Using satellite images of nighttime lights to predict the economic impact of COVID-19 in India

Dasgupta, Nataraj

doi:10.1016/j.asr.2022.05.039

Cited by 18 publications

(5 citation statements)

References 50 publications

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“…Finally, we elaborated on the association between the socio-economic characteristics of the states and the magnitude of their economic loss and the speed of recovery. Several recent studies explored NTL dynamics during the COVID-19 pandemic and concluded that drastic changes did take place [26][27][28]; some studies have confirmed that the NTL data correlate with GDP in the pandemic period at the level of selected cities [29] and at the national level [30,31]. Our study adds value by using the NTL to measure the changes in the quarterly GDP at the subnational level in the U.S. before and during the pandemic period.…”

Section: Discussionmentioning

confidence: 70%

“…In another study, Roberts shows that at the national level, there exists a statistically significant correlation between the quarterly trends in Morocco's overall NTL intensity and the trends in its real Gross Domestic Product (GDP) [30]. Finally, in the most recent study, Dasgupta reports that during the pandemic, the amount of NTL was strongly correlated with GDP at the national level in India [31]. The question, however, remains whether NTL data can track the economic impacts of the COVID-19 crisis at the subnational level.…”

Section: Introductionmentioning

confidence: 95%

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Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Lin¹,

Rybnikova

2023

Geomatics

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Night-time light (NTL) data have been widely used as a remote proxy for the economic performance of regions. The use of these data is more advantageous than the traditional census approach is due to its timeliness, low cost, and comparability between regions and countries. Several recent studies have explored monthly NTL composites produced by the Visible Infrared Imaging Radiometer Suite (VIIRS) and revealed a dimming of the light in some countries during the national lockdowns due to the COVID-19 pandemic. Here, we explicitly tested the extent to which the observed decrease in the amount of NTL is associated with the economic recession at the subnational level. Specifically, we explore how the association between Gross Domestic Product (GDP) and the amount of NTL is modulated by the pandemic and whether NTL data can still serve as a sufficiently reliable proxy for the economic performance of regions even during stressful pandemic periods. For this reason, we use the states of the US and quarterly periods within 2014–2021 as a case study. We start with building a linear mixed effects model linking the state-level quarterly GDPs with the corresponding pre-processed NTL data, additionally controlling only for a long-term trends and seasonal fluctuations. We intentionally do not include other socio-economic predictors, such as population density and structure, in the model, aiming to observe the ‘pure’ explanatory potential of NTL. As it is built only for the pre-COVID-19 period, this model demonstrates a rather good performance, with R2 = 0.60, while its extension across the whole period (2014–2021) leads to a considerable worsening of this (R2 = 0.42), suggesting that not accounting for the COVID-19 phenomenon substantially weakens the ‘natural’ GDP–NTL association. At the same time, the model’s enrichment with COVID-19 dummies restores the model fit to R2 = 0.62. As a plausible application, we estimated the state-level economic losses by comparing actual GDPs in the pandemic period with the corresponding predictions generated by the pre-COVID-19 model. The states’ vulnerability to the crisis varied from ~8 to ~18% (measured as a fraction of the pre-pandemic GDP level in the 4th quarter of 2019), with the largest losses being observed in states with a relatively low pre-pandemic GDP per capita, a low number of remote jobs, and a higher minority ratio.

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Section: Discussionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 95%

Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Lin¹,

Rybnikova

2023

Geomatics

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“…Urban nightlights were the next source of exploration in remote sensing data as they are shown to provide insights into the spatial connectivity of urban spaces [47] and hints of economic activity [20] [37]. Our hypothesis is that urban areas saturated with lighting might be indicated as commercial zones, which may serve as a valuable feature for prediction.…”

Section: Remote Sensingmentioning

confidence: 99%

Invisible Walls: Exploration of Microclimate Effects on Building Energy Consumption in New York City

Dougherty¹,

Jain²

2022

Preprint

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The reduction of greenhouse gases from buildings forms the cornerstone of policy to mitigate the effects of climate change. However, the automation of urban scale building energy modeling systems required to meet global urban demand has proven challenging due to the bespoke characteristics of each city. One such point of uniqueness between cities is that of urban microclimate, which may play a major role in altering the performance of energy efficiency in buildings. This research proposes a way to rapidly collect urban microclimate data through the utilization of satellite readings and climate reanalysis. We then demonstrate the potential utility of this data by composing an analysis against three years of monthly building energy consumption data from New York City. As a whole, microclimate in New York City may be responsible for large swings in urban energy consumption. We estimate that Central Park may reduce the electricity consumption of adjacent buildings by 5-10%, while vegetation overall seems to have no appreciable impact on gas consumption. We find that favorable urban microclimates may decrease the gas consumption of some buildings in New York by 71% while others may increase gas consumption by as much as 221%. Additionally, microclimates may be responsible for the decrease of electricity consumption by 28.6% in regions or increases of 77% consumption in others. This work provides a method of curating global, high resolution microclimate data, allowing researchers to explore the invisible walls of urban microclimate which interact with the buildings around them.

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“…Additionally, satellite data has been crucial in responding to public health crises, aiding understanding and response to the COVID-19 pandemic [53][54][55][56].…”

Section: A Decade Of Remotely Sensed Epidemiology: Academic Literatur...mentioning

confidence: 99%

“Urban-Satellite” estimates in the ABCD Study: Linking Neuroimaging and Mental Health to Satellite Imagery Measurements of Macro Environmental Factors

Goldblatt,

Holz,

Tate

et al. 2023

Preprint

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While numerous studies over the last decade have highlighted the important influence of environmental factors on mental health, globally applicable data on physical surroundings are still limited. Access to such data and the possibility to link them to epidemiological studies is critical to unlocking the relationship of environment, brain and behaviour and promoting positive future mental health outcomes. The Adolescent Brain Cognitive Development (ABCD) Study is the largest ongoing longitudinal and observational study exploring brain development and child health among children from 21 sites across the United States. Here we describe the linking of the ABCD study data with satellite-based “Urban-Satellite” (UrbanSat) variables consisting of 11 satellite-data derived environmental indicators associated with each subject’s residential address at their baseline visit, including land cover and land use, nighttime lights, and population characteristics. We present these UrbanSat variables and provide a review of the current literature that links environmental indicators with mental health, as well as key aspects that must be considered when using satellite data for mental health research. We also highlight and discuss significant links of the satellite data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides the foundation for large-scale environmental epidemiology research.

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Using satellite images of nighttime lights to predict the economic impact of COVID-19 in India

Cited by 18 publications

References 50 publications

Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Invisible Walls: Exploration of Microclimate Effects on Building Energy Consumption in New York City

“Urban-Satellite” estimates in the ABCD Study: Linking Neuroimaging and Mental Health to Satellite Imagery Measurements of Macro Environmental Factors

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