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Visualization and Policy Development:Implications for Poverty Research
West Coast Poverty CentreSeminar on Poverty and Public Policy
Professor Evert LindquistSchool of Public Administration, University of Victoria, Canada
HC Coombs Policy Forum, Australian National University
5 December 2011
Overview of Presentation
• Why Visualization?
• What is Visualization?
• Visualization Literature: Themes
• Guiding Conceptual Framework
• Exploratory Roundtable Questions
• What Might Be Our Expectations?
• Potential Strategic Implications
Informed by
HC Coombs Policy Forum, ANU-APS Exploratory Roundtables
2011 Banff Visual Analytics Summit
VisWeek 2011 Panel on “Visualization and Policy Development: Implications for Theory-Building
2011 APPAM Conference
Why Visualization?
Why Visualization?• Public sector leaders grappling with complexity…
– Problems: intrinsically complex, over time, diverse perspectives
– Interventions: diverse actors working across boundaries & sectors
• Citizens & political leaders consume information in new ways– Broad familiarity digital and web technology; lateral; images; etc.
– Overload/time compression: too much information; too little information!
– Visual techniques: capturing complexity, furthering analysis, communications
– External familiarity implies citizens/leaders judging government capability
• Uneven take-up of visualization techniques in government– Premise (1): governments under-investing in visualization
– Premise (2): pockets of innovation and use across government
– Premise (3): most adoption bottom-up; initiative and practice in domains
– Premise (4): investments made in selective, critical areas (i.e., security)
What is Visualization?
Three Visualization Domains
Information Visualization
Facilitation & Strategic
Thinking
Graphics & Visual
Display
Cognate Strategic Practice- systems thinking- simulations- scenario-building- performance thinking
Graphic/Visual Practitioners - graphic recorders & facilitators- visual practitioners- organizational development- stakeholder development
Graphics and Display- advertising- maps- scientific & architecture- newspapers/magazines- web sites- presentations- animations
Genres- scientific visualization- information visualization- visual & data analytics
Visualization Techniques- spatial data- geospatial data- multivariate data- trees/graphs/networks- text and documents Ward et al (2010)
Types of Structural Representation- graphs, trees, cones- proximity & connectivity techniques- clustering and classification
- distance and word search- multi-dimensional-scaling- network analysis
- glyphs on charts & graphs- virtual structures (WordNet, Wordle)- network representations Chen (2006)
Examples of Visualizations• Online Library of Information on Visualization Environments at
www.otal.umd.edu/Olive
• Many Eyes at http://www-958.ibm.com/software/data/cognos/manyeyes/
• Tableau Public at http://www.tableausoftware.com/public
• Flowing Data at http://flowingdata.com/ (see “Visualization” in the Archives section)
• Infosthetics at http://infosthetics.com/
• Simple Complexity at http://simplecomplexity.net/
• Visual Complexity at http://www.visualcomplexity.com/vc/ (network apps)
• Dynamic Diagrams blog “Information Design Watch” at http://dd.dynamicdiagrams.com/
• The Big Picture at www.public.iastate.edu/~CYBERSTACKS/BigPic.htm
• Junk Charts at http://junkcharts.typepad.com/
• Small Labs Inc. on NYT infographics: http://www.smallmeans.com/new-york-times-infographics/
Information Visualization
http://politicosphere.net/map/
http://www.nytimes.com/interactive/2008/09/15/business/20080916-treemap-graphic.htm
D. Holten, “Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data”IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v. 12, n. 5, SEPT/OCT 2006
http://mashable.com/2011/05/07/bin-laden-visualization/
Graphics and Visual Display
Behind the Qantas emergency landing Globe and Mail Update Published Monday, Nov. 08, 2010 6:45PM EST
Office space in downtown Toronto Globe and Mail Update
Tuesday, Nov. 02, 2010 7:09PM EDT
Facilitation & Strategic Thinking
Some More Examples to Consider...and these are not Dynamic!
UK Foresight Obesity System Map
Three Visualization Domains
Information Visualization
Facilitation & Strategic
Thinking
Graphics & Visual
Display
Cognate Strategic Practice- systems thinking- simulations- scenario-building- performance thinking
Graphic/Visual Practitioners - graphic recorders & facilitators- visual practitioners- organizational development- stakeholder development
Graphics and Display- advertising- maps- scientific & architecture- newspapers/magazines- web sites- presentations- animations
Genres- scientific visualization- information visualization- visual & data analytics
Visualization Techniques- spatial data- geospatial data- multivariate data- trees/graphs/networks- text and documents Ward et al (2010)
Types of Structural Representation- graphs, trees, cones- proximity & connectivity techniques- clustering and classification
- distance and word search- multi-dimensional-scaling- network analysis
- glyphs on charts & graphs- virtual structures (WordNet, Wordle)- network representations Chen (2006)
Lessons, Challenges and OpportunitiesBoundaries permeable – Overlap – No overarching theory of visualization – Rational disposition
• holism and focus. Requires ability to zoom in and out, rotate, and use images to see connections and serve as point of departure for further exploration and re-integration.
• representations involve trade-offs. Representing complexity and the “whole” requires simplifications of complexity, distillations of information.
• visualizations may (or may not) promote exploration. Visual images and imaging may arise out of iterative processes, but can audiences can manipulate visualizations?
• dynamic visualization rocks. Static data and representations are important, but displaying trends and evolving relationships is highly desirable.
• more data streams and perspectives are better. Multiple lines of data and diverse perspectives on semantics are important but this also depends on the task).
• users lag and react differently to visualizations. Cognitive limitations may limit benefit of more sophisticated, along with preferences and lack of prior knowledge.
