Abstract
Nighttime light imagery provides a perspective for studying urbanization and socioeconomic changes. Traditional global regression models have been applied to explore the nonspatial relationship between nighttime lights and population density. In this study, geographically weighted regression (GWR) identifies the spatially varying relationships between population density and nighttime lights in mainland China. However, the rural population does not have a strong relationship with remote-sensing spectral features. The rural population estimation using nighttime light data alone easily identifies meaningless negative population density in the rural area. This study proposes an adaptive non-negative GWR (ANNGWR) to explore the spatial pattern of population density by using nonnegative constraints with an adaptive bandwidth of kernel. The ANNGWR solves the negative value of population density and serious overestimation of the western boundary. The result shows that the ANNGWR provides the best goodness-of-fit compared with linear regression and original GWR. This study applies Moran’s I index to prove that the ANNGWR substantially decreases the spatial autocorrelation of the model residual. The model offers a robust and effective approach for estimating the spatial patterns of regional population density solely on the basis of nighttime light imagery.
Highlights
Population density is a major index for measuring human development and assessing urbanization in urban areas
The geographically weighted regression (GWR) model with spatial weighting considers the spatial heterogeneity in the regression model between nighttime lights and population density
The GWR model can estimate the spatial relationship of nighttime lights and population density
Summary
Population density is a major index for measuring human development and assessing urbanization in urban areas. Gaining accurate and detailed population data is a complicated task in developing countries. During the economic marketization process in numerous developing countries, rural residents migrate rapidly to urban areas while government regulations on migration remain largely rigid. These unchanged regulations reflect governments’ hesitation in promoting human mobility (often for the purpose of social control), and in most cases, this situation considerably affects the release of government statistics on population. Satellite images here provide a useful global viewpoint. These data are helpful in tracing the detailed process of urbanization [1,2,3]
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