Estimation of Mexico’s Informal Economy and Remittances Using Nighttime Imagery

Ghosh, Tilottama; Anderson, Sharolyn; Powell, Rebecca; Sutton, Paul; Elvidge, Christopher D.

doi:10.3390/rs1030418

Cited by 133 publications

(80 citation statements)

References 31 publications

(30 reference statements)

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“…A number of studies have indicated that the nighttime light has a very high correlation with the national and regional economic volume [11,12]. Compared to the census approach, the mapping of nighttime light can help to investigate the economics on a large scale with very low cost; thus, the nighttime light imagery has been used to investigate the regional economics in many countries [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China

et al. 2013

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Historically, the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R 2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation OPEN ACCESSRemote Sens. 2013, 5 3058 to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.

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Section: Introductionmentioning

confidence: 99%

Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China

et al. 2013

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“…Much of this research uses land cover or use maps derived from remotely sensed images and correlate these with economic growth and stability. Geographers were the first to utilize DMSP OLS to examine economic activity [93]. While most existing studies have emphasized associations at high levels of data aggregation such as countries, increasingly applications at the subnational level emerge with efforts led by economists [160,161].…”

Section: Social and Economic Modelingmentioning

confidence: 99%

“…At sub-national scales economic nighttime data lead to rougher estimates of GDP, but offer clues to the magnitude of the informal economy (e.g., North Korea, Mexico, and India) [92,93]. A time series analysis of annual DMSP OLS composite data products was used to estimate changes in economic growth (change in GDP from year to year) [94].…”

Section: Social and Economic Indicesmentioning

confidence: 99%

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

et al. 2014

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This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities' increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on OPEN ACCESSRemote Sens. 2014, 6 3880 three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

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“…Considering the close relationship between NSL and urbanization [63], population [70], economy [71], energy consumption [72], etc., NSL is further found to correlate well with the anthropogenic heat flux density [44,47]. This study does not try to calculate the anthropogenic heat with NSL, but attempts to use NSL to provide relevant information on the anthropogenic heat for better Ta estimation over a large area.…”

Section: Nighttime Light Predictor Independence and Coefficient Dynmentioning

confidence: 99%

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

et al. 2016

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Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001-2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R 2 ) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18-2.60 K in RMSE and 0.00-0.15 in R 2 ).

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Estimation of Mexico’s Informal Economy and Remittances Using Nighttime Imagery

Cited by 133 publications

References 31 publications

Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China

Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

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