Predicting Police Cellphone Locations – Weaponizing Open Data

Predicting And Mapping Arrest Types in San Francisco with LightGBM, R, ggplot2 by Max Woolf.

Max does a great job of using open source data SF OpenData to predict arrest types in San Francisco.

It takes only a small step to realize that Max is also predicting the locations of police officers and their cellphones.

Without police officers, you aren’t going to have many arrests. 😉

Anyone operating a cellphone surveillance device can use Max’s predictions to gather data from police cellphones and other electronic gear. For particular police officers, for particular types of arrests, or at particular times of day, etc.

From the post:

The new hotness in the world of data science is neural networks, which form the basis of deep learning. But while everyone is obsessing about neural networks and how deep learning is magic and can solve any problem if you just stack enough layers, there have been many recent developments in the relatively nonmagical world of machine learning with boring CPUs.

Years before neural networks were the Swiss army knife of data science, there were gradient-boosted machines/gradient-boosted trees. GBMs/GBTs are machine learning methods which are effective on many types of data, and do not require the traditional model assumptions of linear/logistic regression models. Wikipedia has a good article on the advantages of decision tree learning, and visual diagrams of the architecture:

GBMs, as implemented in the Python package scikit-learn, are extremely popular in Kaggle machine learning competitions. But scikit-learn is relatively old, and new technologies have emerged which implement GBMs/GBTs on large datasets with massive parallelization and and in-memory computation. A popular big data machine learning library, H2O, has a famous GBM implementation which, per benchmarks, is over 10x faster than scikit-learn and is optimized for datasets with millions of records. But even faster than H2O is xgboost, which can hit a 5x-10x speed-ups relative to H2O, depending on the dataset size.

Enter LightGBM, a new (October 2016) open-source machine learning framework by Microsoft which, per benchmarks on release, was up to 4x faster than xgboost! (xgboost very recently implemented a technique also used in LightGBM, which reduced the relative speedup to just ~2x). As a result, LightGBM allows for very efficient model building on large datasets without requiring cloud computing or nVidia CUDA GPUs.

A year ago, I wrote an analysis of the types of police arrests in San Francisco, using data from the SF OpenData initiative, with a followup article analyzing the locations of these arrests. Months later, the same source dataset was used for a Kaggle competition. Why not give the dataset another look and test LightGBM out?

Cellphone data gathered as a result of Max’s predictions can be tested against arrest and other police records to establish the presence and/or absence of particular police officers at a crime scene.

After a police office corroborates the presence of a gun in a suspect’s hand, cellphone evidence they were blocks away, in the presence of other police officers, could prove to be inconvenient.

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