Spatial heterogeneity and influencing factors of urban emergency services
TIAN Fengshi1,2, SUN Zhanhui1, ZHENG Xin1, YIN Yanfu1,3
1. Department of Engineering Physics, Tsinghua University, Beijing 100084, China; 2. School of Intelligence Policing, China People's Police University, Langfang 065000, China; 3. Fire and Rescue Department, Ministry of Emergency Management of the People's Republic of China, Beijing 100054, China
Abstract：[Objective] Urban emergency services are mainly divided into fire suppression and technical rescues. Relatively more studies have been conducted on fire suppression, whereas relatively fewer studies have focused on technical rescues. Thus, this study aims to map the spatial distribution of fire suppression and technical rescues on a city scale and build their connections quantitatively to the human population and mobility. The findings are expected to help in the planning of urban emergency services to follow the major task change from fire suppression to technical rescues.[Methods] The global spatial autocorrelation of fire suppression, technical rescues, and their totality — whole emergency services — was assessed using global spatial autocorrelation analysis Moran's I, whereas the local components were indexed with local indicators of spatial association and Getis-Ord G*i. The human population and mobility were modeled through the point of interest (POI) and visitor throughput, respectively. Through the stepwise regression method, five broad categories of POI data (14 minor categories of POI) of the highest sensitivities were selected from the available 30 categories of POI. Their associations with fire suppression, technical rescues, and the whole emergency services were established using the multiscale geographically weighted regression (MGWR) model.[Results] Both fire suppression and technical rescues were found to have a certain degree of spatial clustering, but some differences were noted in the spatial distribution of different emergency service types. Old towns were the concentrated hot spots for fire suppression, whereas the formation of additional clusters of technical rescues was extensively distributed; thus, more multiregional linkage and targeted prevention were required for technical rescues than fire suppression. The POI data of residential premises, office premises (such as office buildings, and public administration and public service institutions), industrial premises (such as industrial parks and mines), educational premises (such as schools, scientific research institutions, and training institutions), and commercial premises (such as supermarkets, convenience stores, home appliance stores, digital device stores, beauty salons), and visitor throughput were closely connected to emergency services. The applied MGWR model overtook both the traditional multiple linear regression and conventional GWR, with the goodness of fit R2 exceeding 0.8 for fire suppression, technical rescues, and overall emergency services. The residual sum of squares and the corrected Akaike information criterion (AICc) had the smallest values in the MGWR model. The correlation of the local visitor throughput and POIs (e.g., residential buildings, offices, and retail stores) to local fire suppressions were approximately spatially uniform. By contrast, the connections of other POIs (e.g., offices, schools, and industrial parks) to fire suppression and technical rescues varied across the present city domain.[Conclusions] The findings indicate that targeted prevention should be employed in the present city's emergency services according to the local POI distribution and should be steered to technical rescues which have become the main part of the overall emergency services. This study provides an important reference for future emergency preparedness and response, regional prevention work, and location planning of new fire stations.
