Abstract: 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 – 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 1999–2010. 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.

Abstract: The increase in artificial light at night (ALAN) is a global concern, while the pattern of ALAN pollution inside urban areas has not yet been fully explored. To fill this gap, we developed a novel method to map fine-scale ALAN pollution patterns in urban bird habitats using high spatial resolution ALAN satellite data. First, an ALAN pollution map was derived from JL1-3B satellite images. Then, the core habitat nodes (CHNs) representing the main habitats for urban birds to inhabit were identified from the land cover map, which was produced using Gaofen2 (GF2) data, and the high probability corridors (HPCs), indicating high connectivity paths, were derived from Circuitscape software. Finally, the ALAN patterns in the CHNs and HPCs were analysed, and the mismatch index was proposed to evaluate the trade-off between human activity ALAN demands and ALAN supply for the protection of urban birds. The results demonstrated that 115 woodland patches covering 4149.0ha were selected as CHNs, and most of the CHNs were large urban parks or scenic spots located in the urban fringe. The 2923 modelled HPCs occupying 1179.2ha were small remaining vegetation patches and vegetated corridors along the major transport arteries. The differences in the ALAN pollution patterns between CHNs and HPCs were mainly determined by the characteristics of the green space patches and the light source types. The polluted regions in the CHNs were clustered in a few regions that suffered from concentrated and intensive ALAN, while most of the CHNs remained unaffected. In contrast, the 727 HPCs were mainly polluted by street lighting was scattered and widely distributed, resulting a more varying influence to birds than that in the CHNs. Relating patterns of the ALAN to bird habitats and connectivity provides meaningful information for comprehensive planning to alleviate the disruptive effects of ALAN pollution.

Abstract: “Urban Sprawl” is a growing concern of citizens, environmental organizations, and governments. Negative impacts often attributed to urban sprawl are traffic congestion, loss of open space, and increased pollutant runoff into natural waterways. Definitions of “Urban Sprawl” range from local patterns of land use and development to aggregate measures of per capita land consumption for given contiguous urban areas (UA). This research creates a measure of per capita land use consumption as an aggregate index for the spatially contiguous urban areas of the conterminous United States with population of 50,000 or greater. Nighttime satellite imagery obtained by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP OLS) is used as a proxy measure of urban extent. The corresponding population of these urban areas is derived from a grid of the block group level data from the 1990 U.S. Census. These numbers are used to develop a regression equation between Ln(Urban Area) and Ln(Urban Population). The ‘scale-adjustment’ mentioned in the title characterizes the “Urban Sprawl” of each of the urban areas by how far above or below they are on the “Sprawl Line” determined by this regression. This “Sprawl Line” allows for a more fair comparison of “Urban Sprawl” between larger and smaller metropolitan areas because a simple measure of per capita land consumption or population density does not account for the natural increase in aggregate population density that occurs as cities grow in population. Cities that have more “Urban Sprawl” by this measure tended to be inland and Midwestern cities such as Minneapolis–St. Paul, Atlanta, Dallas–Ft. Worth, St. Louis, and Kansas City. Surprisingly, west coast cities including Los Angeles had some of the lowest levels of “Urban Sprawl” by this measure. There were many low light levels seen in the nighttime imagery around these major urban areas that were not included in either of the two definitions of urban extent used in this study. These areas may represent a growing commuter-shed of urban workers who do not live in the urban core but nonetheless contribute to many of the impacts typically attributed to “Urban Sprawl”. “Urban Sprawl” is difficult to define precisely partly because public perception of sprawl is likely derived from local land use planning decisions, spatio-demographic change in growing urban areas, and changing values and social mores resulting from differential rates of international migration to the urban areas of the United States. Nonetheless, the aggregate measures derived here are somewhat different than similar previously used measures in that they are ‘scale-adjusted’; also, the spatial patterns of “Urban Sprawl” shown here shed some insight and raise interesting questions about how the dynamics of “Urban Sprawl” are changing.