关键词：时空格局，空间分布，城市人口，空间相关性，POI
摘 要：先进的技术和大数据为城市规划研究带来了新的视野和方法。基于2018年两个典型日期（周末和工作日）的百度热图和POI数据，本文利用ArcGIS分析了武汉市中心人群的时空聚集格局。聚集模式是由人口活动的强度和人群聚集的地方定义的。就时间而言，通过计算一天中捕获的24个瞬间的热值来研究人口聚集强度的日变化。结果表明，在休息日，人们更喜欢在中午和下午出行，在15:00左右达到一天中的最高峰；而在工作日，居民的活动受到通勤的影响，有明显的“早高峰”和“晚高峰”。研究首先通过百度热地图与POI数据之间的空间耦合关系，研究了POI分布密度与人口聚集度之间的空间相关性。其次，提到了不同POI聚集与人口（ICPP）之间的相关指数，以分析周末和工作日高峰时间聚集的目的和程度。基于ICPP，我们从三个方面分析活动：工作日和周末之间不同的ICPP；早晨，下午和晚上之间的不同ICPP；以及不同POI之间的不同ICPP。
Abstract: Advanced technologies and big data have brought new visions and methods to urban planning research. Based on the Baidu heat map and POI data of two typical days (a weekend day and a workday) in 2018, this paper analyses the spatial and temporal aggregation patterns of crowds in the urban centre of Wuhan using ArcGIS. Aggregation patterns are defined by the intensity of population activities and the places where crowds gather. In terms of time, the daily change of population aggregation intensity is studied by counting the heat value of 24 moments captured throughout the day. The results show that on rest days, people prefer to travel around noon and in the afternoon, reaching the highest peak of the day around 15:00, while on workdays, residents' activities are affected by commuting, with obvious 'morning rush hours' and 'evening rush hours'. Firstly, the spatial correlation between the density of POI distribution and the degree of population aggregation has been studied by the spatial coupling relationship between the Baidu heat map and POI data. Secondly, the index of correlation between the aggregation of different POIs and population (ICPP) are mentioned to analyse the purposes and the degrees of aggregation during weekend and workday rush hours. Based on the ICPP, we analyse activities from three aspects: the different ICPPs between the workday and the weekend; the different ICPPs between the morning, afternoon and evening; and the different ICPPs among different POIs.
Key: Spatial and temporal patterns, spatial distribution, urban population, spatial correlation, POI
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