[1] PÁNTYA P. Fire, rescue, disaster management. Experiences from different countries[J]. AARMS, 2018, 17(2):77-94. [2] PÁNTYA P, KUK E. Fire service responses and the need for foreign language knowledge[J]. Journal of Environmental Protection, Safety, Education and Management, 2016, 4(7):26-29. [3] 伍晗,张欣,张琰.火警事件统计系统中英比较分析[J].消防科学与技术, 2021, 40(2):259-262. WU H, ZHANG X, ZHANG Y. Comparison and analysis of fire incident statistics system between China and Britain[J]. Fire Science and Technology, 2021, 40(2):259-262.(in Chinese) [4] 应急管理部消防救援局.中国消防救援年鉴:2021年卷[M].北京:应急管理出版社, 2021. Fire and Rescue Department, Ministry of Emergency Management of the People's Republic of China. China fire and rescue yearbook:2021 volume[M]. Beijing:Emergency Management Press, 2021.(in Chinese) [5] XIA Z L, HAO L, CHEN Y H. An integrated spatial clustering analysis method for identifying urban fire risk locations in a network-constrained environment:A case study in Nanjing, China[J]. International Journal of Geo-Information, 2017, 6(11):370. [6] 何敏,王军,江琴.中国重大事故灾害时空分布特征及危险性评价[J].安徽师范大学学报(自然科学版), 2021, 44(2):160-168. HE M, WANG J, JIANG Q. The temporal-spatial distributions and risk assessment of major accident disasters in China[J]. Journal of Anhui Normal University (Natural Science), 2021, 44(2):160-168.(in Chinese) [7] 杨尧,李功权.火灾发生率空间分异及影响因素研究[J].中国安全生产科学技术, 2018, 14(9):158-163. YANG Y, LI G Q. Study on spatial differentiation and influencing factors of fire incidence[J]. Journal of Safety Science and Technology, 2018, 14(9):158-163.(in Chinese) [8] 傅永财,徐波.中国近年火灾的空间聚集趋势分析[J].消防科学与技术, 2012, 31(4):411-413. FU Y C, XU B. Spatial cluster analysis of Chinese fire statistic[J]. Fire Science and Technology, 2012, 31(4):411-413.(in Chinese) [9] 喻彦,侯心一,高宁,等.上海市2013-2015年重特大交通事故及其伤亡信息的流行特征分析[J].中华疾病控制杂志, 2017, 21(11):1152-1156. YU Y, HOU X Y, GAO N, et al. Epidemic analysis of serious traffic accidents and casualties in Shanghai from 2013 to 2015[J]. Chinese Journal of Disease Control&Prevention, 2017, 21(11):1152-1156.(in Chinese) [10] 向珍君,计玉容,于海玲,等.北京市120院前医疗急救道路交通伤害的空间自相关分析[J].医学信息学杂志, 2022, 43(7):65-68. XIANG Z J, JI Y R, YU H L, et al. Spatial autocorrelation analysis of road traffic injuries of pre-hospital medical emergency in Beijing[J]. Journal of Medical Informatics, 2022, 43(7):65-68.(in Chinese) [11] 李云,翟欣欣,胡雅丽,等.城市道路交通事故的时空特征与规划成因:以深圳市南山区为例[J].深圳大学学报(理工版), 2018, 35(2):197-205. LI Y, ZHAI, HU Y L, et al. An analysis on the relationship between the spatial-temporal characteristics of road traffic accident and the urban planning in the mega-city:A case study of Nanshan District in Shenzhen[J]. Journal of Shenzhen University (Science&Engineering), 2018, 35(2):197-205.(in Chinese) [12] 陆化普,罗圣西,李瑞敏.基于GIS分析的深圳市道路交通事故空间分布特征研究[J].中国公路学报, 2019, 32(8):156-164. LU H P, LUO S X, LI R M. GIS-based spatial patterns analysis of urban road traffic crashes in Shenzhen[J]. China Journal of Highway and Transport, 2019, 32(8):156-164.(in Chinese) [13] JENNINGS C R. Social and economic characteristics as determinants of residential fire risk in urban neighborhoods:A review of the literature[J]. Fire Safety Journal, 2013, 62:13-19. [14] ŠPATENKOVÁ O, VIRRANTAUS K. Discovering spatio-temporal relationships in the distribution of building fires[J]. Fire Safety Journal, 2013, 62:49-63. [15] 赵晓旭.融合多源时空数据的城市火灾危险性评估[J].测绘通报, 2020(5):101-106. ZHAO. Urban fire hazard assessment based on multi-source spatiotemporal data[J]. Bulletin of Surveying and Mapping, 2020(5):101-106.