Telling Stories With Maps Paper

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    Telling Stories With Maps

    WhereConfApril 2, 2012Noah Iliinsky@noahi

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    Why Maps?

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    Maps give context.

    familiar locations

    relative positions trends & patterns, gaps,

    outliers

    http://sanfrancisco.crimespotting.org/

    http://sanfrancisco.crimespotting.org/http://sanfrancisco.crimespotting.org/http://sanfrancisco.crimespotting.org/
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    Maps give context.

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    Why Stories?

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    Narrative brings relevance.

    http://xkcd.com/657http://xkcd.com/657
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    How To Do It

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    Make good choices

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    Make intentional choi

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    Good Choices Are Guided by Three In

    Th i

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    Data Designer

    Reader

    Your

    visualization

    Three inputs.

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    You have goals.

    Y h l

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    You have goals.

    Why are you here?

    Y h l

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    You have goals.

    Why are you here?

    If you cant conciselyarticulate your goal,

    youre doing itwrong.

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    Your Customer Has Needs.

    Your customer has needs

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    Your customer has needs.

    Your success is defined by

    your customers success.

    Your customer has needs

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    Your customer has needs.

    Your success is defined by

    your customers success.

    If you cant satisfy their needyou have failed.

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    Do user research!

    Understand their hopes,dreams, and favorite flavors

    Understand their jargon, identand contexts of use!

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    Data Has Properties

    Data

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    Data

    Wheel size: numeric (accategorical)

    Tire width: continuous

    Price: continuous

    Anti-puncture: binary

    Foldable: binary

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    Now we start designing.

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    Statement of Goals

    Statement of goals.

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    Statement of goals.

    Show the points on a map.

    Statement of goals.

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    g

    Show the points on a map.

    WRON

    G

    Statement of goals.

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    g

    Show the points on a map.

    WRON

    GShow

    the most recent 100 points

    from each categorywithin 50 km of

    our areas of interest.

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    Define Desired Knowledgeefore Structure

    Knowledge before structure.

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    g f

    http://litmus.com/blog/email-client-market-share-infograph

    Knowledge before structure.

    http://litmus.com/blog/email-client-market-share-infographhttp://litmus.com/blog/email-client-market-share-infographhttp://litmus.com/blog/email-client-market-share-infograph
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    g f

    0 JULY 2010 SEPT 2010 NOV 2010 JAN 2011 MARCH 2011 MAY 20

    60

    0

    5

    10

    15

    20

    25

    30

    35

    40

    45

    50

    55

    P

    latforms

    hare%

    DESKTOP

    WEBMAIL

    MOBILE

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    Appropriate Encodings

    Data

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    Wheel size: numeric (accategorical)

    Tire width: continuous

    Price: continuous

    Anti-puncture: binary

    Foldable: binary

    Appropriate encodings

    http://complexdiagrams.com/properties
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    http://ComplexDiagrams.com/properties

    http://complexdiagrams.com/propertieshttp://complexdiagrams.com/propertieshttp://complexdiagrams.com/propertieshttp://complexdiagrams.com/propertieshttp://complexdiagrams.com/properties
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    Encoding well:

    1. Position is everything.2. Color is difficult.

    - @moritz_stefaner

    Position is everything.

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    http://hipmunk.com

    Position is everything.

    http://hipmunk.com/http://hipmunk.com/http://hipmunk.com/
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    absolute & relative departure time (continuous)

    absolute & relative arrival time (continuous)absolute & relative length of trip (continuous)

    stopovers (binary)

    absolute & relative stopover durati

    absolute & relative stopover start &(continuous)

    sort order (ranked)

    Axes give you information for free!

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    West - East

    North- South

    Axes give you information for free!

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    1. ab

    Northerly and Westerly!

    West - East

    North- South

    Axes give you information for free!

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    1. ab

    2. w(th

    West - East

    North- South

    Lack of axes gives you spaghetti!

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    http://commons.wikimedia.org/wiki/File:Spaghetti_alle_vongole.jpg

    http://commons.wikimedia.org/wiki/File:Spaghetti_alle_vongole.jpghttp://creativecommons.org/licenses/by/2.0/deed.enhttp://creativecommons.org/licenses/by/2.0/deed.enhttp://commons.wikimedia.org/wiki/File:Spaghetti_alle_vongole.jpghttp://commons.wikimedia.org/wiki/File:Spaghetti_alle_vongole.jpg
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    Axes are negotiable!http://www.tfl.gov.uk/assets/downloads/standard-tube-map.gif

    http://www.tfl.gov.uk/assets/downloads/standard-tube-map.gifhttp://www.tfl.gov.uk/assets/downloads/standard-tube-map.gif
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    Color is Difficult.

    Color is difficult.

