.004372 2.516015 0.011869 583.46 0.111567 -0.006993 0.002336 2.453005 0.014167 721.91 0.126348 -0.006993 0.001494 3.449896 0.000561 860.37 0.119289 -0.006993 0.001033 3.928186 0.000086 998.82 0.099553 -0.006993 0.000729 3.946652 0.000079 1137.28 0.078796 -0.006993 0.000562 3.617499 0.000297 1275.74 0.078242 -0.006993 0.000437 4.077468 0.000046 1414.19 0.071951 -0.006993 0.000348 4.229806 0.000023 1552.65 0.057735 -0.006993 0.000283 3.845881 0.000120 1691.11 0.054276 -0.006993 0.000232 4.023134 0.000057 3.3. Crime Hotspots and Cool Spots Figure 6 Hotspot and Cold Spot Map Analysis of the Study Area World Journal of Advanced Research and Reviews, 2025, 27(01), 448-460 456 The results of the Hotspot and Cold spot map analysis, derived from the Getis-Ord Gi* statistic, are presented in Figure 6. This map visually distinguishes areas of high crime concentration (hotspots) from areas of low crime concentration (cool spots). The blue color gradient on the map indicates cool spots, representing "peace zones" where crime rates are low, with deeper blue signifying cooler (safer) areas. Conversely, the red color gradient signifies hotspots or "danger zones," where crime rates are higher, with deeper red indicating a more intense concentration of crime. The specific geographic coordinates and names of the identified hotspot and cool spot locations are provided in Tables 8 and 9, respectively. These tables pinpoint critical areas requiring heightened security attention and areas that demonstrate relative safety within the campus environment. Table 8 Hotspot location Hotspot Locations Northings(m) Easting(m) Fajuyi hall 831329.459 6670506.913 Awolowo hall 831672.354 667172.366 Post graduate hall 831787.921 667390.827 Oduduwa lecture theater 831411.225 667932.883 Admin extension 831690115 667678.995 Sport center 831009.351 667805.921 Banking area 830944.885 668089.648 Table 9 Cold spot location Cold spot Location Northings(m) Easting(m) Maintenance 829184.866 667861.644 4. Discussion The findings of this study provide crucial insights into the spatial and temporal dynamics of crime on the Obafemi Awolowo University campus, offering an evidence-based foundation for transitioning towards a more proactive security management paradigm. 4.1. Interpretation of Crime Frequencies and Trends The analysis of crime frequencies reveals that theft consistently accounts for the highest proportion of reported incidents in both academic areas and halls of residence. This prevalence aligns strongly with Routine Activity Theory, which posits that crime occurs when a motivated offender converges with a suitable target in the absence of a capable guardian. On a university campus, personal items like mobile phones and laptops (highly suitable and valuable targets) are frequently left unattended in lecture theaters, libraries, and even student rooms, creating abundant opportunities for opportunistic theft. The higher overall crime frequency observed in academic areas compared to halls of residence could be attributed to the larger number of suitable targets present during active academic hours, combined with a greater influx of non-campus affiliates. The calculated crime rates for OAU, ranging from 1.4 to 5.11 per 1,000 inhabitants between 2017 and 2022, indicate that the campus generally maintains a relatively low crime rate compared to typical urban environments. However, the fluctuation in these rates, particularly the peak in 2019 (5.11 per 1,000) and the sharp decline in 2020 (1.4 per 1,000), offers compelling insights. The significant reduction in crime during 2020 directly correlates with the prolonged campus shutdown due to the COVID-19 pandemic. This observation strongly supports the core tenets of Routine Activity Theory; the drastic reduction in the "suitable target" and "motivated offender" populations (as students and staff were largely absent), coupled with