The accurate measurement of CO₂ emissions is helpful for realizing the goals of “carbon neutralization” and “carbon peak”. However, most current research on CO₂ emission measurements utilizes the traditional energy balance coefficient and top-down methods. The data granularity is large, and most studies are concentrated at the national, provincial, municipal, or district/county administrative unit scale. As an important part of the Guangdong–Hong Kong–Macao Greater Bay Area of China, the Pearl River Delta region has good nighttime light vitality and faces huge carbon emission pressure. Using the Pearl River Delta as the research area, this study constructed an optimized pixel-scale regression model based on NPP-VIIRS (The Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-Orbiting Partnership spacecraft) nighttime light data and CO₂ emissions data at the district and county levels for 2017. In addition, the spatial pattern of CO₂ emissions in the Pearl River Delta was analyzed based on the predicted CO₂ emission status. The results showed that the spatial pattern of CO₂ emissions in the Pearl River Delta had the distinct characteristics of the “center-edge” effect, the spatial spillover effect, and high-value aggregation, which should be considered when making related social or public decisions.

The existing research on security risk often focuses on specific types of crime, overlooking an integrated assessment of security risk by leveraging existing police resources. Thus, we draw on crime geography theories, integrating public security business data, socioeconomic data, and spatial analysis techniques, to identify integrated risk points and areas by examining the distribution of police resources and related factors and their influence on security risk. The findings indicate that security risk areas encompass high-incidence areas of public security issues, locations with concentrations of dangerous individuals and key facilities, and regions with a limited police presence, characterized by dense populations, diverse urban functions, high crime probabilities, and inadequate supervision. While both police resources and security risk are concentrated in urban areas, the latter exhibits a more scattered distribution on the urban periphery, suggesting opportunities to optimize resource allocation by extending police coverage to risk hotspots lacking patrol stations. Notably, Level 1 security risk areas often coincide with areas lacking a police presence, underscoring the need for strategic resource allocation. By comprehensively assessing the impact of police resources and public security data on spatial risk distribution, this study provides valuable insights for public security management and police operations.