Exploring Ghana for Development Gateway

Development Gateway sent a team to the Ghanaian government and requested appropriate maps from AidData that illustrated the advantages of collecting and maintaining spatial datasets. I designed several maps that ranged from a simple chloropleth map to a raster of cropland to a slightly more analytical map that asks how World Bank projects are allocated according to large cities. The  power of spatial data is real #endpreach.

This map is by far the most simple and is most typical for your beginner GIS student. Although there is no clear pattern of World Bank projects to population-dense districts, the follow-up questions to this map could be just as valuable. Why is the presence of projects not markedly higher in the population dense districts? Is there a relationship between type of project and an unmet need (e.g., health, agriculture, education projects in rural areas)?
Perhaps a rookie mistake to ask a question that can’t actually be answered, looking for patterns as a casual observer isn’t particularly helpful in this map. However, I included it because it displays raster data–unarguable satellite imagery–and  offers an interesting quandary: should there be a stronger ag. project presence in areas that a high crop density because this is where farming occurs, or in areas that have low crop density because this area shows an unmet need? Combine this with population, poverty indices, and malnutrition and you could be looking at particularly useful map.
This map shows some simple analysis using buffers around selected “large” cities (50,000+ individuals). Focusing on urban areas, is the money going where the people are?


While these maps aren’t perfect, I think they fulfill the role they were designed for: showing how robust spatial data can answer allocation questions… and how it can spark correlation vs. causation debates.

Exploring Ghana for Development Gateway

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