Regional Features and National Differences in Population Distribution in China’s Border Regions (2000–2015)

Abstract

China is of great geostrategic significance not only in the Asia-Pacific region, but also in the wider world. The population distribution in China's border regions is of significance for the development of China and neighboring countries. Based on the LandScan Population Distribution Database, this study used GIS spatial analysis and statistics to investigate national trends in the population distribution of China's border regions. The study areas were the buffer zones within specified Euclidean distances from the land borders of China. The results showed that: (1) Although the population agglomeration index and population size inside China's border areas have increased, the population density inside China's border areas is still far below China's average population density; (2) Over the last 15 years, the population of China's northwestern border has been sparse, with small increases in the population agglomeration index and population size. China's northeastern and southwestern border regions have several average population density areas, but the northeastern regions have seen a substantial reduction in population size since 2000. Based on our analysis, we propose that the Chinese government introduce supporting policies for the development of border regions to maintain the population size. At the same time, considering the varied living suitability of the natural environment along the northeastern, northwestern, and southwestern borders, "moderate agglomeration" and "moderate evacuation" population development strategies should be implemented in specified areas. Tilt policy should also be implemented in areas of rapid population decline.

Human Activity Intensity and Its Spatial-Temporal Evolution in China’s Border Areas

“…Most border research uses population and land use data for analysis. You et al analyzed population changes within a 200 km buffer zone inside and outside China's borders [55], finding that the population density on the neighboring side was twice as high as on the Chinese side. The comparison of population densities we found is consistent with this, but with a higher HAI on the Chinese side than on the neighboring side.…”

“…However, the demographic analysis based on administrative units has coarsened the spatial differences in population density. In fact, the border population is mainly distributed in the plains, oases, and along the transportation lines [55]. Therefore, using a single datum to identify human activities will produce large deviations.…”

Evolving population distribution in China’s border regions: Spatial differences, driving forces and policy implications

“…Chinese scholars have conducted many studies on frontier residents. At present, the research on such communities includes five key aspects: (1) their distribution characteristics [ 38 ]; (2) population outflow and its influencing factors [ 26 , 27 , 35 – 37 ]; (3) population security’s impact on national security, as well as economic and social development [ 22 , 25 ]; (4) local inhabitants’ marital status [ 47 , 48 ]; and (5) illegal cross-border migrants [ 20 , 49 ]. However, most scholars focus only on some counties.…”

“…Existing research has overlooked the issue of population distribution in these regions. Only a few studies have focused on smaller zones, shorter time scales, and borderlines [ 26 , 27 , 35 – 37 ]; or they considered population distribution and change within one kilometer of each side of the border [ 38 ]. As there is no administrative division within the study of 1 km of the frontier, it is difficult to propose specific policies for population distribution patterns and changes.…”

The Driving Influence of Multi-Dimensional Urbanization on PM2.5 Concentrations in Africa: New Evidence from Multi-Source Remote Sensing Data, 2000–2018

“…The population urbanization level was depicted by the population density, which was considered to be the most direct indicator of the spatial pattern of population distribution [29]. Its data were obtained from the LandScan Global Population Project, developed by the Department of Energy Oak Ridge National Laboratory (ORNL) in Tennessee, USA based on a combination of geographic information systems, image analysis, and multivariate zoning density models at 1 km spatial reso-lution (https://www.satpalda.com/product/landscan/) (accessed on 25 December 2020) [30]. The land urbanization level, characterized by the degree of the artificial impervious surface coverage (impervious surface coverage = impervious surface area/total area) [31], was calculated using a 30 m high-resolution artificial impervious surface product by Professor Gong Peng [32].…”