基于手机定位数据的深圳市热浪人口暴露度分析

doi:10.18306/dlkxjz.2020.02.005

地理科学进展 ›› 2020, Vol. 39 ›› Issue (2): 231-242.doi: 10.18306/dlkxjz.2020.02.005

基于手机定位数据的深圳市热浪人口暴露度分析

谢铖¹, 黄波^2,^3,^4,^*(), 刘晓倩¹, 周涛¹, 王宇¹

^1. 西南交通大学地球科学与环境工程学院,成都 611756
^2. 香港中文大学地理与资源管理学系,香港 999077
^3. 香港中文大学太空与地球信息科学研究所,香港 999077
^4. 香港中文大学深圳研究院,广东深圳 518057

收稿日期:2019-02-18 修回日期:2019-05-14 出版日期:2020-02-28 发布日期:2020-04-28
作者简介:谢铖(1993— ),男,硕士生,研究方向为城市热环境与地理大数据分析。E-mail:gis2013@163.com
基金资助:
国家科技支撑计划项目(2013BAJ05B04);自然资源部城市国土资源监测与仿真重点实验室开放基金资助课题(KF-2015-01-011)

Population exposure to heatwaves in Shenzhen based on mobile phone location data

XIE Cheng¹, HUANG Bo^2,^3,^4,^*(), LIU Xiaoqian¹, ZHOU Tao¹, WANG Yu¹

^1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
^2. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong 999077, China
^3. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong 999077, China
^4. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, Guangdong, China

Received:2019-02-18 Revised:2019-05-14 Online:2020-02-28 Published:2020-04-28
Supported by:
National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2013BAJ05B04);The Project Supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR(KF-2015-01-011)

摘要/Abstract

摘要：

热浪作为城市化特征灾害之一,严重影响着城市居民的生命健康。目前针对热浪的研究主要聚焦基于静态数据的时空模式、风险管理和脆弱性评价分析方向,对动态人口暴露度的研究尚少。论文基于手机定位数据,首先融合深圳市逐时人口与气温时空分布模型,揭示热浪动态人口暴露度水平;其次,构建基于7类城市兴趣点(point of interest,POI)与不同时段人口分布的地理加权回归模型,初步分析了热浪环境下POI对人群行为模式的影响机制。结果显示：① 相比于基准时段(2018年7月28日12:00~18:00),2018年7月26日至8月1日热浪平均辐射范围在7月29日以8.66倍速增长,至7月30日则以18.93倍速跃至峰值,覆盖区域整体呈现西部高于东部、南部低于北部的特征;② 人口在不同时段均表现为明显的带状聚集分布态势,且人口暴露度与气温和人口的动态演变紧密关联,其暴露度同热浪扩散幅度相似,总体呈2.29倍等比增长,辐射范围包括南山区、福田区、罗湖区等城市商业、工业、住宅中心人口密集区域;③ 同类POI在不同时刻、不同POI在相同时刻对人群减少热浪暴露的移动交互行为具有明显的时空驱动机制差异及选择偏好特征。在持续性城市化背景下,该研究方法可为同类的城市灾害人口暴露度分析提供一定的科学参考。

关键词: 手机定位数据, 热浪, 人口暴露度, POI, 地理加权回归, 深圳市

Abstract:

As one of the characteristic disasters of urbanization, heatwaves seriously affect the life and health of urban residents. Existing research on heatwaves mainly focuses on the spatial and temporal pattern based on static data, risk management, and vulnerability assessment, and studies on dynamic population exposure are relatively few. This study first integrated spatial and temporal distribution models of population and temperature hourly in Shenzhen to reveal the dynamic population exposure to heatwaves based on mobile phone location data. Then a set of geographically weighted regression models in different time were built based on seven types of points of interest (POIs) and population distribution to explore the influencing mechanisms of POIs on crowd behavior patterns during the heatwaves. The results show that: 1) Compared with the baseline (12:00 to 18:00 on 28 July 2018), the average radiation range of the heatwaves increases by 8.66 times on 29 July, and jumped to the peak of 18.93 times on 30 July from 26 July to 1 August 2018. The overall coverage shows that temperature in the west was higher than the east and temperature in the south was lower than the north. 2) Population distribution exhibited an obvious zonal distribution of aggregates in different time periods, and population exposure was closely related to the dynamic evolution of temperature and population. The population exposure was similar to that of heatwaves, showing 2.29 times proportional growth. The coverage included densely populated urban commercial, industrial, and residential centers such as Nanshan District, Futian District, and Luohu District. 3) The same type of POIs at different times and the different types of POIs at the same time showed obvious spatial-temporal differences as driving mechanisms and selection preferences in the interactive mobility behavior of reducing population exposure. Under the background of sustainable urbanization, this research can provide a scientific reference for the analysis of population exposure to similar urban hazards and disasters.

