Why I Wrote Data Science for Crime Analysis with Python

I am looking at the table of contents and it looks like the topics are just the same ones as any other Python for data analysis book. Is there anything specific in the book that is specific to "Crime Analysis" or it is just another data analysis task?

"Plug-in could not be loaded". Reminded me of the good old Java Applets / ActiveX days.

`<object data="/images/DS_PythonCrimeAnalysis_EarlyRelease.pdf" width="100%" height="500"></object>` is causing that problem.

Here's the link: https://crimede-coder.com/images/DS_PythonCrimeAnalysis_Earl...

Another book using shrink wrapped software:

https://www.esri.com/en-us/esri-press/browse/modern-policing...

And a software-agnostic book focused on the theory:

https://www.esri.com/en-us/esri-press/browse/understanding-c...

Disclosure: I work at Esri but not on desktop software or the press teams, and I bought both books on Amazon with my own money.

> One critique of predictive analytics is that machine learning is racist. This is misleading – predictive policing is a method to identify areas or people that can benefit the greatest from specific interventions.

This is just complete misrepresentation.

Characterizing machine learning itself as inherently racist is an oversimplification. Predictive tools often use biased data from the past, which can make their predictions unfair. The bias in predictive policing stems from historical over-policing in Black neighborhoods compared to white ones, for one example. Using these biased predictions leads police to focus on the same areas and people repeatedly, creating a self-fulfilling cycle. This happens despite evidence showing that people across different communities commit similar minor crimes at comparable rates. The system essentially reinforces existing patterns of unequal law enforcement rather than reflecting true crime distribution.

I see you've written lots of papers on predicting crime. Have you ever gone back and looked at your predictions vs actual reports?

I wish for once people would try to turn this inward on the system rather than support armed agents of the law to further reinforce harmful systems. You could design a system to see how a particular type of outcome from a law enforcement officer's intervention results in the downstream effects of that intervention. Does that person ever re-offend? Does that person instead never touch the legal system again? If they don't re-offend, what is the LEO doing that we could encourage more officers to practice?

There is research to support the idea that less punitive intervention means less recycling through the CJ system. You could look at prosecutors on a single team, and look at diversionary disposition outcomes, with downstream criminal justice data from CJIS systems, to see what outcomes individual prosecutors are doing and how they're actually meaningfully impacting people's lifelihoods, likelihood to reoffend and community safety. Instead, we just continue to reinforce cycles of harm. It's shameful.