基于大数据的轨道交通站点服务空间特征研究

Research on Service Space Characteristics of Rail Transit Stations Based on Big Data

大数据时代,采用POI等多源数据可分析城市轨道交通站点与其周边用地开发程度之间的关系。在利用Python获取高德地图23大类POI数据的基础上,从轨道交通站点服务空间核密度及混合度2个维度出发,进而运用K-Means聚类方法对合肥轨道交通122个站点进行了分类评价。结果显示:轨道站点服务空间核密度呈现明显的空间分异特征,老城区轨道站点服务空间核密度远高于郊区站点,二环线以外站点服务空间核密度迅速降低。轨道站点周边空间土地利用混合度呈现多中心发展。

With the arrival of the big data era,it has become a reality to analyze the relationship between urban rail transit and urban land use using POI and other multi-source data,and evaluate the degree of land development around the station.Based on the 23 categories of POI data from the Gaud map,this paper measures the spatial kernel density and mixing degree of land use around rail transit stations based on POI big data,and then uses K-Means clustering method to classify and evaluate 122 stations of Hefei rail transit.The results show that the kernel density of rail stations presents obvious spatial differentiation characteristics.The kernel density of rail stations in the old city is much higher than that of suburban stations,and the kernel density of stations outside the Second Ring Road decreases rapidly.The land use mixing degree of the space around the rail station presents a multi-center development.