Track the UK spread of COVID-19: Mapping in Python with geopandas

The motivation was to learn how to do geospatial data handling and plotting in Python, here geopandas. COVID-19 confirmed cases by region seemed like a suitable dataset to learn on. But perhaps to many the image outputs are of more interest than the methods used...

Doubling rates of UK tests, confirmed cases and deaths (updated 20 March'20)

Having plotted regional doubling rates for Confirmed Cases I was curious to see how this relates to the rate of doubling of the Testing and indeed the number of Deaths. In this expansion stage of the spreading, I assume (as a non-specialist in the field) that the rate at which Confirmed Cases double would equal the rate at which the Death count doubles (I.e. a fixed percentage of Confirmed Cases ends in Death). If the number of tests is limited then the doubling rate of Confirmed Cases would be slower. Plotting on a log scale gives straight lines for exponential growth. The slope gives the doubling period. Here doubling periods are: 8 days (Testing); 2.8 days (Confirmed Cases); and (1.8 days) Deaths.

Doubling rates of confirmed cases in March (updated 19 March'20)

Pragmatic criteria for testing result in under reporting for actual cases. Nevertheless it is interesting to look at the growth rate of confirmed cases. Plotting the total number of confirmed cases on a logarithmic count scale, against day, will give a straight line if the data are doubling at a steady rate. The steeper the line the faster the rate. Here I plot the data for the reporting districts in England with the largest number cases (hence least noisy data):

In black 3 lines are drawn to show the expected slopes for data that doubles every 2, 3 or 5 days.

Summary animations: Daily confirmed cases March (updated 20 March'20)

Data sources

• shapefiles:

  • Local Authority Districts (December 2017) Super Generalised Clipped Boundaries in Great Britain https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2017-super-generalised-clipped-boundaries-in-great-britain/geoservice (This effectively masks non-metropolitan regions in the PHE covid19 data, as they report over larger regions in the non-metropolitan places.)
  • Local Authority Districts (December 2019) Boundaries UK BUC at 500m https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2019-boundaries-uk-buc?geometry=-3.947%2C53.302%2C-0.591%2C53.872 (This matches the PHE reporting regions for all but a couple of the reporting regions).

• Public Health England: https://www.gov.uk/government/publications/covid-19-track-coronavirus-cases UTLA cases table.

Might also look at the for following for tabulated, in time, data:

• https://github.com/emmadoughty/Daily_COVID-19/blob/master/COVID19_by_LA.csv
• https://docs.google.com/spreadsheets/d/129bJR5Mgcr5qOQNc96CBWKFfjODToWKRiVKDEg5ybkU/edit#gid=1952384968
• https://github.com/tomwhite/covid-19-uk-data/

Issues

• Some of the regions may not be accurately drawn if local authority boundaries have moved I.e. shapefile slightly out of date.

Technical stuff

Aim: Try out geopandas to colour shapefile polygons by field values. Here load a UK county council boundary shape file and tables of COVID-19 confirmed cases and plot.

Python environment

To get this code to work I build a bespoke python environment:

conda create -n geo_env conda activate geo_env conda config --env --add channels conda-forge conda config --env --set channel_priority strict conda install python=3 geopandas jupyter matplotlib numpy seaborn pysal pandas

Then conda activate geo_env

Or, trying the following to get Spyder working conda create --override-channels -c conda-forge -n covid19 python=3 geopandas jupyter matplotlib numpy seaborn pysal pandas spyder conda activate covid19

But this didn't work for me. I couldn't get Spyder to work with geopandas :-(

author: jpolton

date: 11 March 2020