Truth and Cartography: Spatial Data Visualization

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# Truth and Cartography: Spatial Data Visualization

.class-info[

**Session 17**

.light[HES597: Introduction to Spatial Data in R<br>
Boise State University Human-Environment Systems<br>
Fall 2021]

]

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# Outline for today

- (_Brief_) Intro to data visualization
 
 - Extending data viz to maps
 
 - Common spatial data visualization
 
 - Looking ahead
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name: datviz
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# Introduction to Data Visualization

__What do we mean by data visualization?__
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# Principles vs. Rules

- Lots of examples of _good_ and _bad_ data visualization
  
  - What makes a graphic good (or bad)?
  
  - Who decides?
--

- __Rule:__ externally compels you, through force, threat or punishment, to do the things someone else has deemed good or right.
  
  - __Principle:__ internally motivating because it is a _good practice_; a general statement describing a philosophy that good rules should satisfy
  
  - Rules contribute to the design process, but do not guarantee a satisfactory outcome
  
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> "Graphical excellence is the well-designed presentation of interesting data—a matter of substance, of statistics, and of design … [It] consists of complex ideas  communicated with clarity, precision, and efficiency. … [It] is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space … [It] is nearly always multivariate … And graphical excellence requires telling the truth about the data." 
<footer>--- Edward Tufte</footer>

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# Ugly, Wrong, and Bad

.pull-left[
- _Ugly_: graphic is clear and informative, but has aesthetic issues
  
- _Bad_: graphic is unclear, confusing, or decieving
  
- _Wrong_: the figure is objectively incorrect

]
.pull-right[
<figure>
  <img src="img/09/ch-01-holmes-monstrous-only.png" alt="ZZZ" title="ZZZ" width="100%">
  <figcaption>Monstrous Costs’ by Nigel Holmes from Healy 2018.</figcaption>
</figure>
]

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# Bad and Wrong

- Presentation of the data is (intentionally?) decieving

- Presentation is just incorrect

.pull-left[
<figure>
  <img src="img/09/ch-01-democracy-nyt-version.png" alt="ZZZ" title="ZZZ" width="100%">
  <figcaption>Tricky (from Healy 2018).</figcaption>
</figure>
]

.pull-right[
<figure>
  <img src="img/09/Fox-News-pie-chart.png" alt="ZZZ" title="ZZZ" width="100%">
  <figcaption>Wrong.</figcaption>
</figure>
]
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# Grammar of Graphics (Wilkinson 2005)

- _Grammar_: A set of structural rules that help establish the components of a language

- System and structure of language consist of _syntax_ and _semantics_

- _Grammar of Graphics_: a framework that allows us to concisely describe the components of any graphic

- Follows a layered approach by using defined components to build a visualization

- `ggplot2` is a formal implementation in `R`

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class: middle
<figure>
  <img src="img/09/gg_pyramid.png" alt="ZZZ" title="ZZZ" width="100%">
</figure>

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# Aesthetics: Mapping Data to Visual Elements

.pull-left[

- Define the systematic conversion of data into elements of the visualization

- Are either categorical or continuous (exclusively)

- Examples include `x`, `y`, `fill`, `color`, and `alpha`
]

.pull-right[
<figure>
  <img src="img/09/tbl.png" alt="ZZZ" title="ZZZ" width="100%">
  <figcaption>From Wilke 2019.</figcaption>
</figure>
]
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# Scales

- Scales map data values to their aesthetics

- Must be a one-to-one relationship; each specific data value should map to only one aesthetic

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# Principles of Data Visualization

- Be Honest

- Principle of proportional ink

- Avoid unnecessary 'chart junk'

- Use color judiciously

- Balance data and context
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name: carto
class: center middle main-title section-title-4

# Extending Data Viz to Maps

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# Ugly, Wrong, and Bad Maps

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# Key Issues: Projection

- Thinking about projections

- Scale of the map

- Errors of Omission
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# Cartographic Principles

1. Concept before compilation

2. Hierarchy with harmony (Important things should look important)

3. Simplicity from sacrifice

4. Maximum information at minimum cost

5. Engage emotion to enhance understanding
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name: spviz
class: center middle main-title section-title-4

# Common Spatial Visualizations
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# Choropleth
- Mapping color to geographies

- Common problems

<figure>
  <img src="img/09/ch-07-firstmap-05-1.png" alt="ZZZ" title="ZZZ" width="70%">
  <figcaption>From Healy 2019.</figcaption>
</figure>

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# Cartogram

- Adjusts for differences in area, population, etc

- Common Problems

<figure>
  <img src="img/09/Cartogram.png" alt="ZZZ" title="ZZZ" width="100%">
  <figcaption>From Healy 2019.</figcaption>
</figure>

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name: future

# The Next few weeks

- Static maps: `ggplot2` and `tmap`

- Interactive webmaps: `leaflet` and `mapview` (maybe others)