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It is the fact that crime affects all aspects of human life. Shooting crime has long been a major and serious problem of some US cities. According to 2017 national statistics, Cleveland was ranked as the 5th, Cincinnati as the 9th, and Columbus as the 21st deadliest city in the US. The analysis of any historical crime data reveals that crime is non-randomly distributed in time and space. Based on this notion, hot spots policing has gained its momentum to effectively predict future crime locations and to direct limited resources of the police to the places where the need is greatest. Hots spots policing approach tries to predict shooting locations ahead of time to increase the quality of life. Recent studies; however, pointed out that traditional hot spots policing occasionally predict rare crimes such as homicides and shootings due to their less frequent recurring counts in a given place and time (specifically for shorter time periods such as weeks and months). Given this context, we developed a new shooting prediction system (SHOPS) to explore whether recent dynamic/mobility activity patterns of known violent individuals increase the prediction of short-term fatal and non-fatal shootings compared to the traditional hot spots policing. Findings suggest that SHOPS predicts fatal and non-fatal shooting locations more precisely by identifying fewer hotspot locations. Policy implications of the study were discussed in the conclusion section.