Developing Machine Learning Based Predictive Models for Smart Policing

Simple item page
dc.contributor.authorElluri, Lavanya
dc.contributor.authorMandalapu, Varun
dc.contributor.authorRoy, Nirmalya
dc.date.accessioned2019-11-01T15:56:55Z
dc.date.available2019-11-01T15:56:55Z
dc.date.issued2019-08-01
dc.description2019 IEEE International Conference on Smart Computing (SMARTCOMP)en_US
dc.description.abstractCrimes are problematic where normal social issues are confronted and influence personal satisfaction, financial development, and quality-of-life of a region. There has been a surge in the crime rate over the past couple of years. To reduce the offense rate, law enforcement needs to embrace innovative preventive technological measures. Accurate crime forecasts help to decrease the crime rate. However, predicting criminal activities is difficult due to the high complexity associated with modeling numerous intricate elements. In this work, we employ statistical analysis methods and machine learning models for predicting different types of crimes in New York City, based on 2018 crime datasets. We combine weather, and its temporal attributes like cloud cover, lighting and time of day to identify relevance to crime data. We note that weatherrelated attributes play a negligible role in crime forecasting. We have evaluated the various performance metrics of crime prediction, with and without the consideration of weather datasets, on different types of crime committed. Our proposed methodology will enable law enforcement to make effective decisions on appropriate resource allocation, including backup officers related to crime type and locationen_US
dc.description.sponsorshipThis research is partially supported by the NSF CAREER Award # 1750936 and ONR under grant N00014-15-1-2229.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/8784006en_US
dc.format.extent7 pagesen_US
dc.genreconference proceedings and papers postprintsen_US
dc.identifierdoi:10.13016/m2wr4h-coh0
dc.identifier.citationElluri, Lavanya & Mandalapu, Varun & Roy, Nirmalya. (2019). Developing Machine Learning Based Predictive Models for Smart Policing. https://doi.org/10.1109/SMARTCOMP.2019.00053.en_US
dc.identifier.urihttps://doi.org/10.1109/SMARTCOMP.2019.00053
dc.identifier.urihttp://hdl.handle.net/11603/16020
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights©2019 IEEE
dc.subjectCrime Predictionen_US
dc.subjectWeatheren_US
dc.subjectTemporal featuresen_US
dc.subjectDeep Learningen_US
dc.subjectSmart Policingen_US
dc.titleDeveloping Machine Learning Based Predictive Models for Smart Policingen_US
dc.typeTexten_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Developing Machine Learning based Predictive Models for Smart Policing.pdf
Size:
845.83 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Information Systems Department
UMBC Faculty Collection
UMBC Student Collection