title: Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression.
abstract: An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.
authors.affiliation: State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China. songch35@mail.ustc.edu.cn.
authors.forename: Chao
authors.initials: C
authors.lastname: Song
authors.affiliation: Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, 255 Computing Applications Building, MC-150, 605 E Springfield Ave., Champaign, IL 61820, USA. mpk654@gmail.com.|Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, P.O. Box 80125, 3508 TC Utrecht, The Netherlands. mpk654@gmail.com.
authors.forename: Mei-Po
authors.initials: MP
authors.lastname: Kwan
authors.affiliation: State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China. jpzhu@ustc.edu.cn.
authors.forename: Jiping
authors.initials: J
authors.lastname: Zhu
mesh_terms: D002681:China; D002947:Cities; D005390:Fires / Q000517:prevention & control / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D018986:Motor Vehicles / Q000706:statistics & numerical data; D018570:Risk Assessment; D012621:Seasons; D000068916:Spatial Regression; D014507:Urbanization
keywords: GTWR; GWR; fire risk; global linear regression; heterogeneity; space and time