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    强思维, 陈夏明, 姜开达, 金耀辉. 基于移动网络流量日志的城市时空行为分析[J]. 计算机研究与发展, 2016, 53(4): 932-940. DOI: 10.7544/issn1000-1239.2016.20148278
    引用本文: 强思维, 陈夏明, 姜开达, 金耀辉. 基于移动网络流量日志的城市时空行为分析[J]. 计算机研究与发展, 2016, 53(4): 932-940. DOI: 10.7544/issn1000-1239.2016.20148278
    Qiang Siwei, Chen Xiaming, Jiang Kaida, Jin Yaohui. Urban Spatio-Temporal Behavior Analysis Based on Mobile Network Traffic Logs[J]. Journal of Computer Research and Development, 2016, 53(4): 932-940. DOI: 10.7544/issn1000-1239.2016.20148278
    Citation: Qiang Siwei, Chen Xiaming, Jiang Kaida, Jin Yaohui. Urban Spatio-Temporal Behavior Analysis Based on Mobile Network Traffic Logs[J]. Journal of Computer Research and Development, 2016, 53(4): 932-940. DOI: 10.7544/issn1000-1239.2016.20148278

    基于移动网络流量日志的城市时空行为分析

    Urban Spatio-Temporal Behavior Analysis Based on Mobile Network Traffic Logs

    • 摘要: 城市的空间组织和居民行为研究是城市地理学研究的重点,随着信息技术的快速发展,居民的时空行为对城市空间的组织和结构的影响呈现出日益增加的趋势,因此,对城市空间以及居民时空行为的深入分析具有很高的研究价值.通过采集杭州市区移动3G网络流量日志,首先采用空间点模式的分析方法研究了城市居民的聚集模式,并研究了居民移动的距离、方向等方面的特征;之后采用网格的方法对城市空间进行分块,并以区块为主体研究了热点区块出现的时空点、区块人流的更迭速率、工作日人流的潮汐效应;提出了区块差异指数的概念,并利用其对区块进行聚类,分析了区块间的相关性和区块间距离之间的关系.由于所研究的数据来源于移动3G网络流量日志,因此具有覆盖面广、数据量大等特点,非常适合从大时空尺度层面研究居民和城市空间活动.

       

      Abstract: City organization and residents behavior are one of the key researches in urban geography. With the rapid development of information technology, the impact of residents spatial and temporal behavior on urban spatial organization and structure shows a growing trend, therefore in-depth analysis of the spatio-temporal behavior of city space and urban residents has high research values. After the acquisition of Hangzhou mobile network traffic logs, the gathering patterns of urban residents are studied with spatial point pattern analysis, and the features of moving distance and direction are analyzed. Using grid approach, we divide the urban space into blocks, and focus on the emergence of hotspot point, the change rate of human flow, tidal effects on weekdays, and present the concept of blocks difference index which is used to cluster blocks and analyze the relationship between the correlation of blocks and their distances. Since our research data comes from mobile network traffic logs, it has a wide coverage and a large volume, which is ideal for search on residents and city behavior on large spatio-temporal scales.

       

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