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 E-mail:xuxugis@foxmail.com;zhougeo@126.com
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 E-mail:xuxugis@foxmail.com;zhougeo@126.com
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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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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