Deep Learning for Real Time Crime Forecasting

07/09/2017
by   Bao Wang, et al.
1

Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both space and time. In this work, we adapt the state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et al, AAAI, 2017], to collectively predict crime distribution over the Los Angeles area. Our models are two staged. First, we preprocess the raw crime data. This includes regularization in both space and time to enhance predictable signals. Second, we adapt hierarchical structures of residual convolutional units to train multi-factor crime prediction models. Experiments over a half year period in Los Angeles reveal highly accurate predictive power of our models.

READ FULL TEXT

page 2

page 3

research
11/23/2017

Deep Learning for Real-Time Crime Forecasting and its Ternarization

Real-time crime forecasting is important. However, accurate prediction o...
research
04/02/2018

Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data

We present a generic framework for spatio-temporal (ST) data modeling, a...
research
02/03/2023

Where and How to Improve Graph-based Spatio-temporal Predictors

This paper introduces a novel residual correlation analysis, called AZ-a...
research
01/10/2017

Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

Forecasting the flow of crowds is of great importance to traffic managem...
research
03/07/2022

HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data

Traffic accident forecasting is a significant problem for transportation...
research
05/12/2019

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. ...
research
02/01/2019

Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction

Crime events are known to reveal spatio-temporal patterns, which can be ...

Please sign up or login with your details

Forgot password? Click here to reset