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

4. Source: NHTSA website

5. Source: UC Berkeley “Access”

6. 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 Purpose

7. Edward R.Tufte Source: Tufte, Visual Display of Quantitative Information and Tufte.com 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

8. SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES 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 lie Rule: Don't use unstandardized time series and insultingly simple designs

9. SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES 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 data Rule: 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

10. SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES 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 nature Rule: 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

11. SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES 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

12. SUMMARY OF TUFTE'S MORAL PREMISES, PRINCIPLES, AND RULES 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

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

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

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

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. 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. School-Age Pedestrian Crashes Middle Schools Purpose: Assess school age pedestrian crashes near middle schools. Effective? + 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).

30. City of Des Moines 1995 to 1999 Pedestrian Crashes + 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. Crash Mapping for Safety Audits 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 Fixed Object Struck Graphical Puzzle?

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

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. Source: HSIS Graphics for Before and After Analysis

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

43. 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. Left-Turn and Right Turn Crashes Missouri DOT District 5, 1997-1999 Identification of Access Management Problem Corridors With Missouri Department of Transportation Crash Data and USGS Land Cover Data Overlay

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 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 Des Moines River Bridge (No Access )

45. Systematic Identification of High Crash Locations 1.Horizontal Curves 2.Four-lane, Rural Expressway Intersections 5.Urban, Four-lane Undivided Corridors 3.Fixed Objects 4.Head-on

46. Appropriate staffing level?

47. Incident Location Tool – Graphic User Interface Map Manipulation Buttons Command Menus Map Window 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.

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