基于手机数据的西部城市居民出行特征研究&mdash;&mdash;以西宁市为例

杨喜平; 杨鸿海; 李彬; 李君轶

doi:10.13959/j.issn.1003-2398.2021.01.014

PDF(19026 KB)

人文地理 ›› 2021, Vol. 36 ›› Issue (1) : 115-124. DOI: 10.13959/j.issn.1003-2398.2021.01.014

城市

基于手机数据的西部城市居民出行特征研究——以西宁市为例

杨喜平^1,2,3, 杨鸿海^3,4, 李彬^3,4, 李君轶^1,2

作者信息 +

CHARACTERISTICS OF URBAN HUMAN MOBILITY OF WESTERN CHINA BASED ON MOBILE PHONE DATA: A CASE STUDY OF XINING

YANG Xi-ping^1,2,3, YANG Hong-hai^3,4, LI Bin^3,4, LI Jun-yi^1,2

Author information +

文章历史 +

摘要

基于手机轨迹数据系统的定量分析了西宁市不同群体的出行特征差异，通过定义移动距离、回转半径、出行频次和停留位置个数来定量居民的出行特征，分析不同性别、年龄段、工作日和休息日西宁市居民的出行差异。研究发现：①不同年龄群体出行行为差异显著，其中31—40岁年龄段居民的出行需求最为强烈，大于60岁的老年人出行需求最小；②男性在出行距离、活动范围、出行频次和停留位置个数方面都全部高于女性；③居民在休息日的平均移动距离、出行频次和停留位置个数都显著大于工作日，但休息日和工作日的活动范围并没有显著差异。

Abstract

The spatiotemporal travel behavior is helpful for understanding urban spatial structure, which is meaningful to urban planning and management. With the emergence of big space-time trajectory data, providing a new data for studying large-scale human mobility patterns, which gives insight of human mobility characteristics from macroscopic, collective and spatiotemporal dynamic perspectives. This study focuses on quantifying the difference of human mobility for different groups in Xining using mobile phone data, the displacement distance, radius of gyration, travel frequency and number of stop location are defined to measure the human mobility, then analyzing the difference of human mobility fordifferent gender, ages, workday and weekend.The results find that there are significant different in human mobility for different age groups, residents aged between 30-40 have the strongest travel demand, while those aged over 60 have the smallest travel demand. In terms of travel distance, activity range, travel frequency and number of stop locations, male is significant bigger than female.

导出引用

杨喜平, 杨鸿海, 李彬, 李君轶. 基于手机数据的西部城市居民出行特征研究——以西宁市为例[J]. 人文地理. 2021, 36(1): 115-124 https://doi.org/10.13959/j.issn.1003-2398.2021.01.014

YANG Xi-ping, YANG Hong-hai, LI Bin, LI Jun-yi. CHARACTERISTICS OF URBAN HUMAN MOBILITY OF WESTERN CHINA BASED ON MOBILE PHONE DATA: A CASE STUDY OF XINING[J]. HUMAN GEOGRAPHY. 2021, 36(1): 115-124 https://doi.org/10.13959/j.issn.1003-2398.2021.01.014

中图分类号： K901

参考文献

[1] 柴彦威,申悦,肖作鹏,等.时空间行为研究动态及其实践应用前景[J].地理科学进展,2012,31(6):667-675.[Chai Yanwei, Shen Yue, Xiao Zuopeng, et al. Review for space-time behavior research:Theory frontiers and application in the future[J]. Progress in Geography, 2012,31(6):667-675.]
[2] 赵鹏军,孔璐.TOD对北京市居民通勤影响及其机制研究[J].人文地理,2017,32(5):131-137.[Zhao Pengjun, Kong Lu. The impact of TOD on residents' commuting activities:A case study of Beijing[J]. Human Geography, 2017,32(5):131-137.]
[3] 齐兰兰,周素红.邻里建成环境对居民外出型休闲活动时空差异的影响——以广州市为例[J].地理科学,2018,38(1):31-40.[Qi Lanlan, Zhou Suhong. The influence of neighborhood built environments on the spatial-temporal characteristics of residents daily leisure activities:A case study of Guangzhou[J]. Scientia Geographica Sinica, 2018,38(1):31-40.]
[4] 焦华富,韩会然.中等城市居民购物行为时空决策过程及影响因素——以安徽省芜湖市为例[J].地理学报,2013,68(6):750-761.[JiaoHuafu, Han Huiran. Research on temporal and spatial decision-making process and influencing factors of residents' shopping behavior in medium-sized cities:A case study of Wuhu city in Anhui province[J]. Acta Geographica Sinica, 2013,68(6):750-761.]
[5] 申悦,柴彦威.基于日常活动空间的社会空间分异研究进展[J].地理科学进展,2018,37(6):853-862.[Shen Yue, Chai Yanwei. Progress of research on sociospatial differentiation based on daily activity space of urban residents[J]. Progress in Geography, 2018,37(6):853-862.]
[6] 张文佳,鲁大铭.影响时空行为的建成环境测度与实证研究综述[J].城市发展研究,2019,26(12):9-16.[Zhang Wenjia, Lu Daming. Measuring built environment for spatiotemporal behavior studies:A review[J]. Urban Development Studies, 2019,26(12):9-16.]
[7] 申悦,柴彦威.基于性别比较的北京城市居民活动的时空弹性研究[J]. 地理学报,2017,72(12):2214-2225.[Shen Yue, Chai Yanwei. Space-time flexibility of daily activities and gender differences:A case study of Beijing[J]. Acta Geographica Sinica, 2017,72(12):2214-2225.]
[8] 古杰,周素红,闫小培.生命历程视角下广州市居民日常出行的时空路径分析[J].人文地理,2014,29(3):56-62.[Gu Jie, Zhou Suhong, Yan Xiaopei. The space-time paths of resident' daily travel in Guangzhou from a perspective of life course[J]. Human Geography, 2014,29(3):56-62.]
[9] Wang D G, Zhou M. The built environment and travel behavior in urban China:A literature review[J]. Transportation Research Part D:Transport and Environment, 2017,52:574-585.
[10] 塔娜,柴彦威.理解中国城市生活方式:基于时空行为的研究框架[J].人文地理,2019,34(2):17-23.[Ta Na, Chai Yanwei. Understanding the lifestyle in Chinese cities:A framework based on spacetime behavior research[J]. Human Geography, 2019,34(2):17-23.]
[11] 刘瑜.社会感知视角下的若干人文地理学基本问题再思考[J].地理学报,2016,71(4):564-575.[Liu Yu. Revisiting several basic geographical concepts:A social sensing perspective[J]. Acta Geographica Sinica, 2016,71(4):564-575.]
[12] 张博,邓浩坤,安宁,等.大数据背景下国内人文地理研究进展与方法审视[J].人文地理,2019,34(3):14-21.[Zhang Bo, Deng Haokun, An Ning, et al. The re-examination of human geography research and the research methods in the context of big data[J]. Human Geography, 2019,34(3):14-21.]
[13] 柯文前,俞肇元,陈伟,等.人类时空间行为数据观测体系架构及其关键问题[J].地理研究,2015,34(2):373-383.[Ke Wenqian, Yu Zhaoyuan, Chen Wei, et al. Architecture and key issues for human space-time behavior data observation[J]. Geographical Research, 2015,34(2):373-383.]
[14] 杨喜平,方志祥.移动定位大数据视角下的人群移动模式及城市空间结构研究进展[J].地理科学进展,2018,37(7):880-889.[Yang Xiping, Fang Zhixiang. Recent progress in studying human mobility and urban spatial structure based on mobile location big data[J]. Progress in Geography, 2018,37(7):880-889.]
[15] 龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报,2012,67(10):1339-1352.[Long Ying, Zhang Yu, Cui Chengyin. Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 2012,67(10):1339-1352.]
[16] 王俊蓉,张景秋.基于手机数据的北京城市女性职住关系研究[J]. 人文地理,2019,34(3):30-36.[Wang Junrong, Zhang Jingqiu. A study on the female relationship between employment and residence in Beijing based on cell phone data[J]. Human Geography, 2019,34(3):30-36.]
