{"id":1195,"date":"2016-03-16T00:53:03","date_gmt":"2016-03-16T00:53:03","guid":{"rendered":"http:\/\/blogs.lincoln.ac.nz\/gis\/?p=1195"},"modified":"2023-05-07T04:06:55","modified_gmt":"2023-05-07T04:06:55","slug":"fun-with-cholera","status":"publish","type":"post","link":"https:\/\/blogs.lincoln.ac.nz\/gis\/fun-with-cholera\/","title":{"rendered":"Fun with Cholera!"},"content":{"rendered":"

This post looks at how data can be symbolised in different ways to make for more effective communication using data from John Snow’s mapping of the cholera outbreak in Soho.\u00a0 <\/em><\/p>\n

Well there’s nothing fun about cholera, really.\u00a0 But there are some important lessons we’ve learned about spatial thinking from cholera, particularly with an outbreak in London in 1854<\/a> as well as with map making.\u00a0 Here’s another image of John Snow’s\u00a0original 1854 map of cholera deaths in Soho as a reminder:<\/p>\n

\"Snow-cholera-map-1\"<\/a>
By John Snow – Published by C.F. Cheffins, Lith, Southhampton Buildings, London, England, 1854 in Snow, John. On the Mode of Communication of Cholera, 2nd Ed, John Churchill, New Burlington Street, London, England, 1855.<\/figcaption><\/figure>\n

Snow’s map was an effective example of geographic communication, and the main purpose of this post is to play around with some different ways of portraying those data to\u00a0 get the message across.\u00a0 Let’s start with a digital version\u00a0of the raw data:<\/p>\n

\"Cholera1\"<\/a><\/p>\n

(You can find a copy of these data on J:\\Current_Projects\\SnowGIS.\u00a0 These data were created by Robin Wilson<\/a>)<\/em><\/p>\n

On this map we have some points that represent the locations of deaths overlain on a portion of a UK Ordinance Survey<\/a> map to give us some spatial context.\u00a0 The table shows that each point has a Count attribute that holds the number of deaths occurring at that location (minimum:1, maximum: 15).\u00a0 Showing just the points may suggest a bit of a pattern; it would be tempting from this to look somewhere roughly in the middle as some sort of focus (which is roughly where the pump was), but with each point the same colour and size, it implies a lower clustering than Snow’s map suggests.\u00a0 Perhaps we could better communicate that by showing them in a different way.\u00a0 Next we’ll use points but we’ll also use colour to highlight the number of deaths:<\/p>\n

\"GradColoursRedMap\"<\/a><\/p>\n

Here I used the graduated colours option under the Quantities option in the Symbology tab:<\/p>\n

\"GradColoursRed\"<\/a><\/p>\n

This is a slight improvement – but only slight, methinks.\u00a0 The colours blend in a bit too much against the OS map – it’s basically using the shade to highlight the number of deaths – the darker the colour, the higher the number.\u00a0 This is a common strategy – we naturally tend to associate a darker colour with more “stuff” and our eyes are usually attracted to the darker shades – we can use this to draw our map readers’ attention to those points we wish to highlight (though it might look a bit\u00a0odd to use darker colours for\u00a0the points with lower number of deaths, even if we were trying to\u00a0 highlight that) .\u00a0 I’ll try one more configuration of shade with a different colour ramp, green blending in to red:<\/p>\n

\"GradColoursG2RMap\"<\/a><\/p>\n

Another slight improvement – the colours “guide” the reader in towards the centre, though some of the higher death points are getting a bit hidden, but there does seem to be a drawing of the reader’s eye in towards the centre.\u00a0 This green-to-red colour ramp is a commonly used one that takes advantage of the social engineering that we associate with those colours: green = good or go, yellow\/orange = slow or caution and red = stop or bad.\u00a0 No death is a good thing so maybe the green’s not a great choice.\u00a0 Plus we may well be alienating our colour-blind readers. \u00a0 Perhaps a bit of trial and error with colours would get us a better result.\u00a0 (Mind you, we could also play around with the ranges of our categories, i.e. make the 9 – 15 class larger so that more points are coloured red.) One more quick tweak:<\/p>\n

\"GradColours30TranspMap\"<\/a><\/p>\n

A very subtle change – I’ve just made the OS map 30% transparent (using the Effects toolbar) so that it fades into the background a bit, helping the points to stand out a bit more.<\/p>\n

So far we’ve looked at changing colours but we can also play with size.\u00a0 Next I’ll use graduated symbols so that the size of the point scales with the number of deaths – the larger the circle, the more deaths:<\/p>\n

