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tapestry.md

Table of Contents

1 jonathan corum keynote {#sec-1}

1.1 adjusting stories based on audience - can't do that with a graphic {#sec-1-1}

1.2 have an audience {#sec-1-2}

reader viewer listener user

1.3 focusing on the people rather than the mechanism {#sec-1-3}

1.4 designing for bart simpsons vs lisa simpsons {#sec-1-4}

1.4.1 quick overview vs ability to deep dive {#sec-1-4-1}

1.5 three types of people (science graphics) {#sec-1-5}

1.5.1 high school science student {#sec-1-5-1}

1.5.2 busy commuter {#sec-1-5-2}

how to keep them interested instead of going to their phone, playing a game

1.5.3 his grandmother {#sec-1-5-3}

does it pull together as a cohesive visual whole

1.6 whole goal is to design for someone else {#sec-1-6}

1.7 tensions {#sec-1-7}

1.7.1 range between oversimplification and overwhelming detail {#sec-1-7-1}

1.7.2 explanation vs. decoration {#sec-1-7-2}

1.7.3 storytelling vs interactivity - narrative vs exploration {#sec-1-7-3}

1.8 don't be your own audience {#sec-1-8}

1.9 show ideas + evidence {#sec-1-9}

1.9.1 understand, translate, display, explain {#sec-1-9-1}

1.9.2 find the central idea. find one idea to use as the basis for your graphic {#sec-1-9-2}

1.10 respect the reader - help them through the story {#sec-1-10}

1.10.1 allow for multiple entry points - compartmentalized {#sec-1-10-1}

1.10.2 interactive tools to allow reader to pace themselves through {#sec-1-10-2}

1.10.3 use disparate scales to give context {#sec-1-10-3}

1.11 add meaningful annotations {#sec-1-11}

1.11.1 close proximity between graphics and labels {#sec-1-11-1}

1.11.2 don't make people go back and forth between graphics and labels {#sec-1-11-2}

1.11.3 another way to provide context {#sec-1-11-3}

1.11.4 example: annotate each step in a sequence {#sec-1-11-4}

flea jumping

1.12 show change {#sec-1-12}

1.12.1 motion {#sec-1-12-1}

1.12.2 show large scale, small scale what's happening each step {#sec-1-12-2}

1.12.3 change in form {#sec-1-12-3}

1.13 reduce complexity and opportunities for confusion {#sec-1-13}

1.13.1 adding interface can be adding complexity {#sec-1-13-1}

1.14 reduce tedium {#sec-1-14}

1.14.1 interact with data, not the interface {#sec-1-14-1}

1.14.2 strip out tedious activities - usability {#sec-1-14-2}

1.15 visualization is not explanation {#sec-1-15}

1.15.1 dont let technology drive {#sec-1-15-1}

1.15.2 add enough information beyond your visualization to explain a pattern in data {#sec-1-15-2}

1.15.3 or structure your visualization to reveal and explain patterns {#sec-1-15-3}

1.16 reveal patterns {#sec-1-16}

1.16.1 layer multiple data sets {#sec-1-16-1}

1.17 respect the data {#sec-1-17}

1.17.1 show what's unique about it {#sec-1-17-1}

if your visualization can apply to something completely different, you might not be telling the unique story. detainees vs cups of tea

1.17.2 edit - throw things away {#sec-1-17-2}

throw as much away as possible but actually tell a story

1.18 apply common sense vigorously {#sec-1-18}

2 showing is not explaining, Pat Hanrahan {#sec-2}

2.1 trying to explain Euclid's algorithm for Greatest Common Divisor {#sec-2-1}

2.2 algorithm animation / explanation {#sec-2-2}

2.3 problems with animation {#sec-2-3}

2.3.1 motion is fleeting and transient {#sec-2-3-1}

2.3.2 cannot simultaneously attend to multiple animations {#sec-2-3-2}

2.3.3 … more {#sec-2-3-3}

2.4 Pat showed the animation of the algorithm, but it didn't really explain how the algorim worked {#sec-2-4}

3 Choosing the Right Visual Story, Cheryl Phillips {#sec-3}

aimed at journalists

3.1 What's the story? {#sec-3-1}

3.1.1 data without a theme is just a bunch of data - not a story {#sec-3-1-1}

3.1.2 who what when where why how {#sec-3-1-2}

3.1.3 interview your data. think of it as the man on the street {#sec-3-1-3}

3.2 avoid notebook dump {#sec-3-2}

3.2.1 don't put every last detail in the story {#sec-3-2-1}

3.3 use the nutgraf (theme) to help define a strong visualization {#sec-3-3}

3.4 data is more than numbers – what little stories make up the larger whole which can be visualized? {#sec-3-4}

3.5 example: methadone the politics of pain {#sec-3-5}

3.6 example: family tree of songlaw {#sec-3-6}

4 29, Nigel Holmes {#sec-4}

4.1 29 is not interesting in itself, but interesting in context {#sec-4-1}

4.2 you understand something when you see it next to something you already something understand {#sec-4-2}

4.3 context is the key to understanding {#sec-4-3}

5 The Art of Honest Theft: Evolution of a connected scatterplot, Hannah Fairfield {#sec-5}

How graphics influence each other

5.1 if you move away from plotting time against the horizontal you can reveal interesting trends {#sec-5-1}

5.2 what's next? {#sec-5-2}

5.2.1 one technique: associate ancillary content (animations) with scroll {#sec-5-2-1}

so that extra information shows up in a way that it's tied to what the reader is reading at that moment

