Over-representation reports allow users to compare a geographic region (city, county, or district) to the larger geographic region (county, district, state) in which it resides.

Overrepresentation reports use geo-located crashes based on the reported location of the crash (as it appears in the crash report). Occasionally there is a discrepancy between the reported crash location, and the geo-located crash location - if this is the case, crashes reported inside the selected boundary, but geo-located outside the selected boundary, will not appear in the report. This may lead to discrepancies between crash statistic reporting.

Over-representation reports highlight key differences in metrics between geographic regions, allowing users the ability to quickly identify potential crash types or crash characteristics that may be over-represented in the selected geographic region.

The over-representation report displays all of the metrics contained within the Query, broken into a number of columns. The first column, labeled Filtered displays the geographic region that was identified through the Geo Filter in the Query (either a city, county, or district/region - if configured for your organization). The next column(s) will represent the same metrics for the county, district and/or state in which the filtered geographic region resides.

In the example below, a city has been selected using the City (Geo) filter. The Filtered column contains the metrics for the filtered city. The next two columns, County and State represent the county of the selected city, and the statewide totals for each metric. The metrics are depicted as a percentage of total crashes, allowing for a meaningful comparison across different sized regions.

In the example below, the selected city has 3,224 Intersection Related crashes - which accounts for 44.45% of all crashes for the city. For the county and the state, Intersection Related crashes account for 43.43% and 39.28% of total crashes, respectively.

Over-representation reports highlight key differences in metrics between geographic regions. Any change of +/- 5% or 2x a given metric between geographic regions will be highlighted by the over-representation report. In the example below, Straight Horizontal Alignment crashes account for 68.47% of total crashes, compared to the county, which only experiences 42.64% of the same crash type. Because the number of Straight crashes is greater than 5% larger than the next lowest value, it has been highlighted. Additionally, the county has a significantly larger percentage of Unknown Horizontal Alignment crashes than the selected city, and the state - so it has been highlighted as well.

To generate an Over-representation report, follow the steps outlined below: