One challenge in managing fire hazards in an urban setting is how to optimize the fire service route, increase the response time, and increase service coverage. Recently, this challenge is becoming imminent due to road traffic congestion and insufficient road widths that are common in populated cities in the Southeast Asia regions. One of the urban fire hotspots in populated Jakarta City is Pekojan Urban Village, Tambora Subdistrict. This subdistrict is served by Angke Fire Station located in Pekojan’s southwestern parts. Then this research aims to evaluate and compare optimized routes for fire vehicle dispatched from Angke Fire Station to serve 12 neighborhood units (in Bahasa is RW) in Pekojan. The method used the route optimization and network analysis tools in Geographic Information System (GIS) and its related geospatial data including neighborhood units, road networks, traffic congestion, and fire station locations. Geospatial network analysis of data by GIS has an advantage as a method to design and analyze the routing strategy and determine the most optimized route for fire vehicles. Based on the results and with the fire vehicle speed of 40 km/h, the average optimized route distances to travel from the fire station to RWs were 1.092 km (95%CI: 0.888-1.3 km) with an average response time of 1.638 minutes (95%CI: 0.869-2.41 min.). According to the GIS, model, response time of 1 minute only covers 22.77% of Pekojan areas. By increasing response time to 2 minutes, then fire vehicle can cover 98.9% of Pekojan area (AIC= 0.06). Despite the fact that the fire vehicle routes and response times can be optimized, those routes are challenged by the road traffic congestion. This congestion limits the speeds of fire vehicles to less than 20 km/h, as observed in 11.59% of the optimized routes. The service coverages of fire vehicles was also limited due to the narrow street.
Fire incidents in urban setting were influenced by many factors ranging from population, building density to climatic variables. Currently, fire incident can be estimated using various variables and modeling methods including maximum entropy approach. Then the aim of this study is to model the probable spatial distribution of areas in Jakarta City mainly in North, West, and South districts that are prone to the fire risks. The model was developed using maximum entropy approach using climatic variables as predictors obtained from WordClim database. The model then was confirmed using area under the curve (AUC) values. The climatic models show that North and West parts of Jakarta receiving lower rainfall than South parts. Based on modeled probability distributions of fire risks, North and West parts were having highest probability distributions of fire risks with value of 50%. The AUC validates the probability distributions of fire risks model with AUC value of 0.64 ± 0.07. The results obtained from this study then can be used planning fire prevention.
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