Colman McMahon, DIT School of Computing: Getting Started with Data Visualisation

Fingal County Council

Getting Started with Data Visualization

Colman McMahon
colman@colmanmcmahon.com

2012-05-24 1

Visualisation

MSc Data Analytics
http://www.dit.ie/postgrad/programmes/dt285dt286mscincomputingdataanalytics/

2012-05-24 2

Agenda

1) Background to Data Visualization*
2) Resources
3) Classification of Visualization
4) The Design Process
5) Demonstration

*Disclaimer (and apologies to some), I use the American spelling “visualization”

2012-05-24 3

Take-away Points
1) Open to all

» new domain with many facets

2) Professional-level output is achievable

» practice a few programming and graphic design techniques

3) It's (only) a means to an end

» should affect behaviour

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Background
Data Visualisation
(very briefly)

2012-05-24 5

Charles Joseph Minard
(1781 – 1870)

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William Playfair
(1759 – 1823)

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Broad Street cholera outbreak
(John Snow - 1854)

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Crimea War deaths
(Florence Nightingale - 1858)

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London Underground Map
Harry Beck (1933)

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http://briankerr.wordpress.com/2009/06/08/connections/

John Tukey
(1915 – 2000)

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Edward Tufte

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Hans Rosling

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...and many other giants of
statistics, mathematics, medicine,
design, computing and related fields

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Agenda

2) Resources
3) Classification of Visualisation
5) Demonstration

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Resources
(ever growing)

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Texts (1 of 2)
► R in a Nutshell: A Desktop Quick Reference - Adler, Joseph
► Excel 2007 Dashboards & Reports For Dummies - Alexander, Michael
► Ways of Seeing: Based on the BBC Television Series - Berger, John S.
► Semiology of Graphics: Diagrams, Networks, Maps - Bertin, Jacques
► Statistics in a Nutshell: A Desktop Quick Reference - Boslaugh, Watters
► The Jelly Effect: How to Make Your Communication Stick - Bounds, Andy
► Gamestorming: A Playbook for Innovators, Rulebreakers, and Changemakers - Brown, Sunni
► Sketching User Experiences: Getting the Design Right and the Right Design - Buxton, Bill
► Readings in Information Visualization: Using Vision to Think - Card, Mackinlay and Shneiderman
► The Elements of Graphing Data - Cleveland, William S.
► Visualizing Data - Cleveland, William S.
► Now You See It - Davidson, Cathy N.
► slide:ology: The Art and Science of Creating Great Presentations - Duarte, Nancy
► Art: The Whole Story - Farthing, Stephen
► Information Dashboard Design: The Effective Visual Communication of Data - Few, Stephen
► Now You See It: Simple Visualization Techniques for Quantitative Analysis - Few, Stephen
► Show Me the Numbers: Designing Tables and Graphs to Enlighten - Few, Stephen
► Freelance Design in Practice - Fishel, Cathy
► Art of Plain Talk - Flesch, Rudolf
► The Art of Looking Sideways - Fletcher, Alan
► Graphic Artist's Guild Handbook of Pricing and Ethical Guidelines - Graphic Artists Guild
► Made to Stick: Why Some Ideas Survive and Others Die - Heath, Chip and Dan
► Switch: How to Change Things When Change Is Hard - Heath, Chip and Dan
► Data Analysis with Open Source Tools - Janert, Philipp K.
► We Feel Fine: An Almanac of Human Emotion - Kamvar, Sep
► Turning Numbers into Knowledge: Mastering the Art of Problem Solving - Koomey, Jon
► Elements of Graph Design - Kosslyn, Stephen M.
2012-05-24 17
Andy Kirk, http://www.visualisingdata.com

