At the upcoming American Society of Criminology conference in Philadelphia I will be presenting a talk, Crime Data Visualization for the Future. Here is the abstract:

Open data is a necessary but not sufficient condition for data to be transparent. Understanding how to reduce complicated information into informative displays is an important step for those wishing to understand crime and how the criminal justice system works. I focus the talk on using simple tables and graphs to present complicated information using various examples in criminal justice. Also I describe ways to effectively evaluate the size of effects in regression models, and make black box machine learning models more interpretable.

But I have written a paper to go with the talk as well. You can download that paper here. As always, if you have feedback/suggestions let me know.

Here are some example graphs of plotting the predictions from a random forest model predicting when restaurants in Chicago will fail their inspections.

I present on Wednesday 11/15 at 11 am. You can see the full session here. Here is a quick rundown of the other papers — Marcus was the one who put together the panel.

• A Future Proposal for the Model Crime Report – Marcus Felson
• Crime Data Warehouses and the future of Big Data in Criminology – Martin Andresen
• Can We Unify Criminal Justice Data, Like the Dutch and the Nordics? – Michael Mueller-Smith

So it should be a great set of talks.

I also signed up to present a poster, Mapping Attitudes Towards the Police at Micro Places. This is work with Albany Finn Institute folks, including Jasmine Silver, Sarah McLean, and Rob Worden. Hopefully I will have a paper to share about that soon, but for a teaser on that here is an example map from that work, showing hot spots of dissatisfaction with the police estimated via inverse distance weighting. Update: for those interested, see here for the paper and here for the poster. Stop on by Thursday to check it out!

And here is the abstract:

We demonstrate the utility of mapping community satisfaction with the police at micro places using data from citizen surveys conducted in 2001, 2009 and 2014 in one city. In each survey, respondents provided the nearest intersection to their address. We use inverse distance weighting to map a smooth surface of satisfaction with police over the entire city, which shows broader neighborhood patterns of satisfaction as well as small area hot spots of dissatisfaction. Our results show that hot spots of dissatisfaction with police do not conform to census tract boundaries, but rather align closely with hot spots of crime and police activity. Models predicting satisfaction with police show that local counts of violent crime are the strongest predictors of attitudes towards police, even above individual level predictors of race and age.