Some Ideas for Improving Crash Graphics

1. Some Ideas for Improving Crash Graphics Zachary Hans, Todd Knox and Reg Souleyrette

4. Source: NHTSA website

5. Source: UC Berkeley “Access”

6. Purpose • Because maps are often taken as “truth”, we need to be careful about what we prepare, however, …. • Not enough time/resources/skill/ knowledge to do a good job • Not conveying what is intended • Misleading, confusing • Time lost in redoing work • Is there a science to it? • Need a guide for … • QA/QC • Training new employees

7. Edward R.Tufte Vision for good graphics … • Induce the viewer to think about the substance • Encourage the eye to compare different pieces of data • Reveal data at several levels of detail • Serve a clear purpose • Convey complex ideas with clarity, precision, & efficiency Source: Tufte, Visual Display of Quantitative Information and Tufte.com

8. Principle: Readers are alert and caring; they may be busy, eager to get on with it, but they are not stupid Rule:Don't patronize or distrust audience (corollary -- know your audience) Rule:Don't lieRule:Don't use unstandardized time series and insultingly simple designs SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES

9. Principle: Include statistical and substantive expertise, not just artistic Rule: Maximize the ratio of "data-ink" to total ink (minimize grids, etc., that mask data) Rule:Erase redundant data-ink (within reason) and non-data-ink (most ink should convey useful information) Rule:Forego "chart-junk" Rule: Mobilize every graphical element, perhaps several times over, to show the data Rule:Show all the dataRule: Maximize data density and the size of the data matrix, within reason (graphics area can be shrunk way down) Rule:Comparisons must be enforced within the eye span of the reader SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES

10. Principle: Use graphical methods that organize and order the flow of graphical information to the eyes Rule: Consider purpose of the graphic or report (in-house or external, preliminary or final) Rule: Use relational graphics to explain Rule:Consider shades of gray, in lieu of color, to indicate visual heirarchy Rule: For encoding abstract information, more than 20 to 30 colors frequently produce not just diminishing but negative returns Rule:Use colors found in natureRule: Large areas of pure, bright, or very strong colors have loud, unbearable effects when adjacent to each other Rule: Placing light, bright colors mixed with white next to each other usually produces unpleasant results, especially if the colors are used for large areas Rule: Large areas background or base-colors should do their work most quietly Rule: If a picture is composed of two or more large, enclosed areas in different colors, then the picture falls apart Rule: On bringing color to information, "above all, do no harm." Rule:Use gray shading or screens rather than cross-hatching, to avoid Moire effects SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES

11. Principle: Graphic elegance is often found in simplicity of design and complexity of data Principle: Provide efficient interpretation and facilitate comparisons of data Rule: Hi-density graphics Rule: Comparative and/or multivariate Rule: Usually from large data matrix Rule: Narrative content SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES

12. Miscellaneous Rule: Revise and edit Rule: Words and pictures belong together Rule:Never use pie charts! Rule: If the nature of the data suggests the shape of the graphic, follow that suggestion, otherwise move toward horizontal graphics about 50% wider than tall Rule: Don't omit information from the data -- show everything Rule: For ease in reading, avoid all-caps and sans serif fonts Rule: Make only minimal use of lines in tables, especially vertical lines SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES

13. From: The New Yorker, September 2003

14. Harvard Response to Tufte … KISS

15. Some Examples to Follow … - Avoid graphical puzzles • Organize and order the flow of information • Avoid “chartjunk” • Provide, maintain appropriate context • Some examples we like Tufte, Visual Display of Quantitative Information

16. Purpose of 3-D?

17. Does this encourage eyes to compare difference? Non-data ink Source: NHTSA website

18. No label of values. Is this portion of the graph needed?

19. Indicate pertinent (causal) events, e.g. legislation – speed limit change, BAC, restraints. Tufte: chronology not causation w/o clear mechanism Source: FARS website

20. Source: 2000 Iowa Crash Facts Indication of pertinent events (causal?)

21. - Too much white space – increase data density. - Source? + Context is critical to interpretation!

22. Graphical Integrity • Do not distort the data • Numeric quantities graphic representing, however… • perception of graphical differences vary • Use labeling to defeat graphical distortion & ambiguity • Use a table to show the numbers for small data sets (~20) • Use graphics to show large data sets Source: Tufte, Visual Display of Quantitative Information

23. Crash Rates < 1000 per HMVM Proportional Line Thickness Crash Rates (All) Proportional Line Thickness Sometimes in crash analyses it’s not possible to maintain proportionality.

24. Source: NHTSA website

25. Major Cause of Fatal v. All Crashes Iowa Rural, Secondary Paved Roads 2001 - 2003 What do these numbers mean? + High data density. + Context. +/- Facilitates comparisons, but difficult between tables.

