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

Abstract

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

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.

Figures/Tables 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

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

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