{"id":2546,"date":"2020-05-11T15:20:26","date_gmt":"2020-05-11T03:20:26","guid":{"rendered":"http:\/\/blogs.lincoln.ac.nz\/gis\/?p=2546"},"modified":"2023-05-07T03:19:27","modified_gmt":"2023-05-07T03:19:27","slug":"being-fair-to-covid-19","status":"publish","type":"post","link":"https:\/\/blogs.lincoln.ac.nz\/gis\/being-fair-to-covid-19\/","title":{"rendered":"Being Fair to Covid-19"},"content":{"rendered":"

We take a look at mapping Covid-19 cases in New Zealand and how we can ensure that our interpretations are more realistic.\u00a0 MOH figures in this post are current as at 10 May 2020 at 9.00<\/em><\/p>\n

As we shift a bit closer to Level 2 and a perhaps bit more freedom, it may be an appropriate time to have a look at the spatial aspects of Covid-19.\u00a0 There are lots, but in this post we’ll look at mapping the cases and also trying to ensure that our interpretations are accurate.\u00a0 So, where to start?<\/p>\n

Let’s start with the Ministry of Health, who maintain a webpage of current case numbers<\/a>\u00a0with some useful information:<\/p>\n

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There’s even a map! (Thanks MOH):<\/p>\n

\"\"<\/a>This is, of course, a great way to present these data (I’m a bit biased, I know).\u00a0 But I fear these numbers don’t quite give us the full picture.\u00a0 Let me illustrate by recreating this map so we can play around with it a bit.<\/p>\n

First off, on that same webpage, the MOH lists the number of current cases by district health board (DHB) – here’s a screenshot from that page :<\/p>\n

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As we’ve talked about in a previous post<\/a>, while this is essentially a table of data, it’s spatial data by virtue of having the DHB names, which have locations (areas) implicit in them.\u00a0 Let’s start with those areas.\u00a0 I’ve got a copy of this on J: somewhere but in the short term, I went to Koordinates.com<\/a> and search on “health boards” – there were several versions listed there and I chose the latest one, from 2015:<\/p>\n

\"\"<\/a>StatsNZ <\/a>also has a copy of the same layer.\u00a0 \u00a0Unfortunately, both extend beyond the coastline to take into about the 12\u00a0nautical mile territorial sea b<\/a>aseline<\/a> – I can deal to that later.\u00a0 So I downloaded<\/a> and unzipped a copy\u00a0 and here it is on the map below (note: I selected the\u00a012 nm sea baseline\u00a0polygon and just deleted it – that worked for most of the country except for the Nelson Marlborough DHB – I could remove that if I has a coastline polygon layer but I’m not going to worry about it for this.\u00a0 Also note that the Chathams are included\u00a0 with Canterbury):<\/p>\n

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With the table open you can see the DHB names there – that will be critical for our next step – adding the cases data.\u00a0 So back to the MOH webpage where I copied and pasted the values on the cases into an Excel spreadsheet:<\/p>\n

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Luck is on my side and doing a quick comparison, the names of the DHBs are mostly<\/em> the same in the table and in the layer, so my table join will be pretty straightforward.\u00a0 I’ve got three issues though: macrons in Waitemata and Tairawhiti plus a space in “Mid Central” – my spatial layer has MidCentral.\u00a0 Before saving my spreadsheet, I’ll make those changes and ensure that the first row\u00a0attribute names\u00a0have no spaces, no crazy characters, none starting with a number, save it and add it to my map:<\/p>\n

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Note the <Null> values – these are due to spreadsheet cells that were blank.\u00a0 I’m just going to map the Total cases so I’m not worried about it but if I were I could do a Select by Attribute where “Deceased” IS NULL to select those records and change them to 0 with a field calculation.\u00a0 I would have to do that again for the Change attribute as I can only do field calculations one attribute at a time (or,<\/em> I could have dealt to this in the spreadsheet before joining).<\/p>\n

Next, right-click on the layer name and go to Joins and R\"\"<\/a>elates > Add Join.\u00a0 The tool recognises the layer and the table and I just have to link up the attributes in each that holds the name.\u00a0 When I click Run those numbers get added to the layer’s attribute table:<\/p>\n

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Now I’ve can use the case values in my map and can basically recreate the MOH’s map:<\/p>\n

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Not an exact replica – the more I look at the MOH colour scheme the more I think it’s not linked to the values – more like a world map where colours are chosen to show different countries but no countries with a shared border have the same colour.\u00a0 On this map, the colours have more meaning: the darker the blue, the higher the case numbers.\u00a0 Anyway, we at roughly the same place as the MOH map now.<\/p>\n

But herein lies my issue with this map (and not just this one – many instances like this).\u00a0 On the face of it, we’re comparing the number of cases by region – sweet.\u00a0 But is it a fair representation?\u00a0 Are the regions equal<\/em>?\u00a0 Equal enough that the comparison is valid?\u00a0 To put it another way, Southland and Waitemata have comparable values (216 vs 233).\u00a0 But there are a few differences between them – two obvious ones are area and population.\u00a0 The area one is pretty obvious just by looking at the map, but the population one is less so.\u00a0 I’m going to focus on the population – mainly because the number of cases per km2<\/sup> doesn’t seem to have much real-world meaning in this context.<\/p>\n

So, population – my first thought was StatsNZ as they are the ones doing the counting.\u00a0 My search of their data was fruitless, but I did find this from the Ministry of Health:<\/p>\n

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Clicking through to each DHB shows a population estimate (from 2018) as text.\u00a0 Try as I might, I can’t find an easily downloadable table, so had to enter the values in region by region (couldn’t copy and paste – grrrrrrr….) so had to carefully transpose by hand and double-check to make sure I didn’t make a mistake.\u00a0 Here’s the outcome, grouped by defined interval with an interval size of 200,000 people:<\/p>\n

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The minimum population was the West Coast DHB at 32,410 while the maximum was 628,970 for Waitemata.\u00a0 Keeping in mind our earlier comparison, the population of Southern DHB is just over half of Waitemata at 329,890.\u00a0 The map also shows you the differences in their relative areas.<\/p>\n

Now I’ve got one layer with case numbers and populations in the table.<\/p>\n

What I want to do next is take the different DHB populations into account by dividing the case numbers by the population – standardising the values on a per capita basis.\u00a0 To do this I need to add a new field to the table and do a field calculation.<\/p>\n

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Steps:<\/p>\n