地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (10): 1467-1477.doi: 10.12082/dqxxkx.2018.180224

所属专题: 人口与城市研究

• 地理空间分析综合应用 • 上一篇    下一篇

基于多源数据的北京市朝阳区人口时空格局评估与预测

林文棋1,2(), 陈会宴1,*(), 谢盼1, 李颖1, 陈清凝1, 李栋1   

  1. 1. 北京清华同衡规划设计研究院有限公司,北京 100085
    2. 清华大学建筑学院,北京 100084
  • 收稿日期:2018-05-03 修回日期:2018-07-04 出版日期:2018-10-25 发布日期:2018-10-17
  • 通讯作者: 陈会宴 E-mail:linwq@tsinghua.edu.cn;chenhuiyan_12@163.com
  • 作者简介:

    作者简介:林文棋(1969-),男,福建人,博士,副教授,主要从事城市研究及城乡规划实践。E-mail: linwq@tsinghua.edu.cn

  • 基金资助:
    国家社会科学基金项目(14BGL149);清华大学自主科研计划项目

Spatial-temporal Variation Evaluation and Prediction of Population in Chaoyang District of Beijing Based on Multisource Data

LIN Wenqi1,2(), CHEN Huiyan1,*(), XIE Pan1, LI Ying1, CHEN Qingning1, LI Dong1   

  1. 1. Beijing Tsinghua Tongheng Urban Planning and Design Institute, Beijing 100085, China
    2. School of Architecture, Tsinghua University, Beijing 100084, China
  • Received:2018-05-03 Revised:2018-07-04 Online:2018-10-25 Published:2018-10-17
  • Contact: CHEN Huiyan E-mail:linwq@tsinghua.edu.cn;chenhuiyan_12@163.com
  • Supported by:
    National Social Science Fund Project, No.14BGL149;Tsinghua University Initiative Scientific Research Program.

摘要:

城市人口分布与活动呈现高度的时空动态变化,掌握人口时空变化特征并进行未来预测,对于精准人口评估、有效的政策措施制定、实时的人口预警与调控等具有重要意义。本研究利用以手机信令数据为主的多源时空数据,首先利用地理探测q统计进行探索性数据分析,其次结合贝叶斯模型进行北京市朝阳区居住人口的时空变化探究及时空预测,以期达到对朝阳区人口的动态评估与预测。首先,选用地理探测q统计进行空间异质性探测,用贝叶斯时空层次模型探究基于手机信令数据推算的北京市朝阳区居住人口的总体空间效应、总体时间效应以及局部变化趋势;其次,选用贝叶斯高斯预测过程模型,基于朝阳区各街乡的居住人口及相关人口影响因子数据进行朝阳区各街乡2017年12月的居住人口预测。时空探究表明:朝阳区居住人口在空间上存在完美空间分异,整体呈现沿环路由内向外递增的空间分布格局,整体时间趋势表现为增长,各街乡局部时间变化趋势呈现一定差异。预测的空间分布与实测空间分布整体一致,精度较高,各街乡预测精度不一。结果表明基于贝叶斯理论的时空层次模型和高斯预测过程模型可以为多源时空数据下的多尺度精准识别与人口时空模式挖掘提供有效的方法支撑。

关键词: 多源数据, q统计, 贝叶斯层次模型, 高斯预测过程, 时空格局, 人口预测

Abstract:

Urban population distribution and activities are always the hot research topics. Identifying the spatial-temporal variation and predicting future trends are of great significance for estimating population accurately, making policy effectively, and warning of population booming timely. With the availability of data and the development of data processing technique, multisource data with both spatial and temporal features, such as mobile signaling data, have been used in population studies. In this paper, q-statistic was firstly applied as an exploratory analysis, then Bayesian spatial-temporal models were used to evaluate patterns of urban population and make prediction of future trends. The Chaoyang, Beijing in 2017 was selected as empirical study of this model. The spatially stratified heterogeneity was detected by q-statistic in Geodetector firstly. Then we explored the overall spatial variation, overall time trend and the departures of the local trends from the overall trend of resident population in Chaoyang by use of Bayesian spatial-temporal hierarchical model. Secondly, we applied Bayesian Gaussian predictive process to predict the resident population in December of 2017 by incorporating other relevant influential factors. The results show the perfect spatial stratified heterogeneity for resident population in Chaoyang, and the overall spatial variation demonstrates an increasing trend of population from center to the outside along the main ring road in Beijing. The overall time trend is still growing all over Chaoyang district, while the local trends, which departure from the overall trend of resident population, are different between each sub-districts in Chaoyang. Moreover, the spatial distribution of predicted resident population shows a high consistency with the observed resident population, and the prediction accuracy can be well accepted on the scale of Chaoyang district. However, prediction accuracy shows obvious difference on scale of sub-districts, with the worst prediction accuracy in the capital airport area. These findings show that Bayesian hierarchical model and Bayesian Gaussian predictive process are reliable in empirical study of population evaluation and prediction by effective application of multisource spatial-temporal data. Researches in this paper can be an excellent theoretical and practical support for mining multisource spatial-temporal data and assisting multiscale analysis with Bayesian spatial-temporal model, and provide an important basis for population controlling and early warning in urban population management.

Key words: multisource data, q-statistic, Bayesian hierarchical model, gaussian predictive process, spatial-temporal variation, population prediction