How to Create Coronavirus Time Series Map

Sometimes ago  I wrote a tutorial about creating coronavirus map with python where the case of coronavirus is plotting on a map date by date with a moving slider. There was a question: Is it possible to create such the map using a GIS software? Yes. This tutorial will show you how to create coronavirus map with time series visualization like the figure below using free open source software QGIS.

corona virus time series visualization
Figure 1. Coronavirus time series visualization

Following this tutorial we will learn how to:
  • Getting coronavirus data 
  • Transform data into time series format
  • Ploting the data with QGIS
  • Using Time manager to visualize time series cases

Getting Coronavirus Data

The most important thing in mapping activity whatever it is, is data availability. For creating the time series coronavirus map, we will get the data from John Hopkins University (JHU) Covid-19 data repository on Github. From the Github page you will find some datasets and also the data description. As our mission to create a time series map, look for time series data (shortcut link). The time series data page is look like figure 2. There you can find time series dataset for confirmed, deaths and recovered cases both for US and global in csv format.

Coronavirus time series data
Figure 2. JHU time series dataset

Transforming Coronavirus Data

Select a dataset, for example time_series_covid19_deaths_global.csv. We will see the data in a table like figure 3.

Figure 3. Death cases data

From the table we can see some columns such as Province/State, Country/Region, Lat, Long and a series of date's columns to the right. Well, it is a time series data cause there are a number of date columns. But unfortunately the time series won't work for a GIS visualization using Time Manager Plugin in QGIS, because Time Manager will render based on feature. Each rows is a single feature. Therefore a series of date columns won't work to visualize temporal change. So, what should we do? We have to transform the table from a series of date's columns to a series of date's rows as illustrated in the figure 4.

Figure 4. Table transformation for time series data

To transform the data, I created a little Python code as below. The following code transform the original csv dataset of global death cases directly from the github raw url link. If you want to change to other dataset just change the raw url link.

#COROVIRUS TIMESERIES DATA CONVERSION
#CREATED BY IDEAGORA GEOMATIS WWW.GEODOSE.COM
import urllib.request
import ssl

# HEADER
ssl._create_default_https_context = ssl._create_unverified_context
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
raw_url='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
file=opener.open(raw_url)
mybyte=file.read()
mystr=mybyte.decode("utf8")
file.close()

output=open('F:\Covid\output_death_300520.csv','w') #OUTPUT PATH
data=mystr.split('\n')
head_init=data[0].split(',')
head=head_init[::-1]

#WRITE OUTPUT
output_head='date,country,province,lat,lon,n_death\n'
output.write(output_head)
for i in range(len(head)-4):
    for j in range(1,len(data)-1):
        split_row=data[j].split(',')
        row=split_row[::-1]
        if row[i]!='0':
            date=head[i].rstrip()
            n_death=row[i].rstrip()
            lon=row[head.index('Long')]
            lat=row[head.index('Lat')]
            country=row[head.index('Country/Region')]
            province=row[head.index('Province/State')]
            if country==' South"':
                province=''
                country='South Korea'
            output_row=date+','+country+','+province+','+lat+','+lon+','+n_death+'\n'
            output.write(output_row)
output.close()
print ('Finished. Check the output path!')

After running the code, open the output file. The result will be like figure 5.

Output transformed dataset
Figure 5. Output transformed dataset

Plotting Coronavirus Data in QGIS

After getting and transformed the data. Now let's plot it in QGIS with the following steps.

Firslty, from the Layer menu, select Add Layer, then Add Delimited Text Layer as in figure 6.

Figure 6. Add delimited text layer

The data source manager window will appear like figure 7. Make sure to select Delimited Text. In the File name select the output of transformed csv file. Then in the Geometry Definition option select column's name for X field and Y field. To make sure the data will be parsed correctly, look at the Sample Data part. If the columns are split correctly, then everything is fine. 

Datasource Manager Window
Figure 7. Datasource Manager Window

After pushing the Add button, the points of coronavirus death cases will be plotted on the QGIS map. You will see so many points just like scatter plot. To make it more meaningful, add a reference map or a basemap like CartoDB Dark. You can find this basemap and add it easily to QGIS using Tile+ plugin. Figure 8 shows the coronavirus location all over the world on the CartoDB Dark basemap.   

Coronavirus death cases all over the world
Figure 8. Coronavirus death cases all over the world

Coronavirus Time Series Visualization

Now let's visualize the data with time series, so we can see the change of death cases date by date. To do the time series visualization in QGIS, we are using Time Manager Plugin. The plugin can be found from the Plugins menu as shown in figure 9. If you don't find it, install it using Manage and Instal Plugins menu.

Time Manage Plugin
Figure 9. Time Manage Plugin

After toggling the visibility of the Time Manager plugin, it will docked at the bottom of QGIS window like figure 10.

QGIS with Time Manager Plugin
Figure 10. QGIS with Time Manager Plugin

Let's play with it. Click the Settings button. The time manager setting window will appear. From the window, select Add Layer. Then Select layer and column(s) window will appear as in figure 11. Select the csv data layer. Then choose the Start time column's name. To accumulate the case from starting date to end date, make sure to click the Accumulate features option.  

Time manager setting
Figure 11. Time manager setting

Before playing the time series visualization, make sure to set the Time frame size to 1 days, because we want to see the change date by date (1 day). Now let's see it in action. Push the play button, you should see the time series visualization of corona virus death cases like figure 12.

coronavirus time series visualization
Figure 12. Coronavirus time series visualization

In case you have the date setting problem like figure 13 below. Open the csv file using a spreadsheet software. Then select the date column and do cell formatting by selecting Custom category and set the type to yyyy/mm/dd as shown in figure 14.

time manager date problem
Figure 13. Time manager date problem


Format date column
Figure 14. Format date column

So far we did it. We can see the temporal change of coronavirus death cases date by date. But let's make it more pretty by showing the number of death cases proportional to dot size, so we will see the larger dot for more cases. To do this, we just play with marker symbology setting.

Right click the point layer. In the layer properties window, select control feature symbology icon. Select a marker, chose your favorite color and set the Opacity to something like 30 or 40%. See figure 15.

Marker symbology
Figure 15. Setting layer symbology
 
For the marker size we can't set it directly to death column's name to relate with the death case number due to the size unit. It will be just too big. We need to re-scale it using a log scale. To do this, next to Size option, click edit. The Expression String Builder window will be opened like figure 16. I used natural logarithm (ln). For that, in the expression editor type: ln("n_death").

Dot size expression
Figure 16. Expression string builder window

Done. Now let's play it again. You should see the visualization like figure 17. The death case will be accumulated day by day with larger dot size.

Coronavirus time series temporal change dynamic marker size
Figure 17. Coronavirus time series temporal change with dynamic marker size

That's all the tutorial how to create coronavirus time series map using QGIS. In this tutorial we learned how to get coronavirus dataset, transform it to proper format that can be used in time manager plugin, plotting the case on the map and visualize the temporal change day by day. I hope this tutorial will be useful for you. May the pandemic will be gone from our lovely planet. Thanks for reading!

Watch the tutorial video!



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