Robin's Blog

John Snow’s Cholera data in more formats

In honour of the bicentenary of John Snow’s birth – and because I was asked to by someone via email – I have now released my digitisation of John Snow’s Cholera data in a few other formats: KML and as Google Fusion Tables.

To save you reading my previous blog posts on the subject, I’ll give a brief overview of my data. John Snow produced a famous map in 1854 showing the deaths caused by a cholera outbreak in Soho, London, and the locations of water pumps in the area. By doing this he found there was a significant clustering of the deaths around a certain pump – and removing the handle of the pump stopped the outbreak. This is a bit of a simplification (see Wikipedia or the John Snow Society for more details), but generally covers what happened.

Anyway, I digitised John Snow’s original data and georeferenced it to the Ordnance Survey co-ordinate system, so that I could overlay it on modern maps of that area, as below (using the OS OpenData StreetView data, containing Ordnance Survey data © Crown copyright and database right 2013):

Locations of deaths from Snow's analysis shown on a modern OS Map

while still being able to overlay it on John Snow’s original map:

Snow's original map with the vector data for pumps and deaths overlainAnyway, the data that is available is:

  • Cholera Death locations (Vector) with attribute data giving the number of deaths at each point
  • Pump locations (Vector)
  • John Snow’s original map georeferenced to the Ordnance Survey National Grid (Raster)
  • Current Ordnance Survey maps of the area (from those released under OS OpenData; Contains Ordnance Survey data © Crown copyright and database right 2013Raster)

These are available for download/use in a number of formats:

  • A zip file with the Vector data as Shapefiles and the Raster data as TIFF images
    (this is the original data provided for download by me – and is probably what you want for importing into a GIS system)
  • A zip file with the Vector data as KML files and the Raster data as TIFF images
    (suitable for importing into Google Earth and other products that use KML files)
  • Links to Google Fusion Tables with the vector data already imported
    Cholera Deaths
    Deaths and Pumps together (this dataset has both pump and death points in the same table: pump points have Count values of -999, death points have Count values > 0 which give the number of Cholera deaths at that location)

The latter is particularly cool, I think as it allows you to very easily overlay the data on modern Google Maps data, and should allow some interesting ‘mashups’ to be created. All of the tables are set to be shared publically, so you could be able to copy them (using the Copy Table command in the File menu) and play around with them as much as you want! If you click the Example Map tab then you’ll see a very rudimentary map I’ve created using the data on top of Google Maps (see below) – I’m sure you’ll be able to do far better visualisations than that.

Example map from Google Fusion Tables

The folks at CartoDB have also used this data in one of their tutorials which shows you how to import the data to CartoDB and create a styled map to show the deaths with different sized markers – yet another way you can use the ‘first real GIS data’ in today’s modern web-based GIS tools.

So, enjoy – and please let me know (via the comments below) what you create!

Update: There was a problem with the KML files and Google Fusion Tables that I uploaded yesterday, caused by an incorrect co-ordinate transformation between the Ordnance Survey grid references and latitude/longitude. This has now been fixed and the downloads and tables have been updated. Sorry about this.

Categorised as: Academic, GIS, Python, R


  1. TimSalabim says:

    Hey Robin,
    I am intending to use this in a visualisation course next semester. Thanks for providing the data.

  2. […] • DATA: download the full spreadsheet as a Google Fusion table• Available in more formats here […]

  3. […] • DATA: download the full spreadsheet as a Google Fusion table • Available in more formats here […]

  4. […] Z tej okazji na Robin’s Blog pojawiły się dane zebrane przez Johna Snowa w kilku formatach, między innymi .shp czy .kml. Po szczegóły zapraszam – dane cholery z roku 1854. […]

  5. […] legacy lives on in health maps everywhere. The London School of Hygiene and Tropical Medicine (LSHTM) is […]

  6. Vincent says:

    Awesome job, thank you.

  7. […] problem.  Here are a variety of resources to explore this classic example.  Here is an article that highlights the spatial thinking that produced this map, with KML files and in Google Fusion […]

  8. […] it to a polluted section of the River Thames. Recent “re-visualizations” of Snow’s data by Robin Wilson and Simon Rogers, using digital mapping techniques, make the connection even more […]

  9. […] map is often used as an example of data visualisation – where new information appears by showing data in a […]

  10. Karen O'Neill says:

    Will use this for politics of environmental issues course on origins of public health policies. Thanks!

  11. […] My work on John Snow’s cholera map was featured in the Guardian datablog purely because they found me on Google (my university press […]

  12. Clare Griffiths says:

    This is great, really helpful, thank you!

  13. Great post!
    I’ve just created my John Snow’s Map using Google Maps Engine!
    Related information (in portuguese):

  14. Jo says:

    Thanks!!!! It was great to find the data. I am making a map for Intro to Cart, thanks.

  15. […] I was working for a training on data visualization, I wanted to get a nice visual for John Snow’s cholera dataset. This dataset can actually be found in a nice package of famous historical […]

  16. […] see what can be done with that package, we will use one more time the John Snow’s cholera dataset, discussed in previous posts (one to get a visualisation on a google map background, and the second […]

  17. […] очаги эпидемии холеры в 1854 году, опираясь на данные John Snow`s Cholera data. Результат наглядно показал, что данный пакет отлично […]

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