||Over the last quarter of a century, analyzing the pace of urbanization and urban economic growth in South Asia has become increasingly important. However, a key challenge relates to the absence of spatially disaggregated national accounts data â in particular, the absence of GDP data for sub-national administrative units and individual cities. The absence of such data limits the scope for detailed empirical analysis of spatial patterns of economic growth, particularly across individual urban settlements or cities. This paper aims to test the suitability of DMSP-OLS Nighttime Lights (NTL) data as a proxy for GDP to analyze detailed spatial patterns of urban economic growth across South Asia over the period 1999â2010. It will help to build an understanding of the nature and heterogeneity of spatial patterns of urban economic growth within the region and contribute to the development of a framework for the usage of NTL to investigate such patterns. Geographic Information System (GIS) is employed to identify the cities and urban agglomerations together with their NTL data in South Asia, and spatial statistics are used to analyze the spatial and temporal patterns of NTL growth. This paper adopts descriptive and inferential statistics to determine the quantitative relationship between NTL and population, urban size, and proximity to the coast. This paper reveals that the inter-annually calibrated NTL data is a good proxy for changes in national and sub-national GDP. In South Asia, the urban NTL hot spots are around major cities with populations between 1.3 and 2.6 million in 1999 and 0.5 to 1.3 million in 2010. Cities in the region have also become more clustered and connected forming urban agglomerations. NTL per unit of land in such clusters tends to be higher than in single cities in South Asia. India, Pakistan, and Sri Lanka tend to have higher NTL (economic) growth on average, while Nepal and Bangladesh have lower growth or declining NTL. There exists a very strong positive linear relation between distance to the coast and the total NTL within that distance, which leads to similar NTL growth rates among inland and coastal cities.