Texts (2 of 2)
► Graph Design for the Eye and Mind - Kosslyn, Stephen M.
► Don't Make Me Think: A Common Sense Approach to Web Usability, 2nd Edition - Krug, Steve
► Universal Principles of Design, Revised and Updated - Lidwell - Holden, Butler
► Visual Complexity: Mapping Patterns of Information - Lima, Manuel
► The Power of the 2 x 2 Matrix: Using 2 x 2 Thinking to Solve Business Problems and Make Better Decisions - Lowy, Alex
► How Maps Work: Representation, Visualization, and Design - MacEachren, Alan M.
► The Laws of Simplicity (Simplicity: Design, Technology, Business, Life) - Maeda, John
► Visual Language for Designers: Principles for Creating Graphics that People Understand - Malamed, Connie
► Understanding Comics: The Invisible Art - Mccloud, Scott
► The Chicago Guide to Writing about Numbers (Chicago Guides to Writing, Editing, and Publishing) - Miller, Jane E.
► How to make an IMPACT - Moon, Jon
► Designing Visual Interfaces: Communication Oriented Techniques - Mullet, Kevin
► The Designful Company: How to build a culture of nonstop innovation - Neumeier, Marty
► Emotional Design: Why We Love (or Hate) Everyday Things - Norman, Donald A.
► The Design of Everyday Things - Norman, Donald A.
► Playfair's Commercial and Political Atlas and Statistical Breviary - Playfair, William
► Presentation Zen Design: Simple Design Principles and Techniques to Enhance Your Presentations - Reynolds, Garr
► Presentation Zen: Simple Ideas on Presentation Design and Delivery - Reynolds, Garr
► The Back of the Napkin (Expanded Edition): Solving Problems and Selling Ideas with Pictures - Roam, Dan
► Unfolding the Napkin: The Hands-On Method for Solving Complex Problems with Simple Pictures - Roam, Dan
► Creating More Effective Graphs - Robbins, Naomi B.
► The Craft of Information Visualization: Readings and Reflections - Shneiderman, Ben
► The Visual Display of Quantitative Information - Tufte, Edward R.
► Envisioning Information - Tufte, Edward R.
► Beautiful Evidence - Tufte, Edward R.
► Graphic Discovery: A Trout in the Milk and Other Visual Adventures - Wainer, Howard
► Visual Thinking: for Design - Ware, Colin
► The Grammar of Graphics - Wilkinson, Leland
► Non-Designer's Design Book (3rd Edition) - Williams, Robin
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► Glut: Mastering Information Through the Ages - Wright, Alex

Tools for Analysis, Graphing and Enterprise
► Microsoft Excel http://office.microsoft.com/en-us/excel/
► Open Office Calc http://why.openoffice.org/why_great.html
► Tableau Desktop http://www.tableausoftware.com/products/desktop
► Tableau Public http://www.tableausoftware.com/public/
► TIBCO Spotfire http://spotfire.tibco.com/
► QlikView http://www.qlikview.com/
► Grapheur http://grapheur.com/
► Gephi http://gephi.org/
► Visokio Omniscope http://www.visokio.com/
► Panopticon http://www.panopticon.com/
► Wolfram Mathematica http://www.wolfram.com/mathematica/
► Data Graph http://www.visualdatatools.com/DataGraph/
► OmniGraphSketcher http://www.omnigroup.com/products/omnigraphsketcher
► PLOT http://plot.micw.eu/
► MATLAB http://www.mathworks.com/products/matlab/
► SPSS Visualisation Designer http://www-01.ibm.com/software/analytics/spss/products/statistics/vizdesigner/
► STATA http://www.stata.com/
► Visualize Free http://visualizefree.com/index.jsp
► Dundas http://www.dundas.com/dashboard/
► Wondergraphs http://www.wondergraphs.com/


2012-05-24 19

Visual Programming Languages and Environments
► Adobe Flash http://www.adobe.com/products/flash/
► Processing http://processing.org/
► Processing.js http://processingjs.org/
► R http://www.r-project.org/
► D3 http://mbostock.github.com/d3
► Protovis http://protovis.org/
► Prefuse http://prefuse.org/
► Prefuse Flare http://flare.prefuse.org/
► Impure http://www.impure.com/
► Mondrian http://www.theusrus.de/Mondrian/
► HTML5 http://dev.w3.org/html5/spec/
► Python http://www.python.org/
► Silverlight http://www.silverlight.net/
► Orange http://orange.biolab.si
► paper.js http://paperjs.org/about/
► WebGL http://www.chromeexperiments.com/webgl
► Dejavis http://dejavis.org/stacks
► Simile Widgets http://simile-widgets.org/
► JavaScript InfoVis Toolkit http://thejit.org/
► Juice Kit http://www.juicekit.org/
► Treevis http://treevis.net/

