基于多源地理大数据的城市空间结构研究

Combining with the kernel density analysis， the method for transforming data into regular grids and mapping double factors， we take the main urban area of Dongguan as study area， and use the nighttime light data， POI data and Lacation-based data in 2019 as data source， to obtain the same or different spatial coupling relationship of these three data and compare their relationship with the urban spatial structure. Research demonstrates that the overall spatial distribution trends of these three data types are generally consistent.， but there are different couplings in partial areas： （1） Influenced by the factors such as traffic， functional areas， and “spillover” effect of nighttime light data， the coupling of nighttime light data and POI data are different in the roads， commercial districts， and public service areas in the urban scopes； the coupling of nighttime light data and Lacation-based data are different in logistics industrial parks， schools， and suburban parks. （2） The differences of the spatial distribution of job-housing places， causing the different spatial coupling of POI data and Lacation-based data. The public services and business districts where have complete infrastructure， the density of POI data is higher than Lacation-based data； the infrastructure construction of the residential areas is relatively weak， but the population distribution is concentrated， making the POI density lower than the Lacation-based data. The integration of these three types of spatial data can effectively reflect the spatial structure of the cities and the existing problems.

Keywords： Nighttime light data ; POI （Point of Interest） data ; Lacation-based data ; Spatial coupling ; Urban spatial structure