Malaria and Population Density in Africa

africa_plot1-1

This map shows population density as well as the malaria (plasmodium falciparum) incidence rate per 1000 people in Africa in 2015. Areas of high population density are represented by vertical rises in the lines, and malaria incidence is represented by the color of the lines. The lines are grey where there was no data for malaria incidence.

Malaria is caused by plasmodium parasites, with plasmodium falciparum being the most prevalent type in Africa, and it’s responsible for most of the world’s malaria-related deaths. The map above shows that many large population centers in Africa like Addis Ababa, Cairo, and Johannesburg are at a low risk of malaria. However, there are large swaths of densely populated areas in West Africa that are at a very high risk of malaria, as well as much of Central Africa and southeast towards Mozambique.

About the map and data:

Data on malaria incidence came from the Malaria Atlas Project based out of the University of Oxford. The raster of malaria incidence plotted below was used to make the main map.

malaria_raster_plot

Data on population density was obtained from NASA’s Socioeconomic Data and Applications Center and had global coverage. In order to limit the population density raster to African countries a shape file containing only African countries was merged with the global population density data, creating the plot of the data below.

africa_population_density_plot

Using R the rasters for population density and malaria were merged to put all the needed data into one place. Then, using the rasterToPoints command in the “raster” package, the raster data was transformed into a data frame that could be plotted with ggplot. In order to create the heart monitor-like look of the map each line of latitude had to be grouped together so ggplot could know to draw lines connecting each point of longitude that lay on the same line of latitude. Population data from 10 latitude points above and 10 latitude below each line was aggregated and malaria incidence data was averaged to avoid losing any data while only drawing 1/20th of the latitude lines. The code to create this map can be found on my GitHub page.

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