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

doi:10.18306/dlkxjz.2018.04.009

地理科学进展 ›› 2018, Vol. 37 ›› Issue (4): 535-546.doi: 10.18306/dlkxjz.2018.04.009

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

徐智邦^1,²(), 周亮^1,^2,^3,^*(), 蓝婷⁴, 王中辉^1,², 孙立^1,², 武荣伟⁵

1. 兰州交通大学测绘与地理信息学院,兰州 730070
2. 甘肃省地理国情监测工程实验室,兰州 730070
3. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
4. 福建师范大学地理科学学院,福州 350007
5. 中国科学院新疆生态与地理研究所,乌鲁木齐 830011

收稿日期:2017-08-08 修回日期:2017-12-14 出版日期:2018-04-20 发布日期:2018-04-20
通讯作者: 周亮
作者简介:
作者简介：徐智邦(1991-),男,山东淄博人,硕士研究生,主要从事GIS应用研究,Email：xuxugis@foxmail.com
基金资助:
国家自然科学基金项目(41701173);资源与环境信息系统国家重点实验室开放基金项目(201619);中国博士后基金项目(2016M600121)

Spatial optimization of mega-city fire station distribution based on Point of Interest data: A case study within the 5th Ring Road in Beijing

Zhibang XU^1,²(), Liang ZHOU^1,^2,^3,^*(), Ting LAN⁴, Zhonghui WANG^1,², Li SUN^1,², Rongwei WU⁵

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring , Lanzhou 730070, China
3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
4. School of Geographical Science, Fujian Normal university, Fuzhou 350007, China
5. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China

Received:2017-08-08 Revised:2017-12-14 Online:2018-04-20 Published:2018-04-20
Contact: Liang ZHOU
Supported by:
National Natural Science Foundation of China, No. 41701173;Open Foundation of State Key Laboratory of Resources and Environmental Information System, No. 201619;China Postdoctoral Science Foundation, No.2016M600121

摘要/Abstract

摘要：

消防站的空间布局事关城市发展与城市安全。本文以北京市五环内中心城区为研究区,使用44.34万条POI数据和道路网等相关数据,考虑易燃易爆、人群脆弱等不同特征的火灾风险因子,采用核密度分析、SAVEE模型等方法,识别出研究区内的火灾风险空间分布,进一步借助“位置—分配”模型和网络分析,并结合优化目标对研究区内消防站进行空间优化。主要研究结论为：①按照火灾风险从高到低排序,前10%的火灾风险区域主要集中在CBD—三里屯、北京古玩城—双井、王府井、南锣鼓巷—雍和宫等区域。②现有消防站对全部44.34万个POI请求点5分钟响应时间内的覆盖率为96.46%,总体覆盖效果较好,但在研究区西北和西南部的世纪城—闵庄一带覆盖不足。③综合考虑高火灾风险区、重要火灾风险因子、POI总体覆盖率和个体消防站覆盖面积相关标准等因素,经多次迭加运算分析得到最终需新增15个消防站点。优化后的各指标均有较大提升,可满足研究区的消防需求。

关键词: POI数据, 城市安全, 消防站, 空间优化, 北京

Abstract:

Sound spatial distribution of fire stations is important for urban development and for ensuring urban safety. Based on kernel density analysis and the SAVEE model, and taking into account different types of point of interest (POI) such as flammable and explosive and crowd fragility fire risk factors, 443410 POIs and road network were used to identify the fire risk zones in the downtown area of Beijing. Using the location-allocation fire station spatial optimization model, this study proposed adjustment plans. The main conclusions are as follows: (1) According to the fire risk ranking result, the top 10% fire risk areas are mainly concentrated in the CBD area Sanlitun, Beijing Antique Market Shuangjing, Wangfujing, and Nanluoguxiang-Yonghegong, among others. (2) The coverage rate (within 5 minute response) of the existing fire stations reached 96.46% for all 443,410 POIs, and the overall coverage was good, while some fire risk factors were covered with insufficient response in the northwestern and southwestern parts of the study area (Century City-Minzhuang area), and some fire station coverage area was too large. (3) Considering high fire risk areas, important fire risk factors, POI overall coverage rate, and individual fire station coverage area standards, the analysis by multiple iterations results in the final need of adding 15 new fire stations. With these additions, every performance indicator can be significantly improved and the demand of fire protection in the study area can be fully met.

