Data-driven color palettes for categorical maps

Data-driven color palettesfor categorical maps

NACIS - 2016 - Colorado SpringsLuc Guillemot - UC Berkeley GeographyDavid OSullivan - UC Berkeley Geography

Too many categories: hard to read!

Qualitative maps (and colors) tend to emphasize more differences than similarities.

Categories are not always equally distant to each other, e.g. geodemographics.

We can relate the distance between colors to degrees of difference between categories on the map.

Background:Socio-demographics categories are useful to understand neighborhood change.State of the art:Only a limited number of categories or dimensionscan be represented on a map.Proposition:Project the distance between categorieson a color space.Outline

BackgroundNeighborhood Changeand the uncertainty of ACS data

American Community Survey (ACS):Since 2006, the Census Bureau provides yearly estimatesof population data.ACS data is yearly but uncertain:The margin of errors due to the sampling methodis often more important than the change recorded in the data.Describe types of neighborhoods:Overcome the variable paradigmAny geographic entity is a holistic unity that isa combination of features.Track change:Use census data as a proxy to understand population change and movementsHow to describe socio-demographic features of the Bay?

How to describe socio-demographic features of the Bay?

American Community Survey (ACS):Since 2006, the Census Bureau provides yearly estimatesof population data.ACS data is yearly but uncertain:The margin of errors due to the sampling methodis often more important than the change recorded in the data.Describe types of neighborhoods:Overcome the variable paradigmAny geographic entity is a holistic unity that isa combination of features.Track change:Use census data as a proxy to understand population change and movements

Reducing dimensionalityby clusteringGEOPopAgeEduc13245452455287363632341373356

ACS dataclustering

Geodemographictypology

Background:ACS data and neighborhood ChangeState of the art:Color palettes for categorical mapsProposition:Color dimensions for data dimensionsUsage:Case study

How to read the categories

ColorBrewers12 qualitative colors+3 shades of grey=Hard to read

As a general rule of thumb, cartographers seldom use more than seven classes on a choropleth map. (Harrower & Brewer, 2013)

State of the art:Multivariate data, categorical mapsand colors

Perceptually consistent color spaces:They are based on perceptually consistent color spaces,which means ou can measure a just notifiable differencethat is consistent all over the color space.Tools:ColorBrewer, IWantHue or Colorgorical are tools to selectpredefined palettes. They aim at providing colors thatare the most distinguishable possible.Tools to selectpredefined color palettes

The issue of color spreading:Large coherent groups visually suppress smaller groups and are often visually dominant in an image. Class visibility:locally distinguish the perceptual intensities of coarse and finer spatial structures.Enables more context-dependent color choices, but still focuses on differentiation.

Further research on color palette for maps still focus on differences rather than similarities

(Lee, Sips, & Seidel, 2013)

Danny Dorling s colored cartogram of Chernoff faces

How many dimensions can flatland hold?Dorling, 1991

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Proposition:Project the distance betweencategories on a color space

The distance between categories can be quantified

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Principal Componentson L*a*b* color space

PC1PC2

clusters

Tracts

Common rules for bivariate maps (Trumbo, 1981):Colors should be mutually distinguishableProgression in any direction should make senseDiagonal should be visible to display positive associationWe are interested in relative distances, not a value on an axis: Colors should reflect the relative distance of each clusterswith all the others.The direction of the colormap can be modified without reducing the legends relevance.

Bivariate color mapsBernard et alii, 2015

How to project the distancebetween categories on the color space?Force-Directed Graphdefinition

avantages + inconvninent

undeterministic, distance non completely interpretable, for instance configuration where there is no optimal situationmechanism of stabilization, the stabilization doesnt solve all issues.finds a solution that is not always the best!

its not ideal, but its good enough. Thats a solution that is interested.it works anyway. (shows spatial pattern!)Force Directed GraphGives an optimal relational distribution of clusters,given forces.Not perfect, but optimal:The mechanism of stabilization doesnt solveall issues. Distances are not completely interpretable. But its a solution that allows to detect spatial patterns.

Patternvis a vis avec colorBrewer map!

Color spreading is counterbalanced by sharp contrastsClass visibility is context-wise strengthen for differences that actually matter.

The data itself is used to discriminate how distinctive colors should be.

Color contrastincreases class visibility

Change

Coherent color system:Each line represents a Census Tract, each column is a year.Thanks to the coherent color system, you can track change.Change in color matches the importance of the actual change:Change in the amount of color matches the socio-demographic change.

le systme de couleur, parce quil est cohrant permet de rendre compte du changement,on peut le visualiser sur une matrice.

Data-driven color palettesfor categorical maps

luc.guillemot@gmail.com@lucguillemot