Song Chao, Kwan Mei-Po, Zhu Jiping
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.
Int J Environ Res Public Health. 2017 Apr 8;14(4):396. doi: 10.3390/ijerph14040396.
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.
随着城市的快速发展，火灾发生的数量日益增加，给人类和环境带来了更高的风险。本研究比较了基于地理加权回归的模型，包括地理加权回归（GWR）和整合了空间和时间效应的地理和时间加权回归（GTWR），以及用于城市尺度火灾风险建模的全局线性回归模型（LM）。结果表明，道路密度和企业的空间分布对火灾风险的影响最为强烈，这意味着我们应关注道路和企业密集分布的区域。此外，企业数量较多的地方火灾起火记录较少，这可能是由于严格的管理和预防措施。空间上显著变量数量的变化表明，异质性主要存在于合肥市北部和东部的农村及郊区，在这些地方与人类相关的设施或道路建设仅集中在城市副中心。GTWR能够捕捉变量时空异质性的微小变化，而GWR和LM则无法做到。一种整合空间和时间的方法使我们能够更好地理解火灾风险的动态变化。因此，政府可以利用这些结果在城市尺度上管理消防安全。
