| 摘要: |
| 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。 |
| 关键词: 城市 大数据 锚点 算法 职住 通勤 |
| DOI：10.13791/j.cnki.hsfwest.20170105 |
| 分类号: |
| 基金项目: |
|
| Research on Residence-and-Work Anchor Points Algorithm with Big Data in Urban Research |
|
GAO Shuo,WANG Mingyang,LU Xu,MAO Mingrui
|
| Abstract: |
| 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. |
| Key words: Urban Big Data Anchor Points Algorithm Residence-and-Work Commute |
