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title: DS21S06
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## <span class="censor">Data Studies 2021 // S06. Data Visualisation</span>

<!--image for class-->
<img src="https://hackmd.io/_uploads/HkRQE-MIv.png" width=80%>

<span class="refs">Ed Hawkins, Warming Stripes for Denmark from 1901-2019</span>

Pablo Velasco // Information Studies // [pablov.me](https://pablov.me)

---

## Plan for the day:
* Classic data visualisation/representation

* Modern data visualisation/representation

* Contemporary data visualisation/representation and critiques

---

We will think about data representation among two axis:

* ### Aims:
* effective
* complex
* beautiful
* affective
* critical
* ...
<br>
* ### Main forms/coordinates:
* radial
* geographic
* cartesian
* ...

---

# Classic data representation

----

### Radial maps

<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/48/Hereford-Karte.jpg/1920px-Hereford-Karte.jpg" width="40%">
<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/borgia.jpg" width="45%"><br>

<span class="refs">1. Hereford map (c. 1300): Jerusalem at the centre of the circle</span>
<span class="refs">2. Borgia map (c. 1450): Babilonian, Alexandrian, Carthagian, and Roman empire a the centre (extremes of the world as monstruos races)
</span>

----

### Cartographic maps

<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/regina.jpg" width="35%"><br>
<span class="refs">Europa Regina map (c. 1500)</span>

<!--*Streetview Germany bald spot (why? How does streetview works?)

*borges - “on the exactitude in science”:

*old maps: control elements / borders

*maps power: the north
-->

----

## <span class="censor">*Critical cartography*: <br>modern maps not as an evolution, but a (visual) representation of power</span>

**Data about places (maps)**
* Territory / census
* Extended geographical information: e.g. Google streetview in Germany

**Data between places (flows)**
* Extraterritoriality of data
<span class="refs">Eichenser, K. (2017, November 10). Data Extraterritoriality.</span>
* Reformulation of concepts like "space, "territory", "population"
* E.g. https://www.submarinecablemap.com/ + https://traceroute-online.com

---

# Modern data representation

----

Which one of these is a familiar type of visual information representation?

<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/buno400.jpg" width="45%">

<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/playfair.jpg" width="49%">

<span class="refs">1. Johannes Buno [the 400s] (1672)</span>
<span class="refs">2. William Playfair, Scotland's imports and exports 1780-1781 (1786)</span>

----

### Cartesian coordinates

## <span class="censor">The bivariate graph, with its inexhaustible capacity to spatialize parameters and put them into relation with each other, is <span style="color:pink">an intellectual product of an era in which rationality could be at the service of theoretical and practical knowledge</span></span>
<span class="refs">Drucker, J. (2014). Graphesis: Visual Forms of Knowledge Production.</span>

----

<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/minard.png" width="100%"><br>
<span class="refs">Charles Jospeh Minard, Napoleon's army across Russian Empire of Alexander I (1869)</span>
<!--first data visualisation -->

----

<img src="https://i.imgur.com/B9ASvaG.png" width=45%>

<img src="https://media.sciencephoto.com/image/c0336339/800wm/C0336339-John_Snow_s_cholera_map,_1854.jpg" width="45%">

<span class="refs">John Snow data on the 1854 cholera outbreak in London</span>

---

# Contemporary data visualisation and critiques

----

### Informative *visuals* not only about visual data

* [Everynoise](http://everynoise.com/engenremap.html)

<span class="censor">Every Noise at Once is an ongoing attempt at an algorithmically-generated, readability-adjusted scatter-plot of the musical genre-space, based on data tracked and analyzed for 5,597 genre-shaped distinctions by Spotify as of 2021-10-02. <span style="color:pink">The calibration is fuzzy</span>, but in general <span style="color:pink">down is more organic, up is more mechanical and electric; left is denser and more atmospheric, right is spikier and bouncier</span>.</span>

* [Hamilton](https://graphics.wsj.com/hamilton/)

----

### Visual cues open and constrained data

"In its most basic form, visualization is simply mapping data to geometry and color."
<span class="refs">Yau, N. (2013). Data Points: Visualization That Means</span>

