Interpreting rescue vehicle patterns using geovisual analytics for spatiotemporal resource allocation

Abstract

This article presents a geovisual analytics approach to explore hidden travel patterns of emergency response vehicles in the city of Lahore, Pakistan. The Rescue 1122 is a department that provides emergency response services in Pakistan. These services are like the 911 working in the USA or the 999 emergency services in the UK. The Rescue 1122 emergency vehicles are categorized as fire, rescue, and ambulance services. Different categories of vehicles handle various types of requests depending upon the nature of the emergency. All vehicles are equipped with a Global Positioning System (GPS) tracker. Data obtained from these vehicles generate spatiotemporal sequences. These sequences—also known as trajectories—form the core component of moving object analysis. The aim of this study is to use visualization techniques and to assist government officials for efficient spatiotemporal allocation of the emergency vehicles. Several visualization techniques are applied in this study to get useful insight including clustering, mean time gaps in visits, hotspots, spatiotemporal aggregation and sub-setting, and caller data analysis. The results demonstrate some interesting patterns and highlight the areas that require allocation pre-planning. The findings are beneficial for effective resource planning and for understanding the complexities of a highly urbanized city of Lahore.