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.
Abstract
Viktor Prasanna
Viktor K. Prasanna (V. K. Prasanna Kumar) is Charles Lee Powell Chair in Engineering and is Professor of Electrical Engineering and Professor of Computer Science at the University of Southern California (USC) and serves as the director of the Center for Energy Informatics (CEI). He is an associate member of the Center for Applied Mathematical Sciences (CAMS). He leads the Integrated Optimizations (IO) efforts at the USC-Chevron Center of Excellence for Research and Academic Training on Interactive Smart Oilfield Technologies (CiSoft) at USC and the demand response optimizations in the LA Smart Grid project. His research interests include High-Performance Computing, Parallel and Distributed Systems, Reconfigurable Computing, Cloud Computing, and Smart Energy Systems. He received his BS in Electronics Engineering from the Bangalore University, MS from the School of Automation, Indian Institute of Science, and Ph.D. in Computer Science from the Pennsylvania State University.
Prasanna has published extensively and consulted for industries in the above areas. He is the Steering Committee Co-Chair of the International Parallel & Distributed Processing Symposium (IPDPS) [merged IEEE International Parallel Processing Symposium (IPPS) and Symposium on Parallel and Distributed Processing (SPDP)]. He is the Steering Committee Chair of the International Conference on High-Performance Computing (HiPC). In the past, he has served on the editorial boards of the IEEE Transactions on Very Large Scale Integration (VLSI) Systems, IEEE Transactions on Parallel and Distributed Systems (TPDS), Journal of Pervasive and Mobile Computing, and the Proceedings of the IEEE. He serves on the editorial boards of the Journal of Parallel and Distributed Computing and the ACM Transactions on Reconfigurable Technology and Systems. During 2003-’06, he was the Editor-in-Chief of the IEEE Transactions on Computers. He was the founding chair of the IEEE Computer Society Technical Committee on Parallel Processing. He currently serves as the Editor-in-Chief of the Journal of Parallel and Distributed Computing (JPDC).
Prasanna received the W. Wallace McDowell Award from the IEEE Computer Society in 2015 for his contributions to reconfigurable computing. He received an Outstanding Engineering Alumnus Award from the Pennsylvania State University in 2009 and the Distinguished Alumnus Award from the University Visveswaraya College of Engineering (UVCE) of Bangalore University in 2017. He was appointed Distinguished Professor, Beihang University (BUAA), Beijing, PRC, in 2018. He received a 2019 Distinguished Alumnus Award from the Indian Institute of Science (IISc). He received the 2010 Distinguished Service Award from the IEEE Computer Society. He has received best paper awards at several international forums including ACM Computing Frontiers (CF), IEEE International Parallel and Distributed Processing Symposium (IPDPS), ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, International Conference on Parallel and Distributed Systems (ICPADS), International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), International Conference on Parallel and Distributed Computing and Systems (PDCS), IEEE International Conference on High Performance Switches and Routers (HPSR), among others. His work on regular expression matching received one of the most significant papers in FCCM during its first 20 years award in 2013. He is a recipient of the 2005 Okawa Foundation Grant. He is a Fellow of the IEEE, the Association for Computing Machinery (ACM) and the American Association for Advancement of Science (AAAS).
Performance Period: 08/15/2016 - 07/31/2020
Institution: University of Southern California
Award Number: 1637372