(in Chinese) [16] COSTAFREDA-AUMEDES S, COMAS C, VEGA-GARCIA C. Human-caused fire occurrence modelling in perspective:A review[J]. International Journal of Wildland Fire, 2017, 26(12):983-998. [17] LIU D L, XU Z S, WANG Z Y, et al. Regional evaluation of fire apparatus requirements for petrol stations based on travel times[J]. Process Safety and Environmental Protection, 2020, 135:350-363. [18] 祝明明,罗静,余文昌,等.城市POI火灾风险评估与消防设施布局优化研究:以武汉市主城区为例[J].地域研究与开发, 2018, 37(4):86-91. ZHU M M, LUO J, YU W C, et al. Urban fire risk evaluation and location optimization of fire station based on the POI:A case study of main urban region in Wuhan[J]. Areal Research and Development, 2018, 37(4):86-91.(in Chinese) [19] 疏学明,颜峻,胡俊,等.基于Bayes网络的建筑火灾风险评估模型[J].清华大学学报(自然科学版), 2020, 60(4):321-327. SHU X M, YAN J, HU J, et al. Risk assessment model for building fires based on a Bayesian network[J]. Journal of Tsinghua University (Science and Technology), 2020, 60(4):321-327.(in Chinese) [20] 姜昀呈,孙立坚,肖琨,等.城市消防站选址布局优化研究[J].测绘科学, 2021, 46(9):207-217. JIANG Y C, SUN L J, XIAO K, et al. Optimization for location and layout of urban fire station[J]. Science of Surveying and Mapping, 2021, 46(9):207-217.(in Chinese) [21] KRISP J M, JOLMA A, VIRRANTAUS K. Using explorative spatial analysis to improve fire and rescue services[M]//OOSTEROM P, ZLATANOVA, S, FENDEL E, et al. Geo-information for disaster management. Amsterdam, The Netherlands:Springer, 2005. [22] HU J, SHU X M, XIE S T, et al. Socioeconomic determinants of urban fire risk:A city-wide analysis of 283 Chinese cities from 2013 to 2016[J]. Fire Safety Journal, 2019, 110:102890. [23] SINGH P P, SABNANI C S, KAPSE V S. Hotspot analysis of structure fires in urban agglomeration:A case of Nagpur City, India[J]. Fire, 2021, 4(3):38. [24] 徐智邦,周亮,蓝婷,等.基于POI数据的巨型城市消防站空间优化:以北京市五环内区域为例[J].地理科学进展, 2018, 37(4):535-546. XU Z B, ZHOU L, LAN T, et al. Spatial optimization of mega-city fire station distribution based on Point of Interest data:A case study within the 5th Ring Road in Beijing[J]. Progress in Geography, 2018, 37(4):535-546.(in Chinese) [25] DONG X M, LI Y, PAN Y L, et al. Study on urban fire station planning based on fire risk assessment and GIS technology[J]. Procedia Engineering, 2018, 211:124-130. [26] ZHANG, YAO J, SILA-NOWICKA K, et al. Urban fire dynamics and its association with urban growth:Evidence from Nanjing, China[J]. ISPRS International Journal of Geo-Information, 2020, 9(4):218. [27] ZHANG, YAO J, SILA-NOVICKA K. Exploring spatiotemporal dynamics of urban fires:A case of Nanjing, China[J]. ISPRS International Journal of Geo-Information, 2018, 7(1):7. [28] 李刚,马海霞,刘英杰,等.基于指数函数和的老旧电梯综合风险评价方法[J].中国安全科学学报, 2016, 26(8):69-73. LI G, MA H X, LIU Y J, et al. Method for assessing risk involved in old elevator operation based on sum of exponential functions[J]. China Safety Science Journal, 2016, 26(8):69-73.(in Chinese) [29] 黄华兵,王先伟,柳林.城市暴雨内涝综述:特征、机理、数据与方法[J].地理科学进展, 2021, 40(6):1048-1059. HUANG H B, WANG, LIU L. A review on urban pluvial floods:Characteristics, mechanisms, data, and research methods[J]. Progress in Geography, 2021, 40(6):1048-1059.(in Chinese) [30] 陈立新.消防救援队伍抗洪抢险排涝的实战探索与思考[J].消防科学与技术, 2022, 41(7):982-985. CHEN L X. Practical exploration and thinking of the fire rescue team in flood fighting, emergency rescue and waterlogging drainage[J]. Fire Science and Technology, 2022, 41(7):982-985.