Paper Info
Title
VisCrimePredict - a system for crime trajectory prediction and visualisation from heterogeneous data sources.
Abstract
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.
Year
DOI
Venue
2019
10.1145/3297280.3297388
SAC
Keywords
Field
DocType
LSTM network, Twitter data, crime trajectory, trajectory prediction, visual analytics
Data science,Open data,Social media,Visualization,Predictive analytics,Computer science,Visual analytics,Neighbourhood (mathematics),Artificial neural network,Crime prevention
Conference
ISBN
Citations 
PageRank 
978-1-4503-5933-7
1
0.41
References 
Authors
1
8
Name
Order
Citations
PageRank
Ahsan Morshed110211.75
Abdur Rahim Mohammad Forkan2303.91
Tsai Pei-wei312715.88
Prem Prakash Jayaraman437844.66
Timos K. Sellis549701255.07
Dimitrios Georgakopoulos62554580.54
Irene Moser720921.94
Rajiv Ranjan84747267.72