基于手机信令数据的广州市社区建成环境对居民通勤距离的影响研究

doi:10.3969/j.issn.1004-9479.2023.05.2021485

摘要/Abstract

摘要：

通过优化社区建成环境来优化通勤行为是缓解大城市交通压力的重要举措，实施差异化的社区建成环境政策可能更有助于优化通勤行为。在利用手机信令数据构建居委会之间的职住联系OD数据库并计算居委会的居民平均通勤距离的基础上，构建社区建成环境“5D”指标体系，利用地理加权回归模型（GWR）来分析社区建成环境对居民通勤距离的影响。研究表明，包括密度、多样性、通达性、距离等维度的多项指标对居民平均通勤距离有显著影响。人口密度提高能减少居民平均通勤距离，但区域差异明显，人口密度较高的城郊村居民承受着长距离通勤。混合的土地利用能减少居民平均通勤距离，尤其是减少郊区居民平均通勤距离。地铁通达性在一定程度上增加了区域居民平均通勤距离。通过调控改善中心城区与郊区的居住成本差异，增加中心城区局部功能相对单一区域和郊区的土地利用混合度，增加中心城区局部区域轨道交通建设力度，皆有利于优化居民通勤行为和减少区域通勤距离。

关键词: 手机信令数据, 建成环境, 通勤行为, 空间异质性, 广州市

Abstract:

Optimizing commuting behavior by optimizing the built environment of communities is an important measure to alleviate traffic pressure in big cities. Implementing regionally differentiated policies for community built environment may be more conducive to optimizing commuting behavior. By using mobile phone signaling data to construct the OD database of the job-housing contact among residents' committees and to calculate the average commuting distance of committees' residents, this paper analyzes the impact of community built environment ("5D" indicator) on residents' commuting distance based on the geographically weighted regression model (GWR). It shows that multiple indicators of the built environment of community including density, diversity, accessibility, distance and other dimensions have a significant impact on residents' average commuting distance. High population density areas can reduce the average commuting distance of residents, but the regional differences are obvious. Residents in suburban villages with higher population density bear long-distance commuting. Mixed land use can reduce the average commuting distance of residents, especially an important means to reduce commuting demand in suburbs. The accessibility of the subway has increased the average commuting distance in the area to a certain extent. Regionally differentiated policies for the community built environment, such as improving the difference in housing costs between the central city and the suburbs, increasing the local function of the central city relative to a single area and the land use mix of the suburbs, and increasing the construction of rail transit in the central city, are beneficial to optimize residents. Commuting behavior and reducing regional commuting.

Key words: mobile signaling data, built environment, commuting behavior, spatial heterogeneity, Guangzhou

刘望保, 李彤彤. 基于手机信令数据的广州市社区建成环境对居民通勤距离的影响研究[J]. 世界地理研究, 2023, 32(5): 79-90.

Wangbao LIU, Tongtong LI. Analysis of the impact of community built environment on residents' commuting distance in Guangzhou supported by mobile signaling data[J]. World Regional Studies, 2023, 32(5): 79-90.

图/表 8

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日期范围	起点格网编号	终点格网编号	人数
20190401—20190430	100003	117758	2
20190401—20190430	100015	109079	1
20190401—20190430	100015	198044	1

日期范围	起点格网编号	终点格网编号	人数
20190401—20190430	100003	117758	2
20190401—20190430	100015	109079	1
20190401—20190430	100015	198044	1

变量类型	解释变量	最小值	上四分位数	中位值	下四分位数	最大值	VIF值
密度	居住人口密度/（人/km²）	－0.738	－0.037	0.056	0.070	2.111	1.557
密度	公共服务设施密度/（个/km²）	－1.087	－0.019	0.014	0.123	0.260	2.504
多样性	土地利用多样性	－0.195	－0.081	－0.049	－0.016	0.058	1.762
	经济业态多样性	－0.167	－0.024	0.028	0.055	0.106	1.377
	职住空间平衡度	－0.386	－0.275	－0.135	－0.063	－0.010	1.081
设计	路网密度/（km/km²）	－0.674	－0.075	－0.028	0.047	0.203	3.486
设计	主次干道占比/%	－0.226	－0.059	－0.044	－0.027	0.039	1.126
距离	距公交站点距离/m	－0.923	－0.062	0.056	0.194	0.570	1.221
距离	距地铁站点距离/m	－0.155	－0.057	－0.030	0.003	0.313	1.693
可达性	公交站点密度/（个/km²）	－0.232	－0.033	－0.010	0.023	0.269	3.139
	地铁线路密度/（km/km²）	0.005	0.037	0.060	0.113	0.676	1.315
	CBD可达性/m	0.064	0.201	0.274	0.631	1.139	2.575
最优带宽		257.000
调整R²		0.578

变量类型	解释变量	最小值	上四分位数	中位值	下四分位数	最大值	VIF值
密度	居住人口密度/（人/km²）	－0.738	－0.037	0.056	0.070	2.111	1.557
密度	公共服务设施密度/（个/km²）	－1.087	－0.019	0.014	0.123	0.260	2.504
多样性	土地利用多样性	－0.195	－0.081	－0.049	－0.016	0.058	1.762
	经济业态多样性	－0.167	－0.024	0.028	0.055	0.106	1.377
	职住空间平衡度	－0.386	－0.275	－0.135	－0.063	－0.010	1.081
设计	路网密度/（km/km²）	－0.674	－0.075	－0.028	0.047	0.203	3.486
设计	主次干道占比/%	－0.226	－0.059	－0.044	－0.027	0.039	1.126
距离	距公交站点距离/m	－0.923	－0.062	0.056	0.194	0.570	1.221
距离	距地铁站点距离/m	－0.155	－0.057	－0.030	0.003	0.313	1.693
可达性	公交站点密度/（个/km²）	－0.232	－0.033	－0.010	0.023	0.269	3.139
	地铁线路密度/（km/km²）	0.005	0.037	0.060	0.113	0.676	1.315
	CBD可达性/m	0.064	0.201	0.274	0.631	1.139	2.575
最优带宽		257.000
调整R²		0.578

变量类型	解释变量	最小值	上四分位数	中位数	下四分位数	最大值	VIF值
密度	居住人口密度/（人/km²）	－0.566	0.030	0.070	0.087	1.889	1.557
密度	公共服务设施密度/（个/km²）	－1.349	－0.043	－0.014	0.009	0.203	2.504
多样性	土地利用多样性	－0.181	－0.122	－0.077	－0.042	0.072	1.762
	经济业态多样性	－0.083	－0.010	0.011	0.037	0.101	1.377
	职住空间平衡度	－0.162	－0.047	－0.009	0.031	0.414	1.081
设计	路网密度/（km/km²）	－0.176	－0.038	－0.008	0.139	0.465	3.486
设计	主次干道占比/%	－0.117	－0.030	－0.014	0.014	0.086	1.126
距离	距公交站点距离/m	－0.473	－0.042	0.019	0.099	0.255	1.221
距离	距地铁站点距离/m	－0.088	0.009	0.046	0.108	0.323	1.693
可达性	公交站点密度/（个/km²）	－0.211	－0.002	0.017	0.093	0.590	3.139
	地铁线路密度/（km/km²）	－0.010	0.011	0.024	0.045	0.460	1.315
	CBD可达性/m	－0.082	0.017	0.174	0.567	1.415	2.575
最优带宽		271.000
调整R²		0.549