<img src="https://i.imgur.com/264s6nh.png" width="35%">

**See**: [Periodic table of visualisation methods](http://www.visual-literacy.org/periodic_table/periodic_table.html)

----

### Data can always be represented differently

<img src="https://i.imgur.com/jipIEfq.png" width="80%">

<span class="refs">McInerny, G. (2018). Visualizing data: A view from design space. </span>

----

## <span class="censor">While information may be infinite, the ways of structuring it are not (...) Your choice will be determined by <span style="color:pink">the story you want to tell</span>. Each way will permit a different understanding of the information—within each are many variations</span>
<span class="refs">Wurman, R. S. (2000). Information Anxiety </span>

----

### Interpretation includes affective elements

<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/peck_graphs1.png" width="90%">

<span class="refs">Peck, E. M., Ayuso, S. E., & El-Etr, O. (2019). Data is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania.</span>

----

<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/peck_graphs2.png" width="90%">

You can listen a podcast about this study [here](https://datastori.es/data-is-personal-with-evan-peck/)

<!--42 interviews (rural pennsylvania)

Asking questions about visualizations. And ask them to rank 10 charts on drugs abuse in the US (messy charts).

The map graphs were confusing for people. Assumingly because they could not find their house [who has Google their house in gmaps?]

When people found a personal relation to the graph, people will highly rate the graph.

Easy manipulation
-->

----

### Data is often used to misrepresent information

<img src="https://hackmd.io/_uploads/Hy90sPz8w.jpg" width="25%">
<img src="https://hackmd.io/_uploads/Sy40sPz8P.jpg" width="25%">

<small>[Mona Chalabi](https://www.instagram.com/monachalabi/)</small>

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### A critical visual use of data should consider the un/misrepresented populations

"the rendering of statistical information into graphical form gives it **a simplicity and legibility that hides every aspect of the original interpretative framework** on which the statistical data were constructed."
<span class="refs">Drucker, J. (2011). Humanities approaches to graphical display></span

"When visualizing data, it might be inevitable that **we hide some patterns as we reveal others**. Patterns can be a composite of features that might be optimally revealed in different kinds of charts and not viewable in any single graph."
<span class="refs">McInerny, G. (2018). Visualizing data: A view from design space, 136 </span>

Affective mapping:
https://guns.periscopic.com/
(from data feminism)

----

<img src="https://i.imgur.com/9SCuS5Z.png" width="55%">

<span class="censor">Design Space is envisaged as a hyper-volume of all possible visualization designs with as many dimensions as there are ways to visualize data using coordinate and mapping systems, visual encodings and formatting, scales and sizing, sampling and aggregation methods, etc.</span>

<span class="refs">McInerny, G. (2018). Visualizing data: A view from design space. </span>

----

### Beyond *realists* representations of data

* rethink graphical display according to humanistic principles
* a constructivist instead of a realist approach to data representation
* explicit representation of the subjective expression of perceived phenomena
* represent ambiguity and uncertainty

<img src="https://i.imgur.com/kPPpo5f.jpg" width="45%">

<span class="refs">Drucker, J. (2011). Humanities approaches to graphical display></span>

----

#### Affective small data: *Dear Data* project

<img src="https://pablov.me/pres/media/dd_phone_addiction1.jpg" width="45%">
<img src="https://pablov.me/pres/media/dd_phone_addiction2.jpg" width="45%">

----

<img src="https://pablov.me/pres/media/dd_beautiful_full.jpg" width="45%">

<small>See also: [Quantified selfie project](http://quantifiedselfie.us/) and [Drops (work-in-progress)](https://pablov.me/sandbox/drops/
)</small>

---

# For tomorrow's workshop
* ### bring your ideas for twitter crawling (which theme/topic should we follow?)
* ### make sure you download the "redpill2.csv" file before the workshop (from brigthspace)
* ### the [Mini-project](https://brightspace.au.dk/d2l/le/lessons/25343/units/612784) information is up on brightspace

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