Crime is a major problem in many urban communities. This project focuses on developing a framework for increased security and crime prevention in crime-prone environments by identifying and integrating hitherto disaggregated heterogeneous data and analyzing the causal and spatio-temporal interconnections between constituent parts of a connected community including environmental aspects (i.e., traffic, lighting, poverty levels, business proximity such as banks/ATMs), crime history, and social events. While existing crime prediction and prevention methods focus on the location of the crimes to detect ``hot-zones'', this project takes a fundamentally different, data-driven approach towards integrated multi-scale data analytics for identifying the characteristics and features of crime-prone environments. This high-risk high-payoff project research is based on real-time crime data and interactions with crime prevention and safety agencies. By revealing the connections between crime and environmental, social, and economic factors, this research aims to demonstrate the critical need of an integrated systems approach to crime prevention, instead of focusing on post-crisis management. This interdisciplinary endeavor of developing computational methods for crime prevention across public urban landscapes requires the combination of data mining and statistical methods in space and time to extract useful features and discover models from passive data sets. The proposed project will develop 1) new tools for the fundamental understanding of criminal behavior by analyzing the time varying and location-specific systems and patterns observed as a result of complex processes between interacting cyber-physical entities, and 2) scalable data-driven Nowcasting algorithms for crime prediction that will adapt with the constantly evolving state of criminal activity by continuously learning from a rich set of spatial and demographic features, including traffic, spatial attributes, socio-economic characteristics of neighborhoods, and current time, as well as context. To enable continuous forecasting over streaming data, while maintaining high prediction accuracy and low time complexity, the project will develop and train crime prediction artificial neural networks (CANN) for prediction across space and time. The output of the proposed data-driven models will feed a novel multi-objective optimization formulation that will be used for the integrated optimization of personnel positioning, patrol scheduling and safest route calculation. The resulting decision support environment, will be transferred to the USC Department of Public Safety (DPS), the Los Angeles Police Department (LAPD), and South Park Business Improvement District (SPBID) for integration with their systems to enable decision makers to choose the best course of action at any given time. This project will lead to the development of technology for crime prevention that will be directly applicable to smart and connected communities across the US, with the potential to bring together white and blue-collar residents from mixed urban communities- college campus residents, off-campus neighborhood residents and businesses with their employees, transiting commuters and law enforcement under the theme of making the communities quantifiably more secure. The project will leverage the USC Living Laboratory, a unique ?city within a city? campus and its adjacent neighborhoods as a real-world use case of a connected community of interrelated infrastructures.