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This paper estimates the impact of a differential relaxation of COVID-19 containment policies on aggregate economic activity in India. Following a uniform national lockdown, the Government of India classified all districts into three zones with varying containment measures in May 2020. Using a difference-in-differences approach, the paper estimates the impact of these restrictions on nighttime light intensity, a standard high-frequency proxy for economic activity. To conduct this analysis, pandemic-era, district-level data from a range of novel sources are combined -- monthly nighttime lights from global satellites; Facebook's mobility data from individual smartphone locations; and high-frequency, household-level survey data on income and consumption, supplemented with data from the Indian Census and the Reserve Bank of India. The analysis finds that nighttime light intensity in May was 12.4 percent lower for districts with the most severe restrictions and 1.7 percent lower for districts with intermediate restrictions, compared with districts with the least restrictions. The differences were largest in May, when the different policies were in place, and slowly tapered in June and July. Restricted mobility and lower household income are plausible channels for these results. Stricter containment measures had larger impacts in districts with greater population density of older residents, as well as more services employment and bank credit.

Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are important in UNLS mapping. However, most studies rely solely on census data or nighttime light (NTL) images to map the physical features of UNLSs, which limits UNLS mapping, and few studies perform UNLS mapping from a social function perspective. Point-of-interest (POI) data, which can reflect social activity functions, are needed. As a result, a novel methodological UNLS mapping framework, that integrates NTL images and POI data is required. Consequently, we first extracted high-NTL intensity and high-POI density areas from composite data as areas with high nightlife activity levels. Then, the POI data were analyzed to identify the social functions of leisure spaces revealing that nighttime leisure activities are not abundant in Beijing overall, the total UNLS area in Beijing is 31.08 km2, which accounts for only 0.2% of the total area of Beijing. In addition, the nightlife activities in the central urban area are more abundant than those in the suburbs. The main urban area has the largest UNLS area. Compared with the nightlife landmarks in Beijing established by the government, our results provide more details on the spatial pattern of nighttime leisure activities throughout the city. Our study aims to provide new insights into how multisource data can be leveraged for UNLS mapping to enable researchers to broaden their study scope. This investigation can also help government departments better understand the local nightlife situation to rationally formulate planning and adjustment measures.

Economic inequality at the local level has been shown to be an important predictor of people's political perceptions and preferences. However, research on these questions is hampered by the fact that local inequality is difficult to measure and systematic data collections are rare, in particular in countries of the Global South. We propose a new measure of local inequality derived from nighttime light (NTL) emissions data. Our measure corresponds to the local inequality in per capita nighttime light emissions, using VIIRS-derived nighttime light emissions data and spatial population data from WorldPop. We validate our estimates using local inequality estimates from the Demographic and Health Surveys (DHS) for a sample of African countries. Our results show that nightlight-based inequality estimates correspond well to those derived from survey data, and that the relationship is not due to structural factors such as differences between urban and rural regions. We also present predictive results, where we approximate the (survey-based) level of local inequality with our nighttime light indicator. This illustrates how our approach can be used for new cases where no other data are available.

The objective of this study is to investigate the potential of nighttime light data, acquired with Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) owned by National Oceanic and Atmospheric Administration (NOAA), in predicting urban daily particulate matter (PM)2.5 with an aerodynamic diameter of less than 2.5 µm average concentrations. To achieve the purpose, we firstly extracted two night light indices, the Nighttime Light Intensity Index (NLII) and the Nighttime Saturated Light Area Index (NSLAI) from DMSP/OLS images. Through Gaussian fitting of the relationship between the indices and the daily PM2.5 concentrations data released by the government, we found that the intraday nighttime light indices were all more relevant with the PM2.5 average concentrations of the next day in Shanghai. Therefore, the 56 sets of data, the light indices were collected from 3 November 2013 to 28 December 2013, the daily PM2.5 concentrations data were collected from 4 November 2013 to 29 December 2013, and these were divided into two parts. The first 40 sets were used for training the model of echo state network (ESN). The last 16 sets were used for testing. The value of R2 of predicted results was as high as 0.6318. In summary, the effectiveness of nighttime light data that used for the prediction of urban daily PM2.5 average concentrations was verified in this study.

This paper analyzes an experiment designed to demonstrate the effects of high intensity street lights on nighttime crime. The statistical methodology is developed around an interrupted time series design and illustrates the hazards that can arise if the serial dependence of successive observations is ignored.

AbstractScholars of urban sustainability are increasingly interested in the influence of spatial configuration on local environmental performance, with a focus on urban compactness. Spatial compactness with urban density boosts energy‐efficient construction and infrastructure, subsequently encouraging lower carbon emissions. In this study, we first documented the distributional pattern of spatial compactness in the prefectures of Japan using refined measurement and nighttime light satellite data, which is a more consistent indicator of economic activities. Further, we investigate the underlying impact of this compactness on the local carbon emission intensity with quantitative evidence. The regression results indicate that higher compactness in a prefecture corresponds to lower carbon intensity even in different settings. Moreover, robustness checks verify the consistency of the abatement effects. Additionally, the mitigating role of compactness presents a heterogeneous pattern across sectors. The effect is statistically significant only in the manufacturing and household sectors. These findings add to the value of urban compactness and offer important policy implications for addressing sustainable societies in the long term.