Surprise results stem from curious data analytics models

Real world data analytics models

Madan’s description of how data science should be applied boiled everything down to the basics. “While the data science field is evolving at a rapid pace on the technology front, the whole field can be explained in one word: Patterns. It’s all about learning and making use of analogs (positive patterns) and anti-logs (negative patterns) to enable effective decision making.”

What decisions could be impacted with better insight into bike theft patterns? “If you have to plan a patrol route, we can tell you when and where the crimes are typically happening.” Having a visible and consistent police presence could play an important role in reducing bike thefts in hot spots. In the case of adding more secure options for bike owners, the data could also be used for urban planning. When city officials could clearly visualize the areas of high demand for bike parking and high risk for theft, it was easier to see where the next secure parking solution should be implemented.

As with many college campuses around the country, bike theft was perceived as a persistent issue at ASU. Most of the bicycles were locked with a portable anti-theft device when stolen, so the use of basic locks was obviously not addressing this type of crime. Over $150,000 worth of bikes were taken on an annual basis, and only a fraction of this equipment was ever recovered. This problem wasn’t going to be solved without better information. Madan set about gathering the data to explore the problem in detail.

Evaluation of data analytics models

Key factors for evaluation included geography, time of day, and property being stolen. As he sifted and analyzed the data, it became clear that most of the crime was clustered in a few key areas. In fact, 40% of the overall value of property stolen over a five year period was associated with the areas surrounding just ten buildings. Analysis highlighted peak hours and days when thefts were most likely to happen. This information coupled with additional data gave Neeraj the confidence to propose a potential site for new valet bike parking.

Madan had several objectives when gathering and analyzing data: Identifying bike theft hot spots, measuring the effectiveness of existing valet and secured storage options, and proposing a new valet location for a new walk only zone. He took things even farther by exploring the marketplace for stolen bikes (such as popular online venues where these goods were sold). “We would look at Craigslist phone numbers to find patterns. Then, we handed that information over to the police for investigation.” For example, if the same phone number kept showing up over and over with postings to sell various used bikes across cities, it could indicate that the user was acquiring their inventory in a suspicious manner. Or, if a dealer was also listed as an owner under the same number, that could be a red flag.

In addition to recommending construction of new valet parking, targeted patrols of hot spots during peak times for bike theft, and investigation of popular venues for selling stolen bikes, Madan addressed the issue of student perceptions. He found that a full 60% of students believed their bicycles would be stolen on campus. Addressing this concern would entail actually reducing the incidence of theft, enabling the police to more effectively investigate these crimes, and educating students on smarter bike management.

For Neeraj, this self-initiated project was an opportunity to showcase a very practical side of the discipline of data science. “My stakeholders never knew this could be a data oriented problem. They needed someone to look at it from a different point of view.” Madan received a formal letter of recognition and appreciation for his efforts. The authorities went on to implement his various recommendations. It’s good to know that a few more eyes were opened to the possibilities of data analytics to create real change one bicycle at a time.