• story-telling enhances visualizations. Audiences need context, narrative, and often a guide to parse information.
• designers and users should interact. The best visualizations emerge from dialogue and interaction between the designers and the users.
• innovation, re-discovery and re-packaging. Visualization techniques developed for one purpose can be applied elsewhere; but similar packages branded with different names.
• education/training increasingly essential. There is agreement that a broader circle of users – primary and secondary – should become literate in visualization techniques.
Preliminary GuidingConceptual Framework
Visualization and Public Policy Development
Information Visualization
Facilitation & Strategic
Thinking
Graphics & Visual
DisplayPolicy
Analysis
Policy Engagement
Policy Advising
A
Pirolli & Card (2005), Kang & Card (2011) on intelligence analysts
Pacific Northwest National LaboratorySecurity Directorate Special Programs Data Intensive Computing
http://dicomputing.pnnl.gov/articles/d/i/c/File-DICI-Graphic.jpg_968e.html
Fig. 3 – Visualization in Distributed Public Sector Systems
Information Visualization
Facilitation & Strategic Thinking
Graphics & Visual Display
Policy Analysis
Policy Engagement
Policy Advising
Distributed Governance and Public Sector Systems(central agencies, departments, agencies, networks)
- Where has progress already started? Factors: tasks, culture, recruitment, networks, curiosity, slack, discretion.
- Drivers? Selective investment, curiosity-driven, central edicts.- Where should investments be made? Cost, benefits,
location.
A
B
Fig. 4 – Visualization and Public Sector Governance
Information Visualization
Facilitation & Strategic Thinking
Graphics & Visual Display
Policy Analysis
Policy Engagement
Policy Advising
Research
Data
Analysis
Convocation
Publication
Trends- demographic rollover- generational cognitive styles - hyperlinked world- cost pressures- time pressures- visualizing information- expectations re graphics- great experimentation- open government- vendors multiplying- data availability increase- ICT costs declining- new interfaces for users
Distributed Governance and Public Sector Systems(central agencies, departments, agencies, networks)
- Where has progress already started? Factors: tasks, culture, recruitment, networks, curiosity, slack, discretion.
- Drivers? Selective investment, curiosity-driven, central edicts.- Where should investments be made? Costs, benefits,
location.
Reporting
Media Stories and Political Intelligence
Goals? Criteria? • broader horizons• better use of time• informed dialogue• more use of data• more perspective• greater versatility• strategic interventions• complexity awareness
• problems• admin. effort
Debate & Decisions
PolicyInquiry
A
B
C
Questions AnimatingExploratory Roundtables
Some PreliminaryTheoretical Considerations
Perspectives on Visualization & Policy-Making• Visualization for what? What are the motivations for
producing and using visualization technologies?
• Cognitive styles, bandwidth & channels. How will visualization fit with user needs and environments?
• Visualization as play. From playful and non-aligned use to the horizon-broadening and decision-specific.
• Costs, benefits, impact. Comparisons of credibly producing different visualizations? With other info?
• Visualization and “open government”. Expectations re purely undirected play vs. directed design competitions.
• Is visualization so different? Another form of technical expertise to factor into larger repertoire of policy tool-kit.
Working Expectations
Visualization and Policy-Making: What Might Be Our Expectations?
• Achieving focused impact. Visualization feeding into policy and organizational repertoires for monitoring and decision, and perhaps encouraging consent?
• Developing shared context. Visualizations help develop better, shared perceptions of complex policy challenges?
• Enlightenment/percolation. Visualization as another stream of information with indirect effects on policy-makers?
• Enhancing learning/debate. Visualization as encouraging broader understanding & developing alternative narratives?
• Amplifying difference. Visualization as another weapon for advancing interests, projecting narratives, and marketing?
?
UK Foresight Obesity System Map
Policy Analysis
Policy Engagement
Policy Advising
Vis and Policy Analysis- driven by disciplinary repertoires- varies by government program- bottom-up adoption typically- emphasis on “policy informatics”- data-sifting, exploratory, simulation- another tool; input for analysts
Vis and Policy Advising- driven by minister preference- upstream “illumination” re context- decisions and sensitivity analysis- downstream “communication”- focus on setting the context- key: rapid response; diverse techniques
Vis and Policy Engagement- contested/competing visualizations- “open data” movement challenges- playing with visualizations takes time- my visualization or yours?Some General Thoughts...
- there is overlap across the areas of policy work identified- profound clash between “text-based” government traditions and culture, and “visual-holistic” means of communication- none of these tensions are entirely new....
…but data ≠ visualization ≠ policy communities- it will take years for these techniques to get absorbed into government systems, much like the ideas involving performance management, only now starting to get integrated with advances in data and informatics systems
Potential Strategic Implications
Government Investing in Visualization: Some Strategic Possibilities to Consider
• Galvanize existing visualization expertise
• Develop a system of distributed capabilities
• Linking distinct communities-of-practice and expertise in data & visualization & policy
• Invest in visualization literacy and training
• Develop guidelines for quality and representation
• Keep an eye on the quality of data (and relevance)
• Front-end investment; long-term pay-offs
Community-of-practice parallel to Strategic
Policy Network
Poverty Research and Visualization: Some Strategic Possibilities to Consider
• Complement statistics: visualization literacy & training
• Link data & policy & visualization communities in order to tackle poverty research questions and findings
• Develop guidelines for quality and representation, including quality of data (and relevance)
• Galvanize existing visualization expertise and develop a system of distributed capabilities...
– but adopt a broad perspective on linkages (e.g., obesity), target audiences, types of visualization, multiple representations, etc.
• Front-end investment for long-term pay-offs; but get ready to engage develop facility for rapid turn-around
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