Exploring Spatiotemporal Dynamics of Urban Fires: A Case of Nanjing, China

Abstract

Urban fire occurs within the built environment, usually involving casualties and economic losses, and affects individuals and socioeconomic activities in the surrounding neighborhoods. A good understanding of the spatiotemporal dynamics of fire incidents can offer insights into potential determinants of various fire events, therefore enabling better fire risk estimation which can assist with future allocation of prevention resources and strategic planning of mitigation programs. Using a twelve-year (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) dataset containing the urban fire events in Nanjing, China, this research explores the spatiotemporal dynamics of urban fires using a range of exploratory spatial data analysis (ESDA) approaches. Of particular interest here are the fire incidents involving residential properties and local facilities due to their relatively higher occurrence frequencies. The results indicate that the overall amount of urban fires has greatly increased in the last decade and the spatiotemporal distribution of fire events varies among different incident types. The identified spatiotemporal patterns of urban fires in Nanjing can be linked to the urban development strategies and how they have been reflected in reality in recent years.

Urban Fire Dynamics and Its Association with Urban Growth: Evidence from Nanjing, China

“…For instance, it might be of interest to investigate why there is a descending trend of urban fires during 2004-2008, and why the year 2008 had the lowest number of fire events and whether it was affected by aforementioned factors like weather and enhanced monitoring due to big events (e.g., [5][6][7]). Additionally, as pointed by [10], the intensifying and persistent fire hot spots in the city proper, particularly for the dwelling fire, might be related to the concentration of old residential buildings that lack fire safety devices. Further, in addition to population growth and urban expansion, urban development such as urban regeneration in the city proper and construction of new housing and public facilities in the new towns also involves human activities that could affect urban fire incidence.…”

“…The fire incident data used in this research contained all urban fires (about 39,230) that occurred in Nanjing during 2002-2013, provided by the Fire and Rescue Bureau of Nanjing and included the location and time of each fire event. In addition, all fires were grouped into ten categories based on the official classification (see [10]). The proportion of each fire type was as follows: dwellings (31.1%), grassland (14.9%), facilities (14.6%), refuse (12.7%), vehicles (10.9%), false alarm (3.7%), industrial (2.8%), non-residential buildings (2.5%), retail stores (2.6%) and others (4.3%).…”

An Approach for the Analysis of the Accessibility of Fire Hydrants in Urban Territories

“…Fires are the fourth-largest cause of accidental injury globally (after road accidents, falls and drowning) [7]. Fire risk management is a global issue [8][9][10], where urban safety policies should take serious consideration of this topic. There are many phases for fire risk management, but more emphasis should be given to two strategies, such as [11,12]:…”

Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams

“…Space-time cube (STC) representation has proved to be useful means of conceptualization, analysis and visualization of spatio-temporal events [27,28] and trajectories [29]. It has been used for characterizing various urban phenomena, including crime hotspots [30], urban fires [31], and dengue fever [32], as well as for studying human activity patterns [33,34] and describing big trajectory datasets [35,36].In this paper, we build on a previous study by Hipp et al [25] and present a new method to derive high-resolution spatio-temporal pedestrian density from webcam images. Given the three-dimensional nature of the density, we propose a novel visualization using a continuous space-time cube representation, aiming at providing at-a-glance view of the dynamics of pedestrian density in space and time.…”

“…Space-time cube (STC) representation has proved to be useful means of conceptualization, analysis and visualization of spatio-temporal events [27,28] and trajectories [29]. It has been used for characterizing various urban phenomena, including crime hotspots [30], urban fires [31], and dengue fever [32], as well as for studying human activity patterns [33,34] and describing big trajectory datasets [35,36].…”