[17] 闫晴,李诚固,陈才,等.基于手机信令数据的长春市活动空间特征与社区分异研究[J].人文地理,2018,33(6):35-43.[Yan Qing, Li Chenggu, Chen Cai, et al. Characteristics of activity space and community differentiation in Changchun:A study using mobile phone signaling data[J]. Human Geography, 2018,33(6):35-43.]
[18] 钟炜菁,王德,谢栋灿,等.上海市人口分布与空间活动的动态特征研究——基于手机信令数据的探索[J].地理研究,2017,36(5):972-984.[Zhong Weijing, Wang De, Xie Dongcan, et al. Dynamic characteristics of Shanghai's population distribution using cell phone signaling data[J]. Geographical Research, 2017,36(5):972-984.]
[19] 董路熙,贾梅杰,刘小明,等.基于手机信令数据的居民工作日出行链判别方法[J].桂林理工大学学报,2019,39(4):958-966.[Dong Luxi, Jia Meijie, Liu Xiaoming, et al. Discrimination method of residents trip chains on working days based on mobile phone signaling data[J]. Journal of Guilin University of Technology, 2019,39(4):958-966.]
[20] 郭思慧,文聪聪,何云,等.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.[Guo Sihui, Wen Congcong, He Yun, et al. 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.]
[21] 李秋萍,刘逸诗,巩诗瑶,等.基于居民出行活动特征的个体经济水平推断方法[J].武汉大学学报·信息科学版,2019,44(10):1575-1580.[Li Qiuping, Liu Yishi, Gong Shiyao, et al. Individual income level inference method based on travel behavior of urban residents[J]. Geomatics and Information Science of Wuhan University, 2019,44(10):1575-1580.]
[22] 杨喜平,方志祥,赵志远,等.城市人群聚集消散时空模式探索分析——以深圳市为例[J].地球信息科学学报,2016,18(4):486-492.[Yang Xiping, Fang Zhixiang, Zhao Zhiyuan, et al. Exploring urban human spatio-temporal convergence-dispersion patterns:A case study of Shenzhen city[J]. Journal of Geo-information Science, 2016,18(4):486-492.]
[23] Tu W, Cao J Z, Yue Y, et al. Coupling mobile phone and social media data:A new approach to understanding urban functions and diurnal patterns[J]. International Journal of Geographical Information Science, 2017,31(12):2331-2358.
[24] 丁亮,钮心毅,宋小冬.上海中心城就业中心体系测度——基于手机信令数据的研究[J].地理学报,2016,71(3):484-499.[Ding Liang, Niu Xinyi, Song Xiaodong. Measuring the employment center system in Shanghai central city:A study using mobile phone signaling data[J]. Acta Geographica Sinica, 2016,71(3):484-499.]
[25] Mei-Po Kwan, Tim Schwanen. Geographies of mobility[J]. Annals of the American Association of Geographers, 2016,106:2,243-256.
[26] 柴彦威,谭一洺.中国西部城市居民时空间行为特征研究——以西宁市为例[J].人文地理,2017,32(4):43-50.[Chai Yanwei, Tan Yiming. Examining the space-time behavior patterns of residents in cities of the western China:Evidence from Xining[J]. Human Geography, 2017,32(4):43-50.]
[27] 张雪,柴彦威.西宁城市居民家内外活动时间分配及影响因素——基于结构方程模型的分析[J].地域研究与开发,2017,36(5):161-165.[Zhang Xue, Chai Yanwei. Time allocation of in-home and out-of-home activities and the influence factors of urban residents in Xining city:Based on SEM[J]. Areal Research and Development, 2017,36(5):161-165.]
[28] 谭一洺,柴彦威,关美宝.地理背景的不确定性对时空行为模式分析的影响——基于西宁市的实证研究[J].地理学报,2017,72(4):657-670.[Tan Yiming, Chai Yanwei, Mei-Po Kwan. The impact of the uncertain geographic context on the space-time behavior analysis:A case study of Xining, China[J]. Acta Geographica Sinica, 2017,72(4):657-670.]
[29] 何诗,柴彦威,郭文伯,等.基于整日尺度的城市女性休闲满意度及其影响因素——以西宁市为例[J].经济地理,2019,39(2):224-231.[City women' leisure satisfaction and its influencing factors on a daily scale:A case study of Xining city[J]. Econonmic Geography, 2019,39(2):224-231.]
[30] 林楠,尹凌,赵志远.基于滑动窗口的手机定位数据个体停留区域识别算法[J].地球信息科学学报,2018,20(6):762-771.[Lin Nan, Yin Ling, Zhao Zhiyuan. Detecting individual stay areas from mobile phone location data based on moving windows[J]. Journal of Geo-information Science, 2018,20(6):762-771.]
[31] Moreno F, Pineda A, Fileto R, et al. SMoT+:Extending the SMoT algorithm for discovering stops innested sites[J]. Computing & Informatics, 2014,33(22),327-342.
[32] Zhao Z, Shaw S L, Yin L, et al. The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data[J]. International Journal of Geographical Information Science, 2019,33(7):1471-1495.