Purpose
Develop an interactive descriptive and diagnostic analytic tool that can help target road safety interventions to improve public safety.
Create a base for a future predictive model of ACT road safety.
Description
The Data Driven visualisation is an interactive, online dashboard that allows users to understand the relationship between traffic cameras and road safety, helping to reveal the most dangerous areas on ACT roads. Suburb traffic risks are mapped by suburb along a colour gradient calculated on the basis of collisions causing injury as a function of traffic flow rates.
Data Sources
We used six sources of GovHack open data to retrieve, join and blend:
- Traffic offence (average and maximum infringements) and traffic flow information.
- Fixed and mobile speed camera locations.
- Revenue received by speed camera locations.
- Number of registered vehicles in each ACT suburb.
- Population and proportion who drive to work from ABS Census data by ACT suburbs.
- Vehicle collision and fatality data for the ACT by suburb.
These measures help users to understand how various factors relate to traffic infringements, injuries and fatalities.
Method
By mapping latitudinal and longitudinal camera locations to ABS ESRI suburb polygons, we were able to blend six sources of open data to create a location-based traffic safety visualisation for Canberra.
We conducted exploratory statistical analysis in SPSS as a means of testing the validity of predictive analytics models. We found more points of open data (that were not available) were needed to support this analysis.
We prepared, manipulated and blended our data sources using Microsoft Excel and Alteryx (spatial and other) workflows. We then input all data into Tableau, using a custom Mapbox API map which includes road congestion data from Google Maps.
We presented this information for our video using a Microsoft Surface Hub.
Challenges
The quality of open data on road accidents in the ACT is poor compared to States such as Victoria. This limits the validity of predictive analytics, as there are many unknown variables and factors which influence road safety.