许宁, 尹凌, 胡金星. 从大规模短期规则采样的手机定位数据中识别居民职住地[J]. 武汉大学学报 ( 信息科学版), 2014, 39(6): 750-756. DOI: 10.13203/j.whugis20140085
引用本文: 许宁, 尹凌, 胡金星. 从大规模短期规则采样的手机定位数据中识别居民职住地[J]. 武汉大学学报 ( 信息科学版), 2014, 39(6): 750-756. DOI: 10.13203/j.whugis20140085
XU Ning, YIN Ling, HU Jinxing. Identifying Home-Work Locations from Short-term,Large-scale,andRegularly Sampled Mobile Phone Tracking Data[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 750-756. DOI: 10.13203/j.whugis20140085
Citation: XU Ning, YIN Ling, HU Jinxing. Identifying Home-Work Locations from Short-term,Large-scale,andRegularly Sampled Mobile Phone Tracking Data[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 750-756. DOI: 10.13203/j.whugis20140085

从大规模短期规则采样的手机定位数据中识别居民职住地

Identifying Home-Work Locations from Short-term,Large-scale,andRegularly Sampled Mobile Phone Tracking Data

  • 摘要: 目的 使用大规模手机定位数据获取居民职住地分布是大数据趋势下城市研究的新兴技术。然而,现有的研究主要使用了长期不规则稀疏采样的手机通话数据,对短期规则采样的手机定位数据缺乏尝试。基于大规模短期规则采样的手机定位数据,提出了一种居民职住地识别的方法。这是首次从大规模短期规则采样的手机定位数据中进行居民职住地识别的尝试,并对识别结果进行了较全面的验证。该研究成果为职住平衡等相关城市问题研究探讨了一种新型大规模数据源的可行性,在低成本大幅度提高相关研究的样本代表性和分析结果可靠性上具有重要意义。

     

    Abstract: Objective In urban studies acquistion of individual home-work locations from large-scale mobile phonetracking data is an emerging technology using big data.Long-term irregularly as well as sparsely sam-pled mobile phone call data are widely used in existing studies,but short-term regularly sampled mo-bile phone tracking data are less widely used.This study proposes a home-work location identificationmethod based on short-term,large-scale,and regularly sampled mobile phone tracking data.To theauthors’knowledge,this study is the first effort to identify home-work locations for urban residentsfrom short-term,large-scale,and regularly sampled mobile phone tracking data.The findings of thisstudy evaluate the feasibility of using this new type of large-scale data source for research on urban is-sues such as the job-housing balance,and is of great significance when improving the representative-ness of samples and the reliability of analysis results in home-work locaiton related research effectivelyin terms of low finacial and labor costs.

     

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