mark d. flood department of finance university of maryland · 2019-12-23 · top-down, partially...
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Data visualization and financial stabilityMark D. FloodDepartment of FinanceUniversity of Maryland
Center for Latin American Monetary Studies (CEMLA)Course on Financial StabilityMexico City, 18 September 2019
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Visualization as a human-computer interface• Cognitive amplification to exploit the strengths of human perception
• Humans as pattern recognizers• “A picture is worth 1,000 words”
• Core Functionality• Visual rendering of data – computer presents to the (human) user
• Typically 2-dimensional• 3-dimensional (even multimedia) data renderings are possible
• User interaction – user controls the computer• Zoom• Filter• Details on demand
Render
Control
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What is data visualization?
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Non-interactive Interactive
Static
No user input after initial rendering, and image does not change. “Fixed.”Example: Newspaper infographic
Ongoing user input, but rendering does not change between input events. Example: Spreadsheet chart
Dynamic
No user input after initial rendering, but image may change.Example: Animated GIF
Ongoing user input, and rendering may change between input events. Example: Video game
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Classification of data visualization techniques
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Core functions of visualization• Sensemaking
• Data exploration• Trial-and-error analytics• Compressed decision iterations
• Decision-making• Formal authority• Common knowledge requirement• Agendas and minutes
Sensemaking ParadigmObserve–Orient–Decide–Act (OODA)
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• Rulemaking• Formal authority• Laws, regulations, interpretations• Notice and comment
• Transparency• Audience-specific renderings• Common knowledge requirement• Emphasize facts over interpretations
Image: Wikipedia
Institutional context for visualization
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Numeric GeographicIMF (2013) Federal Reserve (1932)
Network TextualKubelec and Sá (2012) Rönnqvist and Sarlin (2014)
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Common types of financial data
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Putting the human “in the loop”• Shneiderman’s (1996) seven data types:
1. One-dimensionalo “Linear” data – e.g., text documents, source code, alphabetical lists
2. Two-dimensional o Planar data – e.g., geographic maps, floor plans, document layout
3. Three-dimensionalo Real-world objects – e.g., molecules, anatomy, buildings
4. Temporalo Specialization of one-dimensional data, with a time-series history
5. Multidimensionalo Data as points in n-space – e.g., relational and statistical databases
6. Treeo Simple hierarchies, with one parent for each child node
7. Networko Graph structures with arbitrary connections between nodes
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Interactive visualization
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• Federal Open Market Committee (FOMC)• Decisions on short-term monetary policy • Confidential briefing materials (“Bluebook”), September 2007• Absence of interpretation or narrative• Emphasis on uncertainty, both historical and forward-looking
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Visualization for decision-making
Image: Federal Reserve
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• Narrative visualization
The Lead-Up to the Collapse of MF Global Holdings Ltd.OFR Annual Report (2014)
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Visualization for transparency
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• Two views of the DIKW transformation• Data – raw observations, typically factual• Information – data augmented with meaning and/or interpretation• Knowledge – information in context (cultural, historical, organizational)• Wisdom – abstracted, effective understanding of patterns in knowledge
Progressing to Wisdom (Clark, 2010) Winnowing to Wisdom (Hey, 2004)
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Data – Information – Knowledge – Wisdom (DIKW)
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Sensemaking Transparency
Foreign exchange and interest rates Civilian unemployment rateQuax, et al., (2013) Federal Reserve (2013)
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Visualizations for diverse purposes – time series data
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Sensemaking Transparency
Self-organizing financial stability map Financial Stability MonitorVisRisk (2015) Office of Financial Research (2015)
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Visualizations for diverse purposes – financial stability maps
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Interactive types for the human “in the loop”• Shneiderman’s (1996) Mantra
“Overview first, zoom and filter, then details-on-demand”The seven tasks:1. Overview
o Gain an overview of the entire collection2. Zoom
o Zoom in on items of interest3. Filter
o Filter out uninteresting items4. Details-on-demand
o Select an item or group and get details when needed5. Relate
o View relationships among items6. History
o Keep a history of actions to support undo, replay, and progressive refinement7. Extract
o Allow extraction of sub-collections and of the query parameters
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Interactive visualization
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Formalizing visual interaction
Human “in the loop” analysisSarlin (2016)
• Analytical reasoning – facilitated by interactive visual interfaces
• Combines:• Interactive visualization• Automated analysis
• Exploits:• Human visual perception• Expert judgment
• Rapid-feedback, iterative analysis• Software-assisted• Requires development of a
software model
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Visual analytics
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Three levels of human perception1. Pre-attentive perception
• Rapid parallel processing• Basic feature extraction
• Color, texture, orientation, movement, etc.• Transitory storage – very short term – of visual cues• Bottom-up, involuntary processing
2. Pattern perception• Attentive activity• Slower serial processing• Identified patterns are “bound” for a few seconds• Top-down, partially voluntary attention management
3. Visual working memory• Active attention management• Search strategies and visual queries
Patterns
Source
Thought ?