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    http://eusoils.jrc.ec.europa.eu/esdb_archive/serae/GRIMM/erosion/inra/europe/analysis/maps_and_listings/web_erosion/maps_and_listings/altitude_a3.gif

    http://eusoils.jrc.ec.europa.eu/esdb_archive/serae/GRIMM/erosion/inra/europe/analysis/maps_and_listings/web_erosion/maps_and_listings/altitude_a3.gifhttp://eusoils.jrc.ec.europa.eu/esdb_archive/serae/GRIMM/erosion/inra/europe/analysis/maps_and_listings/web_erosion/maps_and_listings/altitude_a3.gifhttp://eusoils.jrc.ec.europa.eu/esdb_archive/serae/GRIMM/erosion/inra/europe/analysis/maps_and_listings/web_erosion/maps_and_listings/altitude_a3.gif
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    Color is not ordered.

    Color is difficult.

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    http://mapsof.net/uploads/static-maps/topographic_(altitude)_map_tamil_nadu.png

    Color is difficult.

    http://mapsof.net/uploads/static-maps/topographic_(altitude)_map_tamil_nadu.pnghttp://mapsof.net/uploads/static-maps/topographic_(altitude)_map_tamil_nadu.pnghttp://mapsof.net/uploads/static-maps/topographic_(altitude)_map_tamil_nadu.png
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    http://www.goldensoftware.com/gallery/gallery-11.shtml

    Not bad...

    Color is meaningful

    http://www.goldensoftware.com/gallery/gallery-11.shtmlhttp://www.goldensoftware.com/gallery/gallery-11.shtmlhttp://www.goldensoftware.com/gallery/gallery-11.shtml
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    Color is meaningful.

    Gender

    Nationality

    Politics

    ReligionMorality

    Nature

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    Abstraction is Useful

    Abstraction is Useful

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    http://www.chartball.com/football/

    Abstraction isUseful

    The Columbia River System

    donald

    gold

    en

    mache

    en

    er prin

    gs

    mica creek

    http://www.chartball.com/football/http://www.chartball.com/football/
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    hood

