基于多源数据的县域主导功能类型划分及其空间结构模式识别

研究县域主导功能类型并分析空间结构模式对区域全面、协调、可持续发展的引导和规划调控具有重要意义。本文以广东省为例,利用K-means聚类分析对其124个县域单位主导功能区类型进行划分;基于腾讯位置大数据,利用位序—规模法则构建空间结构指数,并结合遥感数据对不同主导功能县域的空间结构模式进行识别和分析。研究表明：① 利用统计、遥感、社交媒体以及夜间灯光等多源数据和K-means聚类方法进行县域主导功能区分类,分类结果能客观反映区域特征,广东省县域主导功能可分为生态主导型、农业主导型、工业主导型、中心服务型和均衡发展型5类;② 基于腾讯位置大数据的位序—规模法则方法能够突破城市尺度,结合遥感数据定量分析,可用于中观县域尺度的空间结构模式识别研究;③ 农业主导型和生态主导型县域空间结构指数均值都大于1,呈单中心空间结构特征趋势;工业主导型县域空间结构指数均值小于1,呈多中心空间结构特征趋势;均衡发展型县域空间结构指数均值接近1,不同地区均衡发展型县域所呈现的空间结构特征存在差异;④ 不同主导功能县域之间的空间结构模式差异明显,生态主导型县域多呈单中心极核式,农业主导型县域多呈单中心极核+散点式,均衡发展型县域多种模式并存特征明显,而工业主导型县域呈多中心网络分布模式。

Classification of County Leading Function Types and Pattern Recognition of Its Spatial Structure based on Multi-source Data

It is of great significance to study the types of dominant function at county level and analyze the spatial structure pattern for the guidance and planning regulation of regional comprehensive, coordinated, and sustainable development. Taking Guangdong Province as an example, this paper uses K-means clustering analysis to classify 124 dominant functional areas of county units. Based on the Tencent location big data, the spatial structure index was constructed using the rank-size rule. The spatial structure patterns of the counties with different dominant functions were identified and analyzed by combining remote sensing data. Research results show that, firstly, the use of statistics, remote sensing, social media, night lighting multi-source data, and K means clustering method in county dominant function classification can get nice results, which can objectively reflect the features of area. The dominant function of counties in Guangdong can be divided into five classes, including ecological leading, agricultural leading, industry leadership, center service, and balanced developmental. Secondly, the rank-size law method based on Tencent location big data can break through the city scale. It can be used together with the quantitative analysis of remote sensing data in the study of spatial structure pattern recognition at the meso-county scale. Thirdly, the average value of spatial structure index of agriculture-oriented and ecology-oriented counties is greater than 1, showing a trend of single center spatial structure. The mean value of spatial structure index of industrial dominated county is less than 1, showing a polycentric spatial structure trend. The mean value of spatial structure index of balanced development county is close to 1. There are differences in spatial structure characteristics of balanced development county in different regions. Fourthly, the spatial structure patterns of the counties with different dominant functions were significantly different. The ecology-dominated counties showed a single central pole core pattern, while the agriculture-dominated counties showed a single central pole core and scattered point pattern. The balanced development counties had obvious coexistence characteristics of multiple patterns, while the industrial-dominated counties showed a multi-center network distribution pattern.