DOI: 10.57760/sciencedb.36674
title: Dataset of Spatial Extents of Physical Urban Areas and Main Urban Areas in Chinese Cities (2020)
title: 中国城市实体地域及主城区实体地域范围数据集（2020年）

description: Scientifically identifying the physical urban areas and main urban areas, and developing corresponding vector datasets, is a fundamental prerequisite for urban research. Existing public datasets regarding the spatial extents of Chinese cities have yet to identify urban extents based on statistical urban-rural classifications (urban-rural classification codes), nor have they effectively distinguished main urban areas from urban-rural fringe zones. Furthermore, official vector data for urban boundaries remains unavailable to the general public. In view of this, the present dataset leverages Chinese community/village point data and their urban-rural classification codes, integrated with Global Urban Boundaries (GUB) data. By employing methods such as community/village Voronoi polygon generation, urban-rural type filtering, and spatial overlay fusion, we have developed the first dataset of physical urban areas and main urban areas in Chinese cities based on the statistical attributes of urban-rural classification codes. The dataset covers the spatial extents of 689 Chinese cities for the year 2020, consisting of two Shapefile (.shp) layers: physical urban areas and main urban areas. Validation indicates that the dataset is of high quality. This data provides a spatial baseline for verifying classical theories in urban geography, measuring urbanization processes, and researching urban-rural integration. Simultaneously, it offers data support for territorial spatial planning, the formulation of urban development strategies, and the optimized allocation of spatial resources.
description: Scientifically identifying the physical urban areas and main urban areas, and developing corresponding vector datasets, is a fundamental prerequisite for urban research. Existing public datasets regarding the spatial extents of Chinese cities have yet to identify urban extents based on statistical urban-rural classifications (urban-rural classification codes), nor have they effectively distinguished main urban areas from urban-rural fringe zones. Furthermore, official vector data for urban boundaries remains unavailable to the general public. In view of this, the present dataset leverages Chinese community/village point data and their urban-rural classification codes, integrated with Global Urban Boundaries (GUB) data. By employing methods such as community/village Voronoi polygon generation, urban-rural type filtering, and spatial overlay fusion, we have developed the first dataset of physical urban areas and main urban areas in Chinese cities based on the statistical attributes of urban-rural classification codes. The dataset covers the spatial extents of 689 Chinese cities for the year 2020, consisting of two Shapefile (.shp) layers: physical urban areas and main urban areas. Validation indicates that the dataset is of high quality. This data provides a spatial baseline for verifying classical theories in urban geography, measuring urbanization processes, and researching urban-rural integration. Simultaneously, it offers data support for territorial spatial planning, the formulation of urban development strategies, and the optimized allocation of spatial resources.