    river

    m

    osier

    thed

    alles

    rufus

    arlin

    gton

    board

    man

    irrigon

    umatilla

    burbank

    richlandwest

    richland

    prosser

    mabtongr

    anger

    zilla

    h

    elle

    nsbu

    rgcle

    elu

    m

    topp

    enishw

    apato

    union

    gap

    selah

    thorp

    south

    cleelum

    easto

    n

    bentoncity

    grandview

    pasco

    mattawa

    rock island

    east wenatchee

    tonasket

    okanaganfalls

    moseslake

    wilson

    creek

    odessa

    beverly

    hooper

    colfax

    palouse

    potlatch

    orofino

    kooskia

    kamiah

    white birdlucile

    stanley

    riggins

    horseshoe

    bend

    cascade

    spray

    mt.vernon

    prairiecity

    clarkstonasotin

    oxbow

    huntington

    nyssa

    weiser

    fruitland

    payette

    new

    plymouth

    emmett

    parma

    hom

    edale

    adrian

    marsin

    g

    gran

    d

    view

    bruneau

    hammett

    glennsferry

    bliss

    b

    g

    ha

    ke

    kinghill

    notus

    middleton

    star

    eagle

    ontario

    vale

    harper

    juntura

    elginim

    bler

    islandcity

    lagrande

    dayville

    cascade

    locks

    maupin

    warm springs

    terrebonne

    prineville

    pre

    scott

    columbia

    city

    milwaukie

    oak grove

    gladstone

    oregon city

    st. paul

    harrisburg

    sprin

    gfield

    low

    ell

    oakr

    idge

    lakeoswego

    west linn

    wilsonville

    newberg

    dundee

    dayton

    independence

    st.helens

    fairview

    rainier

    kelso

    castlerock

    mossyrock

    warre

    nton

    as

    toria

    hamm

    ond

    chinook

    skamo

    kawa

    cath

    lamet

    kalama

    camas

    washougal

    northbonneville

    stevenson

    carson

    whitesalmon

    bingen

    lyle

    roosevelt

    dallesport

    wishram

    ilwaco

    vader

    toledo

    randle

    packwood

    dext

    er

    sunriver

    three rivers

    garden

    city

    rom

    e

    owyh

    ee

    vantage

    entiat

    pateros

    brewster

    bridgeport

    grand

    coulee

    lincoln

    hunters

    montrose

    balfour

    kettlefalls

    northportmetaline metaline falls

    bosw

    ell

    wyn

    ndel

    creston

    marcusorient

    grand

    forks

    midway

    curlew

    deer

    meadows

    sevenbays

    ninemilefalls

    otisorchards

    stateline

    postfalls

    huetter

    st.maries

    calder

    avery

    elmercity

    couleedam

    inchelium

    trail

    genelle

    castlegar

    edgewood

    fauquier

    nakusp

    spillim

    edgew

    ate

    radium

    hot

    sp

    win

    derm

    ere

    fairm

    ont

    hotspr

    ingsi

    nverm

    ere

    malott

    okanogan

    omak

    riverside

    osoyooscawston

    keremeos

    hedley

    princeton

    oliver

    kaleden

    oroville

    millwood

    spokanevalley

    libertylake

    harrison

    ionecusick

    usk newport

    priest

    river

    oldtown

    dover

    sandpoint

    ponde

    ray

    kooten

    ai b

    onnersferry

    troy

    libby

    rexford

    wasa

    moyiesprings

    trout

    creek

    thomps

    on

    falls

    clark

    fork

    eastho

    pe

    hope

    plains

    pa

    kragm

    ont

    elko

    elkford

    wardner

    canal

    flats

    noxo

    nheron

    st.re

    g

    su

    kaslo

    ainsworth

    longview

    portland

    kennewick

    vancouver

    salem

    albanybend

    corvallis

    eugene

    johnday

    lewiston

    caldwell

    boise

    yakima

    wenatchee

    penticton

    nelson

    spokane

    coeur dalene

    revelstoke

    COLUMBIA

    OKANOGAN

    KETTLE

    KOOTENAYDUNCAN

    SPOKANE& ST. JOE

    SIMILKAMEEN

    WILLAMETTE

    DESCHUTES

    CROOKED

    JOHN DAY

    SNAKE

    YAKIMA

    GRANDE RONDE

    SALMO

    PAYETTE

    MALHEUR

    OWYHEE

    BRUNEAU

    BBOISE

    PALOUSE

    CRAB

    CLEARWATER& SELWAYCOWLITZ

    CA NV

    D

    OR

    WA

    BC

    http://somethingaboutmaps.wordpress.com/river-maps/columbia-river/

    http://somethingaboutmaps.wordpress.com/river-maps/columbia-river/http://somethingaboutmaps.wordpress.com/river-maps/columbia-river/http://somethingaboutmaps.wordpress.com/river-maps/columbia-river/
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    Use Defaults.

    Use defaults.

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    http://elections.nytimes.com/2008/president/whos-ahead/key-states/map.html

    Unless...

    http://elections.nytimes.com/2008/president/whos-ahead/key-states/map.htmlhttp://elections.nytimes.com/2008/president/whos-ahead/key-states/map.htmlhttp://elections.nytimes.com/2008/president/whos-ahead/key-states/map.html
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    http://elections.nytimes.com/2008/president/whos-ahead/key-states/map.html

    http://elections.nytimes.com/2008/president/whos-ahead/key-states/map.htmlhttp://elections.nytimes.com/2008/president/whos-ahead/key-states/map.htmlhttp://elections.nytimes.com/2008/president/whos-ahead/key-states/map.html
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    Unless youve gotsomething better.

    Appropriate encodings

    http://complexdiagrams.com/propertieshttp://complexdiagrams.com/properties
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    http://ComplexDiagrams com/properties

    Design strategies

    h

    http://complexdiagrams.com/propertieshttp://complexdiagrams.com/propertieshttp://complexdiagrams.com/propertieshttp://complexdiagrams.com/propertieshttp://complexdiagrams.com/properties
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    Limit the data & detail you include

    Use position for your most important

    relationship(s)Try different axes

    Consider default formats

    Use color for categories, not rankEncode other data and relationships with

    appropriate properties

    Intentional Communication from Data to Display

    http://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://bit.ly/aB3CJyhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoAhttp://oreil.ly/n4KEoA
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    @noahigmail: iliinskyComplexDiagrams.com

    Thank you!

    Noah Iliinsky & Julie Steele

    Data

    Visualizations

    Designing

    How to do it(this talk)

    How t(20 cas

    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  • 7/29/2019 Telling Stories With Maps Paper

    56/72

    What do you want to show?

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    57/72

    What questions are you trying to answer?

    What do you want to show?

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    58/72

    What actions/decisions are you trying to enable

    What questions are you trying to answer?

    What do you want to show?

  • 7/29/2019 Telling Stories With Maps Paper

    59/72

    Who is consuming this data?

    What are their needs?

    Wh i i hi d ?

  • 7/29/2019 Telling Stories With Maps Paper

    60/72

    Who is consuming this data?

    If not you:

    What are the ir priori ties?

    What are there biases?

    What are the ir limitations?

    What dont you know about them?

  • 7/29/2019 Telling Stories With Maps Paper

    61/72

    What data dimensions do you have to play with?

    Wh f h

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    62/72

    What types of data do you have?

    categorical (grouped)

    ordinal (ranked, time)

    quantitati ve (numeric)

    relational (hierarchy, influence, etc.)

    location (... its complicated...)

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    63/72

    What are the key relat ionships? (probably)

  • 7/29/2019 Telling Stories With Maps Paper

    64/72

    What are the key relationships?

    What data is required to show them?

  • 7/29/2019 Telling Stories With Maps Paper

    65/72

    Show the relationship between A and B [and C...across X [and Y] from m to n.

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    66/72

    What data are you actually going to use, based ogoal?

    [A, B, X (from m to n)]

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    67/72

    How can you use position to reveal your keyrelationship(s)?

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    68/72

    Is there a good default format for this kind ofrelationship?

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    69/72

    List (at least) three possible combinat ions of axe

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    70/72

    What are good opt ions for encoding your other ddimensions?

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    71/72

    Pick encodings for your remaining data dimensio

    See how that looks.

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    72/72

    Iterate, iterate, iterate.