Prediction of Multi-Scale Socioeconomic Parameters from Long-Term Nighttime Lights Satellite Data Using Decision Tree Regression: A Case Study of Chongqing, China

Abstract

The Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and the Suomi National Polar-Orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light (NTL) data provide an adequate proxy for reflecting human and economic activities. In this paper, we first proposed a novel data processing framework to modify the sensor variation and fit the calibrated DMSP/OLS data and NPP/VIIRS data into one unique long-term, sequential, time-series nighttime-lights data at an accuracy higher than 0.950. Both the supersaturation and digital value range have been optimized through a machine learning based process. The calibrated NTL data were regressed against six socioeconomic factors at multi-scales using decision tree regression (DTR) analysis. For a fast-developing city in China—Chongqing, the DTR provides a reliable regression model over 0.8 (R2), as well explains the variation of factor importance. With the multi-scaled analysis, we matched the long-term time-series NTL indices with appropriate study scale to find out that the city and sub-city region are best studied using NTL mean and stander derivation, while NTL sum and standard deviation could be better applied the scale of suburban districts. The significant factor number and importance value also vary with the scale of analysis. More significant factors are related to NTL at a smaller scale. With such information, we can understand how the city develops at different levels through NTL changes and which factors are the most significant in these development processes at a particular scale. The development of an entire city could be comprehensively explained and insightful information can be produced for urban planners to make more accurate development plans in future.