Search

We propose a mechanism based on regional inequality in economic activity to explain the heterogeneity in the spread of COVID-19 and test it using data from India. Contagion is expected to spread at a higher rate in regions characterized by greater movement of goods and services. We argue that mobility is higher in regions with greater degree of intra-regional inequality in economic activity. Such regions are usually characterized by a core-periphery economic structure in which the periphery is dependent on the core for the supply of jobs, goods, and services. Such dependence leads to a greater degree of mobility between the core and periphery, which in turn leads to higher rate of contagion. Using nightlight data to measure regional inequality, we find evidence in support of our hypothesis. Using mobility data, we provide direct evidence in support of our proposed channel; the positive relationship between regional inequality and COVID-19 infection is driven by mobility. Our findings suggest that policy responses to contain COVID-19 contagion need to be heterogeneous across India, where the priority areas can be chosen ex ante based on a regional inequality-based criterion. [ABSTRACT FROM AUTHOR]

Purpose: The field study was to understand older adults' reactions to and use of different low-light conditions while walking to bathrooms in the dark in their homes. Low-light conditions included participants' usual nightlights and a destination-based LED strip lighting system. Background: Older adults encounter fall accidents while going to bathrooms at night due to low illuminance levels. They also fear falling due to previous fall histories or visual impairments. This field study tested and compared a destination-based LED strip lighting system with their usual nightlights on their movement and fear of falls. Methods: Fifteen older adults from an independent living facility participated in the within-subject design experiment, walking under two scenarios in random order: with usual nightlights turned on or with the destination-based LED strip lighting system turned on. Body-worn sensors were used to collect participants' movement behaviors, and subjective questionnaires were used to understand participants' anxiety under the two low-light conditions. Further, semi-structured interviews were conducted to understand their nightlight usage patterns and their evaluations of the destination-based LED strip lighting system. Results: Participants walked more smoothly under the destination-based LED strip lighting system scenario. However, the anxiety states were not statistically different between the two scenarios. Conclusion: Visual cues in the dark can benefit older adults' safe movement. However, the application of the lab-effective LED strip lighting system in home settings should consider older adults' floor plans and their furniture layout, both indoor and outdoor ambient lighting sources, and their lifestyles. [ABSTRACT FROM AUTHOR]

The high sensitivity and advanced onboard calibration on the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables accurate measurements of low light radiances which leads to enhanced quantitative applications at night. The finer spatial resolution of DNB also allows users to examine social economic activities at urban scales. Given the growing interest in the use of the DNB data, there is a pressing need for better understanding of the calibration stability and absolute accuracy of the DNB at low radiances. The low light calibration accuracy was previously estimated at a moderate 15% using extended sources while the long-term stability has yet to be characterized. There are also several science related questions to be answered, for example, how the Earth's atmosphere and surface variability contribute to the stability of the DNB measured radiances; how to separate them from instrument calibration stability; whether or not SI (International System of Units) traceable active light sources can be designed and installed at selected sites to monitor the calibration stability, radiometric and geolocation accuracy, and point spread functions of the DNB; furthermore, whether or not such active light sources can be used for detecting environmental changes, such as aerosols. This paper explores the quantitative analysis of nightlight point sources, such as those from fishing vessels, bridges, and cities, using fundamental radiometry and radiative transfer, which would be useful for a number of applications including search and rescue in severe weather events, as well as calibration/validation of the DNB. Time series of the bridge light data are used to assess the stability of the light measurements and the calibration of VIIRS DNB. It was found that the light radiant power computed from the VIIRS DNB data matched relatively well with independent assessments based on the in situ light installations, although estimates have to be made due to limited ground truth data and lack of suitable radiative transfer models. Results from time series analysis are encouraging in potentially being able to detect anomalies in the DNB calibration. The study also suggests that accurate ground based active lights, when properly designed and installed, can be used to monitor the stability of the VIIRS DNB calibration at near the specified minimum radiances (3 nW.cm-2.sr-1), and potentially can be used to monitor the environmental changes as well. [ABSTRACT FROM AUTHOR]

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research. [ABSTRACT FROM AUTHOR]

Various environmental criminology theories and empirical studies have linked the urban environment to crime. The crime pattern theory, in particular, argues that edges, either social or physical, affect crime. A recent study has combined both social and physical edges to derive composite edges. A composite edge index measured by NPP-VIIRS satellite nightlights at the census tract level is found to be related to street robbery and burglary. Nightlight images of Luojia 1-01, launched in June 2018, have a much higher spatial resolution than that of NPP-VIIRS. This study applies Luojia 1-01 nightlight data to measure composite edges by nightlight gradients at the smaller census block group level. The effects of the composite edges on street robbery and burglary are explored by negative binomial models. Results show that composite edges measured by Luojia 1-01 nightlight data improve the fitness of models noticeably on street robbery but not on burglary. Nightlight gradients make a statistically significant and positive impact on the street robbery rate, but an insignificant and negative impact on the burglary rate. Furthermore, the composite edge effect on street robbery is more substantial than that on burglary. In sum, this study provides evidence that Luojia 1-01 nightlight imagery can help explain crime at the aggregated block group level, but its impact on crime varies by crime type. • Make the first attempt of using Luojia 1-01 nightlight imagery to study urban crime. • Confirm that Luojia 1-01 is applicable for crime research at the census block group level. • Reveal that composite edge effects are more substantial on street robbery than burglary. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm (BUNTUS—Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions. [ABSTRACT FROM AUTHOR]

More than quarter of underprivileged global population, who lack access to basic sanitation and clean drinking water, live in India. Consequently, every year, millions suffer with enteric diseases from drinking faecal-contaminated groundwater. The UN Sustainable Development Goal lists access to safe water and basic sanitation for all by 2030, as their sixth goal. For the first time, the role of economic improvement on decrease in water-borne faecal pathogens was studied across Indo-Ganges–Brahmaputra river basin (IGB) for almost last three decades, to delineate the long-term improvement trends of groundwater quality across India, as a consequence of development. Long-term temporal (1990–2017) and high-resolution spatial (administrative block scale, n = 2217 ) datasets of water-borne faecal pathogen concentration in groundwater and satellite-based nightlight (NL) were used to investigate the statistical trends and causal relationships. Linear and nonlinear (Hodrick–Prescott) trend analyses, panel data analyses, Bayesian vector autoregression (VAR) and lead–lag causality (LLC) analyses were performed on aforesaid culled datasets. However, the efficiency of development in alleviating the water quality and public health, and relationship with economic development, has not been well understood. Here, for the first time, using long-term, high-spatial resolution ( n = 2217 ), annual in-situ measurements and multivariate statistical models, we show that the spatially variable groundwater faecal pathogen concentration (FC, 2002–2017, - 1.39 ± 0.01 %/yr) has been significantly decreased across the basin. In most areas, increasing satellite-based NL plays a significant role (NL , 1992–2013, 3.05 ± 0.01 %/yr) in reduction of FC. However, in areas with low literacy rate surpass development. Enhanced decrease of faecal coliform concentration in groundwater possibly signifies the implementation of Clean India Mission since 2014. [ABSTRACT FROM AUTHOR]