Key words: Point of Interest (POI) data, urban safety, fire station, spatial optimization, Beijing

徐智邦, 周亮, 蓝婷, 王中辉, 孙立, 武荣伟. 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.

Zhibang XU, Liang ZHOU, Ting LAN, Zhonghui WANG, Li SUN, Rongwei WU. 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.

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参考文献 25

[26]	McKenzie G, Janowicz K.2015. Where is also about time: A location-distortion model to improve reverse geocoding using behavior-driven temporal semantic signatures[J]. Computers, Environment and Urban Systems, 54: 1-13. doi: 10.1016/j.compenvurbsys.2015.05.003
[27]	Menezes R C, Pizzolato N D.2014. locating public schools in fast expanding areas: Application of the capacitated p-median and maximal covering location models[J]. Pesquisa Operacional, 34(2): 301-317. doi: 10.1590/0101-7438.2014.034.02.0301
[28]	Murray A T.2013. Optimising the spatial location of urban fire stations[J]. Fire Safety Journal, 62: 64-71. doi: 10.1016/j.firesaf.2013.03.002
[29]	Plane D R, Hendrick T E.1977. Mathematical programming and the location of fire companies for the Denver fire department[J]. Operations Research, 25(4): 563-578. doi: 10.1287/opre.25.4.563
[30]	Reilly J M, Mirchandani P B.1985. Development and application of a fire station placement model[J]. Fire Technology, 21(3): 181-198. doi: 10.1007/BF01039973
[31]	Yang L L, Jones B F, Yang S-H.2007. A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms[J]. European Journal of Operational Research, 181(2): 903-915. doi: 10.1016/j.ejor.2006.07.003
[1]	陈驰, 任爱珠. 2003. 消防站布局优化的计算机方法[J]. 清华大学学报: 自然科学版, 43(10): 1390-1393. doi: 10.3321/j.issn:1000-0054.2003.10.025
	[Chen C, Ren A Z.2003. Optimization of fire station locations using computer[J]. Journal of Tsinghua University: Science & Technology, 43(10): 1390-1393.] doi: 10.3321/j.issn:1000-0054.2003.10.025
[2]	陈鸿. 2007. 城市消防站空间布局优化研究[D]. 上海: 同济大学.
	[Chen H.2007. Study on optimization of the spatial distribution of urban fire station[D]. Shanghai, China: Tongji University.]
[3]	陈韶阳, 程镇燕, Loh D K.2012. 基于SAVEE方法的海岛空间价值评价: 以南沙群岛为例[J]. 海洋环境科学, 31(1): 107-110.
	[Chen S Y, Cheng Z Y, Loh D K.2012. Islands valuation of spatial appraisal based on SAVEE method: With the Nansha Islands as an example[J]. Marine Environmental Science, 31(1): 107-110.]
[4]	陈蔚珊, 柳林, 梁育填. 2016. 基于POI数据的广州零售商业中心热点识别与业态集聚特征分析[J]. 地理研究, 35(4): 703-716. doi: 10.11821/dlyj201604009
	[Chen W S, Liu L, Liang Y T.2016. Retail center recognition and spatial aggregating feature analysis of retail formats in Guangzhou based on POI data[J]. Geographical Research, 35(4): 703-716.] doi: 10.11821/dlyj201604009
[5]	池娇, 焦利民, 董婷, 等. 2016. 基于POI数据的城市功能区定量识别及其可视化[J]. 测绘地理信息, 41(2): 68-73.
	[Chi J, Jiao L M, Dong T, et al.2016. Quantitative identification and visualization of urban functional area based on POI data[J]. Journal of Geomatics, 41(2): 68-73.]
[6]	杜兰, 葛军莲, 王宏志, 等. 2014. 基于POI网络信息的景区最优游客接待中心选址研究: 以南京钟山景区智慧旅游为例[J]. 华中师范大学学报: 自然科学版, 48(4): 613-619.
	[Du L, Ge J L, Wang H Z, et al.2014. Study on scenic-oriented optimal site selection of tourist service center based on the POI network data: A case of intelligent tourism of Zhongshan Scenic, Nanjing[J]. Journal of Huazhong Normal University: Natural Sciences, 48(4): 613-619.]
[7]	沈体雁, 周麟, 王利伟, 等. 2015. 服务业区位选择的交通网络指向研究: 以北京城市中心区为例[J]. 地理科学进展, 34(8): 947-956. doi: 10.18306/dlkxjz.2015.08.002
	[Shen T Y, Zhou L, Wang L W, et al.2015. Traffic network point of services location choice: A case study of the Central City Area of Beijing[J]. Progress in Geography, 34(8): 947-956.] doi: 10.18306/dlkxjz.2015.08.002
[8]	索超, 张浩. 2015. 高铁站点周边商务空间的影响因素与发展建议: 基于沪宁沿线POI数据的实证[J]. 城市规划, 39(7): 43-49. doi: 10.11819/cpr20150708a
	[Suo C, Zhang H.2015. Influencing factors and development proposals of business space around HSR station: A case study of cities along Shanghai-Nanjing HSR with POI data[J]. City Planning Review, 39(7): 43-49.] doi: 10.11819/cpr20150708a