2012-05-24 20

Tools for Analysis, Graphing and Enterprise
► Microsoft Excel http://office.microsoft.com/en-us/excel/
► Open Office Calc http://why.openoffice.org/why_great.html
► Tableau Desktop http://www.tableausoftware.com/products/desktop
► Tableau Public http://www.tableausoftware.com/public/
► TIBCO Spotfire http://spotfire.tibco.com/
► QlikView http://www.qlikview.com/
► Grapheur http://grapheur.com/
► Gephi http://gephi.org/
► Visokio Omniscope http://www.visokio.com/
► Panopticon http://www.panopticon.com/
► Wolfram Mathematica http://www.wolfram.com/mathematica/
► Data Graph http://www.visualdatatools.com/DataGraph/
► OmniGraphSketcher http://www.omnigroup.com/products/omnigraphsketcher
► PLOT http://plot.micw.eu/
► MATLAB http://www.mathworks.com/products/matlab/
► SPSS Visualisation Designer http://www-01.ibm.com/software/analytics/spss/products/statistics/vizdesigner/
► STATA http://www.stata.com/
► Visualize Free http://visualizefree.com/index.jsp
► Dundas http://www.dundas.com/dashboard/
► Wondergraphs http://www.wondergraphs.com/

2012-05-24 21

Google's Charting and Visualisation Tools
► Google Docs https://docs.google.com/?pli=1#home
► Google Fusion Tables http://www.google.com/fusiontables/Home?pli=1
► Google Chart API http://code.google.com/apis/chart/
► Google Visualization API http://code.google.com/apis/visualization/documentation/gallery.html
► Google Motion Chart & Public Data Explorer
http://www.google.com/publicdata/home
► Google Insights for Search http://www.google.com/insights/search/#
► Google Zeitgeist http://www.google.com/intl/en/press/zeitgeist2010/
► Google Ngram Viewer http://ngrams.googlelabs.com/
► Google Analytics http://www.google.com/intl/en_uk/analytics/
► Google.org Philanthropy http://www.google.org/#one
► Google Wonder Wheel http://www.google.com/landing/searchtips/engineers.html
► GraphViz http://code.google.com/apis/chart/docs/gallery/graphviz.html
► Choosel http://code.google.com/p/choosel/
► Data Appeal http://dataappeal.com/


2012-05-24 22

Tools for Mapping
► Google Maps & Google Earth http://www.google.co.uk/help/maps/tour/
► ArcGIS http://www.arcgis.com/home/index.html
► GeoCommons http://geocommons.com/
► OpenHeatMap http://www.openheatmap.com/
► Indiemapper http://indiemapper.com/
► InstantAtlas http://www.instantatlas.com/Choose_your_language.xhtml
► Target Map http://www.targetmap.com/
► TileMill http://tilemill.com/index.html
► Polymaps http://polymaps.org/
► Color Brewer http://colorbrewer2.org/
► Dotspotting http://dotspotting.org/
► DataMaps.eu http://www.datamaps.eu/
► GeoTime http://geotime.com/


2012-05-24 23

Specialist Tools and Visualisation Communities
► Many Eyes http://www-958.ibm.com/software/data/cognos/manyeyes/
► Visual.ly http://visual.ly/
► Visualizing Player http://www.visualizing.org/
► Number Picture http://numberpicture.com/
► Parallel Sets http://eagereyes.org/parallel-sets
► Dipity http://www.dipity.com/
► Wordle http://www.wordle.net/
► Tagxedo http://www.tagxedo.com/
► VisualEyes http://www.viseyes.org/
► Wordlings http://wordlin.gs/
► Chartle http://www.chartle.net/
► ChartsBin http://chartsbin.com/
► Simple Usability http://www.simpleusability.com/services/usability/eye-tracking
► Fineo http://fineo.densitydesign.org/custom/


2012-05-24 24

Combination of Many Disciplines
Given complexity of data, insights from diverse fields are required to provide
meaningful solutions:

Statistics Graphic Design
Data Mining
Computer Science
Data/Info Visualisation
(Ben Fry – “Visualizing Data”)

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Pick an area of interest/define your requirements, then drill down...

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Primary Texts

2012-05-24 27

“Designing Data Visualizations”

Designing Data Visualizations
Intentional Communication from Data to Display

Noah Iliinsky and Julie Steele

Publisher: O'Reilly Media (September 29, 2011)

ISBN-10: 1449312284

2012-05-24 28

“Visualize This”

Visualize This
The Flowing Data Guide to Design, Visualization and Statistics

Nathan Yau

Publisher: Wiley (July 20, 2011)

ISBN-10: 0470944889

2012-05-24 29

“Visualizing Data”

Visualizing Data
Ben Fry

Publisher: O'Reilly Media (January 11, 2008)

ISBN-10: 1449312284

2011/12 30

Course tools
(all free/open source)

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R Project
http://www.r-project.org/

