March 25, 2013
Gang Graffiti App.

I receive frequent emails asking to develop a graffiti identification and interpretation app. When it comes to developing apps that are already in existence, I will only redesign the wheel if I can make it better. The Department of Homeland Security (‟DHS”) in collaboration with Purdue University has already developed a gang graffiti app that is top-notch, but you will not find it in any app store. The app, GARI (‟Graffiti Automatic Recognition and Interpretation”), is only available to law enforcement agencies by emailing Purdue University at You can view the June 2012 DHS fact sheet containing GARI information by visiting

Gangs are a serious threat to public safety throughout the United States. Gang members are continuously migrating from urban cities to suburban areas. They are responsible for an increasing percentage of crime and violence in many communities.

According to the National Gang Threat Assessment, approximately 1 million gang members belonging to more than 20,000 gangs were criminally active within all 50 states and the District of Columbia as of September 2008. Criminal gangs commit as much as 80% of the crime in many communities according to law enforcement officials throughout the nation.

Street gang graffiti is their most common way to communicate messages, including challenges, warnings or intimidation to rival gangs. It is, however, an excellent way to track gang affiliation and growth, or even sometimes to obtain membership information.

The goal of this project is to use the knowledge gained from our work in mobile devices and applications and leverage it towards the development of a mobile-based system capable of image analysis. This system will provide an accurate and useful output to a user based on a database of gang graffiti images.

The image analysis includes obtaining the metadata (geoposition, date and time) and extracting relevant features (e.g., color, shape) from the gang graffiti image. The information is sent to a server and compared against the graffiti image database. The matched results are sent back to the device where the user can then review the results and provide extra inputs to refine information. Once the graffiti is completely decoded and interpreted, it is labeled and added to the database.


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