A new perspective to map the supply and demand of artificial night light based on Loujia1-01 and urban big data

The notable increase in artificial night light (ANL) induced by the rapid urbanization process has been widely studied, but a deep understanding of the supply and demand status of ANL is still lacking. This paper attempts to map the supply and demand of ANL from the human perspective by using advanced Loujia1-01 nighttime imagery and social media derived population density (PD) data, which provides a new tool for light regulation in urban management. The bivariate clustering based k-means algorithm and template matching technique are integrated to delineate mismatch regions at the block scale to further analyze the underlying reason for unbalanced status. The results showed that the high supply but low demand (HSLD) ANL status was the leading component in the mismatch regions, occupying more than 650,000 ha and mainly occurring in the city center. The HSLD proportion was considerable in terms of public services (44%), commercial (40%), industrial (39%), transportation (56%), and green space areas (53%). Moreover, the HSLD area notably increased 946 ha over time from 18:00 to 22:00. The measurements for validation obtained by field investigation showed highly linear relationship with ANL (R2 = 0.75) and PD (R2 = 0.62), and the mapping results were consistent with the actual conditions. This study reveals the highly unbalanced ANL status, and appeals to planners for the establishment of optimal lighting regulations to alleviate disruptive effects.