{"id":2686,"date":"2020-09-24T20:19:42","date_gmt":"2020-09-24T08:19:42","guid":{"rendered":"http:\/\/blogs.lincoln.ac.nz\/gis\/?p=2686"},"modified":"2023-05-07T03:13:29","modified_gmt":"2023-05-07T03:13:29","slug":"deleting-smokers-part-2","status":"publish","type":"post","link":"https:\/\/blogs.lincoln.ac.nz\/gis\/deleting-smokers-part-2\/","title":{"rendered":"Deleting Smokers – Part 2"},"content":{"rendered":"

Part 2 of this series looks at refining our script pseudo-code to get input data prepped for further analysis<\/em><\/p>\n

Way back when the dinosaurs roamed the earth,\u00a0we saw the first post<\/a> in this series on developing a Python script for some analysis.\u00a0 With Python being the topic of the moment in the current GIS course, I’ve been encouraged to pick up where I left off and continue developing this script.\u00a0 You might recall that the aim was to take a collection of points (here being tobacco retailers) and test out the effect of reducing the number on tobacco retailers, of limiting availability.\u00a0 This would mean systematically removing retailer points and then evaluating average additional costs to consumers.\u00a0 This is not a straightforward thing to do at all, not least because we needed to have some objective way of removing retailers.\u00a0 We decided we might look at imposing a minimum distance between retailers, such as 500 m, and then using that criterion as a way to remove them.\u00a0 That’s the theory behind this and what I’ve been trying to figure out is how I can set the data up to do this.\u00a0 I’ll reinsert a figure used in the previous post to illustrate:<\/p>\n

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https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0091743515000274<\/em><\/figcaption><\/figure>\n

The key here is being able to set up what the authors refer to as a “proximity relationship”, meaning one relationship between two points and how far apart they are.\u00a0 A script would randomly select a proximity relationship and delete one of the two points (again, at random) and then continue iterating through all the points again until there were no points closer than a certain distance.\u00a0 Unfortunately, there’s no off-the-shelf tool that I’m aware of that will do this, so I’ve got to come up with some systematic way of doing this.<\/p>\n

Initially, there were two main tools that came to mind.<\/p>\n