Unveiling the Drivers of Unplanned Urbanization: A High-Resolution Night Light Development Index Approach for Assessing Regional Inequality and Urban Growth in Dhaka

Rapid unplanned urbanization is a common phenomenon in developing countries and the necessity for making an urban area sustainable and livable is one of the major challenges of today’s world. While the timely identification of the factors contributing to rapid unplanned urbanization is essential, uncovering the core reasons behind this phenomenon remains one of the most challenging tasks. The Night Light Development Index (NLDI) can track and monitor urbanization over time and can be used for understanding and optimizing the use of urban space at night due to its timely availability and accessibility. Previous research has only been conducted using VIIRS or DMP OLS satellite imagery to calculate the NLDI. However, the temporal and spatial resolution of these types of imagery are quite poor and require a lot of preprocessing. In this research, these complexities are addressed by introducing a modified NLDI, leveraging high-resolution nighttime imagery from NASA’s Black Marble product suite and population data from NASA’s Socioeconomic Data and Applications Center (SEDAC). This study examines the core drivers of unplanned urbanization in Dhaka by analyzing changes in regional inequality, electrification patterns, and GDP in relation to nighttime light, amidst the significant challenges posed by rapid urbanization in developing countries. The results indicate significant transformations in Dhaka’s urban land cover change (ULCC), with substantial increases in built-up areas correlating with heightened electrification and economic activity/GDP, as evidenced by NTL radiance patterns. Furthermore, the modified NLDI effectively captures the nuances of urban development and inequality, offering insights into regional disparities and urban planning challenges. We find that the income inequality or the NLDI value is the highest in the Biman Bandar area (0.910), followed by Pallabi (0.665), Turag (0.640), and Khilkhet (0.587). These values indicate significant income disparity between affluent and impoverished populations within these regions.