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Pages 45-52

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From page 45...
... B-1 This appendix discusses the methodology used to select a sample of jurisdictions for in-depth review of distracted driving laws, educational and outreach efforts, enforcement strategies, and legislation challenges and successes. B.1 Proposed Sample of Jurisdictions As indicated in the project work plan, following an initial scan of existing distracted driving legislation, a cluster analysis of states and provinces with similarly structured legislation was conducted to group the jurisdictions for further study.
From page 46...
... B-2 Using Electronic Devices While Driving: Legislation and Enforcement Implications of population characteristics, so that the information gathered covered a range of laws, challenges, and populations. The sample represents jurisdictions with stronger distracted driving laws somewhat more heavily, since the objective of this project is to develop model legislation as well as best practices for education and enforcement.
From page 47...
... Proposed Sample of Jurisdictions and Methodology B-3 law, or without a particular element in the law, was assigned a score of 0 for the variable. In general, more points indicate a stricter law.
From page 48...
... B-4 Using Electronic Devices While Driving: Legislation and Enforcement Implications In both data sources, publicly owned vehicles were excluded because privately owned vehicles are of primary interest for this analysis and because for both states and provinces the data sources note that the publicly owned vehicle counts may be of poor quality. B.2.2.5 Vehicle Miles Traveled, 2017 For states, these data were obtained from FHWA (https://www.fhwa.dot.gov/policyinformation/ statistics/2017/)
From page 49...
... Proposed Sample of Jurisdictions and Methodology B-5 -0.57 (p < 0.0001) , which is moderate-to-strong, negative, and significantly different from zero.
From page 50...
... B-6 Using Electronic Devices While Driving: Legislation and Enforcement Implications B.2.4.2 K-means The K-means clustering algorithm is somewhat more advanced because instead of working iteratively by joining pairs, it is able to find optimal clusters across the whole dataset at once. It works by first randomly assigning each observation to a cluster.
From page 51...
... Proposed Sample of Jurisdictions and Methodology B-7 Note that these cluster descriptions are intentionally general, and each jurisdiction in a cluster may not perfectly agree with the high-level description. However, since the goal is simply to group jurisdictions that are roughly similar in order to end up with variety in the final sample, these clusters and descriptions work well.
From page 52...
... B-8 Using Electronic Devices While Driving: Legislation and Enforcement Implications the upper portion of the plot. The Canadian provinces are shown in tan (also with stricter laws, since they all have law scores > 0)

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