地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (4): 523-531.doi: 10.12082/dqxxkx.2018.170536

• 地球信息科学理论与方法 • 上一篇    下一篇

基于手机定位数据的城市人口分布近实时预测

陈丽娜1,2(), 吴升1,2, 陈洁3,*(), 李明晓3,4, 陆锋3   

  1. 1. 福州大学福建省空间信息工程研究中心,福州 350002
    2. 海西政务大数据应用协同创新中心,福州 350002
    3. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    4. 中国科学院大学,北京 100049
  • 收稿日期:2017-11-27 修回日期:2018-02-28 出版日期:2018-04-20 发布日期:2018-04-20
  • 通讯作者: 陈洁 E-mail:chenln@lreis.ac.cn;chenj@lreis.ac.cn
  • 作者简介:

    作者简介:陈丽娜(1993-),女,福建泉州人,硕士生,研究方向为地理信息服务。E-mail: chenln@lreis.ac.cn

  • 基金资助:
    国家自然科学基金面上项目(41571431);特色研究所培育建设服务项目(TSYJS03);福建省科技创新平台建设项目(2015H2001)

The Near-real-time Prediction of Urban Population Distributions Based on Mobile Phone Location Data

CHEN Lina1,2(), WU Sheng1,2, CHEN Jie3,*(), LI Mingxiao3,4, LU Feng3   

  1. 1. Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China
    2. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350002, China
    3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    4. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-11-27 Revised:2018-02-28 Online:2018-04-20 Published:2018-04-20
  • Contact: CHEN Jie E-mail:chenln@lreis.ac.cn;chenj@lreis.ac.cn
  • Supported by:
    National Natural Science Foundation of China, No.41571431;Cultivate Project of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ,No.TSYJS03;Fujian Provincial Science and Technology Innovation Platform Construction Project,China, No.2015H2001.

摘要:

精细时空尺度下城市人口分布的近实时预测可为优化公共资源配置、协助城市交通诱导、制定公共安全应急预案、探索城市居民活动规律等提供重要科学依据。本文采用城市手机定位数据,基于时间序列分析方法,分别建立参数预测模型和非参数预测模型,对精细尺度下的城市人口空间分布开展近实时预测。预测结果表明,基于时间序列分析方法的预测模型可为精细尺度下的城市人口分布近实时预测提供方法支持;在本文实验条件下,从人口规模、时空分布、多时间尺度、特殊事件等多个角度评估模型精度,非参数预测模型其预测误差均小于参数预测模型,且预测结果更为稳定。

关键词: 城市人口, 精细尺度, 手机数据, 近实时预测, 时间序列分析

Abstract:

The near-real-time prediction of urban populations at the fine-grained scales can provide an important scientific basis in many fields, such as optimizing the allocation of public resources, assisting urban traffic guidance, making the early warning in urban emergencies, as well as exploring daily life patterns of urban residents. In this study, based on time series analysis method, a parameter prediction model (i.e., the Autoregressive Integrated Moving Average model) and a non-parameter prediction model (i.e., the K-Nearest Neighboring model) are constructed to predict urban populations in large spatial and temporal scales. The spatial resolution is 0.005 arc-degree and the temporal resolution is 30 minutes. When applying these two prediction models to a large mobile phone location dataset, the results demonstrate that both of them can be helpful to the near-real-time prediction of urban populations. In particular, the non-parameter prediction model produced more stable prediction results with lower error than the parameter prediction model, from the perspectives of prediction error distributions by grid population, prediction error distributions in space and time, prediction error at different temporal granularities, and prediction error distributions under a special event.

Key words: urban population, fine-grained scale, mobile phone location data, near-real-time prediction, time series analysis