This paper investigates the association between night lights and GDP estimates for India at the district level. While many studies are finding a high degree of association between economic activity as measured through the Gross Domestic Product (GDP) and night lights internationally, there is a lack of understanding of whether and how night light data are correlated with economic activity at the sub-national level in emerging economies. This achieves more significance in economic monitoring and policy-making as estimates of GDP are not available at geographically disaggregated level, and even if available there is a large time lag involved before they are released. Stable light data obtained from night time images of 2008 captured by Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) satellite are used in the study. The data records artificial lights from human habitations from the earth surface and is a surrogate of the level of development of an area. The data on GDP at the district level for the year 2008 have been sourced from Indicus Analytics that has used data from government sources and a method of estimation suggested by the Central Statistical Office of the Government of India. Using multinomial non-linear regression techniques the paper finds that indeed GDP at the district level is significantly explained by night lights in the area. It also finds that the non-linearity is much stronger for metropolitan cities where GDP levels are far higher than a linear model can explain. Conversely, in areas where agriculture and forestry activities are higher, the use of night lights in a linear model overestimates the GDP.

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Health and development are intricately related. Although India has made significant progress in the last few decades in the health sector and overall growth in GDP, there are still large regional differences in both health and development. The main objective of this paper is to develop techniques for the prediction of health indicators for all the districts of India and examine the correlations between health and development. The level of electrification and district domestic product (DDP) are considered as two fundamental indicators of development in this research. These data, along with health metrics and the information from two nighttime satellite images, were used to propose the models. These successfully predicted the health indicators with less than a 7%-10% error. The chosen health metrics, such as crude birth rate (CBR) and maternal mortality rate (MMR), were mapped for the whole country at the district level. These metrics showed very strong correlation with development indicators (correlation coefficients ranging from 0.92 to 0.99 at the 99% confidence interval). This is the first attempt to use Visible Infrared Imaging Radiometer Suite (VIIRS) (satellite) imagery in a socio-economic study. This paper endorses the observation that areas with a higher DDP and level of electrification have overall better health conditions.

The number and size of urban settlements are increasing in all the continents of the world at a rapid pace. Urban sprawl is associated not only with changes in landcover and area, but also ecological, climate and social transformations. Mapping the growth and spread of urban areas is important. Remote sensing has long been used to map human settlements. Today the availability of a large number of satellites and sensors, determining the appropriate image to map urban area is a research area itself. This study compares two satellite images: Landsat Enhanced Thematic Mapper data and Defence Meteorological Satellite Program, Operational Linescan System image to map the urban footprint of the city of Hyderabad, India. Landsat ETM data is captured during the daytime and gives spectral reflectance values while the DMSP-OLS data captures artificial lights from human settlements at night and produces brightness information. The results show an accuracy of more than 90% in the classification and delineation of urban, suburban and rural landcover types. This study shows that in addition to spectral reflectance captured by satellites from different features on the earth surface during the daytime, differences in the degree of brightness of the lights emitted from urban areas at night is also an effective indicator in delineating landcover types.

Urbanization, that is the movement of population from rural to urban locations, is a process that has been occurring for hundreds of years, but is increasingly prevalent in today's world. In 2008 most of the global population was resident in urban areas. It has been predicted that in the coming years, an increasing number of people will be living in cities; especially in the developing countries within in Asia and Latin America. This study considers the case of India, the second most populated country in the world, with a present total population exceeding 1 billion. It focuses on the state of Maharashtra (including the mega-city of Mumbai and its surrounds -the largest in India with a population of approximately 18.1 million). The Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) is a spaceborne system that detects visible light and thermal emissions of the earth at night. The data is collected nightly, on a global basis. The aim of this paper is to correlate the values obtained from the radiance calibrated DMSP/OLS night-time images of 2001 with population data. The spatial resolution of DMSP/OLS images is approximately 1 Km. This paper asks the question over what range of spatial scales does DMSP/OLS have utility in retrieving metrics of urbanization.

COVID-19 has emerged as a widely researched topic and the academia has taken interest in the effects of COVID-19 in various sectors of human life and society. Most of the bibliometric research addresses scientific contributions in medicine, health, and virology related topics, with very little emphasis on social sciences. Therefore, to address this gap, a bibliometric analysis of research related to COVID-19 in the subject area of social sciences was performed on selected publications from January 2020 to mid-2021. A total of 9289 articles were analysed to identify major emerging themes of Covid-19 and social sciences and how research collaborations between countries have helped in communicating critical issues to academia. The empirical results indicate the dominance of psychology and business economics subjects in the social sciences sphere, with the emerging themes as psychosocial problems among people, economics, the outbreak of SARS, and discussions on the quality of life in terms of surroundings and environment. The study also suggests that more collaborations between social scientists working in different nations is required to explore the less focussed themes addressing the local challenges of poor nations.

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