2012-05-24 32

R Studio
http://rstudio.org

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R & R Studio stack

R-Studio

R

Must have R
Computer OS
for R Studio
to work

2012-05-24 34

Inkscape
http://inkscape.org/

2012-05-24 35

Python
http://python.org/

► Download & install

» http://wiki.python.org/moin/BeginnersGuide/Download
► Beginners Guide

» http://wiki.python.org/moin/BeginnersGuide/NonProgrammers

2012-05-24 36

Beautiful Soup
http://www.crummy.com/software/BeautifulSoup/

2012-05-24 37

Notepad++
http://notepad-plus-plus.org/

2012-05-24 38

7Zip
http://www.7-zip.org/

2012-05-24 39

Calibre
http://calibre-ebook.com/

2012-05-24 40

Agenda

2) Resources
5) Demonstration

2012-05-24 41

Classification of Visualization

2012-05-24 42

“Designing Data Visualizations”

Designing Data Visualizations
Intentional Communication from Data to Display

Noah Iliinsky and Julie Steele

Publisher: O'Reilly Media (September 29, 2011)

ISBN-10: 1449312284

2012-05-24 43

Classifications of Visualizations

1 Complexity

2 Infographics Data Viz

3 Exploration Explanation

4 Informative Persuasive Visual Art

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(Data Visualisations)

(Infographics)

Figure1-2. The difference between infographics and data visualization may be loosely determined
2012-05-24 45
by the method of generation, the quantity of data represented, and the degree of aesthetic
treatment applied.

Infographics
Infographics is useful term for referring to visual representation of data that is:

» manually drawn (and therefore a custom treatment of the information)

» specific to the data at hand (and therefore non-trivial to recreate with
different data)

» aesthetically rich (strong visual content meant to draw the eye and hold
interest)

» relatively data—poor (because each piece of information must be manually
encoded)

2012-05-24 46


1 Complexity




2012-05-24 49

(Data Visualisations)

(Infographics)

Figure1-2. The difference between infographics and data visualization may be loosely determined
2012-05-24 50
by the method of generation, the quantity of data represented, and the degree of aesthetic
treatment applied.

Data Visualization
The terms data visualization and information visualization refer to any visual
representation of data that is:

» algorithmically drawn (may have custom touches but is largely rendered with
the help of computerized methods);

» easy to regenerate with different data (the same form may be re-purposed to
represent different datasets with similar dimensions or characteristics);

» often aesthetically barren (data is not decorated); and

» relatively data-rich (large volumes of data are welcome and viable, in contrast
to infographics)

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Figure 4-47: Unemployment rates with fitted LOESS curve

2012-05-24 52


1 Complexity




2012-05-24 54

Exploration vs Explanation

Exploratory visualization:
103123101123425832
453246502163409218
► The dataset 3640634102
9236401326432654
736147236421523452
123453456856
141232343576 (1) (2)
153465

► The mind of the designer

Explanatory visualization:
?
► The mind of the designer

► The mind of the reader
(3)

2012-05-24 55

"Holy Trinity"
Designer-Reader-Data

Reader

Informative Persuasive

Data Visual Art Designer

Figure 1-4. The nature of the visualization depends on which relationship (between two of the three components) is dominant.

2012-05-24 56


1 Complexity




2012-05-24 57

Informative

2012-05-24 58
http://www.irisheconomy.ie/wp-content/uploads/2009/05/unemployment.gif

Persuasive

2012-05-24 59

2012-05-24 60

http://www.flickr.com/photos/robertpalmer/3743826461/sizes/l/in/photostream/

Visual Art

2012-05-24 61
Nora Ligorano and Marshall Reese designed a project that converts Twitter streams into a woven fiber-optic tapestry
http://ligoranoreese.net/hber-optic-tapestry)


1 Complexity




2012-05-24 62

Agenda

2) Resources
5) Demonstration

2012-05-24 63

The Design Process

2012-05-24 64

“Visualizing Data”

Visualizing Data
Ben Fry

Publisher: O'Reilly Media (January 11, 2008)

ISBN-10: 0596514557

2011/12 65

Reconcile through single process...