26. Distortion:Detail v. Aggregation • Detail • aid in identification of specific cases • avoid deceptive effects of aggregation • may become cluttered with detail • Aggregation • can show rates • reduces redundancy & complexity • can mask and distort data Spatial, temporal, attribute??? Source: Tufte, Visual Display of Quantitative Information

27. Avoid Graphical Puzzles • Graphic puzzle: must be interpreted through verbal rather than visual process • Non-puzzle: visual to verbal is quickly learned, automatic, and implicit • Color often generates puzzles • Varying shades of gray have a natural visual hierarchy Source: Tufte, Visual Display of Quantitative Information

28. Graphical puzzle?

29. School-Age Pedestrian Crashes Purpose: Assess school age pedestrian crashes near middle schools. Effective? City of Des Moines 1995 to 1999 Ages 5 to 19 August 15 to June 15 Monday to Friday 7:00 a.m. to 5:00 p.m. + Significant detail – age and injury severity. - Graphical puzzle? - Many crashes may occur at same location. Not apparent. - Context? All pedestrian crashes during these times (next slide). Middle Schools

30. Pedestrian Crashes City of Des Moines 1995 to 1999 + Significant detail – age and injury severity. + Provides context for school age crashes, partially. - Graphical puzzle? - Many crashes may occur at same location. Not apparent.

31. Case studies in Highway Safety

32. Fixed Object Struck Crash Mapping for Safety Audits Graphical Puzzle? • Crashes are almost unique (many possible combinations of attributes) • Severity - Type - Conditions • Cause - Time/day - … • Need many more data points than categories for statistical significance • A similar effect on mapping of crashes

33. Improved presentation through symbology. Distorted scale.

34. Detail/Aggregation for Crash Mapping • Crash data are inherently spatial – a map is a natural display mode • Crashes are “rare” statistically • Crash spacing is sometimes too sparse (wasted space) or too crowded (overlapping)

35. Crash Severity Multiple representations of same crash data. Injuries (by Severity) Is the message clearly conveyed? Crash Frequency (by Road Inventory Segments) What is the impact of data aggregation? Crash Frequency (by County-Route)

36. Segmentation: EURORAP

37. Double Counting / Omission Sometimes we have a difficult choice … double count or omit relationships. e.g., crashes often have multiple causes – to which (set) do we attribute/focus mitigation efforts?

38. Does not facilitate comparison. • Double counting • Order? Factor based?

39. + Ordered. Appropriate? + Facilitates comparisons. - Double counting. - Lacking context?

40. Graphics for Before and After Analysis: Identifying Yoked Pairs • Selection Set • 4 or more lanes • AADT w/in 20% of study segment • City population w/in 20% of study city • Segment length similar • Visual Checks for Similarity (context) • Land uses • Access density • Other (railroads, trails, etc) • Crashes (segment should have crashes for comparability)

41. Graphics for Before and After Analysis Source: HSIS

42. Graphics for Off-System Analysis: Complementing the Database

43. Identification of Access Management Problem Corridors With Missouri Department of Transportation Crash Data and USGS Land Cover Data Overlay Left-Turn and Right Turn Crashes Missouri DOT District 5, 1997-1999 Aerial view of Missouri Boulevard, Jefferson City. Five-Lane With Two-Way Left-Turn Lane and High Driveway Density. Missouri Boulevard Ground Level View--Strip Commercial Land Use and High Traffic Volume.

44. Using GIS Crash Data To Visualize Access Management Problems Example Corridor: US 6 (Euclid/Douglas Avenues) In Des Moines, Iowa • On relatively well-managed portion of • corridor to the West (far left-hand side of map)--few mid-block • crashes, intersection crashes are the majority • On less-managed portions of • corridor (center and right-hand • side of map)--mid-block • crashes are much more common Des Moines River Bridge (No Access) • Height of stacked bars • reflects the number of • crashes at locations along • US 6 in 1996 • Crash locations on other routes are shown as green dots

45. Systematic Identification of High Crash Locations • Head-on • Horizontal • Curves • Urban, Four-lane • Undivided Corridors • Four-lane, Rural • Expressway Intersections • Fixed • Objects

46. Appropriate staffing level?

47. Command Menus Map Manipulation Buttons Crash Data Collection • Incident Location Tool • GIS-based • >230 agencies • >60% Crashes • Drivers Services • Remaining % • TraCS Utility, used by several states • Improved crash locations, resulting in improved analyses. Map Window Incident Location Tool – Graphic User Interface

48. Questions/Comments? reg@iastate.edu Thanks!