Designing Information Displays Claremont Graduate University Professional Development Workshop August 23, 2015 Tarek Azzam Ph.D. 8 6 4 2 0-2 -4-6 Site 5 Site 7 Site 1 Site 4 Site 2 Site 3 Site 6 Overview Principles of Data Visualization Applying Principles to Practice Interactive Data Displays Data Dashboards Qualitative Data Displays Tools & Resources 1
Activity #1 We will conduct a small data visualization study, and see the results later in the day. 2
Importance Source: http://graphs.gapminder.org/world/ http://guns.periscopic.com/?year=2013 3
Personal Annual Report Source: http://feltron.com Perception Rules Following perception-based rules, we can present our data in such a way that important patterns stand out. If we disobey the rules our data will be incomprehensible. -Colin Ware (2000) 4
Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press This one graph This one graph Excel Example Source: Tufte, E. (2006). Beautiful Evidence. Cheshire, CT: Graphics Press. 5
How Does the Mind See? Gestalt Principles Proximity Similarity Continuity Enclosure Closure Connection Why are these principles important? They help us reduce non-data ink and enhance relevant data ink. Reduce non-data ink Subtract unnecessary non-data ink De-emphasize and regularize the remaining non-data ink Enhance data ink Subtract unnecessary data ink Emphasize the most important data ink Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 6
Reduce non-data ink Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press Emphasize important data ink Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 7
Proximity Objects that are close together are perceived as a group. Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 8
Similarity Objects that share similar attributes (e.g. color or shape) are perceived as a group. Source: http://www.scholarpedia.org/article/gestalt_principles xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx xxxxx 9
Harder to decipher (Too many attributes: Shape & Hue) Easier to decipher (Just one attribute: Hue) Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 10
Harder to decipher (Not enough hue contrast) Easier to decipher (Greater hue contrast) Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press USD 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 0 1 2 3 4 5 6 Pay Grade Female Male Source: Few, S. (2009). Now You See it. Oakland, CA: Analytics Press 11
Shape Differentiation USD 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 0 1 2 3 4 5 6 Pay Grade Female Male Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 1. Red 2. Green 3. Yellow 4. Blue 5. Black 6. White 7. Pink 8. Gray 9. Orange 10. Brown 11. Purple Hues that are distinct enough to be used together Source: Berlin & Kay (1969) Basic Color Terms: Their Universality and Evolution. Berkeley: University of California Press. 12
Color Context Which is the lighter colored box? Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press Color Context Which is the lighter colored box? Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 13
White text on a black Background works well. Yellow text on a white Background works poorly. Black text on a white Background works best. Blue text on a black Background works poorly. Enclosure Objects that appear to have a boundary around them (e.g. formed by a line or area of common color) are perceived as a group. Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 14
Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press Closure Open structures are perceived as closed, complete, and regular whenever there is a way that they can be reasonably interpreted as such. Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 15
15 Closure 10 5 0 1 2 3 4 5 15 10 5 0 1 2 3 4 5 Continuity Objects that are aligned together or appear to be a continuation of one another are perceived as a group. Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 16
Continuity Site5 Site4 Site3 Site2 Site1 0 5 10 15 Continuity School/Teacher Score Jefferson Smith 350 Anderson 345 Holly 357 Ing 364 Washington Brenda 323 Wade 333 Pat 453 17
Connection Objects that are connected (e.g. by a line) are perceived as a group. 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 20 15 10 5 0 1 2 3 4 5 6 7 8 9 Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press Activity http://www.perceptualedge.com/example14.php 18
Activity http://www.perceptualedge.com/example12.php Telling a Fuller Visual Story It s always tricky to balance encoding too much or too little data when visually representing information. The key is to prioritize your data elements and build a visual tool that reflects these priorities. 19
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Activity #2 The following is a table containing data about Napoléon s failed invasion of Russia and his subsequent retreat from Russia in 1812. If you were asked to visually represent this data how would you do it? Date Temprature (C) Location Army Size Oct. 18 1812 0 Moscow 100000 Oct. 24 1812 0 Obninsk 90000 Nov. 9 1812-9 Smolensk 75000 Nov. 14 1812-21 Mogliev 70000 Nov. 20 1812-11 Barysau 60000 Nov. 28 1812-20 Minsk 45000 Dec. 1 1812-24 Malasziecna 35000 Dec. 6 1812-30 Vilnius 20000 Dec. 7 1812-26 Kaunas 15000 Source: Tufte, E. (2006). Beautiful Evidence. Cheshire, CT: Graphics Press. 21
