Understanding the characteristics of the traditional village distribution contributes to the formulation of relevant protection and development strategies. We adopted a series of spatial analysis methods to investigate the characteristics of the traditional village distribution in China by using the watershed as the research unit. Moreover, we conducted quantitative and qualitative analyses of the relevant influencing factors affecting the distribution pattern using Geodetector and mathematical statistics. The findings indicate that traditional villages are distributed unevenly across watershed units. High–High clusters tend to occur at the boundaries of first-level watersheds. Traditional villages have a clear agglomeration trend in space, with a concentrated and contiguous distribution pattern based on the “core density area–ring-core expansion group–belt area”. The key factors affecting the traditional village distribution are annual precipitation, annual average temperature, and river density. The traditional village number has a clear inverted U-shaped relationship with the annual average temperature, river density, and road density. The study reveals the complex and various characteristics of the traditional village distribution and its influence mechanism and offers scientific advice for traditional villages’ future protection and development.

Establishing the National Comprehensive Disaster-Reduction Demonstration Community (NCDDC) is crucial for enhancing comprehensive disaster risk reduction at the grassroots level in China. Studying the distribution characteristics and influencing factors of NCDDCs can guide future NCDDC layout optimization and related policy adjustments. Using the standard deviation ellipse, nearest neighbor index, kernel density, spatial autocorrelation, and Geodetector, we analyzed the spatiotemporal distribution characteristics of NCDDCs in China from 2008 to 2021 and detected their influencing factors. The findings are as follows: (1) NCDDCs exhibit an uneven distribution at different scales, including spatial, urban–rural, and county scales. (2) The spatial distribution of NCDDCs mainly follows a northwest–southeast pattern during 2008–2014 and shows a northeast–southwest trend after 2014. (3) The positive spatial correlation and spatial agglomeration of NCDDCs increase annually. (4) NCDDCs show a concentrated and contiguous distribution pattern in 2021, based on “core density zone–ring-core decreasing area–ring-core expansion group–Ɔ-shaped area–belt-shaped area”. (5) The main factors affecting the NCDDC distribution are hospital density, road density, GDP density, and population density, with factors’ interactions exhibiting bilinear and nonlinear enhancement effects. This study reveals the NCDDC spatiotemporal distribution characteristics and its influence mechanism, providing a scientific basis for future NCDDC layout optimization and related policy adjustments.