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• W4313488468 abstract "To achieve an effective emergency response and road safety, this study aims to assist a semi-automated dynamic system to analyze and predict the spatial distribution and temporal pattern of road crashes. Kasur, an intermediate city of Pakistan, was selected and data including location, time and reasons of accidents for five years (2014–2018) was utilized. Radar charts, Getis-Ord Gi* statistic, Moran’s I spatial auto-correlation, and time series indices were engaged to present temporal, spatial and spatial-temporal variation of accidents, using python-based tools and jupyter notebook. A dynamic user interface was created using Github and Tableau to visualize a real-time zoom-able spatiotemporal variation of accidents. The results explain that out of 12 months, October faces the peak while April sees the least of road accidents. 7am is the peak hour for accidents and the weekends record a significantly higher number of road accidents as compared to weekdays. The city core witnesses the major hotspot areas with huge cluster of accidents. The findings contribute towards a well-informed decision support system, the knowledge of spatial analytics and its application in road safety science, and the preparedness of the rescue agencies for rapid response to reduce the impacts of road accidents." @default.
• W4313488468 created "2023-01-06" @default.
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• W4313488468 date "2023-01-04" @default.
• W4313488468 modified "2023-09-27" @default.
• W4313488468 title "Risk reduction via spatial and temporal visualization of road accidents: a way forward for emergency response optimization in developing countries" @default.
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• W4313488468 doi "https://doi.org/10.1080/17457300.2022.2164312" @default.
• W4313488468 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36597796" @default.
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