► Must reconcile the various elements
through a single process

► The process begins with:

» a set of numbers

» a question

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Visualization Goals - Technical
1) Highlight data features in order of
their importance

2) Reveal patterns

3) Simultaneously show features across
multiple dimensions

» e.g. time, quantity & geography

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Visualization Goals - People
► The goal of your visualization will be informed by:

» Your own goals and motivations

» The needs of your reader ?
• need for specific information

• to change the reader’s opinions or behaviour

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Data Visualization Process
-7 Stages-

acquire parse filter mine represent refine interact

► Iteration & combination

» demonstrates how later decisions can affect earlier stages

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Data Process – 7 Stages

1) Acquire Obtain the data (file, disk, over network)

2) Parse Provide some structure for the data's meaning, and order it into
categories

3) Filter Remove all but the data of interest

4) Mine Apply methods from statistics or data mining as a way to discern
patterns or place the data in mathematical context

5) Represent Choose a basic visual model, such as a bar graph, list or tree

6) Refine Improve the basic representation to make it clearer and
more visually engaging

7) Interact Add methods for manipulating the data or controlling what
features are visible

(may not need every step in every project)
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Represent
► Rule #1 - function then form

► The visual design elements should enhance and enable the function

► The key to a successful visualization is making good design choices

» elegance, simplicity, efficiency

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Encodings

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Agenda

2) Resources
5) Demonstration

2012-05-24 73

Demonstration
(walk-through followed by demo)

2012-05-24 74

“Visualize This”

Visualize This
The Flowing Data Guide to Design, Visualization and Statistics

Nathan Yau

Publisher: Wiley (July 20, 2011))

ISBN-10: 0470944889

2012-05-24 75

R Project
http://www.r-project.org/

2012-05-24 76

R Studio
http://rstudio.org

2012-05-24 77

The R Script
► A file in the R format

► Allows you to save your scripting work

► File (or Ctrl+Shift+N)

» New

• R Script

► Hit “Run” (or Ctrl + Enter) after each
command

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The R Script

2011/12 79

The R Script pane

2011/12 80

Installing packages
Package installation in R Studio
► Option 1 (R or R Studio)

» Type the following commands into
the console or R script:
» install.packages(packagename)

» library (packagename)

► Option 2 (R Studio)

» Use GUI as show on right ->

Activate package

2011/12 81

Python
http://python.org/

► Download & install

» http://wiki.python.org/moin/BeginnersGuide/Download
► Beginners Guide

» http://wiki.python.org/moin/BeginnersGuide/NonProgrammers

2012-05-24 82

Beautiful Soup
http://www.crummy.com/software/BeautifulSoup/

2012-05-24 83

Inkscape
http://inkscape.org/

2012-05-24 84

Process
(roughly)

Beautiful
colorize Soup counties
/cmd _svg .svg
.py
(data crunched)
(writes to a
(run colorize_svg (or double-click new file)
.py) to run)

(uses BS & Python)

2012-05-24 85

Chapter 8: Visualizing Spatial Relationships

► What to Look For
► Specific Locations

» Just Points

• Map with Dots

• Map with Lines

» Scaled Points

• Map with Bubbles
► Regions

» Color by Data

• Map Counties

• Map Countries

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Map the points

2011/12 87

New

Map with Dots
R script
file

► R, although limited in mapping functionality, makes placing dots on a map easy

► The maps package does most of the work

» install via Package Installer or console.

► Next step: Load the data. Use the Costco locations that you just geocoded, or load it
directly from the URL

costcos <read.csv("http://book.flowingdata.com/ch08/geocode/costcos
geocoded.csv", sep=",")

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Mapping – first layer
► When you create your maps, it’s useful to think of them as layers (regardless of the
software in use).
► The bottom layer is usually the base map that shows geographical boundaries, and then
you place data layers on top of that.
► In this case the bottom layer is a map of the United States, and the second layer is
Costco locations

map(database="state")

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Figure 8-2: Plain map of the United States

Mapping – second layer
► The second layer, or Costco’s, are then mapped with the symbols() function.

symbols(costcos$Longitude, costcos$Latitude,
circles=rep(1, length(costcos$Longitude)), inches=0.05, add=TRUE)

symbols()

2011/12 Figure 8-3: Map of Costco locations 91

Change colours
► Change the colors of both the map and the circles so that the locations stand out and
boundary lines sit in the background

map(database="state", col="#cccccc")
symbols(costcos$Longitude, costcos$Latitude, bg="#e2373f", fg="#ffffff",
lwd=0.5, circles=rep(1, length(costcos$Longitude)),
inches=0.05, add=TRUE)

2011/12 92

Figure 8-4: Using color with mapped locations

Result?
► Not bad for a few lines of code. Costco has clearly focused on opening locations on the
coasts with clusters in southern and northern California, northwest Washington, and in
the northeast of the country.

