Mitigating Vehicle Theftin San Francisco

by Jaime Lopez & Joshua Yarno

• We have been contracted to conduct research for Crime Analytics, a consulting firm hired by the SF City Council. Crime Analytics goal is to:▪ 1) identify the vehicle theft clusters, ▪ 2) locate public transportation near cluster criminals could use for ingress, and ▪ 3) determine what public infrastructure near hot spots can be augmented to mitigate vehicle

theft.

I. Abstract

• Vehicle thefts are one of the most prevalent forms of crime in San Francisco and rose 28% in 2016. (Alexander & Cabanatuan, 2017)

• In 2016 there were 6418 vehicle thefts or attempted vehicle thefts in the city. San Francisco also ranks as third in the nation for vehicle thefts “with 631.7 car thefts per 100,000 people.” (Jornsby 2016)

• Each instance of theft costs an average of $500 in vehicle repair and incurs an annual cost of $13 million for the entire community. (Friedersdorf, 2016)

II. Background

• Crime data, school location, poverty, and public transportation overlays were obtained from the DataSFinformation portal.

• The San Francisco basemap and network topology were provided by Dr. John Radke.

• Friedersdorf, Conor. “Why Can't San Francisco Stop Its Epidemic of Window Smashing?” The Atlantic, Atlantic Media Company, 26 Apr. 2016.

• Alexander, Kurtis, and Michael Cabanatuan. “As car break-Ins jump 28 percent in San Francisco, police shuffle response.” SF Gate, Hearst Communications, 1 Sept. 2017.

• Thorsby, Devon. “10 Places With the Highest Rates of Car Theft in the U.S.” U.S. News and World Report, U.S. News & World Report L.P., 29 Dec. 2016.

IV. Data & Bibliography

• Point density only shows where crimes have occurred and doesn’t take the proximity of other thefts into account. Past criminal activity shows a trend but isn’t necessarily predictive an exact geographic location. It also showed vehicle theft concentrations in downtown areas only with slight variation across the city.

• The Hot Spot analysis tool showed us 12 areas that are hotspots across each day of the week. Network Analysis identified four BART locations that could be potentially used for criminal ingress near vehicle theft hotspots.• To perform a Suitability Analysis we decided to focus our work on the following “Opportunities”: Car Theft Locations, School Sites with High Density (with larger school populations), Street Junctions, and Bart Locations. Our

thought process took into account a desire to maximize the potential benefits that could result from strategic placement of cameras. • We first focused on where car thefts happened. From there, we looked at locations with the highest school densities. Placing the cameras in such locations would help deter car thefts, and perhaps other criminal activity, near

school sites with large student populations, thereby also providing such locations with additional safety precautions. • Once these school sites were identified, relative to where the hot spots for car theft activity were located, we then took into account the nearest street junctions that were also close to BART stations. The street junctions

would represent the actual physical location of where the cameras would be placed, as intersections would likely provide the great visibility for each camera. The decision to place cameras at intersections with proximity to BART locations is based on the reasoning that car theft perpetrators likely would be using the BART system to arrive at the scene of a car theft.

• To refine our Suitability Analysis we decided to also focus our work on the following “Constraints”: Areas where our heat map would show the most car theft activity. We would also be narrowing down our data to car thefts occurring only from Monday to Friday.

V. Results

• What we concluded were three separate small clusters that satisfied our suitability analysis. This would theoretically provide us with the best locations to maximize the potential car-theft deterrence that such cameras would provide.

• What we could not conclude was whether the poverty levels of any neighborhoods in the city where car thefts occurred had any significant correlation with the frequency of such activity. Therefore, we would then be left assuming that car theft would be committed by individuals or groups outside the hot spots.

VI. Conclusions

III. Methodology

Vehicle Thefts: Point Density Vehicle Thefts: Network AnalysisVehicle Thefts: Hot Spots and Outliers Vehicle Thefts: Suitability Analysis