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Road Traffic Injuries in California

Pedestrian Fatalities and Injury Rates By Transport Mode, 2002-2010

By Health [source]

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About this dataset

This table contains data on the number of annual fatal and severe road traffic injuries per population and per miles traveled by transport mode, for the state of California and its various regions, counties, county divisions, cities/towns, and census tracts. Road traffic injury is an important public health issue in California; it ranks second among leading causes of death for people under 45 in the state with an average of 4,018 fatalities per year (2006-2010). In addition to this terrible statistic are also elevated risks for certain population subgroups; Native American male pedestrians experience 4 times the death rate as Whites or Asians while African-Americans and Latinos experience twice the death rate as Whites or Asians.

This dataset has been generated through a combination of datasets--SWITRS (Statewide Integrated Traffic Records System), CHP (California Highway Patrol), 2002-2010 data from TIMS (Transportation Injury Mapping System)--and presents itself as part of a healthy community indicators project from the Office of Health Equity. By looking at this data users can learn about which communities are bearing a disproportionate share in terms of pedestrian/car fatalities due to road traffic injuries without taking into account additional factors such as socioeconomic status or gender. Through understanding these statistics more accurately we can begin to take steps towards promoting safe transportation practices across all communities

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How to use the dataset

Welcome to the Road Traffic Injury dataset! This dataset contains information on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode in California from 2002-2010. We hope that this data will be useful to you in understanding trends, evaluating safety policies, and tracking changes in transportation safety over time.

In this guide, we’ll provide an overview of the dataset so you can start making use of it. We’ll cover what each column means and how you can use them for further analysis and exploration.

The columns in this dataset include detailed information about each road traffic injury event:

• ind_definition: Definition of the indicator – i.e., whether it is a rate (per population) or a risk ratio (relative to some reference group).  
  

• reportyear: Year of the report; 
  

• race_eth_code/name: Race/ethnicity code and name provided;  
  

• geotype/value/name: Type of geographic area included as well as its corresponding value or name; 
  

• county_fips/name: FIPS code for counties, as well as their corresponding names;   
  

• region_code/name: Region codes with accompanying region names provided respectively;

• mode: Mode of transportation associated with these events (motorcycles, pedestrians, buses & rail passengers);

• severity : Severity level (fatal or severe);

• • 11): Number of injuries occurring within that time period within each race ethinic category (injuries, totalpop [its total population], poprate [the rate by which there are injuries happening]) ;

12)- 15): Confidence Intervals associated with 95% Lower & Upper Limits (LL 95CI [Lower than 95% range] & UL95CI [Upper than 95% range]) by population rates (poprate) & miles traveled rates (avmtrate)

16): Standard Error Rates calculated by both Population Rate(poprate) & Miles Traveled Rate(amtrate) ; 19), 20), 23)}: Relative Risk Ration Rates providing values compared bottom line across geographic regions respectively {Population Rate(CA RR poprate), Miles Traveled Rate()) ; 21), 22}, 24), 25 => Decile Rankings arranging breakdowns from 1-10 into 10 respective categories calculations

Research Ideas

• The dataset can be used to develop maps that show impact of traffic injuries in different areas by race, geotype and mode.
• It can be used to measure the performance of safety improvement interventions by comparing changes in injury rates at certain county or cities before and after safety tactics have been implemented.
• It could also be used to study the effects of individual driving behaviors on collision related injury rates by analyzing data from counties with disparate levels of enforcement

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: Open Database License (ODbL) v1.0

• You are free to:
  • Share - copy and redistribute the material in any medium or format.
  • Adapt - remix, transform, and build upon the material for any purpose, even commercially.
• You must:
  • Give appropriate credit - Provide a link to the license, and indicate if changes were made.
  • ShareAlike - You must distribute your contributions under the same license as the original.
  • Keep intact - all notices that refer to this license, including copyright notices.
  • No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material.
  • No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: RoadTrafficInjuries_DD.csv

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File: rows.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Health.