基于出租车GPS数据的居民就医时空特征分析

城市医疗服务在很大程度上影响着城市居民的生活质量,在公共服务领域中发挥着极其重要的作用。近年来,中国城市化发展过程中产生了海量的大数据,基于这些海量数据分析居民就医出行特征对于优化和改善城市医疗资源布局具有重要意义。本文以北京市主要医疗机构空间位置数据为基础,基于出租车GPS移动轨迹数据,采用时空统计分析方法,研究了出租车出行模式下的居民就医出行时空特征。结果表明,利用医院的OD（Origin-Destination）网络结构特征分析,可以识别出不同医院的服务范围以及受众的时空分布模式。市区尤其是四环以内医院的就医网络密集、紧凑,就医密度较高,而四环以外尤其是郊区周边,就医网络稀疏、分散,医疗资源的级别及地理位置影响了居民的就医倾向。本研究基于浮动车GPS数据开展居民就医时空行为模式挖掘研究,可以为城市医疗资源供需分析和优化配置提供决策支持。

Analysis of Spatial-temporal Characteristics of Resident Travel for Hospitals Based on Taxi GPS Data

Urban medical services have a great impact on the quality of life of urban residents and play an extremely important role in the field of public services. In recent years, with the rapid development of China's urbanization, large amounts of large data have been produced in the daily life of urban residents. Based on these massive data, the analysis of residents' medical travel characteristics is of great significance to optimize and improve the layout of urban medical resources. With the help of spatial location data of Beijing's main medical institutions and data of the taxi GPS movement trajectory, the space-time statistical analysis method is used to study the spatial and temporal characteristics of residents' medical travel by the taxi. The results show that using the OD (Origin-Destination) network structure characteristics of the hospital, the service scope of different hospitals and the spatial and temporal distribution pattern of the audience can be identified. The medical network of the hospitals in the urban area, especially within the forth ring, is dense and compact, and the medical density is high, while outside the forth ring and about suburbs, the medical network is sparse and scattered. The result in some degree shows that the geographical location of medical resources affects the residents' medical tendency. This study used the floating car GPS data to carry out the research on the spatial and temporal behavior patterns of residents' medical treatment, which can provide decision-making support for the supply and demand analysis and optimal allocation of urban medical resources.