居民出行活动特征与收入水平的关系——以上海市为例

doi:10.18306/dlkxjz.2017.09.012

摘要/Abstract

摘要：

居民出行活动与居民的收入水平关系是公共交通、城市地理研究的重要问题。传统获取居民出行活动信息主要基于问卷调查的方式,不仅成本高、样本量有限,且研究局限于定性讨论,研究结果易因受访者的主观意识而产生偏颇。随着信息技术的革新,传感器记录的大规模人类活动信息为研究居民出行活动特征与居民收入水平关系提供了可能性。本文利用上海市居民时空轨迹数据,从居民出行活动的角度出发,首先构建居民出行活动指标,并利用主成分分析法提取居民出行活动特征的主要成分;然后对主成分进行K-Means聚类,并针对不同出行活动特征的类别,分析居民出行活动特征与居民收入水平的关系,结果表明：①居民出行地点多样性与居民出行范围大小是反映居民出行活动特征的主要成分;②移动范围越小、移动地点多样性越低的居民类别,其平均工资水平越高;③不同移动性特征的类别平均收入水平差异与各类别居民工作地的产业发展有关。研究结论可为城市规划及相关经济政策制定提供参考。

关键词: 出行活动, 移动性指标, 收入水平, 主成分分析, K-Means均值聚类, 上海市

Abstract:

The relationship between income and travel behavior characteristics of urban residents is of great concern in urban geography. Income level of residents is an important indicator measuring regional social development, thus understanding this relationship is of great significance for city planning. Before the Big Data Age, due to the lack of residents' travel behavior information, it was difficult to study this relationship. However, along with the innovation of information technology, the use of ubiquitous sensors, such as mobile phones, has produced a large amount of human activity information, enabling the research on the relationship between residents' travel behaviors and income levels. In this study, based on the activity trajectory data in Shanghai Municipality from 27 December 2015 to 6 January 2016, we extracted a series of residents' mobility indicator data to measure mobility characteristics and conducted principal components analyses to extract the major components. We adopted the K-Means clustering method to classify residents into mobility groups and analyzed the feature of each group. Furthermore, the distribution of workplaces is shown to verify the difference in income levels between different mobility groups. Our results show that: (1) diversity of places to travel to and range of travel are two major components measuring residents' travel behavior; (2) residents who have smaller travel range and go to fewer places have higher average salary; (3) between the mobility groups, difference in income levels relate to industrial setup. These results may be useful for city planners to make efficient economic policies.

Key words: travel behavior, mobility indicator, income level, principal component analysis, K-Means clustering, Shanghai Municipality

郭思慧, 文聪聪, 何云, 裴韬. 居民出行活动特征与收入水平的关系——以上海市为例[J]. 地理科学进展, 2017, 36(9): 1158-1166.

Sihui GUO, Congcong WEN, Yun HE, Tao PEI. Relationship between travel behavior and income level of urban residents:A case study in Shanghai Municipality[J]. PROGRESS IN GEOGRAPHY, 2017, 36(9): 1158-1166.

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数据	时间	数据量	详细说明
居民时空轨迹数据	2015年12月27日-2016年1月6日	642548名用户5776605条轨迹	用户在24小时内的每整点的经纬度坐标
上海各区平均工资数据	2016年	16条	据16个区县内8208个企业发布薪酬统计而得

日期	ID	经度	纬度	…	经度	纬度
20151230	ff7cfb0e717cc3a48af443209168ef92	121.28	31.35	…	121.28	31.34
20160101	bf3f04034044fd5c9d5f9445c3a3eda3	121.38	31.22	…	121.38	31.21

地区	月平均工资/元	样本企业个数	地区	月平均工资/元	样本企业个数
金山区	4590	80	宝山区	5340	214
虹口区	4890	250	嘉定区	5560	258
杨浦区	5200	282	松江区	5680	1584
青浦区	5230	166	普陀区	5790	432
奉贤区	5240	159	徐汇区	5960	477
闵行区	5280	422	浦东新区	5980	617
长宁区	5300	290	黄浦区	6100	258
崇明县	5310	103	静安区	7240	380

	相关系数	节假日				工作日
	相关系数	成分1		成分2	成分3	成分1	成分2	成分3
居民活动特征指标	地点个数	0.843	0.191	0.345		0.947		-0.118
	移动熵	0.946	0.193	-0.551		0.885	0.130	0.395
	空间多样性	0.730				0.490		0.812
	回旋半径	0.283	0.893			0.179	0.930
	最远距离	0.398	0.872			0.383	0.869
	平均出行距离	0.555	0.741			0.698	0.627
	职住距离	-0.220	0.604	-0.155			0.622	0.131
	地点频次			0.925			-0.167	-0.912
方差解释	方差解释度	0.342	0.320	0.165		0.326	0.307	0.211
方差解释	累计方差解释度	0.342	0.662	0.827	0.326		0.633	0.845

类别	地点个数	移动熵	空间多样性	回旋半径/m	最远距离/m	平均出行距离/m	职住距离/m	月平均工资/元
3	6.12	1.38	1.89	1235.65	3265.69	1447.18	1449.56	5841.57
10	9.71	1.92	2.05	3804.35	10562.14	5270.43	3961.99	5821.59
4	11.24	2.16	2.15	6341.63	18256.65	9729.46	6304.11	5780.97
9	12.20	2.31	2.22	9265.10	27781.88	14510.48	7496.84	5757.01
5	16.04	2.75	2.41	14630.49	46858.75	28486.24	10598.36	5749.18
1	13.98	2.53	2.32	11889.93	36941.71	20799.34	8860.73	5745.92
8	19.29	3.08	2.53	16699.62	55862.56	39102.57	12831.57	5730.38
2	22.57	3.37	2.62	18836.34	65044.98	52204.91	16626.65	5720.75
7	24.97	3.58	2.68	21438.91	76405.06	69622.45	20768.52	5698.25
6	26.47	3.70	2.69	25747.73	94385.03	96397.05	27879.74	5637.16