GIS-Based Spatial Analysis of Accident Hotspots: A Nigerian Case Study

Abstract

This study identified high-risk locations (hotspots) using geographic information systems (GIS) and spatial analysis. Five years of accident data (2013–2017) for the Lokoja-Abuja-Kaduna highway in Nigeria were used. The accident concentration analysis was conducted using the mean center analysis and Kernel density estimation method. These locations were further verified using Moran’s I statistics (spatial autocorrelation) to determine their clustering with statistical significance. Fishnet polygon and network spatial weight matrix approaches of the Getis–Ord Gi* statistic were used in the hotspot analysis. Hotspots exist for 2013, 2014, and 2017 with a significance level between 95–99%. However, hotspots for 2015 and 2016 have a low significance level and the pattern is random. The spatial autocorrelation analysis of the overall accident locations and the Moran’s I statistic showed that the distribution of the accidents on the study route is random. Thus, preventive measures for hotspot locations should be based on a yearly hotspot analysis. The average daily traffic values of 31,270 and 16,303 were obtained for the northbound and southbound directions of the Abaji–Abuja section. The results show that hotspot locations with high confidence levels are at points where there are geometric features.

Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System

“…While the study gave valuable insight into risk zones and accident severities, data limitations and unaccounted factors, such as road characteristics and infrastructure, are still to be considered. Furthermore, a study of the Lokoja-Abuja-Kaduna highway in Nigeria by Afolayan et al (2022) used the KDE method and weighted mean center analysis to show the hotspots of accidents between the Sabon Gida and Yangoji curves [20]. The analyses of the years 2013, 2014, and 2017 showed significant accidents to occur at the Abaji Bridge, Gen. hospt.…”

“…This method interprets the spatial clustering of high values as a positive value, whereas negative values indicate spatial clustering of low values [54]. This is yet again a commonly used hotspot identification tool used by many researchers [5,20,36,49,50,55]. Utilizing KDE alongside the Getis-Ord Gi* technique provides insight into how the population density correlates with accident hotspots.…”

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

“…Moreover, a GIS is a potential approach for spatial analysis to determine the accident hotspots. Identifying the locations of highfrequency accidents, or "hotspots", is one of the most crucial tasks in the effort to decrease the number of traffic accidents [25][26][27][28]. The locations of traffic accident hotspots can be determined via the locations of accident clusters [13,29].…”