[9]	许泽宁, 高晓路. 2016. 基于电子地图兴趣点的城市建成区边界识别方法[J]. 地理学报, 71(6): 928-939. doi: 10.11821/dlxb201606003
	[Xu Z N, Gao X L.2016. A novel method for identifying the boundary of urban built-up areas with POI data[J]. Acta Geographica Sinica, 71(6): 928-939.] doi: 10.11821/dlxb201606003
[10]	薛德升, 曾献君. 2016. 中国人口城镇化质量评价及省际差异分析[J]. 地理学报, 71(2): 194-204. doi: 10.11821/dlxb201602002
	[Xue D S, Zeng X J.2016. Evaluation of China's urbanization quality and analysis of its spatial pattern transformation based on the modern life index[J]. Acta Geographica Sinica, 71(2): 194-204.] doi: 10.11821/dlxb201602002
[11]	杨帆, 徐建刚, 周亮. 2016. 基于DBSCAN空间聚类的广州市区餐饮集群识别及空间特征分析[J]. 经济地理, 36(10): 110-116.
	[Yang F, Xu J G, Zhou L.2016. Cluster identification and spatial characteristics of catering in Guangzhou based on DBSCAN spatial clustering[J]. Economic Geography, 36(10): 110-116.]
[12]	俞艳, 郭庆胜, 何建华, 等. 2005. 顾及地理网络特征的城市消防站布局渐进优化[J]. 武汉大学学报: 信息科学版, 30(4): 333-336. doi: 10.3321/j.issn:1671-8860.2005.04.013
	[Yu Y, Guo Q S, He J H, et al.2005. Gradual optimization of urban fire station location based on geographical network attribute[J]. Geomatics and Information Science of Wuhan University, 30(4): 333-336.] doi: 10.3321/j.issn:1671-8860.2005.04.013
[13]	张刚. 2016. 基于空间分析的城市火灾风险评估与应用: 以西安为例[J]. 城市规划, 40(8): 59-64. doi: 10.11819/cpr20160810a
	[Zhang G.2016. Urban fire risk evaluation and its application based on spatial analysis: A case study of Xi’an[J]. City Planning Review, 40(8): 59-64.] doi: 10.11819/cpr20160810a
[14]	郑红梅, 陈国良, 王玮, 等. 2008. 火灾风险评估相关概念辨析[J]. 中国安全科学学报, 18(6): 75-79.
	[Zheng H-M, Chen G-L, Wang W, et al.2008. Discussion on the difference and similarity of some terms related to fire risk assessment[J]. China Safety Science Journal, 18(6): 75-79.]
[15]	Badri M A, Mortagy A K, Alsayed C A.1998. A multi-objective model for locating fire stations[J]. European Journal of Operational Research, 110(2): 243-260. doi: 10.1016/S0377-2217(97)00247-6
[16]	Chevalier P, Thomas I, Geraets D, et al.2012. Locating fire stations: An integrated approach for Belgium[J]. Socio-Economic Planning Sciences, 46(2): 173-182. doi: 10.1016/j.seps.2012.02.003
[17]	Cooper L.1963. Location-allocation problems[J]. Operations Research, 11(3): 331-343. doi: 10.1287/opre.11.3.331
[18]	Erden T, Coşkun M Z.2010. Multi-criteria site selection for fire services: The interaction with analytic hierarchy process and geographic information systems[J]. Natural Hazards and Earth System Sciences, 10(10): 2127-2134. doi: 10.5194/nhess-10-2127-2010
[19]	Habibi K, Lotfi S, Koohsari M J.2008. Spatial analysis of urban fire station locations by integrating AHP model and IO logic using GIS (a case study of zone 6 of Tehran)[J]. Journal of Applied Sciences, 8(19): 3302-3315. doi: 10.3923/jas.2008.3302.3315
[20]	Harper P R, Shahani A K, Gallagher J E, et al.2005. Planning health services with explicit geographical considerations: A stochastic location-allocation approach[J]. Omega, 33(2): 141-152. doi: 10.1016/j.omega.2004.03.011
[21]	Helly W.1975. Urban systems models[M]. New York, NY: Academic Press.
[22]	Li X, Xu G D, Chen E H, et al.2015. Learning recency based comparative choice towards point-of-interest recommendation[J]. Expert Systems with Applications, 42(9): 4274-4283. doi: 10.1016/j.eswa.2015.01.054
[23]	Li X P, Zhao Z X, Zhu X Y, et al.2011. Covering models and optimization techniques for emergency response facility location and planning: a review[J]. Mathematical Methods of Operations Research, 74(3): 281-310. doi: 10.1007/s00186-011-0363-4
[24]	Loh D K, Holtfrerich D R, Van Stipdonk S E P.1998. Automated construction of rulebases for forest resource planning[J]. Computers and Electronics in Agriculture, 21(2): 117-133. doi: 10.1016/S0168-1699(98)00030-1
[25]	Loh D K, Hsieh Y T C, Choo Y K, et al.1994. Integration of a rule-based expert system with GIS through a relational database management system for forest resource management[J]. Computers and Electronics in Agriculture, 11(2-3): 215-228. doi: 10.1016/0168-1699(94)90009-4

