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Data Dashboard Disclaimers

What’s the data being shown? 

The data used in our dashboards is Stats19 data; it covers injury collisions on public roads reported to/recorded by the Police. The collection and recording of the Stats19 data is governed by the Department for Transport.

Only collisions within the Devon County Council area are included. Torbay and Plymouth are not included as they form their own unitary councils and have their own Road Safety departments.

The Council works with the Police and Department for Transport to quality check and validate the data. While every reasonable effort is made to ensure that the information provided is correct, no guarantees for the accuracy of information are made.


Injury Severity

Serious injury examples: Any type of fracture, internal injury, severe cuts, crushing, burns, concussion, severe shock, hospital in-patient. Click on this ‘i’ button to see SLIGHT injury definitions and more info

Examples of slight injury include sprains, neck whiplash injury, bruises, slight cuts, slight shock requiring roadside attention.

Please note that the Department for Transport (DFT) Stats19 recording rules state that if a road casualty died more than 30 days after the collision their injury should be recorded as serious. Also note that anyone who died as a result of a confirmed medical episode or confirmed as suicide are not included in this Stats19 dataset.


Area Figures (Districts, Towns, Parishes)

These are statistics that relate to which area the collision occurred in (as opposed to where the person injured lived).

Where a boundary map of areas is shown the darker the colour the higher the count of casualties. Note that comparing geographic performance is a complex picture as each area varies in traffic patterns and volumes, distribution of major roads and population.

The areas typically shown relate to the Devon County Council area. Torbay and Plymouth are separate Unitary Council so we do not routinely collect their data.


Casualties vs Collisions

Casualty numbers refer to the number of people hurt per incident; collision numbers are the count of incidents themselves. Casualty numbers are usually higher than collision numbers as often a collision often involves more than one casualty.



This is the persons age at time of injury, where their age is known/recorded.



The data shows where gender was known/disclosed.


Urban / Rural Classification – LSOA’s (Lower Super Output Areas)

Lower Super Output Areas have been used to determine whether the collision occurred on a rural or urban road.

Note this is a new method used for 2020 data outputs, previously we used a local generic definition of town areas with 7,000+ population.

More information on LSOA: LSOAs, LEPs and lookups : A beginner’s guide to statistical geographies (


Casualty Distance From Home

The distance between where the collision occurred and the home postcode of the person injured. It uses the centre point of the postcode area of the person, this centre point is known as the postcode centroid.


Adjusted vs Non Adjusted Data  

In December 2015 serious injury casualties started to be recorded more accurately due to a change in the reporting system used by the Police called CRaSH. Some serious injuries may previously have been classified as slight injuries which means that the 2016 data and earlier will show a lower number of serious casualties than after 2016. Our reports usually show unadjusted figures by default.

The Department for Transport have researched a method to back calculate the statistical probability of a record injury severity being miscoded but this method is currently too sensitive to use in small numbers so any projected adjustments are shown for the whole of Devon across all road users – the projected figures can be seen in our Annual Stats Report (see Area Comparison section).


Statistical Test: Poisson Distribution

ROSPA (Royal Society for Prevention of Accidents) guidelines for using statistical probability within road safety accident investigation context suggests the Poisson distribution is used when identifying the possibility of an annual change being down to random fluctuation. Calculations look at the probability of the latest year being significantly different from the current ten year average (the ten years include the latest year).

We use this test in various data tables, and illustrate any significant results using flag icons.

  • A red flag indicates a significant increase in the latest year compared to the ten year average. The confidence value is higher than 95%.
  • An amber flag represents borderline significant increase equating to a confidence value between 85-94%
  • A green flag represents a significant decrease in the latest year.


Statistical Test: Chi Square Value

This can be used to compare data with a control set to determine if the difference between the control is significant. Often used where percentages are analysed e.g. darkness percentage, single vehicle percentage.



More Info About Stats19 Data

Please see our Stats19 data FAQs page for a full list of questions and answers about this dataset.