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

Data Sources

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.

Data Cleaning

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.

Use of intersection to produce usable data for Stata

• 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.
• 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.
• 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

• When producing heat maps, one should know that there exist mathematical tools that allow you to enhance the spatial shapes that your data.
• 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.

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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