Objective: Build an App that can use satellite imagery to asses rural electrification
Why? 1.6 billion people in the world do not have access to electricity, and the effect of rural electrification programs have not been understood very well. Using satellite imagery one could track rural electrification programs over time to understand how electricity affects socio-economic indicators (economic activity, poverty levels), population density and migration patterns.
Suggestion on how to do this:
1. Produce map of lights since 1992 using DMSP data
Where has change happened?
2. Overlap this with population density changes
Has electricity affected where people live?
3. Economic activity
What’s the range of the effect of electricity (all went up? 15%-40%)
With what data?
U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS).
Distribution of economic activity (Doll et al. 2000, Ebener et al. 2005, Ghosh et al. 2009), Density of constructed surfaces (Elvidge et al. 2007) Poverty levels (Elvidge et al. 2009)
Reference material: Elvidge, C. D., Baugh, K. E., Sutton, P.C., Bhaduri, B., Tuttle, B. T., Ghosh, T., et al. (2010). Who's in the Dark—Satellite Based Estimates of Electrification Rates. Urban Remote Sensing, 211–224.
Elvidge, C. D., P. C. Sutton, T. Ghosh, B. T. Tuttle, K. E. Baugh, B. Bhaduri, and E. Bright , 2009a. A Global Poverty Map Derived from Satellite Data, Computers and Geosciences 35, pp. 1652 – 1660.
Ebener, S., C. Murray, A. Tandon and C,. Elvidge. 2005. From wealth to health: modeling the distribution of income per capita at the sub-national level using nighttime lights imagery. International Journal of Health Geographics 4, pp. 5-11.
Ghosh, T., Anderson, S., Powell, R. L., Sutton, P. C., Elvidge, C. D., 2009. Estimation of Mexico’s Informal Economy and Remittances Using Nighttime Imagery, Remote Sensing, 1 (3), pp. 418-444.
Bhaduri, B., E. Bright, P. Coleman, and J. Dobson. 2002. LandScan: Locating people is what matters. Geoinformatics 5, pp. 34-37.
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