Advancing urban connectivity measurements for SDG 11.a with SDGSAT-1 nighttime light data in urban agglomerations

Abstract

Urban agglomerations, formed by multiple cities connected through transportation and information networks, play a pivotal role in the sustainable development of human settlements and communities. This study introduces a data-driven approach to analyze urban connectivity using high-precision 10-meter panchromatic nighttime light (NTL) data from SDGSAT-1, integrated with OpenStreetMap (OSM) road data, to support SDG 11.a for sustainable cities and communities. We develop image enhancement and network extraction methods to accurately detect urban road networks and assess connectivity. By incorporating geographical adjustment factors and integrating urban geographic information, we construct a series of indicators to evaluate the resource flow capacities of urban agglomerations using social network models. The study focuses on three major urban agglomerations: the Chengdu-Chongqing Urban Agglomeration (CCUA), the Shandong Peninsula Urban Agglomeration (SPUA), and Liaoning Province (Liaoning). Through these case studies, we extract urban connectivity networks and analyze their resource flow capabilities. This approach provides valuable insights into the intensity and efficiency of urban resource circulation, offering data-driven support for fostering sustainable urban development in alignment with SDG 11.a.