{"id":2613,"date":"2020-07-17T10:39:39","date_gmt":"2020-07-16T22:39:39","guid":{"rendered":"http:\/\/blogs.lincoln.ac.nz\/gis\/?p=2613"},"modified":"2023-05-07T03:17:41","modified_gmt":"2023-05-07T03:17:41","slug":"mapping-covid-19-vulnerability","status":"publish","type":"post","link":"https:\/\/blogs.lincoln.ac.nz\/gis\/mapping-covid-19-vulnerability\/","title":{"rendered":"Mapping Covid-19 Vulnerability"},"content":{"rendered":"

This post covers the mapping of South Island communities and their vulnerability to Covid-19 reinfection as part of an ERST202 class project.<\/em><\/p>\n

While semester 1 feels like a long time ago now, the threats from Covid-19 are still remarkably present.\u00a0 Recent incidents around returning Kiwis and their efforts to break out of quarantine illustrate just how vulnerable our borders are to reinfection and a dreaded “second wave”.\u00a0 The team of 5 million did an awesome job of taking NZ cases down to zero, but it’s easy to see that even one case in the wrong place at the wrong time could take us right back to level 4.\u00a0 There are certainly some spatial components to people being at threat, and there are also some spatial components behind what might make some areas more vulnerable than others, in the event that community transmission rears its ugly head.\u00a0 Things like the number of older people, or the medical resources in an area.\u00a0 At the end of last semester, the good folk of ERST202 took on mapping some of these factors for several South Island communities as part of a class project.<\/p>\n

This is not a new idea.\u00a0 The US Centres for Disease Control (CDC) have developed a Social Vulnerability Index<\/a> (SVI) with an interactive map <\/a>that visualises how vulnerable different areas are to a range of events (you may need to run this in FireFox or Edge [never thought I’d recommend someone using Edge..<\/em>.]).<\/p>\n

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The colour codings allow you to tell quickly how at risk a particular area (US counties in this image) is compared to others, given the legend.<\/p>\n

Underneath the bonnet of this map is a fairly complex set of demographic factors (15 in all across four themes) that contribute to this:<\/p>\n

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https:\/\/svi.cdc.gov\/Documents\/Publications\/SVI_Community_Materials\/atriskguidance.pdf<\/em><\/figcaption><\/figure>\n

There are some challenges to doing something like this, not least of which is how do you set up these factors so that they are comparable in a logical and defensible way.\u00a0 (We hit on some similar issues here<\/a>.)\u00a0 The CDC’s approach was to rank each area unit based on the number of people (or things) fitting each criterion.\u00a0 These were then divided by the largest number of people (or things) across the whole area so that each factor is rescaled to\u00a0a value between 0 and 1.\u00a0 Combining them all and rescaling them\u00a0 again reflects the influence of each factor and makes each area comparable.\u00a0 This also means that different numbers can occur depending on whether the analysis is nation-wide, or state-wide, or county-wide.<\/p>\n

For our class project, we adopted a similar approach though simplified things down to just four factors:<\/p>\n