基于大数据的城市居民职住锚点计算方法研究

Research on Residence-and-Work Anchor Points Algorithm with Big Data in Urban Research

居住和就业是两个重要的居民时空行为要素,通勤行为规律能够直接反映城市空间结构特征,而大数据的发展对城市职住通勤研究提供了新的数据源与方法论。本文通过比较分析各个居民职住锚点计算方法,针对网络位置大数据提出基于密度的聚类算法;并以北京市东部及北三县地区为例进行案例分析。结论发现:基于密度的聚类算法速度快、准确度高,适合网络大数据在城市研究中的应用。

Residence and work are two of the most important time and space behavior elements for citizens. To a great extent, commuting pattern reflects spatial structure of a city. Nowadays, the development of information and communication techniques provides new data sources and methodology for urban studies. This paper introduces former algorithms for calculating residence-and-work anchor points, and puts forward a new clustering algorithm for internet LBS data based on DBSCAN. A case with the data produced by this new algorithm, commuting patterns of eastern Beijing and Beisanxian, was introduced afterwards. In conclusion, it＇s found that the new algorithm for residence-and-work anchor points has satisfactory speed and accuracy, and is suitable for the application of LBS data in urban researches.