This is part five in a five part series on Analytics and GIS. Part one looked at a job posting in Edmonton, part two on scoring, part three on model builder, and part four on discrimination.
I’m excited for Edmonton and Edmontonians. The decision to hire a predictive analyst for road safety is an awesome one, and one that ought to generate real results well into the future.
There will be no real way for any individual Edmontonian to know if their life was saved as a result of the recommendations realized and applied as a result of this program. On the aggregate, over time however, fewer fatalities and serious injuries should accrue. I’d like to see a long term goal of 0 fatalities in Edmonton. Why should people die just because they made a silly mistake? Do we really believe that anybody making a genuine mistake is deserving of death?
The other way of thinking about it is that the odds of something truly horrible happening to each individual will go down just a little bit. Ultimately, health care costs will come down – and that’s something that nearly every Canadian is in favor of.
Analytics and GIS: Other Applications
Whereas I believe, rightly or wrongly from my experience in the Canadian Transportation Research Forum (CTRF), that most solutions in logistics and transportation economics are pretty much optimized (hollowed out), other areas are just now emerging.
- Uber is using analytics and GIS to bust up one of the last remaining FDR policies in transportation.
- Canada’s competition bureau ruling against CREA promises to open up more innovation in the scoring of neighborhoods and the relationship to real estate.
- Maybe our elected officials would set better policy if they felt comfortable engaging with the evidence?
So now that you’re aware of potential solution sets, is there something else you can think of doing with them?
I’m Christopher Berry.
I tweet about analytics @cjpberry
I write at christopherberry.ca