Geo Spatial Data

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The proliferation of remote sensing technology has created new opportunities for high-resolution, affordable geospatial data, which have great potential as a source of impact evaluation data.

Guidelines

Repositories of Spatial Data

The following are repositories of spatial data. In the following sites pull in spatial data from a variety of sources.

• Google Earth Engine: Stores petabytes of satellite imagery on google's cloud. Search datasets here.
• Socio Economic Data and Applications Center (SEDAC): Provides links to a number of spatially referenced datasets.
• AidData geo.query: Allows users to extract data to administrative boundaries.

Satellite-Based Datasets

The following are commonly used datasets from satellite imagery or derived from satellite imagery.

• Nighttime Lights: Nighttime lights have increasingly been used as a metric for local economic development. The Visible Infrared Imaging Radiometer Suite (VIIRS) provides monthly data from April 2012 to the present at a 300m resolution. The Defense Meteorological Satellite Program (DMSP) provides nighttime light data on an annual basis from 1992 to 2013.
• Landcover: There are a number of datasets that classify the earth into different land cover categories.
  • ESA Land Cover: The European Space Agency (ESA) has global landcover dataset, produced on an annual basis from 1992 to 2015 at a 300 meter resolution.
• Landsat:

Georeferenced Data Sources

• Afrobarometer: Afrobarometer has surveyed attitudes on democracy, governance and society across 36 countries in Africa in 6 survey rounds from 1999 to 2015. In partnership with AidData, Afrobarometer has recently geocoded the surveys.

Impact Evaluation with Geospatial Data

The emergence of georeferenced data has provided opportunities to evaluate foreign investments at lower costs than traditional RCTs. These evaluations have been dubbed Geospatial Impact Evaluations (GIEs); see here for a working paper from AidData that describes methods and applications to perform GIEs. The paper describes a number of papers that conduct GIEs. In addition, it highlights two R packages that employ methods relevant to using geospatial data: (1) geoMATCH, which employs matching while accounting for geographic spillover from treatment to control units and (2) geoSIMEX, which allows users to account for spatial imprecision in analysis.

Examples of Papers

• Many influential papers using these type of data have been published in journals

  ** J. Vernon Henderson, Adam Storeygard, and David N. Weil. 2012. Measuring Economic Growth from Outer Space. In American Economic Review, 102(2): 994-1028.  Link: http://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.102.2.994

  ** Dave Donaldson and Adam Storeygard. 2016. 'The View from Above: Applications of Satellite Data in Economics'. Journal of Economic Perspectives, 30(4):171-198. Link:  http://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.30.4.171

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This article is part of the topic Data Sources

Additional Resources

• Documentation for QGIS which covers a lot of topics in great detail: https://docs.qgis.org/2.2/en/docs/index.html