Policy Management: How data and information impacts the ability to make policy decisions:

Transcription

1 Policy Management: How data and information impacts the ability to make policy decisions: Louis Cripps Regional Transportation District, Asset Management Denver, Colorado

2 Quick exercise... What do these look like?

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6 Apophenia or Patternicity The experience of perceiving patterns or connections in random or meaningless data.

7 Message - WE are built to see patterns. Our awareness of this can help us guard against seeing patterns in data that may not exist.

8 Goals of this presentation: A discussion based in fun Help us ask better questions of our data Learn to watch for common errors Improve decisions based on data How to ask better questions Understanding Measurements

9 Transit Example We can see seasonality in data where none exists.

10 Average Monthly Boardings 2000 to January February March April May June July August September October November December

11 Average Monthly Boardings with Control Limits Average Monthly Boardings Lower Bound Upper Bound

12 With Minimum and Maximum Observed Ridership

13 Tukey s B Post Hoc Test Group 1 = October has Unusually High Ridership Group 2 = June, July and December have Unusually Low Ridership July 4,882,658 December 4,916,457 June 5,014,757 February 5,216,807 5,216,807 January 5,269,090 5,269,090 November 5,317,047 5,317,047 May 5,375,980 5,375,980 August 5,495,968 5,495,968 March 5,543,448 5,543,448 April 5,596,196 5,596,196 September 5,612,340 5,612,340 October 5,979,804

14 Instruments and Tools

15 Data Analysis and Visualization It is difficult to see the message in rows and columns of data. The message if properly displayed is easy to see and conveys an accurate story.

16 Difficult to quickly see signals

17 Easy to quickly see signals

18 Easy to quickly see signals

19 Seeing a pattern that is not there Context cues Failure to see a pattern Coincidence How do you know? Tools Statistics, control charts and other information were created for the purpose of preventing ourselves from relying on our instincts Statistics can also hide important information [A common statistic can hide] o Variability

20 The Average Perhaps the most commonly used statistic is the average City one average temp 65 City two average temp 70 Question: Should I pack a coat?

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22 Averages Can Hide Variability Inconsistent Consistent Run Run Run Run Run Run Run Run Run Run Run Run Run Run Run Run Run Run Run Run Average

23 The Average Works well: Little variability Small changes don t matter, but magnitude does Works poorly: Variability impacts decision

24 Definition of Measurement Quantitatively expressed reduction of uncertainty based on one or more observation.

25 A problem well stated is a problem half solved Charles Kettering Clarification If it matters at all it is detectable / observable If it is detectable, it can be detected as an amount or range of possible amounts If it can be detected as a range of possible amounts, it can be measured.

26 It s a numbers game Scales Different audiences Different purposes Linear Temperature Ordinal Star rating Exponential Microorganism Logarithmic PH of Solutions

27 Ordinal scale

28 Interval or Linear Scale Numerical scale in which intervals have the same interpretation throughout. Fahrenheit scale of temperature Difference between 30 and 40 degrees represent the same temp change as between 80 and 90 degrees. Can t mix Celsius vs. Fahrenheit

29 Ratio Scales Money, Voltage Math can be applied to these measures Zero position indicates the absence of quantity being measured

30 Dash-boards and Data Displays Gee Whiz Curve Accuracy versus Precision Context

31 Gee Whiz Goodness "Before" "After"

32 Same Data 100% Goodness 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% "Before" "After"

33 What Excel thinks you want 101% 100% Goodness 100% 99% Goodness 99% 98% 98% 97% "Before" "After"

34 Graphic displays can illustrate variability Number of Stops with Average Weekday Boarding Level More than 0, Less than or more, less than or more, less than or more, less than or more, less than or more

35 Refining graphic information displays 3000 Number of Stops with Average Weekday Boarding Level to.99 1 to to to to to to to to 1000 Over 1000

36 Proportion is Contextual 60% Ridership Change in Percentage 50% 40% 33% 20% 0% 0% -20% -40% -60% -50% January February March April

37 Context 60% Ridership Change in Percentage 1,200,000 40% 1,000,000 20% 800,000 0% January February March April 600,000 Ridership Change in Percentage Actual Ridership -20% 400,000-40% 200,000-60% 0

38 Side Effects of Exact In todays world we expect precision. But can precision lead to a poor decision?

39 Presenting Ideas How can we help our audience understand our data?

40 Understanding Magnitude Million Billion Trillion Seconds Minutes Hours Days Years 1 Million 1,000,000 16, Billion 1,000,000,000 16,666, ,778 11, Trillion 1,000,000,000,000 16,666,666, ,777,778 11,574,074 31,709.79

41 Finished Thank you Come on, we covered this It s just a cup of coffee