基于手机定位数据的城市人口分布近实时预测

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。

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

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.