Key words: mobile phone location data, heatwaves, population exposure, POI, geographically weighted regression, Shenzhen

谢铖, 黄波, 刘晓倩, 周涛, 王宇. 基于手机定位数据的深圳市热浪人口暴露度分析[J]. 地理科学进展, 2020, 39(2): 231-242.

XIE Cheng, HUANG Bo, LIU Xiaoqian, ZHOU Tao, WANG Yu. Population exposure to heatwaves in Shenzhen based on mobile phone location data[J]. PROGRESS IN GEOGRAPHY, 2020, 39(2): 231-242.

图/表 13

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编号	发布时间	取消时间	预警类型	发布区域是否覆盖全境陆地
1	2018-07-11T05:20	2018-07-12T18:00	高温	是
2	2018-07-16T17:00	2018-07-18T06:10	台风	否
3	2018-07-17T12:25	2018-07-17T20:00	高温	是
4	2018-07-21T07:45	2018-07-22T15:55	高温	是
5	2018-07-22T17:00	2018-07-24T11:00	台风	是
6	2018-07-28T11:45	2018-08-03T17:45	高温	是
7	2018-08-06T09:05	2018-08-09T19:30	高温	是
8	2018-08-09T17:00	2018-08-15T17:00	台风	是
9	2018-08-25T09:00	2018-08-26T15:30	高温	是
10	2018-09-11T11:00	2018-09-13T06:30	台风	否
11	2018-09-14T12:30	2018-09-17T14:10	台风	是
12	2018-09-14T09:10	2018-09-15T20:00	高温	是

解释变量	相关性		多重共线性
解释变量	相关系数	相关性强弱	膨胀系数(VIF)	共线性强弱
交通设施	0.710	强	8.49	较强
购物场所	0.780	强	6.66	无
科教文化	0.702	强	7.35	无
餐饮	0.810	极强	19.87	较强
商务住宅	0.781	强	5.70	无
医疗保健	0.772	强	13.22	较强
生活服务	0.801	极强	32.65	较强
风景名胜	0.268	弱	1.32	无
酒店住宿	0.578	较强	2.24	无
金融服务	0.585	较强	3.49	无
公司企业	0.746	强	3.52	无

日期	12:00	13:00	14:00	15:00	16:00	17:00	18:00
2018-07-26	0	0	2.94	0	0	0	0
2018-07-27	0	0	0	0	0	0	0
2018-07-28	0	0	1.00	10.88	4.10	14.70	0.45
2018-07-29	0	0	52.62	86.52	86.52	74.14	1.00
2018-07-30	83.42	100	99.76	100	91.90	91.95	53.70
2018-07-31	0	0	2.72	0	0	0	0
2018-08-01	0	0	0	0	0	0	0

日期	12:00	13:00	14:00	15:00	16:00	17:00	18:00
2018-07-26	0	0	1.47	0	0	0	0
2018-07-27	0	0	0	0	0	0	0
2018-07-28	0	0	1.54	15.00	5.61	20.31	1.47
2018-07-29	0	0	45.12	93.26	93.17	70.69	1.16
2018-07-30	77.43	100.00	99.80	100.00	97.72	97.55	64.33
2018-07-31	0	0	2.43	0	0	0	0
2018-08-01	0	0	0	0	0	0	0

基于手机定位数据的深圳市热浪人口暴露度分析

Population exposure to heatwaves in Shenzhen based on mobile phone location data

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