Perception
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Perceptual Processing
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Example: Representing relationships• Connectedness is more powerful than:
• Proximity, matched color, matched size, matched shape
Continuous contours Symbolic indicatorsPre-attentive processing Active interpretation needed
1
4 3
2 1
4 3
2
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Exploiting human perception
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Image source: Ware (2013)
Features with visual salience• Orientation
• Shape
• Size
• Color
• Light/dark
• Convex/concave
• Enclosure
• Shape
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Pre-attentive features
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Example: Color contrast “pops out”• Exploits pre-attentive channels for color
Find the cherries in the treeImage source: Ware (2013)
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Exploiting human perception – Visual salience
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Find the orange square• Competition for pre-attentive channels sequential search
Image source: Cross (2008)
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Visual salience failure
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Some texture distinctions are available pre-attentively• Repetitive small-scale patterns in larger-scale regions
Image source: Tuceryan and Jain (1998)
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Texture recognition
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Distinction in edge sharpness focuses attention• Sharpness is captured pre-attentively
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Contour recognition
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Does the yellow ball obscure a rectangle?• Closure: Inference of integral forms (even when absent)
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Gestalt perception
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Connectedness dominates the other mechanisms
Connectedness
1
4 3
2 1
4 3
2
Proximity
Similarity
1
4 3
2
Common Region
1
4 3
2
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Gestalt mechanisms for pattern perception
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Similar shapes are grouped• Regular alignment creates an axis of symmetry
Axis of SquaresAxis of Diamonds
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Gestalt symmetry and similarity
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Measured versus perceived light• Luminance
• Physically measured amount of light• Emitted or reflected by a source
• Brightness• Perceived amount of light• Typically light emitted by a source
• Lightness• Perceived amount of light• Typically light reflected by a source
Simultaneous Brightness
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Lightness and Brightness
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Study of symbols and their meaning• How do “signs” create meaning?
• Systems of signs are social constructs that impart meaning• Signifier – physical representation that conveys meaning
• Icon – signifier that resembles the signified• Symbol – signifier with no resemblance• Index – clue that only occurs in conjunction with the signified
• Signified – personal interpretation of a signifier• Paradigm – set of signifiers or signifieds with shared features
or functionality• Syntagm – framework of relationships among the signifiers
(for example, syntax of a language)
• Visual semiotics • Focus on visual signs
• Color, texture, orientation, movement, etc.• Physical juxtaposition of signifiers on the page/screen
Icon
“CLOUD”Symbol
Index
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Visual Semiotics
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Mixing shape and meaning• Icons are representational – can carry socialized meaning
• Useful in pedagogical contexts, as a shorthand• Typographic glyphs are typically phonetic• Abstract symbols are useful as neutral indicators of data
Typographic Glyph
Abstract Symbol
“Delete” Icon
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Semiotics in practice
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Maximizing data density Maximizing decoration
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“Chart Junk”Optimize the “signal-to-noise” ratio in your visualizationsEvery rendered element should convey a specific meaning
• Data or metadata attributes
• When in doubt, omit it• Volume of “ink” should be proportional to the importance of the element• Fill the canvas – it is precious
• Area is O(x2) for x = length
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What would FRED do? (You probably should do that too)https://fred.stlouisfed.org
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Juxtaposition as a source of meaning – Time-series plots
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Juxtaposition as a source of meaning – Small multiples
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Reading suggestions
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• C. W. Choo (2005), The Knowing Organization: How Organizations Use Information to Construct Meaning, Create Knowledge, and Make Decisions, Oxford U. Press.
• M. Flood, V. Lemieux, M. Varga, and B. W. Wong (2016), “The application of visual analytics to financial stability monitoring,” J. of Financial Stability, 27, 180-197.
• D. Keim, J. Kohlhammer, G. Ellis and F. Mansmann (eds.) (2010), Mastering the Information Age –Solving Problems with Visual Analytics, Eurographics Association.
• S. Ko, I. Cho, S. Afzal, C. Yau, J. Chae, A. Malik, K. Beck, Y. Jang, W. Ribarsky and D. Ebert (2016), “A Survey on Visual Analysis Approaches for Financial Data,” Computer Graphics Forum, 35(3), 599-617.
• V. Lemieux, B. Fisher and T. Dang (2014), “The visual analysis of financial data,” in: Brose, Flood, Krishna and Nichols (eds.), The Handbook of Financial Data and Risk Information: Vol. 2: Software and Data, Cambridge U. Press, 279–326.
• P. Sarlin (2016), “Macroprudential oversight, risk communication and visualization,” J. of Financial Stability, 27, 160-179.
• P. Sarlin and T. Peltonen (2013), “Mapping the State of Financial Stability,” J. of International Financial Markets, Institutions and Money, 26, 46–76.
• T. Samara (2017), Making and Breaking the Grid, 2nd Edition: A Graphic Design Layout Workshop, Rockport Publishers.
• B. Shneiderman, C. Plaisant, M. Cohen and S. Jacobs (2017), Designing the User Interface: Strategies for Effective Human-Computer Interaction, 6th ed., Prentice Hall.
• E. Tufte (2001), The Visual Display of Quantitative Information, 2nd ed., Graphics Press. • C. Ware (2013), Information Visualization: Perception for Design, 3rd ed., Morgan Kaufmann.• L. Wilkinson (2005), The Grammar of Graphics, 2nd ed., Springer Verlag.