Geographic Data Science

Visualisation of Point Patterns
Dani Arribas-Bel

Visualization of PPs

Three routes (today):

  • One-to-one mapping ↔︎ “Scatter plot”
  • Aggregate ↔︎ “Histogram”
  • Smooth ↔︎ KDE

One-to-one

One-to-one

  • Intuitive
  • Effective in small datasets
  • Limited as size increases until useless

One-to-one

Aggregation

Points meet polygons

Use polygon boundaries and count points per area

[Insert your skills for choropleth mapping here!!!]

But, the polygons need to “make sense” (their delineation needs to relate to the point generating process)

Hex-binning

If no polygon boundary seems like a good candidate for aggregation…

…draw a hexagonal (or squared) tesselation!!!

Hexagons…

  • Are regular
  • Exhaust the space (Unlike circles)
  • Have many sides (minimize boundary problems)

But…

(Arbitrary) aggregation may induce MAUP (see Block D)

+

Points usually represent events that affect only part of the population and hence are best considered as rates

Kernel Density Estimation

Kernel Density Estimation

Estimate the (continuous) observed distribution of a variable

  • Probability of finding an observation at a given point
  • “Continuous histogram”
  • Solves (much of) the MAUP problem, but not the underlying population issue

[Source]

Bivariate (spatial) KDE

Probability of finding observations at a given point in space

  • Bivariate version: distribution of pairs of values
  • In space: values are coordinates (XY), locations
  • Continuous “version” of a choropleth

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A course on Geographic Data Science by Dani Arribas-Bel is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.