Web Mapping & Analysis

Statistical visualisation

Dani Arribas-Bel

Today

  • Visualisation
  • (Web) Maps
  • Choropleths

Visualisation

What?

“Visual representations of datasets designed to help people carry out tasks more effectively”

Munzner (2016)

When?

A human “superpower”:

  • Keep the human in the loop
  • Augment memory/internal representation
  • Ask new questions rather than only answer existing ones

Why?

  • Bridges human and machine
  • Relies on vision (high-volume, parallel throughput)
  • External representations work around limits of internal cognition/memory

How?

what-why-how

↓

data-task-idiom

Most ineffective designs are due to a poor match

Domain-specific → Abstract form

How?

Vis is multi-use:

  • Exploring
  • Checking pre-conceived ideas
  • Long-term use in workflows/processes
  • Presentation

A tool that serves well for one task can be poorly suited for another

Elements (and limitations)

  • Computer (time)
  • Human (memory & attention)
  • Display (capacity)

Design trade-off’s

  • Beauty Vs Elegance
  • “No picture can communicate the truth, the whole truth, and nothing but the truth” (Munzner, 2016)
  • …

Data (Web) Maps

Tufte (1983)

“The most extensive data maps […] place millions of bits of information on a single page before our eyes. No other method for the display of statistical information is so powerful”

Designing good maps

Maps fulfill several needs

MacEachren & Kraak (1997) identify three main dimensions:

  • Knowledge of what is being plotted
  • Target audience
  • Degree of interactivity

Map Cube

[Source]

Choropleths

Choropleths

Thematic map in which values of a variable are encoded using a color gradient of some sort
  • Encode value using the color channel
  • Values are classified into groups (bins)
  • Information loss as a trade off for simplicity

Classification choices

  • N. of bins
  • How to bin?
  • Colors

How many bins?

  • Trade-off: detail Vs cognitive load
  • Exact number depends on purpose of the map
  • Usually not more than 12

How to bin?

Unique values

  • Categorical data
  • No gradient (reflect it with the color scheme!!!)
  • Examples: Religion, country of origin…

Unique values

Equal interval

  • Take the value span of the data to represent and split it equally
  • Splitting happens based on the numerical value
  • Gives more weight to outliers if the distribution is skewed

Quantiles

  • Regardless of numerical values, split the distribution keeping the same amount of values in each bin
  • Splitting based on the rank of the value
  • If distribution is skewed, it can put very different values in the same bin

Other

  • Fisher-Jenks
  • Natural breaks
  • Outlier maps: box maps, std. maps…

Color schemes

Align with your purpose

  • Categories, non-ordered Qualitative
  • Graduated, sequential Sequential
  • Graduated, divergent Divergent

TIP: check ColorBrewer for guidance

Tips

  • Think of the purpose of the map
  • Explore by trying different classification alternatives
  • Combine (Geo)visualisation with other statistical devices

Creative Commons License
Web Mapping & Analysis by Dani Arribas-Bel is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.