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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。
The relationship between residents ’travel activities and residents’ income level is an important issue in public transportation and urban geography research. The traditional method for obtaining information on residents ’travel activities is mainly based on the questionnaire. It not only has high cost and limited sample size, but also limited the research to qualitative discussion. The research results are easily biased by the respondents’ subjective consciousness. With the innovation of information technology, the information of large-scale human activities recorded by sensors provides the possibility to study the relationship between the characteristics of residents ’travel activities and the residents’ income level. Based on the data of Shanghai residents’ space-time trajectory, from the perspective of residents’ travel activities, this paper constructs the index of residents’ travel activities and extracts the main components of the travel characteristics of residents by using the principal component analysis. Then the K- The results show that: (1) The diversity of residents ’travel locations and the range of residents’ travel range are the main components that reflect the characteristics of residents ’travel activities; (2) The characteristics of residents’ (3) The average income level difference of different mobility characteristics is related to the industrial development in each type of resident’s working place. The conclusion of the study can provide reference for the city planning and related economic policy making.