Using night time lights to find regional inequality in India and its relationship with economic development

Singhal, Abhishek; Sahu, Sohini; Chattopadhyay, Siddhartha; Mukherjee, Abhijit; Bhanja, Soumendra N.

doi:10.1371/journal.pone.0241907

Cited by 28 publications

(20 citation statements)

References 16 publications

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“…There are several ways to numerically contrast Figures 1 and 2. A salient approach is to use spatial inequality statistics, as ever more studies use DMSP data to estimate inequality [39,40,48]. The overstated lit area in Figure 1 from DMSP blurring [24] makes it harder to distinguish areas of concentrated activity from other areas.…”

Section: Results Using Earlier Ntl Productsmentioning

confidence: 99%

“…A consequence of mean-reverting errors is understated inequality between places as NTL estimates revert toward their mean. Some studies have considered inequality as an aspect of economic performance by using DMSP data as a proxy in places that lack timely or fine resolution sub-national GDP data [39,40]. However, validation studies show that DMSP data understate spatial inequality, especially in urban and high density areas, with this pattern holding across developed and developing regions of the world [25,33].…”

Section: Related Literature On Ntl Validation Studiesmentioning

confidence: 99%

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Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Gibson

Boe‐Gibson

2021

Remote Sensing

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Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.

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Section: Results Using Earlier Ntl Productsmentioning

confidence: 99%

Section: Related Literature On Ntl Validation Studiesmentioning

confidence: 99%

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Gibson

Boe‐Gibson

2021

Remote Sensing

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show abstract

“…A consequence of mean-reverting errors is understated inequality between places as NTL estimates revert towards their mean. Some studies consider inequality as an aspect of economic performance, using DMSP data as a proxy in places that lack timely or fine resolution sub-national GDP data [39,40]. Yet validation studies show that DMSP data understate spatial inequality, especially in urban and high density areas, with this pattern holding across developed and developing regions of the world [25,33].…”

Section: Related Literature On Ntl Validation Studiesmentioning

confidence: 99%

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Gibson¹,

Boe‐Gibson²

2021

Preprint

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Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional Gross Domestic Product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defence Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. Yet applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001-19 time-series of GDP for over 3100 US counties as a benchmark to examine the usefulness of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts are made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries are used to examine what types of economic activity are best proxied by NTL data and comparisons are also made with the predictive performance of earlier NTL data products.

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“…The nighttime lights of the world have emerged as the most reliable and globally consistent dataset for various scientific studies and applications. Some examples of the use of nighttime light images in scientific studies include mapping urban areas [1,2], human ecological footprint [3], measuring socio-economic indices [4][5][6][7][8], population [9], human health and biological impacts [10], effects of a pandemic such as COVID-19 [11,12], studying impacts of war and natural disasters [13,14], boat detection and illegal fishing activities [15,16], and detection of fires, flares, and other infrared (IR) emitters [17].…”

Section: Introductionmentioning

confidence: 99%

Extending the DMSP Nighttime Lights Time Series beyond 2013

et al. 2021

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Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the years, the EOG has developed automatic algorithms to make Stable Lights composites from the OLS visible band data by removing the transient lights from fires and fishing boats. The ephemeral lights are removed based on their high brightness and short duration. However, the six original satellites collecting DMSP data gradually shifted from day/night orbit to dawn/dusk orbit, which is to an earlier overpass time. At the beginning of 2014, the F18 satellite was no longer collecting usable nighttime data, and the focus had shifted to processing global nighttime images from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Nevertheless, it was soon discovered that the F15 and F16 satellites had started collecting pre-dawn nighttime data from 2012 onwards. Therefore, the established algorithms of the previous years were extended to process OLS data from 2013 onwards. Moreover, the existence of nighttime data from three overpass times for the year 2013–DMSP satellites F18 and F15 from early evening and pre-dawn, respectively, and the VIIRS from after midnight, made it possible to intercalibrate the images of three different overpass times and study the diurnal pattern of nighttime lights.

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Using night time lights to find regional inequality in India and its relationship with economic development

Cited by 28 publications

References 16 publications

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Extending the DMSP Nighttime Lights Time Series beyond 2013

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