https://www.zencos.com/blog/text-mining-examples-advanced-analytics/

Crime Prevention: Stop Human Trafficking

Human trafficking affects over 40 million people each year. It is a sad crime that reaches vulnerable populations, such as children. Many have a strong desire to see this crime stopped. One of those is Tom Sabo, who was very disturbed by this issue after attending a human trafficking symposium. He wanted to put his text mining skills into action.

Using text analytics and AI, he was able to create useful models that law enforcement could use. One model pulled together several text-based data sources: police reports, newspaper articles, recent prosecutions, and a shady classified advertising website. The goal was to find relevant patterns in the text that Sabo could then incorporate into a predictive analytics model.

Sabo used the police statements from a specific New York jurisdiction. He then linked this data to other events outside of the state and even outside of the country.

He saw these trends related not only to who was involved, but also, where the events were happening.

This model was subsequently used to identify these situations quicker and allowed law enforcement to act.

Questions:

1. Please identify three most typical BI related technologies used by Tom Sabo in this case study, and briefly discuss how they help Tom in data analysis.

2. As discussed in our lecture, the financial departments of many companies also do predictive analytics. Compare the analytics done by such financial departments and the one by Tom Sabo, what differences can you identify in terms of the technologies applied by them?