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Int. J. Environ. Res. Public Health 2017, 14(4), 396;

Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression

State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China
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
Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, P.O. Box 80125, 3508 TC Utrecht, The Netherlands
Author to whom correspondence should be addressed.
Academic Editor: Jason K. Levy
Received: 12 February 2017 / Revised: 28 March 2017 / Accepted: 5 April 2017 / Published: 8 April 2017
(This article belongs to the Special Issue Human Health, Risk Analysis and Environmental Hazards)
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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. View Full-Text
Keywords: GTWR; GWR; heterogeneity; space and time; global linear regression; fire risk GTWR; GWR; heterogeneity; space and time; global linear regression; fire risk

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Song, C.; Kwan, M.-P.; Zhu, J. Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression. Int. J. Environ. Res. Public Health 2017, 14, 396.

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