Bilim, Atiye2024-12-102024-12-1020250264-27511873-6084https://doi.org/10.1016/j.cities.2024.105523https://hdl.handle.net/20.500.13091/9648Identifying the locations of pedestrian crashes in cities contributes to the full implementation of sustainable transportation for all road users. This study examines the pedestrian crash data of Konya, T & uuml;rkiye, from 2017 to 2022. First, the dangerous and safe road networks in Konya province were identified. Then, spatial and temporal analyses were performed. Kernel density estimation analysis was used to identify five critical locations with high pedestrian crash density. Hotspot analysis was used to identify roads where crashes showed clustering characteristics. Buffer analysis was used to analyse the effectiveness of traffic lights in preventing crashes and three locations where traffic lights were inadequate in preventing crashes were identified. According to the temporal evaluation, pedestrian crashes increased the most at 8 am and 6 pm. Locations where the number of crashes decreased over time were identified by examining the temporal changes in pedestrian crashes using space-time cube analysis. This study highlights the positive effects of improvements in the pedestrian environment on pedestrian safety. New hotspots have emerged in several areas. Therefore, this approach is valuable for early prevention. With the methodology used in the study, the locations of pedestrian crashes and the cause-and-effect relationships of crashes can be evaluated.eninfo:eu-repo/semantics/closedAccessPedestrian crashSpatial analysisTemporal analysisKernel densityHot spotSpace time cubeKernel Density-EstimationSpatial StatisticsBuilt EnvironmentInjury SeverityCrash AnalysisNetworkGisIntersectionsAccidentsPatternsArticle10.1016/j.cities.2024.1055232-s2.0-85207566679