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

Abstract

Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution of the population at a large scale because of their low spatial resolution. The new generation of Luojia1-01 NTL data can be used for fine-scale social and economic analysis with its high spatial resolution and quantitative range. However, due to the geometry and background noise of the data themselves, the accuracy of the original NTL data is still low. Points-of-interest (POI) also can be used to map the population spatialization, but the indicative relationship between the POI and population is not clear, especially in rural and urban areas with different landscape structures. To solve the above-mentioned problems, this study proposes an improved nighttime light (INTL) index to better use the Luojia1-01 NTL data. Meanwhile, a zonal classification model based on INTL and impervious surface area is proposed to distinguish urban and rural areas. Compared with previous research and existing datasets, our result had the highest accuracy (R² = 0.86). This study explains that the INTL index is applicable to population spatialization research with the emergence of high-resolution and multispectral NTL satellite data. Moreover, the zonal classification model in this research can significantly improve the accuracy of population spatialization in rural areas. This study provides a possible way to use NTL and POI data in other social and economic spatialization research.

Study on Spatialization and Spatial Pattern of Population Based on Multi-Source Data—A Case Study of the Urban Agglomeration on the North Slope of Tianshan Mountain in Xinjiang, China

“…Consequently, techniques utilizing remote sensing data from nighttime lights [8][9][10][11][12] and land use classifications [13][14][15][16] to spatialize population information have gained traction. Moreover, the advent of computational artificial intelligence has led to the application of geographically weighted regression [17][18][19][20][21] and machine learning methods [22,23] for producing fine-grained visual representations of population distribution using both traditional geographic and geographical big data [24][25][26][27][28][29][30].…”

Grid-Scale Poverty Assessment by Integrating High-Resolution Nighttime Light and Spatial Big Data—A Case Study in the Pearl River Delta

“…To this end, spatial big data have been gradually considered to overcome the limitations of nighttime light data. In particular, a number of previous studies have shown that POIs can effectively reflect socio-economic activities and urban vitality [51,55], while housing price is also an important indicator of economic and residential conditions [56,57]. Therefore, it is feasible to combine these various different data sources for fine-scale poverty estimation.…”

A 100-m gridded population dataset of China’s seventh census using ensemble learning and geospatial big data

“…Timely and reliable gridded population data are highly desired to meet this demand, especially in countries experiencing rapid urbanization and internal migration like China. In China, informed decision-making and sustainable urban development greatly depend on timely and accurate gridded population distribution data (Chen et al, 2020b;Cheng et al, 2020;Guo et al, 2023;Tu et al, 2022). The Seventh National Population Census of China, conducted in 2020, presents a valuable opportunity to produce the required up-to-date and reliable gridded population data.…”