Even though vehicle thefts accounted for only 3.9% (10,099) of all crimes in Chicago last year, 62% of the stolen vehicles were recovered with severe damage says the Chicago Police department. Most often the vehicles are stolen by organized rings to be sold on black-markets or shipped overseas, and stripped for parts and resold to various body-shops, or are even resold to unsuspecting customers. In Chicago, 78.9% of the vehicles are stolen from streets, alleys and alongside sidewalks, 8.6% from buildings other than residences, 6.7% from parking lots, 5.5% from residences, and 0.3% from the airports.
The map below shows a hot-spot analysis of the communities that are most and least affected by vehicle theft. The visualization shows statistically significant (statistically significant is the likelihood that a theft is caused by something other than mere random chance) hot-spots in red where a high number of thefts occur and statistically significant cold-spots in blue where few or no thefts occur.
Communities most-prone to vehicle theft (not safe): Uptown (3) in the north, or Austin (25), Avondale (21), Logan Square (22), Hermosa (20), Humboldt Park (23), West Town (24), East/West Garfield Parks (26, 27), Near West Side (28), North Lawndale (29) in the west , or any south central parts of Chicago, namely Chicago Lawn (66), East/West Englewoods (67, 68), Greater Grand Crossing (69), South Shore (43), Auburn Gresham (71) are prone to vehicle thefts.
Communities least-prone to vehicle theft (safe): Edison Park (9), Norwood Park (10), Jefferson Park (11), Forest Glen (12), North Park (13), Dunning (17), Portage Park (15), Lincoln Square (4), North Center (5), Lincoln Park (7) in the north and Bridgeport (60), New City (61), Garfield Ridge (56), Clearing (64), Ashburn (70), West Pullman (53), Morgan Park (75), Beverly (72), Washington Heights (73), East Side (52) and Calumet Heights (48) in the south are least prone to vehicle thefts.
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The above visualization includes 2 major types of spatial analysis techniques. The vehicle theft locations were geocoded using the addresses and then, Getis-Ord Gi* statistic was used to generate a hot-spot analysis to identify statistically significant clusters.
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