Browse by author
Lookup NU author(s):
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© 2019 Association for Computing Machinery. Open multidimensional data from social media and similar sources often carries insightful information on social issues. With the increase of high volume data and the proliferation of visual analytics platforms, it becomes easier for users to interact with and select meaningful information from large data sets. The prevention of crime is a crucial issue for law-enforcing agencies tasked with maintaining societal stability. The ability to visualise crime patterns and predict imminent incidents accurately opens new possibilities in crime prevention. In this paper, we present VisCrimePredict, a system that uses visual and predictive analytics to map out crimes that occurred in a region/neighbourhood. VisCrimePredict is underpinned by a novel algorithm that creates trajectories from heterogeneous data sources such as open data and social media with the aim to report incidents of crime. VisCrimePredict uses a Long Short Term Memory (LSTM) algorithm for trajectory prediction. A proof of concept implementation of VisCrimePredict and an experimental evaluation of crime trajectory prediction accuracy using LSTM neural network concludes the paper.
Author(s): Morshed A, Forkan ARM, Tsai P-W, Jayaraman PP, Sellis T, Georgakopoulos D, Moser I, Ranjan R
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: SAC '19 34th ACM/SIGAPP Symposium on Applied Computing
Year of Conference: 2019
Online publication date: 12/04/2019
Acceptance date: 08/04/2019
Publisher: Association for Computing Machinery
Library holdings: Search Newcastle University Library for this item