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| 基于手机信令数据的城市居民非通勤出行群体画像——以苏州市为例 | |
| 引用本文： | 付晓,陈梓丹,黄洁.基于手机信令数据的城市居民非通勤出行群体画像——以苏州市为例[J].地理科学,2022,42(10):1727-1734. |
| 作者姓名： | 付晓 陈梓丹 黄洁 |
| 作者单位： | 1.东南大学交通学院，江苏 南京 211189 2.中国科学院地理科学与资源研究所中国科学院区域可持续发展分析与模拟重点实验室，北京 100101 |
| 基金项目： | 教育部人文社会科学研究青年基金项目(21YJC790030);国家自然科学基金项目(42171403);东南大学至善青年学者支持计划(2242021R41162) |
| 摘 要： | 构建考虑多维特征的城市居民非通勤出行群体画像概念模型，提出一种按序结合相关系数矩阵与二阶聚类的方法，以进行非通勤出行群体画像。利用苏州市手机信令数据，基于非通勤出行时空规律和社会属性将城市居民出行者进行群体划分，并结合城市居民非通勤出行群体画像概念模型对不同类型非通勤出行群体进行多维度解析。结果显示：① 城市居民出行者可划分为：活跃?波动?工作日主导型群体、非活跃?稳定?均衡型群体。② 不同类型非通勤出行群体画像在多维特征上存在显著差异。③ 根据群体画像标签关联分析，除显性关联外，群体画像不同标签间存在隐性关联。 |
| 关 键 词： | 非通勤出行 群体画像 二阶聚类 隐性标签关联 大数据 |
| 收稿时间： | 2021-10-19 |
| 修稿时间： | 2022-02-15 |
Non-work Travel Group Profiles of Urban Residents Based on Mobile Phone Signaling Data: A Case of Suzhou |
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| Fu Xiao,Chen Zidan,Huang Jie.Non-work Travel Group Profiles of Urban Residents Based on Mobile Phone Signaling Data: A Case of Suzhou[J].Scientia Geographica Sinica,2022,42(10):1727-1734. | |
| Authors: | Fu Xiao Chen Zidan Huang Jie |
| Institution: | 1. School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China 2. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
| Abstract: | A conceptual model of non-work travel group profile for urban residents is proposed in this paper, and a sequential combination of correlation coefficient matrix and two-step clustering method is applied to constructing the non-work travel group profiles. In the case study, using Suzhou mobile phone signaling data, the urban travelers are grouped based on spatiotemporal non-work travel information and social attributes. Combined with the conceptual model of non-work travel group profile, a detailed discussion on the group profiles of different types of non-work travelers is given. Results show that the urban travelers can be divided into two groups: active-fluctuating-workday oriented group and inactive-stable-balanced group. Significant differences exist in multi-dimensional features among different types of non-work travel group profiles. The association analysis of group profile labels shows that, in addition to the explicit association, there are implicit associations between different labels of group profile. |
| Keywords: | non-work travel group profile two-step clustering method implicit label association big data |
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