I have been provided a dataset of drink driving incidents aggregated into postcodes. This is where the incident happened, not where the driver lives.
Postcode 1 - 12 incidents
Postcode 2 - 163 incidents
This is for an entire state in Australia, so just showing total counts will pretty much be a population map.
Therefore I will need to do a bit of normalisation.
With postcodes not being equal-area, my obvious choices are either area or population (which I have the information for.
Population might not be accurate, as there might be factors like particular rural areas being notorious for drink driving due to no public transport, or other factors.
Area seems incorrect, as lets say we have two equal area postcodes, one rural and one urban, is it fair to normalise by area alone?
I then considered other options such as calculating the total lengths of roads within each suburb and using that.
I don't have traffic data, but in an ideal world, would using some sort of traffic count data be also an option?
The essential question I want the map to answer is, "Where are the hotspot areas for drink driving within this state of Australia?"
How would you go about this?
The news guy didnt quite follow the script/advice I provided, and I really wanted to avoid 3D, but you know media. Anyhow, not perfect, but appreciate your help!