Developing Non-Negative Spatial Autoregressive Models for Better Exploring Relation Between Nighttime Light Images and Land Use Types

Exploring the relationship between nighttime light and land use is of great significance to understanding human nighttime activities and studying socioeconomic phenomena. Models have been studied to explain the relationships, but the existing studies seldom consider the spatial autocorrelation of night light data, which leads to large regression residuals and an inaccurate regression correlation between night light and land use. In this paper, two non-negative spatial autoregressive models are proposed for the spatial lag model and spatial error model, respectively, which use a spatial adjacency matrix to calculate the spatial autocorrelation effect of light in adjacent pixels on the central pixel. The application scenarios of the two models were analyzed, and the contribution of various land use types to nighttime light in different study areas are further discussed. Experiments in Berlin, Massachusetts and Shenzhen showed that the proposed methods have better correlations with the reference data compared with the non-negative least-squares method, better reflecting the luminous situation of different land use types at night. Furthermore, the proposed model and the obtained relationship between nighttime light and land use types can be utilized for other applications of nighttime light images in the population, GDP and carbon emissions for better exploring the relationship between nighttime remote sensing brightness and socioeconomic activities.