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Figure 8-4: Using color with mapped locations

Anything missing?
(US geography question)

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Alaska & Hawaii
► Alaska and Hawaii are in the “world” database, so you need to map the entire world

map(database="world", col="#cccccc")
symbols(costcos$Longitude, costcos$Latitude, bg="#e2373f", fg="#ffffff",
lwd=0.3, circles=rep(1, length(costcos$Longitude)),
inches=0.03, add=TRUE)

2011/12 95

Figure 8-5: World map of Costco locations

State specific
► Say you want to only map Costco locations
for a few states. You can do that with the
region argument.

map(database="state",
region=c("California", "Nevada", "Oregon",
  "Washington"), col="#cccccc")
symbols(costcos$Longitude,
costcos$Latitude, bg="#e2373f",
fg="#ffffff",
  lwd=0.5, circles=rep(1,
length(costcos$Longitude)), inches=0.05,
  add=TRUE)

► Some dots are not in any of those states

» easy to remove in Inkscape

2011/12 96

Figure 8-6: Costco locations in selected states



» Just Points

• Map with Dots

• Map with Lines

» Scaled Points

► Regions

» Color by Data

• Map Counties

• Map Countries

2011/12 97

Figure 8-7: Drawing a location trace

2011/12 98

New

Map with Lines
R script
file

► Draw the lines by simply plugging in the two columns into lines(). Also specify color
(col) and line width (lwd).
lines(faketrace$longitude, faketrace$latitude, col="#bb4cd4", lwd=2)
► Now also add dots, exactly like you just did with the Costco locations

symbols(faketrace$longitude, faketrace$latitude, lwd=1,
bg="#bb4cd4", fg="#ffffff", circles=rep(1,
length(faketrace$longitude)), inches=0.05, add=TRUE)

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Figure 8-7: Drawing a location trace

Figure 8-8: Drawing worldwide connections

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Drawing Connections
► It could be interesting to draw lines from one location to all the others

map(database="world", col="#cccccc")
for (i in 2:length(faketrace$longitude)1) {
       lngs < c(faketrace$longitude[8], faketrace$longitude[i])
       lats < c(faketrace$latitude[8], faketrace$latitude[i])
       lines(lngs, lats, col="#bb4cd4", lwd=2)
} (run function as a block)

► Isn’t very informative, but maybe
you can find a good use for it
► The point here is that you can draw a
map and then use R’s other graphics
functions to draw whatever you want
using latitude and longitude
coordinates.
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Figure 8-8: Drawing worldwide connections



» Just Points

• Map with Dots

• Map with Lines

» Scaled Points

► Regions

» Color by Data

• Map Counties

• Map Countries

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Figure 8-10: Rates more clearly explained for a wider audience
2011/12 103

Scaled Points
► Usually,don’t just have a location

» also have other values, e.g

• sales volume

• city population

► Use the principle of bubble plot and
apply it to a map

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New
R script
file

► The code is almost the same as when you mapped Costco locations, but remember you
just passed a vector of ones for circle size in the symbols() function. Instead, we use the
sqrt() of the rates to indicate size.

fertility <
    read.csv("http://book.flowingdata.com/ch08/points/adolfertility.csv")
map(‘world’, fill = FALSE, col = "#cccccc")
symbols(fertility$longitude, fertility$latitude,
    circles=sqrt(fertility$ad_fert_rate), add=TRUE,
    inches=0.15, bg="#93ceef", fg="#ffffff")

2011/12 105

Figure 8-9: Adolescent fertility rate worldwide

Figure 8-10: Rates more clearly explained for a wider audience
2011/12 106



» Just Points

• Map with Dots

• Map with Lines

» Scaled Points

► Regions

» Color by Data

• Map Counties

• Map Countries

2011/12 107

Regions

► Mapping points can take you only so far
because they represent only single
locations.

► Large scale data is usually aggregated
over whole counties, states, countries,
and continents

► Use Python and SVG to generate map

» Python - to process the data
http://www.nevron.com/Gallery.DiagramFor.NET.Maps.ChoroplethMaps.aspx

» SVG - for the map

2011/12 108

Color By Data
► Choropleth maps are the most common way to map regional data
► Based on some metric, regions are colored following a color scale that you define

Figure 8-11: Choropleth map framework
2011/12 109

Using colours
► When you have your color scheme, you have two more things to do:

» Scale - decide how the colors you picked match up to the data range

» Location - assign colors to each region based on your choice

2011/12 110

http://gismapcatalog.blogspot.com/2010/07/standardized-choropleth-map.html



» Just Points

• Map with Dots

• Map with Lines

» Scaled Points

► Regions

» Color by Data

• Map Counties

• Map Countries

2011/12 111

Unemployment by county

2011/12 112

Connect data & map

Unemployment
rates

Beautiful Soup
Python New map
“colorize_svg.py”

Blank
map

2011/12 113

Connect data & map

Beautiful Soup
Python

“colorize_svg.py”

2011/12 114

File structure

2011/12 115

Get data
► U.S. Bureau of Labor Statistics provides county-level unemployment
data every month
► Download the data at
http://book.flowingdata.com/ch08/regions/unemploymentaug2010.txt.
► There are six columns:

1) is a code specific to the Bureau of Labor Statistics
2) and 3) are a unique id specifying county
4) is the county name and
5) is the month the rate is an estimate of
6) is the estimated percentage of people in the county who are
unemployed

► For the purposes of this example, only interested in COUNTY ID (FIPS) and the RATE

2011/12 116

US Unemployment figures (BLS)

LAUS_CODE,STATE_FIPS,COUNTY_FIPS,COUNTY,MONTH,RATE
CN010010,01,001,"Autauga County, AL",Aug10(p),8.1
PA011000,01,003,"Baldwin County, AL",Aug10(p),8.2
CN010050,01,005,"Barbour County, AL",Aug10(p),11.6
CN010070,01,007,"Bibb County, AL",Aug10(p),10.1
CN010090,01,009,"Blount County, AL",Aug10(p),8.3
CN010110,01,011,"Bullock County, AL",Aug10(p),15.0
CN010130,01,013,"Butler County, AL",Aug10(p),12.2
PA010250,01,015,"Calhoun County, AL",Aug10(p),9.1
CN010170,01,017,"Chambers County, AL",Aug10(p),13.6
CN010190,01,019,"Cherokee County, AL",Aug10(p),8.8
CN010210,01,021,"Chilton County, AL",Aug10(p),9.4
CN010230,01,023,"Choctaw County, AL",Aug10(p),11.1
CN010250,01,025,"Clarke County, AL",Aug10(p),15.8
CN010270,01,027,"Clay County, AL",Aug10(p),13.3
CN010290,01,029,"Cleburne County, AL",Aug10(p),8.4
CN010310,01,031,"Coffee County, AL",Aug10(p),7.3
PA010900,01,033,"Colbert County, AL",Aug10(p),9.2
CN010350,01,035,"Conecuh County, AL",Aug10(p),15.4
CN010370,01,037,"Coosa County, AL",Aug10(p),12.2
2011/12 117

Get map

► Blank map from Wikimedia Commons:
http://commons.wikimedia.org/wiki/File
:USA_Counties_with_FIPS_and_names.svg

► download SVG file and save as
counties.svg, in the same directory

that you save the unemployment data

2011/12 118

Download the SVG file

2011/12 http://commons.wikimedia.org/wiki/File:USA_Counties_with_FIPS_and_names.svg 119

SVG map file
► SVG (scalable vector graphics) is an XML file
► It’s text with tags, and you can edit it in a text editor like you would an HTML file
► The browser or image viewer reads the XML, and the XML tells the browser what to show,
such as the colors to use and shapes to draw.

2011/12 120

Figure 8-15: Blank SVG county map from Wikimedia Commons

2011/12 121

SVG - colour of each state
► Change the fill color of each county to match the corresponding unemployment rate

<path
style="fontsize:12px;fill:#d0d0d0;fillrule:nonzero;stroke:#000000;stroke
opacity:1;strokewidth:0.1;strokemiterlimit:4;strokedasharray:none;stroke
linecap:butt;markerstart:none;strokelinejoin:bevel"

► There are more than 3,000 counties so use Beautiful Soup to make parsing XML and HTML
easy

2011/12 122

Load the elements
(create a small script/program)

colorize.svg.py
► Open a blank file in the same directory
as your SVG map and unemployment
data
► Save it as colorize_svg.py
► Follow instructions from book to
construct the script

2011/12 123

Connect data & map)
► The challenge is to somehow link the unemployment data to the county map
► The linkage = the FIPS codes (Federal Information Processing Standard)

Underemployment
rates

FIPS codes

Blank
map

2011/12 124

US Unemployment figures (BLS)