(in Chinese) [31] 柳林,宋广文,周素红,等.城市空间结构对惠州市中心城区交通事故影响的时间差异分析[J].地理科学, 2015, 35(1):75-83. LIU L, SONG G W, ZHOU S H, et al. Temporally impact of urban structure on city traffic accidents in Huizhou[J]. Scientia Geographica Sinica, 2015, 35(1):75-83.(in Chinese) [32] 庞哲,谢波,肖扬谋,等.城市蔓延对交通事故的影响研究:以武汉市为例[J].现代城市研究, 2020(11):1-9. PANG Z, XIE B, XIAO Y M, et al. The impact factor of urban sprawl on traffic crashes:A case study of Wuhan city[J]. Modern Urban Research, 2020(11):1-9.(in Chinese) [33] 郑依玲,谢波,南贤淑,等.城市土地利用对交通事故的影响因素与作用机制研究:以武汉市为例[J].现代城市研究, 2020(2):42-49. ZHENG Y L, XIE B, NAN X S, et al. The impact factor and mechanism of urban land use on traffic accidents:A case study of Wuhan City[J]. Modern Urban Research, 2020(2):42-49.(in Chinese) [34] WANG H J, YAO H X, KIFER D, et al. Non-stationary model for crime rate inference using modern urban data[J]. IEEE Transactions on Big Data, 2019, 5(2):180-194. [35] WANG H J. Ubran computing with mobility data:A unified approach[D]. University Park, USA:The Pennsylvania State University, 2018. [36] 黄颙昊,杨新苗,岳锦涛.基于多尺度地理加权回归模型的城市道路骑行流量分析[J].清华大学学报(自然科学版), 2022, 62(7):1132-1141. HUANG Y H, YANG X M, YUE J T. Urban street bicycle flow analysis based on multi-scale geographically weighted regression model[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(7):1132-1141.(in Chinese) [37] ANSELIN L, SYABRI I, KHO Y. GeoDa:An introduction to spatial data analysis[M]//FISCHER M M, GETIC A. Handbook of applied spatial analysis. Berlin, Germany:Springer, 2010:73-89. [38] KORTER G O, OLUBUSOYE O E, SALISU A A. Spatial analysis of road traffic crashes in Oyo state of Nigeria[J]. Journal of Sustainable Development, 2014, 7(4):151-164. [39] ANSELIN L. Local indicators of spatial association:LISA[J]. Geographical Analysis, 1995, 27(2):93-115. [40] GETIS A, ORD J K. The analysis of spatial association by use of distance statistics[M]//ANSELIN L, REY S J. Perspectives on spatial data analysis. Berlin, Germany:Springer, 2010. [41] DRUKKER D M, PENG H, PRUCHA I R, et al. Creating and managing spatial-weighting matrices with the spmat command[J]. The Stata Journal, 2013, 13(2):242-286. [42] FOTHERINGHAM A S, BRUNSDON C, CHARLTON M. Quantitative geography:Perspectives on spatial data analysis[M]. London, UK:SAGE, 2000. [43] 沈体雁,于瀚辰,周麟,等.北京市二手住宅价格影响机制:基于多尺度地理加权回归模型(MGWR)的研究[J].经济地理, 2020, 40(3):75-83. SHEN T Y, YU H C, ZHOU L, et al. On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression:Scale law of spatial heterogeneity[J]. Economic Geography, 2020, 40(3):75-83.(in Chinese) [44] FOTHERINGHAM A S, YANG W B, KANG W. Multiscale geographically weighted regression (MGWR)[J]. Annals of the American Association of Geographers, 2017, 107(6):1247-1265. [45] YU H C, FOTHERINGHAM A S, LI Z Q, et al. Inference in multiscale geographically weighted regression[J]. Geographical Analysis, 2020, 52(1):87-106. [46] BRUNSDON C, FOTHERINGHAM A S, CHARLTON M E. Geographically weighted regression:A method for exploring spatial nonstationarity[J]. Geographical Analysis, 1996, 28(4):281-298. [47] BOWMAN A W. An alternative method of cross-validation for the smoothing of density estimates[J]. Biometrika, 1984, 71(2):353-360. [48] FOTHERINGHAM A S, BRUNSDON C, CHARLTON M. Geographically weighted regression:The analysis of spatially varying relationships[M]. New York, USA:John Wiley&Sons, 2003.
2023, Vol. 63 
