Debopriya Ghosh
ABSTRACT
One of the challenges governments and communities face to achieve smart city goals is dealing with enormous amount of data available - sensors, devices, social media, Web activities and commerce, tracking devices, all generate enormous amount of data, so called Big Data. Our goal is to empower the city government and its citizens to create a safer city by enabling crime and risk analysis of unstructured crime reports, criminal history of suspects, auto-license data, location-specific data, etc. for crime fighting efforts. We present intelligent solutions for Data-based Smart City Platform in Newark, NJ. We used a Machine Learning approach to automate and help crime analysts identify the connected entities and events by collecting, integrating and analyzing diverse data sources to generate alerts and predictions for new knowledge and insights that lead to better decision making and optimized actions.
- Aquin, Mathieu, John Davies, and Enrico Motta. "Smart Cities' Data: Challenges and Opportunities for Semantic Technologies." IEEE Internet Computing 6 (2015): 66--70.Google Scholar
- Bowen, J. E. (1994). "An expert system for police investigators of economic crimes." Expert Systems with Applications 7(2): 235--248.Google ScholarCross Ref
- Brahan, J. W., Lam, K. P., Chan, H., and Leung, W. (1998). "AICAMS: Artificial intelligence crime analysis and management system." Knowledge-Based Systems 11: 355--361.Google ScholarDigital Library
- Cardie, C. et al. 2008. A Study in Rule-specific Issue Categorization for e-Rulemaking. Proceedings of the 2008 International Conference on Digital Government Research (Montreal, Canada, 2008), 244--253. Google ScholarDigital Library
- Chen, Hsinchun, Wingyan Chung, Jennifer Jie Xu, Gang Wang, Yi Qin, and Michael Chau. "Crime data mining: a general framework and some examples." Computer 37, no. 4 (2004): 50--56. Google ScholarDigital Library
- Gravano L., Ipeirotis P.G., Koudas N., and Srivastava D. "Text joins in an RDBMS for web data integration." In Proceedings of the 12th international conference on World Wide Web 2003 May 20 (pp. 90--101). Google ScholarDigital Library
- Hagen, L., Harrison, T. M., Uzuner, Ö., Fake, T., LaManna, D., & Kotfila, C. (2015). Introducing Textual Analysis Tools for Policy Informatics: A Case Study of E-petitions. Presented at the Proceedings of the 16th Annual International Conference on Digital Government Research. Google ScholarDigital Library
- Hauk, Roslin V., and Hsinchun Chen. "COPLINK: A case of intelligent analysis and knowledge management." In Proceedings of the 20th international conference on Information Systems, pp. 15--28. Association for Information Systems, 1999. Tavel, P. 2007. Modeling and Simulation Design. AK Peters Ltd., Natick, MA. Google ScholarDigital Library
- Hu, Paul Jen-Hwa, Hsinchun Chen, Han-fen Hu, Cathy Larson, and Cynthia Butierez. "Law enforcement officers' acceptance of advanced e-government technology: A survey study of COPLINK Mobile." Electronic Commerce Research and Applications 10, no. 1 (2011): 6--16. Google ScholarDigital Library
- IBM Entity Analytics Solutions. http://www-01.ibm.com/software/data/identity-insight-solutions/law-enforcement.html.Google Scholar
- Ku, C.H. et al. 2008. Natural Language Processing and e-Government: Crime Information Extraction from Heterogeneous Data Sources. Proceedings of the 2008 International Conference on Digital Government Research (Montreal, Canada, 2008), 162--170. Google ScholarDigital Library
- Ku, C.-H. and Leroy, G. 2014. A decision support system: Automated crime report analysis and classification for e-government. Government Information Quarterly. 31, 4 (Oct. 2014), 534--544.Google ScholarCross Ref
- M. Chau, J.J. Xu, and H. Chen, "Extracting Meaningful Entities from Police Narrative Reports, Proc. Nat'l Conf. Digital Government Research, Digital Government Research Center,2002, pp. 271--275. Google ScholarDigital Library
- Pliant, L. (1996). "High-technology solutions." The Police Chief 5(38): 38--51.Google Scholar
- Reaves, Brian A. Local police departments (2007). DIANE Publishing, 2011.Google Scholar
- Yuan, Shengcheng, Soon Ae Chun, Yi Liu, Hui Zhang, & Nabil R. Adam (2015) Agent Driving Behavior Modeling for Traffic Simulation and Emergency Decision Support, 2015 Winter Simulation Conference, 2015. Google ScholarDigital Library
- Wan, Yuan, Hengqing Tong, and Yanfang Deng. "Local Latent Semantic Analysis Based on Support Vector Machine for Imbalanced Text Categorization." In Applied Informatics and Communication, pp. 321--329. Springer Berlin Heidelberg, 2011.Google Scholar
- http://www.nij.gov/journals/274/Pages/challenge-generate-innovation.aspxGoogle Scholar
- https://www.whitehouse.gov/the-press-office/2015/09/14/fact-sheet-administration-announces-new-smart-cities-inititive-helpGoogle Scholar
- Big Data-based Smart City Platform: Real-Time Crime Analysis
Recommendations
Smart city data analysis
DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information SystemsSmart City is one of the vital issues in the next coming years as it is estimated that more number of people will be migrating towards city and by 2040 cities is populated by 70% of the world's population. This will give raise to the city management ...
The role of big data in smart city
We provide a vision of big data analytics to support smart cities.We proposed future business model with the aim of managing big data for smart city.We identify and discuss business and technological research challenges.We provide a description of ...
A Comparison of Smart City Development and Big Data Analytics Adoption Approaches
SMARTGREENS 2018: Proceedings of the 7th International Conference on Smart Cities and Green ICT SystemsThis paper intends to elucidate the similarities between smart city development and big data analytics adoption. Both concepts promise new opportunities: smart cities to improve citizens' life quality and big data analytics to drive companies towards ...
Comments