\"GradColoursGradSymbMap\"<\/a><\/p>\n

Perhaps better – what do you think?\u00a0 Note that I’ve also tried to emphasise the negative nature of these data by using red.\u00a0 So, returning to Snow, he used stacked symbols to make his point – we can try a couple of similar techniques.\u00a0 From Properties > Symbology I’ve opted to use the Bar\/Column chart with the Count attribute:<\/p>\n

\"GradColoursBarMap\"<\/a><\/p>\n

With this result:<\/p>\n

\"GradColoursBarChartMap\"<\/a><\/p>\n

Yikes!\u00a0 I think we’ve taken a few steps backward with this one! \u00a0Each bar is associated with one of the points but so that things don’t overlap, the bars are shifted outward with callout lines connecting each to its location. \u00a0In some contexts, this option would work fine, but here it just dilutes the story – the information has gotten diffused.\u00a0 Let’s try that again but this time we’ll untick the “Prevent chart overlap” box:<\/p>\n

\"GradColoursBarChartOverlapMap\"<\/a><\/p>\n

Well, we’re sort of returning to Snow’s original version here, though to my mind, it lacks the same impact of Snow’s map (some detail below):<\/p>\n

\"Snow-cholera-map-1-detail\"<\/a>
Source as above<\/figcaption><\/figure>\n

We’d have to work hard to recreate this digitally, i.e. with the stacks oriented perpendicular to the streets.\u00a0 That’s not a trivial task without some scripting so we won’t go down that road here.\u00a0 We could add a bit of 3D depth to our bars here:<\/p>\n

\"GradColoursBarChart3DMap\"<\/a><\/p>\n

Neat, but does it really make it any more effective?\u00a0 I don’t think so.\u00a0 Let’s follow this 3D idea and try to visualise this in ArcScene:<\/p>\n

\"Cholera3D\"<\/a><\/p>\n

Each point has been extruded based on the Count attribute (truth be told, it’s Count * 10 to make them large enough to see at this scale). \u00a0Not bad, though this one is a bit more effective in ArcScene where you can actually move it around and zoom in and out. \u00a0Nonetheless, it does help to highlight things a bit more.<\/p>\n

Let’s look at one final option – heat maps.\u00a0 These are layers that allow us to map the density of point-based spatial phenomena. \u00a0Another time we’ll cover “hot spot” analysis which applies a much more rigorous, statistical approach to clustering of points.\u00a0 I’ll switch gears here and use ArcGIS Online to do this next bit, mainly because it’s a lot easier to do there than from\u00a0ArcMap.<\/p>\n

\"CholeraHotSpots\"<\/a><\/p>\n

Now I don’t know about you but, of all we’ve done in this post, I think this is the only thing that even comes close to bettering Snow’s map.\u00a0 This is the result of some statistical modelling of the point densities and helps to identify where the significant clusters are.\u00a0 There’s not doubt with this where the “epicentre” of the outbreak was, nor that it was related to the access people had to the pump via the street (an artifact of the original address-based data, really).\u00a0 This type of analysis is used for all sorts of things, from epidemiology (thanks for that, John Snow<\/a>) to criminology to animal movements<\/a> – perhaps we’ll cover those in more detail in a later post.\u00a0 Iinteresting to note that with all the digital tools at our disposal in ArcMap, it’s arguable if we’ve topped a hand-drawn map from 1854.<\/p>\n

So we’ve had a brief tour through some of the tools of the trade of map making, from colours and shades, to size, to charts, 3D and finally hot spots.\u00a0 Map making is ultimately a form of communication, albeit visual, and just as we might put a lot of thought into the words we choose in an essay, it pays to think carefully about what you’re wanting to say, who you’re saying it to, and how best to say it.\u00a0 They say a picture is worth a thousand words – well perhaps a map is sometimes worth more than that.<\/p>\n

C<\/p>\n

All images by the author unless otherwise stated.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

This post looks at how data can be symbolised in different ways to make for more effective communication using data from John Snow’s mapping of the cholera outbreak in Soho.\u00a0 Well there’s nothing fun about cholera, really.\u00a0 But there are some important lessons we’ve learned about spatial thinking from cholera, particularly with an outbreak in […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1195","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/1195","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/comments?post=1195"}],"version-history":[{"count":2,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/1195\/revisions"}],"predecessor-version":[{"id":4972,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/posts\/1195\/revisions\/4972"}],"wp:attachment":[{"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/media?parent=1195"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/categories?post=1195"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.lincoln.ac.nz\/gis\/wp-json\/wp\/v2\/tags?post=1195"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}