## 2 Research Background  
### Application of POI Data and Night Lights Remote Sensing Data  
Nighttime lights detected by satellites are increasingly used by economists to reflect the absence or perceived inaccuracy of traditional economic statistics such as national or regional GDP [22]. Therefore, light remote sensing data can objectively reflect human socio-economic activities, production, and living conditions. The two main sources of night light data are the Defense Meteorological Satellite Program Operational Linescan System (DMSP for short) and the Day-Night Band (DNB) of the VIIRS [22]. Existing research has controversies regarding the application of data from different sources. For instance, DMSP data are unsuitable for studying small areas or examining boundary effects [22]. Consequently, more research is shifting towards the utilization of VIIRS data, which are considered effective in addressing the limitations associated with the DMSP data [22]. Therefore, when employing light remote sensing data to analyze social-economic activities, it is crucial to acknowledge potential issues with data from various sources. It helps prevent shortcomings and facilitates effective data application and analysis [23]. The night light remote sensing data objectively reflect human socio-economic activities, production, and living conditions. NTL RSD are free from the excessive interference of human factors, have high data accuracy, and cost less when it comes to long-term ongoing data collection [24], including data on the underground economy or the informal economy; NTL RSD also more comprehensively reflect economic activity [25]. Enabling researchers to observe the economic activities of different regions for a long time without being affected by the price level [26], NTL RSD are often used as a proxy indicator of gross domestic product (GDP) and its growth rate, or to adjust and evaluate real GDP through NTL RSD [27; 28; 29]. Second, NTL RSD can be used to measure the spatial distribution of social and economic activities. Currently, traditional social and economic data are based primarily on administrative divisions of statistics [26]. It is difficult to identify the hot spots of social activities and economic concentration points in geographic space with high quality; NTL RSD can overcome the limits of administrative units and reflect the spatial distribution characteristics of social and economic activities in a more detailed and comprehensive manner. Further, NTL RSD play an important role in the urban development process [30], nighttime economic hotspots [31], the distribution characteristics of large cities [32], and the determination of urban agglomeration [30]. Finally, NTL remote sensing images can be used to monitor disasters and environmental pollution by estimating regional resource consumption and carbon emissions [33; 34], evaluating the impact of natural disasters on the environment and the economy [35], and analyzing the impact of war or regional conflict on national socio-economic dynamics [36; 37]. The application of POI data in urban and regional economies helps us to understand the social and economic functions, vitality, and development of cities [38]. POI data can be used to identify urban land use types and functional areas [39], the spatial characteristics of human activity [40; 41], industrial spatial development [42; 43], the urbanization process [44], and population and economic distribution [39; 45]. POI data are highly feasible for the above functions, which can enable the rapid identification of urban functional areas, population size, and business development. POI can also provide a reference for exploring the distribution law of different service facilities such as catering, accommodations, transport, sightseeing, entertainment, and shopping in urban spaces. POI data facilitate the exploration of urban spatial structures based on big data and support the planning of urban construction and development.