Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method

Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and planning. In the past, nighttime light (NTL) data were generally used to map of at a large scale because their low resolution. The new generation Luojia1-01 NTL can be for fine-scale social economic analysis with its high resolution quantitative range. However, due geometry background noise themselves, accuracy original is still low. Points-of-interest (POI) also spatialization, but indicative relationship between POI not clear, especially rural areas different landscape structures. To solve above-mentioned problems, this study proposes improved (INTL) index better use data. Meanwhile, zonal classification model based on INTL impervious surface area proposed distinguish areas. Compared previous research existing datasets, our result had highest ( R ² = 0.86). This explains that applicable spatialization emergence high-resolution multispectral satellite Moreover, significantly improve provides possible way other research.