#VisualizeNoMalaria: Let’s all help build an anti-Malaria dataset

As well as just being plain old fun, data can also be an enabler for “good” in the world. Several organisations are clearly aware of this; both Tableau and Alteryx now have wings specifically for doing good. There are whole organisations set up to promote beneficial uses of data, such as DataKind, and a bunch of people write reports on the topic – for example Nesta’s report “Data for good“.

And it’s not hard to get involved. Here’s a simple task you can do in a few minutes (or a few weeks if you have the time) from the comfort of your home, thanks to a collaboration between Tableau, PATH and the Zambian government: Help them map Zambian buildings.

Whyso? For the cause of eliminating of the scourge of malaria from Zambia. In order to effectively target resources at malaria hotspots (and in future to predict where the disease might flare up); they’re

developing maps that improve our understanding of the existing topology—both the natural and man-made structures that are hospitable to malaria. The team can use this information to respond quickly with medicine to follow up and treat individual malaria cases. The team can also deploy resources such as indoor spraying and bed nets to effectively protect families living in the immediate vicinity.

Zambia isn’t like Manhattan. There’s no nice straightforward grid of streets that even a crazy tourist could understand with minimal training. There’s no 3d-Google-Earth-building level type resource available. The task at hand is therefore establishing, from satellite photos, a detailed map of where buildings and hence people are. One day no doubt an AI will be employed for this job, but right now it remains one for us humans.

Full instructions are in the Tableau blog post, but honestly, it’s pretty easy:

  • If you don’t already have an OpenStreetMap user account, make a free one here.
  • Go to http://tasks.hotosm.org/project/1985 and log in with the OpenStreetMap account
  • Click a square of map, “edit in iD editor”, scan around the map looking for buildings and have fun drawing a box on top of them.

It may not be a particularly fascinating activity for you to do over the long term, but it’s more fun than a game of Threes – and you’ll be helping to build a dataset that may one day save a serious amount of lives, amongst other potential uses.

Well done to all concerned for making it so easy! And if you’ve never poked around the fantastic collaborative project that is OpenStreetMap itself, there’s a bunch of interesting stuff available there for the geographically-inclined data analyst.

 

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