Calculating traffic accident rates

In this tutorial, we will calculate the rate of traffic accidents (number of accidents per 1,000 people) for Bristol, UK. We will be using the following datasets. The first one is available in the demo tables section of the CARTO Data Warehouse, while the latter two are freely available in our Spatial Data Catalog.

  • Bristol traffic accidents (CARTO Data Warehouse)

  • Census 2021 - United Kingdom (Output Area) [2021] (Office for National Statistics)

  • Lower Tier Local Authority (Office for National Statistics)

Alternatively, you could use a different traffic accident dataset from another source (or a dataset on a different topic, such as crime incidence or service provision), and use a different demographic boundary dataset from our Spatial Data Catalog to create your own custom analysis.

Step 1: Converting accident data to Spatial Indexes

In this step, you'll convert the individual accident point data to aggregated H3 cells.

  1. Create a Workflow using the CARTO Data Warehouse connection.

  2. First, drag the Lower Tier Local Authority data onto the canvas. It can be found under Sources > Data Observatory > Office for National Statistics.

  3. Connect this to a Simple Filter component. Set the filter to do_label is equal to "Bristol, City of".

  4. Next, connect the filter results to a H3 Polyfill component, and set the resolution to 9. This will create a H3 grid covering the Bristol area.

  5. Now, drag the Bristol traffic accidents table onto the canvas. It can be found under Sources > Connection > CARTO Data Warehouse > demo tables.

  6. Connect this to a H3 from GeoPoint component, setting the resolution of this to 9 also. This will create a H3 index for each input point.

  7. Connect the output of H3 from GeoPoint to a Group by component. Set the group by column to H3, and the aggregation column to H3 (count). The result of this will be a table with a count for the number of accidents within each H3 cell.

  8. In the final stage for this section, add a Join component. Connect the H3 Polyfill component to the top input, the Group by component to the bottom input, and set the join type to Left.

  9. Run!

The result of this will be a H3 index covering the Bristol area with a count for the number of accidents which have taken place within each cell. Now let's put those counts into context!

Step 2: Enrich the grid with population data

In this section of the tutorial, we will enrich the H3 grid we have just created with population data from the UK Census.

  1. Drag the Census 2021 - United Kingdom (Output Area) [2021] table onto the canvas from Sources > Connections > Office for National Statistics.

  2. Drag an Enrich H3 Grid onto the canvas. Connect the Join component (Step 1 point 4) to the top input, and the Census data to the bottom output.

  3. The component should detect the H3 and geometry columns by default. From the Variables drop down, add "ts001_001_ff424509" (total population, you can reference Variable descriptions for any dataset on our Data Observatory) and specify the aggregation method as SUM. This will estimate the total population living in each H3 cell based on the area of overlap with each Census Output Area.

Run the workflow.

Step 3: Calculating the accident rate & hotspot analysis

Now we have all of the variables collected into the H3 support geography, we can start to turn this into insights.

  1. First, we'll calculate the accident rate. Connect the output of Enrich H3 Grid to a new Create Column component. Call the new column "rate".

  2. Set the expression as CASE WHEN h3_count_joined IS NULL THEN 0 ELSE h3_count_joined/(ts001_001_ff424509_sum/1000) END. This code calculates the number of accidents per 1,000 people, unless there has been no accident in the area, in which case the accident rate is set to 0.

  3. Now, let's explore hotspots of high accident rates . Connect the output of Create Column to a new Getis Ord component which is the hotspot function we will be using. Set the value column as "rate" (i.e. the variable we just created), the kernel to gaussian and the neighborhood size to 3. Learn more about this process here.

  4. Finally, connect the results of this to a Simple Filter, and the filter condition to where the p_value is equal to or less than 0.05; this means we can be 95% confident that the locations we are looking at are a statistically significant hotspot.

You can explore the results below!

💡 Note that to be able to visualize a H3 index in CARTO Builder, the field containing the index must be called H3.

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