The Prediction Modelling Analysis of Regional Economic Activity Based on Nighttime Light in Central Java Province

Abstract

Economic growth and human activity are two closely interrelated aspects. Central Java Province, as the region with the second-highest number of regencies/cities in Indonesia, exhibits a complex diversity of economic characteristics. This study aims to develop a predictive model of regional economic activity using nighttime light (NTL) satellite imagery as an alternative indicator for forecasting Gross Regional Domestic Product (GRDP). NTL VIIRS data from 2013 to 2023 were analysed through linear regression and machine learning to project nighttime brightness levels up to 2043 at five-year intervals. The regression model produced a strong relationship (R2 = 0.85) between NTL and GRDP, with projections indicating an 83% increase in brightness and a 53% increase in GRDP by 2043 compared to the 2023 baseline. The outputs consist of spatial projection maps of nighttime brightness and estimates of GRDP values that can be analysed both temporally and spatially. These findings highlight the potential of remote sensing data to support data-driven regional economic prediction and inform spatial planning policies.