Paper Info
Title
Crime Hot Spot Forecasting: A Recurrent Model with Spatial and Temporal Information
Abstract
Crime is a major social problem in the United States, threatening public safety and disrupting the economy. Understanding patterns in criminal activity allows for the prediction of future high-risk crime “hot spots” and enables police precincts to more effectively allocate officers to prevent or respond to incidents. With the ever-increasing ability of states and organizations to collect and store detailed data tracking crime occurrence, a significant amount of data with spatial and temporal information has been collected. How to use the benefit of massive spatial-temporal information to precisely predict the regional crime rates becomes necessary. The recurrent neural network model has been widely proven effective for detecting the temporal patterns in a time series. In this study, we propose the Spatio-Temporal neural network (STNN) to precisely forecast crime hot spots with embedding spatial information. We evaluate the model using call-for-service data provided by the Portland, Oregon Police Bureau (PPB) for a 5-year period from March 2012 through the end of December 2016. We show that our STNN model outperforms a number of classical machine learning approaches and some alternative neural network architectures.
Year
DOI
Venue
2017
10.1109/ICBK.2017.3
2017 IEEE International Conference on Big Knowledge (ICBK)
Keywords
Field
DocType
crime hot spot forecasting,spatio-temporal neural network,massive spatial-temporal information,recurrent neural network model,time series,call-for-service data,STNN model
Spatial analysis,Data mining,Hot spot (veterinary medicine),Embedding,Tracking system,Recurrent neural network,Engineering,Artificial neural network
Conference
ISBN
Citations 
PageRank 
978-1-5386-3121-8
4
0.69
References 
Authors
0
4
Name
Order
Citations
PageRank
Yong Zhuang125413.88
Matthew Almeida240.69
Melissa Morabito340.69
Wei Ding483472.61