Army Size 110000 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 0-5 -10-15 -20-25 Temp. (Celsius) -30-35 Encoding only two elements (Army Size & Temperature) 22
Where are the priorities here? Source: http://necsi.org/projects/mclemens/syshier.gif 23
Adding performance information 9 8 7 6 5 4 3 2 1 0 Site 5 Site 1 Site 4 Site 2 Site 7 Site 3 Site 6 Planned Adding performance information 24
8 6 4 2 0 Average -2-4 -6 Site 5 Site 7 Site 1 Site 4 Site 2 Site 3 Site 6 Representing deviations from the average Excel Graphs Example USD 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 0 1 2 3 4 5 6 Pay Grade Female Male Source: Few, S. (2009). Now You See it. Oakland, CA: Analytics Press 25
Box Plot 50 th percentile Mean (optional) Source: Few, S. (2009). Now You See it. Oakland, CA: Analytics Press Source: Few, S. (2009). Now You See it. Oakland, CA: Analytics Press 26
Source: Few, S. (2004). Show Me the Numbers. Oakland, CA: Analytics Press 12 10 8 6 4 Program Start Date Start Staff Trained End of Staff Training 2 0 Time 1 Time 2 Time 3 Time 4 Time 5 Time 6 Time 7 Adding contextual information Events Example: https://docs.google.com/spreadsheets How to: https://www.youtube.com/watch?v=vdxevxj5luu 27
Bubble chart Example: https://docs.google.com/spreadsheets How to: https://www.youtube.com/watch?v=60ugtmz75va Heat Map Source: Christopher Lysy How to: https://www.youtube.com/watch?v=cegsbpnuzq4 28
Multivariate Data Displays Math Low Average High Reading Social Studies Science Source: Few, S. (2009). Now You See it. Oakland, CA: Analytics Press Low Performance Average Performance High Performance 29
High Math Low Math Average Social Studies High Reading High Social Studies High Reading Average Science Low Science Avoid 3-D 30
A better way to represent the same information. This allows you to easily compare the shapes of the distribution. 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 Treatment Time 1 Time 2 Time 3 Time 4 Time 5 Control Time 1 Time 2 Time 3 Time 4 Time 5 Comparison Time 1 Time 2 Time 3 Time 4 Time 5 Avoid Pie Charts Is site 4 or site 2 larger? 31
16 14 12 10 8 6 4 2 0 Site1 Site2 Site3 Site4 It is easier to make distinctions in a bar chart. Truthful Representation Both these graphs represent the same data. 16500 15500 14500 13500 12500 11500 10500 9500 8500 1 2 3 4 5 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 1 2 3 4 5 Attention: The data scale begins above zero to reveal a subtle trend improvement. 32
Geography & Data Visualization Source: http://users.rcn.com/jkimball.ma.ultranet/biologypages/e/epidemiology.html Introduction to GIS Geographic Information Systems (GIS) is a method of linking quantitative or qualitative data to geographic markers and locations. This ability allows evaluators to create maps that combine program information with geographic characteristics that surround them. 33
Potential Applications in Evaluation GIS in Evaluation Formative: - Conduct needs assessment -Track program implementation/activities -Track barriers or supporting factors to program implementation Summative - Investigate the relationship between environment and outcomes. -Track change in outcomes over time. - Document participant experiences. Quick GIS Logistics GIS in many of these examples utilizes a databaselike system to create the maps. This means that the format of the data would look almost exactly like a database sheet in Excel or SPSS, with the only difference being the addition of some form of geographic variable such as: An address GPS coordinate City name, street name Census block Some states and counties also have GIS-based websites that allow users to create their own maps using publically available data. (eg. Healthycity.org) Typically each state has a GIS office that can provide additional resources to users although the breadth and depth of these resources vary from state to state 34
You can import this data using a standard database format (i.e. Excel) Scale matters Quick GIS Logistics Change in map-scale often results in changes in the observed pattern of the results (e.g. a map created at the state level will hide details that may appear in a county- or city-level map). Scale can also affect the type of data that can be accessed and embedded within maps. Some data or variables may only be available at the state or county level, while others can be accessed at census tract level or even individual household level. These varying scales can enable you to create maps with different levels of resolution and detail, but be aware that selecting the appropriate scale for an analysis is a critical step in this process. Scale can be determined by the program s expected impact area. http://www.gislounge.com/understanding-scale/ 35
Layering is a key capability of GIS Distribution on tobacco billboards ( ) Mapping Context: Why is it important? Source: Luke, Esmundo & Bloom (2000) 36
Location of each public school (radius.5 miles) and the location of tobacco billboards. Use existing GIS resources & databases to represent community characteristics. Source: http://www.healthycity.org/ 37
Show change over time http://www.latimes.com/science/la-me-g-california-drought-map-htmlstory.html Track change over time http://www.cartifact.com/webmaps/homeless/ 38
