Satellite Data for the Social Sciences: Measuring Rural Electrification with Nighttime Lights
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Remote sensing data has the potential to revolutionize social science. One ofthe most prominent examples of this is the Nighttime Lights dataset, whichprovides digital measures of nighttime luminosity from 1992 to 2013. Thisstudy evaluates the Nighttime Lights data against detailed rural electrificationdata from the 2011 Census of India. The results suggest that many nighttimeluminosity measures derived from satellite data are surprisingly accurate formeasuring rural electrification, even at the village level and using simple statisticaltools. We also demonstrate that this accuracy can be substantially improvedby using of better GIS maps, basic geoprocessing tools, and particularaggregations of nighttime luminosity. Nighttime luminosity performs worsein measuring financial inclusion or proxies of poverty, however, an' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["remote sensing, satellite data, night lights, electrification, India"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Remote sensing data has the potential to revolutionize social science. One ofthe most prominent examples of this is the Nighttime Lights dataset, whichprovides digital measures of nighttime luminosity from 1992 to 2013. Thisstudy evaluates the Nighttime Lights data against detailed rural electrificationdata from the 2011 Census of India. The results suggest that many nighttimeluminosity measures derived from satellite data are surprisingly accurate formeasuring rural electrification, even at the village level and using simple statisticaltools. We also demonstrate that this accuracy can be substantially improvedby using of better GIS maps, basic geoprocessing tools, and particularaggregations of nighttime luminosity. Nighttime luminosity performs worsein measuring financial inclusion or proxies of poverty, however, an' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Remote sensing data has the potential to revolutionize social science. One ofthe most prominent examples of this is the Nighttime Lights dataset, whichprovides digital measures of nighttime luminosity from 1992 to 2013. Thisstudy evaluates the Nighttime Lights data against detailed rural electrificationdata from the 2011 Census of India. The results suggest that many nighttimeluminosity measures derived from satellite data are surprisingly accurate formeasuring rural electrification, even at the village level and using simple statisticaltools. We also demonstrate that this accuracy can be substantially improvedby using of better GIS maps, basic geoprocessing tools, and particularaggregations of nighttime luminosity. Nighttime luminosity performs worsein measuring financial inclusion or proxies of poverty, however, an
remote sensing, satellite data, night lights, electrification, India