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How Spatial Polygons Shape Our World

Amelia McNamara, OpenVisConf 2017

This repo contains a PDF version of the slides for my 2017 OpenVisConf talk, How Spatial Polygons Shape Our World. The video of me giving the talk is available on youtube, and the full transcript is on the conference website.

References and links:

All maps are wrong:

Combining incompatible data

  • With rectangular data, you could use the RStudio data wrangling cheatsheet to join two types of data. With "incompatible spatial units" this doesn't work.
  • The Modifiable Areal Unit Problem is the problem that aggregating point data to different polygons can have huge effects on the visual distribution.
  • It shouldn't surprise us that this happens. Check out the histogram essay I've been working on with a collaborator to see this in a one-dimensional case.

Gerrymandering

Downscaling, upscaling, sidescaling

Thanks to:

  • Aran Lunzer, my collaborator, who has been thinking about how aggregation impacts histograms and maps with me for many years.
  • Pierre Goovaerts, whose talk on geostatistics in practice at UCLA in 2014 started me thinking about the change of support problem.
  • Friedrich Hartmann, who introduced me to dasymetric mapping and the MAUP at IEEEVis in 2014 and pointed me toward cogran.js right before my talk.
  • Moon Duchin, one of the mathematicians solving gerrymandering.
  • Richard Casey Sadler, who works on public health problems in Michigan and discovered many problems with the Flint water data.
  • Matt Brehmer, for sending me references to cartogram effectiveness measures.

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Materials for 2017 OpenVisConf talk, How Spatial Polygons Shape Our World

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