Recent slew of violence-related events have incitedaflood of discussions revolving around issues such as gun control and domestic safetyabroad. An issue that hits close to home is the concern of safety at urbanuniversities. Studying the geo temporal distribution of crime within and arounda university setting is important for understanding crime type occurrence patterns.These patterns can be mined from alert messages posted by universities onvarious media outlets, such as email, Twitter ,and Facebook. We believe thatthe knowledge inferred from this data can be a crucial factor in creating asafe environment to protect students, faculty members, and administration. Theobserved patterns can help devise more effective crime prevention practiceswithin and around a university campus, such as the optimization of thedeployment of law enforcement resources according to recognized temporal andlocation patterns or the modification of patrol routes of police officers.Additionally, the observed geo-temporal patterns may help establish joint crimeprevention programs between a university and the city. This research projectaims to develop a system that automatically collects crime-logged data frompublicly available sources, organizes it for mining, and creates visual miningtools to explore the data. We use Google Maps to render the datageographically.
Keywords: Spatial-temporal patterns, heat maps, geographical information system, crimedata, data mining, SDM (Sub-Divisional Magistrate).