Nighttime light imagery or mobile phone footprints: Which better reflects urban socio-economics at the grid level? A case study in the Pearl River Delta, China

Abstract

Traditional socioeconomic censuses rely on manual statistical surveys at the administrative division level, incurring significant costs while also facing the issue of data fabrication. The lack of information at the fine-scale spatial level limits more accurate policy formulation at the local and global levels. Nighttime lights have been proven to reflect human activities and estimate socio-economic indicators. Meanwhile, with the widespread use of smart devices, mobile phone data recorded as sensor data also provide various information about human footprints. This research elucidates the revealing ability of mobile phone footprints (MOB) and nighttime lights (NTL) to estimate various socio-economic indicators at a fine grid scale, establishing them as valuable proxies for understanding complex urban patterns. A comparative analysis within the Pearl River Delta (PRD), China demonstrates MOB's superior capacity in accurately reflecting socio-economic indicators such as population density and gross domestic product (GDP) distribution, effectively mitigating the oversaturation shortcomings of NTL in reflecting socioeconomic conditions. Especially in urban built-up areas, MOB and NTL data synergistically provide a refined depiction of socio-economic conditions, with MOB elucidating urban structure and density, and NTL closely associated with the service sector's footprint. The insights of the study highlight the value of integrating MOB and NTL data to refine the accuracy of socioeconomic indicators, which could be instrumental in the creation of nuanced urban planning and policy interventions. Such data-driven approaches promise to more effectively address socioeconomic inequalities and support sustainable urban development initiatives.