5.2.2 focusing on immersive content {#sec-5-2-2}

5.2.3 it's important to carve out time, even just 10%, to play {#sec-5-2-3}

6 the why axis, Bryan Connor {#sec-6}

6.1 nick felt (?) was inspiration {#sec-6-1}

6.2 is a critic on the why axis, but doesn't mean that in a negative way {#sec-6-2}

6.3 "the finished piece frequently acs as a seductive screen that distracts us from the higher level of investigation" {#sec-6-3}

6.4 move past being psychics into being an investigator {#sec-6-4}

as a critic, move from guessing to asking

6.5 once you know the objective of the visualization you're able to judge whether it succeeded or failed {#sec-6-5}

7 visual storytelling in the age of data, Robert Kosara {#sec-7}

7.1 academics don't get the idea of presenting data, communicating data. it's just an afterthought {#sec-7-1}

7.2 argues that stylizing charts is quite useful {#sec-7-2}

example: monstrous data by Nigel Holmes

7.3 there's a danger to telling stories {#sec-7-3}

7.3.1 can lead you down the wrong path {#sec-7-3-1}

example: driving an electric car in the parking lot until the battery runs down

7.4 story telling potential of charts {#sec-7-4}

7.4.1 story depth vs story depth {#sec-7-4-1}

story depth | visualization | ? |

simple charts

information graphics

tells a story

7.5 narrative ties facts together {#sec-7-5}

7.5.1 provides causality {#sec-7-5-1}

7.5.2 walks you through a story {#sec-7-5-2}

7.6 storytelling affordances {#sec-7-6}

7.6.1 the form which lends itself to storytelling {#sec-7-6-1}

7.6.2 what are they? {#sec-7-6-2}

  • reading direction, left to right\

  • in the famous napoleon chart, the area gets thinner\

  • follow along a line, like following a journey on a map
    uses the driving safety in fits and starts article as example

  • animations\

  • direction - the bush admin vs. obama admin us job loss bar chart
    effective way of walking you through developments example: gap minder regional differences in health and income

7.7 dimensions {#sec-7-7}

7.7.1 narrative - tells a story {#sec-7-7-1}

7.7.2 facts - story depth {#sec-7-7-2}

7.7.3 focus - tells a story {#sec-7-7-3}

kind of the natural enemy of more data

  • you must be selective in presenting data for it to be a story\

7.7.4 information scent - story depth {#sec-7-7-4}

  • hints used to guide people, indicate that there's more data
    example: the jobless rate for people like you

  • present a lot of information, but focus only on one bit.
    provide other data in a less visually prominent manner

7.7.5 author - tells a story {#sec-7-7-5}

7.7.6 audience - story depth {#sec-7-7-6}

7.7.7 the top right corner: visual data stories {#sec-7-7-7}

we're at the cusp of something amazing and powerful that goes way beyond what's out there right now

8 sites, resources, examples {#sec-8}

8.1 13 point jonathan corum {#sec-8-1}

8.2 long jump olympic shadow video {#sec-8-2}

8.3 nolan reimond strike zone pitches video {#sec-8-3}

8.4 flea jumping {#sec-8-4}

8.5 512 paths to the white house {#sec-8-5}

8.6 gun permits in new york {#sec-8-6}

8.7 where 50k guns in chicago came from {#sec-8-7}

8.8 a chicago divided by killings {#sec-8-8}

8.9 the guantanamo docket, a history of the detainee population {#sec-8-9}

8.10 the us census has an API {#sec-8-10}

8.10.1 within a few weeks the us census bureau will release the history of every US community for the last 20 years {#sec-8-10-1}

8.11 methadone the politics of pain {#sec-8-11}

8.12 elephants dying out in zoos seattle times {#sec-8-12}

8.12.1 seattletimes.com/elephants {#sec-8-12-1}

8.12.2 the family tree of thonglaw {#sec-8-12-2}

8.13 "driving shifts into reverse" unconventional mixing of axes {#sec-8-13}

8.14 Driving safety, in fits and starts, hanna fairfield {#sec-8-14}

8.15 snow fall, the avalanche at tunnel creek {#sec-8-15}

influenced by "the invention of hugo cabret"

8.16 "twenty eleven" {#sec-8-16}

8.17 "with olga" {#sec-8-17}

8.18 chris ware {#sec-8-18}

8.18.1 comic {#sec-8-18-1}

8.19 stephen few {#sec-8-19}

8.20 nigel, "Monstrous data" {#sec-8-20}

8.21 US job loss bar chart, bush administration vs. obama administration {#sec-8-21}

clever to make the bars point downward - association downward with worse

8.22 the jobless rate for people like you {#sec-8-22}

9 summary thoughts {#sec-9}

9.1 presenting data in a way that supports your story {#sec-9-1}

9.2 the primacy of story {#sec-9-2}

9.3 story is really just another way of conveying formation {#sec-9-3}

9.4 story is a tool, data viz is a tool, the intersection of story and data viz {#sec-9-4}

9.5 what is a story? {#sec-9-5}

9.5.1 actors {#sec-9-5-1}

9.5.2 causality? {#sec-9-5-2}

9.6 obvious application to journalism. but where else? {#sec-9-6}

9.7 taking a data perspective seems to bring you out of the realm of emotion {#sec-9-7}

9.8 {#sec-9-8}

Date: 2013-02-27 16:44:34 EST

Author: Daniel Higginbotham

Org version 7.8.03 with Emacs version 24

Validate XHTML 1.0

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