兴趣点、夜光遥感和微博签到的空间耦合分析——以北京市为例

Spatial Coupling Analysis Between POI,Luminous Remote Sensing and Weibo Sign-in Data:A Case Study of Beijing

针对POI(兴趣点)、夜光遥感和微博签到数据的空间耦合关系与联合应用在城市空间结构研究中缺乏的现状,选取2016年的以上3种数据对北京市空间分布进行耦合研究。通过核密度分析与叠加分析对3种数据进行网格化,之后利用双因素制图法对3种数据两两之间的空间耦合关系可视化,得到3组数据空间耦合相同及相异区域,并进一步比较其与城市空间结构的联系。研究表明,3种数据在北京市的空间分布高度一致,都呈现出空间高耦合状态;夜光亮度和POI在机场、景区等大范围同质化区域存在一些耦合相异区;夜光亮度和微博签到在有工厂或夜间行车等情况的区域也有耦合相异的情况,其分布与老龄化比重分布有相似性;POI高于微博签到的地区正是表现出北京城市化高度完善的区域已经出现了基础建设POI供大于求的情况。本研究表明将3种数据结合分析,可以有效地体现城市空间结构。

Aiming at the actuality that the spatial coupling analysis and the integrated application between POI(point of interest),luminous remote sensing and Weibo sign-in data are few researched on urban spatial structure,these three data of2016 were chosen to conduct a coupling study on Beijing spatial distribution.Through kernel density analysis and superposition analysis,these three data were into regular grids,and then the spatial coupling relationship between any two of these three data sets were visualized by two-factor cartography,the same and different spatial coupling areas of the three groups of data were extracted,and the relationship between them and urban spatial structure were further compared.This research showed that the spatial distribution of these three data sets in Beijing were highly consistent,and they presented a high spatial coupling state.There were some coupling dissimilar areas between the nighttime light brightness and POI distribution density in large-scale homogenization areas such as airports and scenic spots.There were some coupling dissimilar areas between the nighttime light brightness and Sina Weibo sign-in density in areas of factory or night driving.The distribution of coupling dissimilar areas was also similar to the aging population in Beijing.The areas where the POI was higher than that signed by Weibo were the areas where Beijing’s urbanization is highly improved.It may indicate that the infrastructure POI supply was more than human’s need.The research in this paper showed that urban analysis combining these three data can effectively reflect urban spatial structures.