居民出行活动特征与收入水平的关系——以上海市为例
Relationship between travel behavior and income level of urban residents: A case study in Shanghai Municipality
查看参考文献22篇
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文摘
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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。 |
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其他语种文摘
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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. |
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来源
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地理科学进展
,2017,36(9):1158-1166 【核心库】
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DOI
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10.18306/dlkxjz.2017.09.012
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关键词
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出行活动
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移动性指标
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收入水平
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主成分分析
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K-Means均值聚类
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上海市
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地址
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中国科学院地理科学与资源研究所, 中国科学院资源与环境信息系统国家重点实验室, 北京, 100101
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中国科学院大学, 北京, 100049
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1007-6301 |
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学科
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公路运输 |
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基金
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国家自然科学基金
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文献收藏号
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CSCD:6081276
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参考文献 共
22
共2页
|
|
1.
丁亮. 上海中心城区商业中心空间特征研究.
城市规划学刊,2017(1):63-70
|
CSCD被引
23
次
|
|
|
|
|
2.
丁威. 基于活动的居民出行行为研究综述.
人文地理,2008(3):85-91
|
CSCD被引
4
次
|
|
|
|
|
3.
高见. 大数据揭示经济发展状况.
电子科技大学学报,2016,45(4):625-633
|
CSCD被引
11
次
|
|
|
|
|
4.
陆锡明. 上海市第五次居民出行调查与交通特征研究.
城市交通,2011,9(5):1-7
|
CSCD被引
7
次
|
|
|
|
|
5.
张文尝. 中国城市居民出行的时空特征及影响因素研究.
地理科学,2007,27(6):737-742
|
CSCD被引
13
次
|
|
|
|
|
6.
周素红. 基于T-GIS的广州市居民日常活动时空关系.
地理学报,2010,65(12):1454-1463
|
CSCD被引
33
次
|
|
|
|
|
7.
Blumenstock J. Predicting poverty and wealth from mobile phone metadata.
Science,2015,350:1073-1076
|
CSCD被引
22
次
|
|
|
|
|
8.
Brockmann D. The scaling laws of human travel.
Nature,2006,439:462-465
|
CSCD被引
80
次
|
|
|
|
|
9.
Calabrese F. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example.
Transportation Research Part C: Emerging Technologies,2013,26(1):301-313
|
CSCD被引
23
次
|
|
|
|
|
10.
Calinski T. A dendrite method for cluster analysis.
Communications in Statistics,1974,3(1):1-27
|
CSCD被引
125
次
|
|
|
|
|
11.
Cliff N. The eigenvalues-greater-than-one rule and the reliability of components.
Psychological Bulletin,1988,103(2):276-279
|
CSCD被引
2
次
|
|
|
|
|
12.
Eagle N. Network diversity and economic development.
Science,2010,328:1029-1031
|
CSCD被引
16
次
|
|
|
|
|
13.
Frias-Martinez V. Forecasting socioeconomic trends with cell phone records.
Proceedings of the 3rd ACM symposium on computing for development,2013:15
|
CSCD被引
1
次
|
|
|
|
|
14.
Giannotti F. Unveiling the complexity of human mobility by querying and mining massive trajectory data.
The VLDB Journal,2011,20(5):695-719
|
CSCD被引
18
次
|
|
|
|
|
15.
Gonzalez M C. Understanding individual human mobility patterns.
Nature,2008,453:779-782
|
CSCD被引
214
次
|
|
|
|
|
16.
Kaiser H F. Coefficient alpha for a principal component and the Kaiser-Guttman rule.
Psychological Reports,1991,68(3):855-858
|
CSCD被引
7
次
|
|
|
|
|
17.
Kang C. Analyzing and geo-visualizing individual human mobility patterns using mobile call records.
Geoinformatics, 2010 18th international conference on Geoinformatics,2010:1-7
|
CSCD被引
1
次
|
|
|
|
|
18.
Rencher A C. Interpretation of canonical discriminant functions, canonical variates, and principal components.
The American Statistician,1992,46(3):217-225
|
CSCD被引
2
次
|
|
|
|
|
19.
Smith-Clarke C. Poverty on the cheap: Estimating poverty maps using aggregated mobile communication networks.
Proceedings of the SIGCHI conference on human factors in computing systems,2014:511-520
|
CSCD被引
2
次
|
|
|
|
|
20.
Song C M. Limits of predictability in human mobility.
Science,2010,327:1018-1021
|
CSCD被引
163
次
|
|
|
|
|
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