• Laura Whiting

A COVID-19 Choropleth Map for Indonesia

Previously I have written about making choropleth maps in Stata, with examples using COVID19 data for Australia and New Zealand. In this post we create another choropleth map, this time with COVID19 data from Indonesia. I have acquired the requisite GIS ESRI shape files for Indonesia from data.humdata.org here as well as the COVID19 data by province here.

We start by first importing the map data and making sure the map works. In the command pane:

This gives the following output, as expected:

Note: this will save the two files in your current working directory, not the directory the database and shapefiles are saved in. You can move them, however make sure both files are always together in the same folder.

Let's test the map. In the command pane:

Our basemap appears to have imported correctly. Now to get the COVID19 data into Stata. In the command pane:

The data appear to have imported correctly, however there is a discrepancy with one of the province names. In the map data Jakarta is recorded as "Dki Jakarta" however in the COVID19 data it is recorded as "DKI Jakarta". While the only difference here is capitals, if the names are not exactly the same the data will not merge correctly. To change the COVID19 province spelling with the map province spelling I use the replace command:

I will now merge the COVID19 data with the map data. In the command pane:

Like the maps of New Zealand and Australia, this map is showing infection count data. It does not take into account the different population sizes of the different districts. Like before, I will now merge province population data (previously obtained from Wikipedia). This allows me to draw a map showing the percentage of population that has contracted the virus. In the command pane:

We can see the most obvious changes through the bottom string of land, where the percentage of population infected shows a relatively low number of infections compared to the basic count data. Conversely, the area of land on the right shows a comparatively high percentage of the population infected despite the lower number of cases.

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