Visualization and Policy Development: Implications for Poverty Research

1. Visualization and Policy Development:Implications for Poverty Research West Coast Poverty Centre Seminar on Poverty and Public Policy Professor Evert Lindquist School of Public Administration, University of Victoria, Canada HC Coombs Policy Forum, Australian National University 5 December 2011

2. Overview of Presentation • 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? • What is Visualization? • Visualization Literature: Themes • Guiding Conceptual Framework • Exploratory Roundtable Questions • What Might Be Our Expectations? • Potential Strategic Implications

3. Why Visualization?

4. 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)

5. What is Visualization?

6. Three Visualization Domains • Genres • scientific visualization • information visualization • visual & data analytics • Graphics and Display • advertising • maps • scientific & architecture • newspapers/magazines • web sites • presentations • animations Graphics & Visual Display Information Visualization • Visualization Techniques • spatial data • geospatial data • multivariate data • trees/graphs/networks • text and documents • Ward et al (2010) Facilitation & Strategic Thinking • Graphic/Visual Practitioners • graphic recorders & facilitators • visual practitioners • organizational development • stakeholder development • 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) • Cognate Strategic Practice • systems thinking • simulations • scenario-building • performance thinking

7. 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/

8. Information Visualization

11. http://politicosphere.net/map/

13. http://www.nytimes.com/interactive/2008/09/15/business/20080916-treemap-graphic.htmhttp://www.nytimes.com/interactive/2008/09/15/business/20080916-treemap-graphic.htm

16. 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

17. http://blogs.wsj.com/wtk/

19. http://mashable.com/2011/05/07/bin-laden-visualization/

22. Graphics and Visual Display

26. http://www.gapminder.org/videos/poor-beats-rich/

29. Behind the Qantas emergency landing Globe and Mail Update Published Monday, Nov. 08, 2010 6:45PM EST

30. Office space in downtown Toronto Globe and Mail UpdateTuesday, Nov. 02, 2010 7:09PM EDT

32. Facilitation & Strategic Thinking

33. Some More Examples to Consider...and these are not Dynamic!

36. UK Foresight Obesity System Map

38. Three Visualization Domains • Genres • scientific visualization • information visualization • visual & data analytics • Graphics and Display • advertising • maps • scientific & architecture • newspapers/magazines • web sites • presentations • animations Graphics & Visual Display Information Visualization • Visualization Techniques • spatial data • geospatial data • multivariate data • trees/graphs/networks • text and documents • Ward et al (2010) Facilitation & Strategic Thinking • Graphic/Visual Practitioners • graphic recorders & facilitators • visual practitioners • organizational development • stakeholder development • 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) • Cognate Strategic Practice • systems thinking • simulations • scenario-building • performance thinking

39. Lessons, Challenges and Opportunities Boundaries 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.

40. Preliminary GuidingConceptual Framework

41. Visualization and Public Policy Development Policy Advising Policy Analysis Graphics & Visual Display Information Visualization Policy Engagement A Facilitation & Strategic Thinking Pirolli & Card (2005), Kang & Card (2011) on intelligence analysts

42. Pacific Northwest National Laboratory Security Directorate Special Programs  Data Intensive Computing http://dicomputing.pnnl.gov/articles/d/i/c/File-DICI-Graphic.jpg_968e.html

43. Fig. 3 – Visualization in Distributed Public Sector Systems Policy Advising Policy Analysis Graphics & Visual Display Information Visualization Policy Engagement A Facilitation & Strategic Thinking B • 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.

44. Fig. 4 – Visualization and Public Sector Governance 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 Policy Inquiry Analysis Publication Research Data Convocation Debate & Decisions Reporting C Policy Advising Policy Analysis Graphics & Visual Display Information Visualization • 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 Policy Engagement A Facilitation & Strategic Thinking B • 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.

45. Questions AnimatingExploratory Roundtables

47. Some PreliminaryTheoretical Considerations

48. 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.

49. Working Expectations

50. 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?