It's #MapInfoMonday again,
Covid-19 is still very much part of our everyday lives. Today, I wanted to share some examples of thematic maps based on the Covid-19 data from Johns Hopkins University.
I downloaded the data from April 25th, 2021, loaded it into MapInfo Pro, and created points based on the data's latitude and longitude. This gives me a map like the one here below.
As you can see the number of points varies across the World. It depends on the administrative level that the data has been collected. For the US, it's collected per county which is quite detailed. Other countries like Australia collect it at a state level. And most countries collect it, or share it, at a country level: One point per country.
You can of course create a thematic based on these points. You can use the Graduated Symbol theme and make the size of the symbols depend upon for example the number of deaths at a given point. You can see an example of this in the image below.
However, this might not be the best representation of the data as it doesn't take the area covered or the population into account.
Let's change how the data is represented on the map from a point to a polygon. I download the World Bank Official Boundaries to get a dataset with countries of the World. It comes as a Shapefile so I opened it into MapInfo Pro and saved it into a nativeX tab file instead.
The boundaries are shown as white lines in the map below.
We can now use the boundaries for our thematic map and join these with the Covid-19 points to summarize the data where the points fall with a boundary.
In the dialog Create Thematic Map Step 2 of 3, I select the table with country boundaries and select the Join... option in the Field dropdown. This lets me add a temporary column to the boundaries based on the sum of deaths where the point from the Covid-19 dataset is within the polygon from the Country dataset.
In this first example, I created a basic Ranges thematic map that illustrates the total number of deaths within each polygon (country).
This map suffers from the same problems as the first Graduated Symbol thematic based on the points. It doesn't take the size of the countries or their population into account.
I can also use a Dot Density thematic map. This will add a point within the boundaries for x number of deaths. In this way, you take the size of the countries into account as the points will be closer to each in a small country compared to a large country with the same number of deaths.
You can make the points appear denser by changing the value they represent. On the map above, each point represents 5,000 deaths. In the map below, each point represents 1,000 deaths.
Another option is to use the Ranges thematic and take the population or the size of the country into account.
In the example below, I divide the summarized number of deaths with the population and multiply this by 1000. This will give me the number of death per 1,000 in habitants. You can also calculate this per 1,000,000 inhabitants if you prefer. And you could have chosen to compare the number of deaths to the size of the country instead.
Here's the resulting map showing the number of deaths per 1,000 inhabitants.
You can use the bins for the thematic layer in the Layer List to control which bins to show and hide on the map. Below, I only show the countries that have been hit hardest when you compare the deaths to the number of inhabitants.
The examples above use Covid-19 data but the methods can be used for most data that somehow relate to population data.
Please note that the numbers shown above might vary from any official numbers. In the calculations above, I have used the dataset Daily reports (csse_covid_19_daily_reports). I used the population number from the World Bank Official Boundaries dataset. This population count is mostly from 2017 with a few records having counts from years prior to this. 127 of the records from the Daily Reports did not fall within any boundaries in the World Bank Official Boundaries. Of these almost 90 records had a lat/long at (0,0).
------------------------------
Peter Horsbøll Møller
Principal Presales Consultant | Distinguished Engineer
Precisely | Trust in Data
------------------------------