Temporal and Spatial Pattern Evolution and Influencing Factors of the National Comprehensive Disaster-Reduction Demonstration Community in China

Abstract

A comprehensive disaster-reduction demonstration community (CDRDC) is imperative for building community resilience when confronted with disasters. However, the temporal characteristics and spatial heterogeneity of CDRDC in China are rarely involved in relevant research, and the influencing factor selection dimension is relatively singular. Based on the list of CDRDCs in China from 2008 to 2020, this study analyzed the spatial–temporal evolution pattern of CDRDCs at different scales and explored the correlation between CDRDCs and influencing factors such as population, GDP, disaster frequency and natural disaster risk. We also deduced the theoretical distribution and the key development areas of CDRDCs, analyzed the problems faced by the establishment of CDRDCs in different regions of China, and put forward targeted optimization countermeasures for community-based disaster mitigation. The results are as follows: (1) The distribution of CDRDCs shows significant regional differences, with 50% concentrated in the eastern region. (2) The number of CDRDCs is closely related to population, GDP, and natural disaster risk, but it has a poor correlation with disaster frequency. (3) The capital circle, the Yangtze River Delta, the Pearl River Delta, eastern Fujian, and eastern Hubei will be the key development areas of CDRDCs in the future. The research results can provide a theoretical basis and technical support for the layout optimization of CDRDCs in China.

Distribution Characteristics and Influencing Factors of the National Comprehensive Disaster-Reduction Demonstration Community in China

“…This finding contradicts the general understanding that areas with higher disaster hazard and risk should have more NCDDCs. Our study shows that the individual impact of disaster factors on the NCDDC distribution is relatively weak, consistent with previous research [37,38]. However, their explanatory power on the NCDDC distribution significantly increases when disaster factors interact with hospital density, GDP density, population density, and road density.…”

“…Road density affects the evacuation of people during disasters, which helps improve resilience [41]. In China, the natural disasters that pose the greatest threat to people and property are earthquakes Combining the relevant literature [36][37][38] and data availability, we selected nine potential influencing factors to discuss the spatial distribution of NCDDCs in five dimensions: disaster, medical services, government agency accessibility, transportation, and socioeconomics. The higher the population density, the higher the exposure and the greater the potential losses caused by a disaster event [39].…”

“…Furthermore, the central region of China has shown particular vulnerability to disasters over the past decade [66]. This region is characterized by a significant concentration of rural populations [67], leading to more severe impacts of disasters on the rural population [38]. Therefore, it is necessary to strengthen comprehensive disaster mitigation capacity in rural areas through the establishment of rural-type NCDDCs.…”

“…China's Western region has a relatively low number of NCDDCs, with 58 out of 74 counties in Tibet lacking NCDDCs. Compared to the Central-Eastern region, the Western region of China faces numerous challenges, including an underdeveloped economy, a scattered population, limited medical resources, inadequate road infrastructure, and high disaster risk [14,38]. Achieving harmonious and synchronized NCDDC development between Western and Central-Eastern China is very difficult.…”

“…Li et al [37] investigated the spatial distribution and influencing factors of NCDDCs in the Beijing-Tianjin-Hebei region using the kernel density, nearest neighbor index, geographic centralization index, Gini coefficient, imbalance index, and Geodetector. Ma et al [38] examined the evolutionary characteristics of NCDDCs in China from 2008 to 2020 at various administrative unit scales and determined the impact mechanisms using Pearson correlation analysis.…”