https://docs.google.com/document/d/1klu-PNb3EDmZI19RV4oP24hMsHcyQMsa-cVhV8b82iY/editShared Notes on OpenVis Conf 2018 TalksLink: https://tinyurl.com/ovc2018-notes

In past years, you’ve all done a great job documenting the conference here. We might format and share this more formally this year, if we can get student help :) Keep an eye on #openvisconf for twitter content if you want to include photos (lanyard permitting), by the way.

Day 1

Keynote: Moritz Stefaner● About

○ Job title: “Truth & Beauty Operator”○ Runs Data Stories

● Multiplicity project at the 123data exhibition in Paris● About: Can we draw a portrait of the city as seen through thousands of pictures● Uses t-sne dimensionality reduction to visualize the features from the last layer of a

pretrained neural network to position images of Paris● As vis becomes a more professional field, we need shared ethics and standards● Mention of rasterfairy.py, color cleanup in photoshop, and a few hacks to fill out the

photo grid● Handwriting in datavis to communicate: there is authorship involved that influences the

outcome● Peak spotting on German train system● How to maximise the number of people on a train without overcrowding● Avoiding number decoration (just a KPI with a circle and an icon) and data explorer

(“here’s all the data, good luck”).● Craft data experiences, create solution for people, active learning● Several different views; days, trains, maps, routes● To understand the data and user, use real data from the start, allow people to dive into

data, what it affords. Created Tableau dashboard prototypes before moving to D3. Useful for basis of discussion.

● Doubt the data, find out what’s missing, make deficiencies clear and design for them● Move on to IA, think about what users need at the start and what detail should be lower

down after clicking.

Ian Johnson● Machine learning for data visualization

● t-SNE to put high dimensional data into lower dimensional space with different points far away from each other

● Quick, Draw! dataset● How Long Does it Take to (Quick) Draw a Dog? by Jim Vallandingham● How Do You Draw a Circle? by Nikhil Sonnad● Forma Fluens by Visual AI Lab @ IBM Research● Visual Averages by Country by Kyle McDonald● sketch-rnn by David Ha● Bad Flamingos by Colin Morris● Visualizing Data using t-SNE by Laurens van der Maaten● How to Use t-SNE Effectively by Wattenberg, Viegas, Johnson● Deep Learning for Human Beings by Chris Olah

Heather Krause● @datassist● Underlying assumptions make a big difference in the type of dataviz. Not same

assumptions between people creating and consuming the data. Example: what is average class size? Question isn’t specific enough.

● 4 common fallacies:○ Ecological fallacy

■ Answering the question at a different level○ Causal fallacy

■ Including or excluding variables changes the question you’re answering○ Simpson’s paradox

■ Answering a question that you think is important rather than letting the nuances of the data tell you the important question

○ Prosecutor’s fallacy■ Answering a question with confused conditional probability.

● Ecological fallacy:○ Try to answer question using data at a different level of analysis.○ Example: individual’s health about smoking, but data based at country level○ Probably working at aggregate data, so need to make statements at that level.

● Causal fallacy:○ If doing a causal analysis, and confounders/mediator variables are in the model,

then causal analysis is problematic● Simpson’s Paradox

○ What happens when including 3rd variable in your model.○ the way we visualize and choose the variables to include. So need to be

succinct about question trying to answer.Variables included in the model tell the data what to say by

● Prosecutor’s Fallacy○ “This one keeps me up at night”○ P(A|B) != P(B|A)○ Important when doing conditional probability.○ For example: chances of having a heart attack given high cholesterol are not the

same as chances of high cholesterol given a heart attack.

Maarten Lambrechts - XenographicsSlides: http://slides.com/maartenzam/openvis-18#/

● We are living at a time where we have data which is fundamentally different than before, so needs new kinds of visualizations.

● Xenographics -> graphics people haven’t seen before○ ...or published on http://xeno.graphics

● 1 star technique: Flip Axes○ Small screens have consequences for the graphics we produce.○ Horizontal -> Vertical○ Everything is a convention, can be relearned, but comes with a cost (need to

relearn)● 2 star: more data in a smaller space

○ Overlapping bars in bar chart, “piled bars”○ Horizon charts○ Break-n-stack scales

○ “Xenographphobia”: fear of weird charts“Our readers won’t understand, so please make a bar chart”

● 3 stars: fuse two visualizations together; crossbreed chart types○ Scatter plot of line charts, scatter plot of pie charts/bar charts, etc.○ Bar chart of tree maps○ Packed bars, https://packedbars.com ○ Spark bar chart

● Aside: naming can create silos. So need to harmonize the graphonyms● 4 stars: add dimensions

○ Animated bubble chart: 6 dimensions○ Data encoded into multiple glyphs

● 5 stars: crossbreed data aspects○ Map of small multiples○ Temporal cartogram○ Xenographic matrix on xeno.graphics○ For example, time evolution of a hierarchy

● Research-practice gap is quite deep○ Researches should publicize○ Practitioners should read papers○ Tool builders: implement new techniques○ Inbetweeners: step up and be bridge

Benjamin Bach● Designing embedded visualizations for augmented reality (AR canvas).● “Embedded data representations”

○ Non-situated viz: Data on desktop, far away from the thing.○ Situated viz: tablet, standing next to things.○ Embedded viz: augmented reality○ Embedded physicalization: some physical mark

● Hardware: “hololens”, “meta”○ Are we at the beginning, going somewhere, or already amazing?

