基于空间点模式分析的城市管理事件空间分布及演化——以武汉市江汉区为例

城市网格化管理系统经过多年运行积累了大量历史事件数据, 这类事件数据在空间上呈现明显集聚分布。确定事件发生的空间分布以及衡量空间分布的集聚程度, 能够为城市管理资源的合理调配、划分提供重要的决策支持。本文应用空间点模式分析方法, 对2011 年1-8 月间武汉市江汉区城市网格化管理系统中的两类主体事件(占道经营和垃圾处理类)进行分析, 研究发现:占道经营事件的“热点”区域1-8 月总体呈减少趋势, 而垃圾处理事件的“热点”区域整体呈递增趋势;两类事件呈现明显的空间集聚, 其特征空间尺度都为1000 m左右。研究表明, 空间点模式分析方法能够为城市管理者提供一种针对城市事件空间集聚模式的直观的可视化分析手段, 以及对空间集聚程度的定量分析方法, 并可为进一步统计建模分析奠定基础。

Spatial distribution and evolution of city management events based on the spatial point pattern analysis: A case study of Jianghan District, Wuhan City

Large amounts of data of historical events have been accumulated for many years through the operations of the Urban Grid Management System. These events are spatially aggregated. By examining the spatial distribution of these events and measuring the corresponding aggregation levels, we can provide important support for making sound decisions on allocation and distribution of urban management resources. Spatial point pattern analysis studies the distribution patterns of geographical point entities or events, and has been widely used in various disciplines including criminal statistics, ecology and public health. In this paper, we investigated two major types of city management events in Jianghan District, Wuhan City, traffic-blocking stall and garbage disposal events, from January to August in 2011. The results show that: the number of hotspots with traffic-blocking stall events had a decreasing trend, while that of garbage disposal events had an increasing trend. Both types of events presented significant spatial aggregation with roughly the same spatial scale of 1000 m. The spatiotemporal aggregation index indicated strong spatiotemporal correlations for both types of events where the space difference is below 500 m and time difference below 3 hours. The research shows that: with the help of spatial point pattern analysis, we can provide city managers with efficient visual analytics tools to identify spatial aggregation patterns of the events, and a quantitative method to measure the degree of spatial aggregation, which also lays a foundation for further statistical modeling.