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== Read First ==
== Read First ==
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.  
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 ==
== Guidelines ==
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*[http://geo.aiddata.org/query/#!/ AidData geo.query]: Allows users to extract data to administrative boundaries.
*[http://geo.aiddata.org/query/#!/ AidData geo.query]: Allows users to extract data to administrative boundaries.


===Data Cleaning===
===Satellite-Based Datasets===
 
The following are commonly used datasets from satellite imagery or derived from satellite imagery.
Although dealing with limited amount of data, and maybe having acquired the data from formal sources, GIS data need a stringent review, which often lead to quite an amount of cleaning. Most of the time you will spend in using GIS data will/should be spent there.
*Nighttime Lights
 
*Landcover
===Use of intersection to produce usable data for Stata===
*Landsat
 
*For a particular project that has a spatial component, you first need to have geographical polygons that are representative of your project. For instance, the polygons that shows the limits of your villages.
*Then, for some particular themes you will only find data in the form of GIS data. Examples of this are hospitals and clinics, or rainfall. The data in question is then intrinsically accompanied with coordinates, that allows to position the information in a spatial setting.
*So then, after importing the particular data to a GIS software, you overlay your new data to the geographical layer that represents your project.
*Once done, and simply intersect both layers, and ask for instance for a mean over your geographical polygons, or for a maximum or for a count. In QGIS, for instance, you will find a bunch of these useful spacial operations in the Vector/Geoprocessing Tools section.
*These tools also allow you for instance to substract polygons from others; Then why not intersecting with data layer afterwards? It is much useful.
*All that needs to be done then is to export your newly generated data.
 
===Data Interpolation===
 
This is useful for those who want to generate information in between spatial measurements. You will not want to this is several settings, since the granularity of your data has a value. However for some themes, like for the level of a groundwater table, or for the level of a ground contamination, this is useful as the nature of the subject itself (ex: contamination) is continuous.


*The main thing one should know, is that the mathematical method you chose for the interpolation has a large impact on your results.
===Georeferenced Data Sources===
*In case of doubts (or in much of cases, let's say), one should chose to use krigging, since the interpolation (let's visualize it as a surface) goes through the measurement points exactly. It's distance at a measurement point is equal to zero.
*[http://afrobarometer.org/data/geocoded-data 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.  
*GIS softwares usually have modules that allow to do interpolation. They also allow to do dynamic modelling (state of your variables, in space, with time).
*The interpolation of your data lead to the production of heat maps.


===Heat Maps===
===Analysis===
*When producing heat maps, one should know that there exist mathematical tools that allow you to enhance the spatial shapes that your 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 [http://docs.aiddata.org/ad4/pdfs/wps44_a_primer_on_geospatial_impact_evaluation_methods_tools_and_applications.pdf 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) [https://github.com/itpir/geoMatch geoMATCH], which employs matching while accounting for geographic spillover from treatment to control units and (2) [https://github.com/itpir/geoSIMEX geoSIMEX], which allows users to account for spatial imprecision in analysis.  
*These "tools" are in fact transformations on your surface, such as first difference, Fourrier, ect. They can provide a much better definition of your results, and even allow you to "see" something that you might have missed when not using them.


===Examples of Papers===
===Examples of Papers===

Revision as of 20:02, 6 November 2017

Read First

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

Data Sources

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

Satellite-Based Datasets

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

  • Nighttime Lights
  • Landcover
  • 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.

Analysis

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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