A surface network based method for studying urban hierarchies by night time light remote sensing data

Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies.