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

许宁; 尹凌; 胡金星

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

许宁^1,2,
尹凌^2,,
胡金星²

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

基金项目: 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)

详细信息

作者简介:
许宁,硕士生。现从事时空数据挖掘研究。

通讯作者:
尹凌

中图分类号: P208
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出版历程
- 收稿日期: 2014-01-26
- 修回日期: 2014-06-04
- 发布日期: 2014-06-04

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

¹School of Geosciences and Info-Physics,Central South University,Changsha 410000,China;²Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China

Funds: The National Natural Science Foundation of China, No. 41301440; the Strategically New Industry Development Special Foundation of Shenzhen, No.JCYJ20130401170306842. 756

More Information

Author Bio:
XU Ning,postgraduate,specializes in spatial-temporal data mining.

Corresponding author:
YIN Ling

摘要

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。
- 手机定位数据 /
- 时空数据挖掘 /
- 职住平衡 /
- 通勤距离 /
- 深圳市
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.
- mobile phone tracking data /
- spatio-temporal data mining /
- job-housing balance /
- commu-ting distance /
- Shenzhen City