Identification of Urban Agglomeration Spatial Range Based on Social and Remote-Sensing Data—For Evaluating Development Level of Urban Agglomeration

Abstract

The accurate identification of urban agglomeration spatial area is helpful in understanding the internal spatial relationship under urban expansion and in evaluating the development level of urban agglomeration. Previous studies on the identification of spatial areas often ignore the functional distribution and development of urban agglomerations by only using nighttime light data (NTL). In this study, a new method is firstly proposed to identify the accurate spatial area of urban agglomerations by fusing night light data (NTL) and point of interest data (POI); then an object-oriented method is used by this study to identify the spatial area, finally the identification results obtained by different data are verified. The results show that the accuracy identified by NTL data is 82.90% with the Kappa coefficient of 0.6563, the accuracy identified by POI data is 81.90% with the Kappa coefficient of 0.6441, and the accuracy after data fusion is 90.70%, with the Kappa coefficient of 0.8123. The fusion of these two kinds of data has higher accuracy in identifying the spatial area of urban agglomeration, which can play a more important role in evaluating the development level of urban agglomeration; this study proposes a feasible method and path for urban agglomeration spatial area identification, which is not only helpful to optimize the spatial structure of urban agglomeration, but also to formulate the spatial development policy of urban agglomeration.