Can provide you with a street view of a community http://www.washingtonpost.com/blogs/wonkblog/wp/2014/04/25/stunningly-rapid-urban-development-seen-through-google-street-view/ Google Maps Provide Program Descriptions 39
Google Maps Provide Program Descriptions Track clusters or program service areas 40
Show Program Connections & Networks Visually Document Program Activities & Stories Mural Restoration Project 41
Visually Illustrate Program Outcomes Poor Outcomes Average Outcomes Good Outcomes Factors Effecting School Attendance http://proceedings.esri.com/library/userconf/mug10/papers/gis_based_method_study_effect_distance_student_attendance_rates.p df 42
Google maps could be embedded in dashboards. Google Engine Lite (support will end in 2016) https://mapsengine.google.com 43
Bing Maps for Office http://office.microsoft.com/en-us/store/bing-maps-wa102957661.aspx Paper & Pencil Cholera Outbreaks http://www.arcgis.com/home/item.html?id=f02dc5b9d9d84c55a79264e0d338bf88 Source: http://users.rcn.com/jkimball.ma.ultranet/biologypages/e/epidemiology.html John Snow 1855 44
Cartodb.com ArcGIS Explorer Desktop http://www.esri.com/software/arcgis/explorer 45
It should be noted that GIS is best applied in evaluations of programs that: Span a relatively large geographic area Contain Multi-implementation sites/locations Have access/collect stakeholder geographic locations (i.e. address) limitations Access to geo-data on participants Privacy issues Training on complex GIS software Expense of the software There are standards and rules to get results 46
It should be noted that GIS is best applied in evaluations of programs that: Span a relatively large geographic area Contain Multi-implementation sites/locations Have access/collect stakeholder geographic locations (i.e. address) http://flowingdata.com/2014/07/07/19-maps-that-will-blow-your-mind/ 47
Interactive Data Visual Analysis Filtering. Highlighting. Grouping. Zooming/Panning. Re-visualizing. Aggregating/Disaggregating. Drilling. Re-scaling Source: 2009 Stephen Few, Perceptual Edge 48
Interactive Information Display 49
Other Examples 50
Interactive Reports Source: http://cam.wem.mb.ca/#page_1 51
What is a dashboard? Visual display of the most important information needed to achieve one or more objectives consolidated on a single screen (or page) so it can be monitored and understood at a glance. Stephen Few Dashboards Smith, V. S. (2013). Data Dashboard as Evaluation and Research Communication Tool. New Directions for Evaluation, 2013(140), 21-45. 52
Track program progress and/or impact over time 35 Student Program Enrollment 30 25 Number of Students 20 15 10 Number of Current Students Target 5 0 Time 1 Time 2 Time 3 Implementation against expected results (plan vs actual) Student Subject Performance 120% 100% 80% Very Good Good Fair Poor Value Target Test Scores 60% 40% 20% 0% Math Reading Science Subject Area How to create bullet graphs: https://www.youtube.com/watch?v=samwaiwuviq 53
Dashboards in Excel Dashboards in Tableau 54
http://www.tableausoftware.com/public/gallery/austin-charter-schools?utm_medium=referral&utm_source=pulsenews http://paintbynumbersblog.blogspot.com/2014/02/taking-look-at-what-minimum-wage-means.html 55
http://highereddatastories.blogspot.com/2014/06/us-post-secondary-offerings.html Qualitative Display Source: http://many-eyes.com 56
Qualitative Data Display Types Source: Stuart Henderson & Eden Segal Qualitative Display 57
http://many-eyes.com Many-eyes.com Data Display Tools http://many-eyes.com 58
http://www.nytimes.com/interactive/2014/08/13/upshot/where-people-in-each-state-were-born.html http://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html?_r=0 59
http://www.brightpointinc.com/interactive/political_influence/index.html?source=d3js Results of Small Study 60
Software JMP Website: http://www.jmp.com/ Tableau Tools & Resources Website: http://www.tableausoftware.com/ Spotfire Website: http://spotfire.tibco.com/ Adobe Flash Website: http://www.adobe.com/products/flash/ SwishMax Website: http://www.swishzone.com/index.php?area=products&product=max ESRI (GIS) Website: http://www.esri.com/ Websites http://maps.google.com http://many-eyes.com http://www.gapminder.org/ http://stephanieevergreen.com/blog/ Tools & Resources Azzam, T., Evergreen, S., Germuth, A. A., & Kistler, S. J. (2013). Data Visualization and Evaluation. New Directions for Evaluation, 2013(139), 7-32. Evergreen, S. D. (2014). Presenting Data Effectively: Communicating Your Findings for Maximum Impact. SAGE Publications. Few, S. (2004). Show Me The Numbers: Designing Tables and Graphs to Enlighten. Oakland, CA: Analytics Press. Few, S. (2006). Information Dashboard Design. North Sebastopol, CA: O Reilly Press. Few, S. (2009). Now You See it. Oakland, CA: Analytics Press. Koomey, J. (2008). Turning Numbers into Knowledge. Oakland, CA: Analytics Press. Tufte, E. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press. Tufte, E. (1990). Envisioning Information. Cheshire, CT: Graphics Press. Tufte, E. (1997). Visual Explanations. Cheshire, CT: Graphics Press. Tufte, E. (2006). Beautiful Evidence. Cheshire, CT: Graphics Press. Koomey, J. (2008). Turning Numbers into Knowledge. Oakland, CA: Analytics Press. 61
Thank You 62