## <span class="censor">Data Studies 2020 // S05</span>
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<img src="https://hackmd.io/_uploads/HkRQE-MIv.png" width=100%>
<small>*Ed Hawkins, Warming Stripes for Denmark from 1901-2019</small>
Pablo Velasco // Information Studies // [pablov.me](https://pablov.me)
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## Plan for the day:
* Cultural analytics
* Classic visualisations
* Modern visualisations
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REMINDER
* Platforms as markets + modular technological architectures (Gawer 2014; Rieder & Sire 2014)
* “Infrastructures that can be programmed and built on” (Bogost & Montfort 2009)
* “Platform-ready” data: websites designed to be compatible to e.g. Facebook platform (Helmond 2015)
* Open (e.g. Wikipedia) / Closed (e.g. Facebook) / Public with controlled access (e.g. Twitter)
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### How to gather data from platforms?
<span class="fragment fade-up">
1. scrape<br>
2. api <br>
3. documentation/reverse engineering <br>
4. ask nicely<br>
</span>
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**Manovich’s cultural analytics + instagram (Phototrails project)**
* Selfie city (http://selfiecity.net/)
* gender, age, smiles (all estimated through Mturk)
* Instagram city (http://phototrails.info/instagram-cities/) imageplot, mturk)
* software studies lab tools [qtip + imageplot](http://lab.softwarestudies.com/p/software-for-digital-humanities.html)
**Platform effects**: "how variation in technology adoption creates online social networks that differ systematically from the underlying social networks" (Malik and Pfeffer 2016)
**When we analyze data from a platform, are we looking at people using the platform or are we looking at the platform using people? Is the modulation of "raw" data produced by technical or social effects?**
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## Visualisations (classical)
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<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/48/Hereford-Karte.jpg/1920px-Hereford-Karte.jpg" width="45%">
<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/borgia.jpg" width="45%"><br>
<small>1. Hereford map (c. 1300): Jerusalem at the centre of the circle<br>
2. Borgia map (c. 1450): Babilonian, Alexandrian, Carthagian, and Roman empire a the centre (extremes of the world as monstruos races)
</small>
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<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/regina.jpg" width="35%"><br>
<small>Europa Regina map (c. 1500)</small>
<!--*Streetview Germany bald spot (why? How does streetview works?)
*borges - “on the exactitude in science”:
*old maps: control elements / borders
*maps power: the north
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Critical cartography: modern maps not as an evolution
**Data about places (maps)**
* Territory / census
* Extended geographical information: e.g. Google streetview in Germany
**Data between places (flows)**
* Extraterritoriality of data (Eichenser)
* Reformulation of concepts like "space, "territory", "population"
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### Short activity:
Visit Traceroute https://traceroute-online.com and trace a route from any domain. You can try to approximate the geolocation of ip results [here].(https://whatismyipaddress.com/ip-lookup)
* How many "coutries" did data flow through?
* What kind of territory are we talking about when we think about data flows?
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<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/buno400.jpg" width="70%"><br>
<small>Johannes Buno [the 400s] (1672)</small>
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<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/playfair.jpg" width="80%"><br>
<small>William Playfair, Scotland's imports and exports 1780-1781 (1786)</small>
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<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/minard.png" width="100%"><br>
<small>Charles Jospeh Minard, Napoleon's army across Russian Empire of Alexander I (1869)</small>
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## Visualisations (modern)
[Periodic table of visualisation methods](http://www.visual-literacy.org/periodic_table/periodic_table.html)
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### Graphesis (Drucker 2014)
* "information graphics": metrics expressed as graphics
* "visual epistemology": ways of knowing, visually
* Visualisations are "arguments in graphical from"
* Playfair: rationality (cartesian quadrants)
* Ways to visualise *interpretation* are needed (graphics for humanism)
> The bivariate graph, with its inexhaustible capacity to spatialize parameters and put them into relation with each other, is an intellectual product of an era in which rationality could be at the service of theoretical and practical knowledge
<!--Vision metaphors of enlightenment, obscure, clear, being bright, shed light upon-->
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#### "Non-innocent" Data
<img src="https://hackmd.io/_uploads/Hy90sPz8w.jpg" width="20%">
<img src="https://hackmd.io/_uploads/Sy40sPz8P.jpg" width="20%">
<small>[Mona Chalabi](https://www.instagram.com/monachalabi/)</small>
Also, spurious correlations (https://www.tylervigen.com/spurious-correlations) and Anscombe's quartet example (McInery 2018)
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Data is personal (Peck et al. 2019)
<img src="https://gitlab.com/xpablov/data-studies/-/raw/master/DS19/S05/peck_graphs1.png" width="90%">
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<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
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**Visualizations**
* A narrative
* Directed to an audience
* Presenting particular information
* Hide patterns as others are revealed (McInery 2018)
> While information may be infinite, the ways of structuring it are not (...) Your choice will be determined by the story you want to tell. Each way will permit a different understanding of the information—within each are many variations
(Wurman, Information Anxiety 2000)
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#### Some Examples of good informative visuals
* [Codebases: millions of lines of code](https://informationisbeautiful.net/visualizations/million-lines-of-code/)
* [Snake Oil superfoods](https://informationisbeautiful.net/visualizations/snake-oil-superfoods/)
* [Everynoise](http://everynoise.com/engenremap.html)
* [Hamilton](https://graphics.wsj.com/hamilton/)
<!--*Codebases : good at portraying the magnitude and evolution of code repositories
*everynoise scatterplot: “The calibration is fuzzy, but in general down is more organic, up is more mechanical and electric; left is denser and more atmospheric, right is spikier and bouncier.” - search danish
*hamilton: phonetic language
*Dear data project: intimate small data
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#### Intimate 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%">
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<img src="https://pablov.me/pres/media/dd_beautiful_full.jpg" width="45%">
<small>See also: [Quantified selfie project](http://quantifiedselfie.us/)</small>
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### Short activity:
* Choose and analyse a beautiful data vis at https://informationisbeautiful.net/
* Which kind of visualisation is it? (you can check the periodic table)
* What kind of data types is showing?
* How "innocent" is the data? How *cooked* or *raw*?
#### OR
* Analyse the structural logic of [Everynoise](http://everynoise.com/engenremap.html)?
* How is data in this page arranged?
* How the categories are made? Is this an automated process? Crowdsourced? The work of the editors?
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