LAUS_CODE,STATE_FIPS,COUNTY_FIPS,COUNTY,MONTH,RATE
CN010010,01,001,"Autauga County, AL",Aug10(p),8.1
PA011000,01,003,"Baldwin County, AL",Aug10(p),8.2
CN010050,01,005,"Barbour County, AL",Aug10(p),11.6
CN010070,01,007,"Bibb County, AL",Aug10(p),10.1
CN010090,01,009,"Blount County, AL",Aug10(p),8.3
CN010110,01,011,"Bullock County, AL",Aug10(p),15.0
CN010130,01,013,"Butler County, AL",Aug10(p),12.2
PA010250,01,015,"Calhoun County, AL",Aug10(p),9.1
CN010170,01,017,"Chambers County, AL",Aug10(p),13.6
CN010190,01,019,"Cherokee County, AL",Aug10(p),8.8
CN010210,01,021,"Chilton County, AL",Aug10(p),9.4
CN010230,01,023,"Choctaw County, AL",Aug10(p),11.1
CN010250,01,025,"Clarke County, AL",Aug10(p),15.8
CN010270,01,027,"Clay County, AL",Aug10(p),13.3
CN010290,01,029,"Cleburne County, AL",Aug10(p),8.4
CN010310,01,031,"Coffee County, AL",Aug10(p),7.3
PA010900,01,033,"Colbert County, AL",Aug10(p),9.2
CN010350,01,035,"Conecuh County, AL",Aug10(p),15.4
CN010370,01,037,"Coosa County, AL",Aug10(p),12.2
2011/12 125

Connect data & SVG (map)
► Each path in the SVG file has a unique id

» combined FIPS state and county FIPS code:

id="01001"
inkscape:label="Autauga, AL”

2011/12 126

Run the Python script
$ python colorize_svg.py > colored_map.svg

2011/12 127

Possible code problem...
unemployment = {}
rates_only = [] # To
In book... calculate quartiles
min_value = 100; max_value
= 0; past_header = False
for row in reader:
    if not past_header:
        past_header = True
        continue
    try:
        full_fips = row[1]
+ row[2]
        rate =
float( row[5].strip() )

unemployment[full_fips] =
rate

rates_only.append(rate)
    except:
Finished script...         pass
2011/12 128

http://book.flowingdata.com/ch08/regions/colorize_svg.py.txt

Figure 8-18: Choropleth map showing unemployment rates

► Open your new choropleth map in a modern browser such as Firefox, Safari, or Chrome
or in Inkscape to see the fruits of your labor

2011/12 129

Next... unemployment rates divided by quartiles

2011/12 130

Define thresholds is by quartiles
► Another common way to define thresholds is by quartiles

» This means that a quarter of the counties have rates below 6.9 percent, another
quarter between 6.9 and 8.7, one between 8.7 and 10.8, and the last quarter is
greater than 10.8 percent

# Quantile scale
if rate > 10.8:
  color_class = 3
elif rate > 8.7:
  color_class = 2
elif rate > 6.9:
  color_class = 1
else:
  color_class = 0

2011/12 131

Define thresholds is by quartiles
► Use four colors to represents a quarter of the regions

» one shade per quarter

colors = ["#f2f0f7", "#cbc9e2", "#9e9ac8", "#6a51a3"]

2011/12 132

Quartiles for re-use
► Instead of hard-coding the values 6.9, 8.7, and 10.8 in your code, you can replace those
values with q1, q2, and q3, respectively.

» The advantage of calculating the values programmatically is that you can reuse the
code with a different dataset just by changing the CSV file

# Quartiles
rates_only.sort()
q1_index = int( 0.25 * len(rates_only) )
q1 = rates_only[q1_index]



2011/12 133

Modify the script
(or create a new one)

colorize.svg.py
► Follow instructions in book to construct
the next script example

► Minor alterations

2011/12 134

Figure 8-19: Unemployment rates divided by quartiles

2011/12 135

Customise and reuse

► You can edit the SVG file in Inkscape,
change border colors and sizes, and add
annotation to make it a complete
graphic for a larger audience (hint: It
still needs a legend) and that fits with
the theme of your project.

► The code is reusable - you can apply it
to other datasets that use the FIPS
code.

2011/12 136

In action...

2012-05-24 137

Summary

1) Background to Data Visualization
2) Resources
3) Classification of Visualization
5) Demonstration

2012-05-24 138

Take-away Points
1) Open to all

» new domain with many facets

2) Professional-level output is achievable

» practice a few programming and graphic design techniques

3) It's (only) a means to an end

» should affect behaviour

2012-05-24 139

The end.

Thank you :)

2012-05-24 140

Colman McMahon, DIT School of Computing: Getting Started with Data Visualisation

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