● 3D dataviz○ 3d scatterplots, Projections, Spatial data○ Manipulation experiment results:

■ Desktop: pretty good. People good with the mouse■ Hololens: ok■ Tablet: not great

● Silent AR : when augmented reality blends into environment. Hard to separate, does not clutter environment.

● Scenario: augment a bookstore.

○ Many decisions. What marks to show, when, etc.○ Traditional canvas is just an empty rectangle. Free to create anything. Apply own

rules.○ AR canvas:

■ Context data■ Artifacts: objects in the space, like the books. Sizes, locations, angles.

Some objects movable.■ Navigator: the person moving around in the space. Field of view, focus,

occluded spaces.■ Visualization: marks, layouts, etc.■ Activity: navigator has a task in mind. Huge variety.■ Scene: the entire library. Lighting. Reflections. People moving in space.

Clutter.● Decisions to make about AR visualizations:

○ 2d/3d○ Adaptability○ Clutter○ Labels and legends

● Nature can provide inspiration: tracks in snow, moss on trees, corrosion

Amanda Cox and Kevin Quealy

Miriah Meyer● ‘Proxy’ framework for understanding user requirements.● Select proxies: task - action, object, measure.● Visualisation: not answer, but inspiration to answer.● Pathline : alternative to heatmaps for gene expression data● Data visualisation column from nature methods , in part written by Bang Wong, designer

who worked with Miriah.

Jan Willem Tulp● ESA Star Mapper http://sci.esa.int/star_mapper/● neat sketching of WebGL stack: https://twitter.com/tillnm/status/996032597296930818● http://www.worldwateratlas.org/ ● Gaia’s Stellar Family Portrait http://sci.esa.int/gaia-stellar-family-portrait/

Jo WoodWhy not How!

● Show different Iterations in one Document to make your thoughts and decisions visible and seeable for other people

https://github.com/gicentre/litvis

● The Design Ladder○ Step 1 -> No-Design○ Step 2 -> Design asForm-Giving○ Step 3 -> Design as Process○ Step 4 -> Design as Strategy

Brian JacobsADVENTURES IN SPACE AND TIMESlides: http://work.briantjacobs.com/share/open_vis_2018_btj.pdfhttps://www.nationalgeographic.com/magazine/2017/06/nodosaur-3d-interactive-dinosaur-fossil

Day 2

Dataveyes

● Beautiful interactive data visualization accessible to the public domain.● Novel ideas for data collection and quantification.● Data privacy was discussed, in addition to how shared data can risk sensitive human

activity (reveal secret military bases).● Debate about who owns data in response to this report

https://www.generationlibre.eu/en/data-a-moi/ ● Theoretical questions were asked regarding data and its future use with AI.

Shan Carter

● Transitioned from a career in journalism to academia with a bit of cultural shock, a decrease in UI quality.

● Working on addressing the research gap for machine learning with https://distill.pub/

Matthew KaySlides: http://www.mjskay.com/presentations/openvisconf2018-bayes-uncertainty-2.pdf

Use theatre seats to communicate risk. wapo.st/2fCYvDWQuantile dot plots help people make better decisions

Nadja PopovichPart of the 12 person NYT climate change team. Arctic sea ice decline https://www.nytimes.com/interactive/2017/09/22/climate/arctic-sea-ice-shrinking-trend-watch.html One way to make it more relatable is to use maps eg. https://www.nytimes.com/interactive/2018/03/16/climate/us-winter-warming.html

Erik Escoffier

● OpenStreetMap: the map is really a byproduct of the database● Basic data model: Nodes, ways and relations● Also has concept of tags, which can be very detailed https://taginfo.openstreetmap.org/ ● For example, a subway station is a node, a subway line is a relation between nodes● How to consume OSM data? Overpass: frontend to API with query language

https://overpass-turbo.eu/ ● For large scale analysis: Overpass not best tool, can crash or hit API limits● One example: OSM QA tiles https://osmlab.github.io/osm-qa-tiles/ plus TileReduce

(MapReduce implementation for tiles) to get the names of all hairdressers in France● Slides and code available at: http://satellitestud.io/openvisconf/ ● Source code: https://github.com/satellitestudio/osm-the-planet-is-the-dataset● EmojiMaps: https://nerik.github.io/Leaflet.Emoji/#emoji_nyc

Steven Franconeri● Steven summarized the talk here:

https://twitter.com/SteveFranconeri/status/996309494929018880

Federica FragapaneData visualisation design, how it connect to readers

- Way of connection - Ideal way of interpretation?- What topic interest target audience?- Analyse personal reaction on visualization to create connection- Emotional connection

- Providing a clean, rational personal data- Picture can be optional, especially, respect data provider’s privacy- Empathy generator

Aaron Williams- Data visualisation help facilitating discussion.

Project to map segregation across America https://www.washingtonpost.com/graphics/2018/national/segregation-us-cities/Segregation can be broken down into 5 dimensions: evenness, exposure, concentration, centralization, clustering. https://www.census.gov/hhes/www/housing/resseg/pdf/massey.pdf Mapping segregation, calculated using Theil’s Index

Topi Tjukanov

Sisi Wei- In the perspective of game UI, one way of improving UX

- Letting users redesign our work- Could we make public more expert in terms of data interpretation?- Visualise the entire system, use Nicky Case’s loopy http://ncase.me/loopy/- Patreon member of https://pudding.cool/, get to see behind the scenes eg.

coding in the open.