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,and Regularly Sampled Mobile Phone Tracking Data

作　　者：许宁[1,2] 尹凌[2] 胡金星[2] 

机构地区：[1]中南大学地球科学与信息物理学院,湖南长沙410000 [2]中国科学院深圳先进技术研究院,广东深圳518055

出　　处：《武汉大学学报（信息科学版）》2014年第6期750-756,共7页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金资助项目(41301440);深圳市战略新兴产业发展专项资金资助项目(JCYJ20130401170306842)~~

摘　　要：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。In urban studies acquistion of individual home-work locations from large-scale mobile phone tracking data is an emerging technology using big data. Long-term irregularly as well as sparsely sampled mobile phone call data are widely used in existing studies, but short-term regularly sampled mobile phone tracking data are less widely used. This study proposes a home-work location identification method based on short-term, large-scale, and regularly sampled mobile phone tracking data. To the authors~ knowledge, this study is the first effort to identify home-work locations for urban residents from short term, large scale, and regularly sampled mobile phone tracking data. The findings of this study 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 effectively in terms of low finacial and labor costs.

关 键 词：手机定位数据 时空数据挖掘 职住平衡 通勤距离 深圳市 

分 类 号：P208[天文地球—地图制图学与地理信息工程]