火灾风险因子	包含的POI类型
易燃易爆类	加油站、加气站、化工厂、工业园区、仓库仓储等
人群脆弱类	学校(幼儿园、小学、中学)、医院(诊所、疾病预防机构等)、婴儿服务场所等
人流密集类	商业经营场所(商场、商业街、娱乐场所等)、交通场站(机场、火车站、汽车站、地铁公交车站等)
重点保护类	政府机关(区县级及以上)、外国机构、公检法机构(除消防站)、科研机构、图书馆、博物馆、档案馆、历史遗迹等
一般消防类	住宅区、写字楼、公司企业(除化工厂和仓库)、产业园区(除工业园区)、各类餐厅、公共设施、银行、宾馆酒店、便利店、体育休闲服务场所、生活服务场所、物流速递(非仓储)、公园广场、政府机关(区县级以下)、高等院校、医药保健销售点等所有其他POI类型
紧急避险类	紧急避难场所

火灾风险因子	覆盖率/%	未覆盖区域
易燃易爆类	87.55	西五环的西山一带、南五环的金星乡区域
人群脆弱类	95.92	西四环的世纪城区域、西北四五环之间的区域、西五环的丰台西站一带、南五环的长丰园和金星乡区域、东五环广渠路立交到观音堂一带
人流密集类	95.32	西四环的世纪城区域、西北四五环之间的区域、西五环的丰台西站一带、南五环的长丰园和金星乡区域、东五环广渠路立交到观音堂一带、世界公园一带
重点保护类	97.09	西四环的世纪城区域、南四环的总部基地十一区和十二区一带

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

Spatial optimization of mega-city fire station distribution based on Point of Interest data: A case study within the 5th Ring Road in Beijing

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