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| Deep Learning for Real-Time Crime Forecasting and Its Ternarization |
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Citation： |
Bao WANG,Penghang YIN,Andrea Louise BERTOZZI,P. Jeffrey BRANTINGHAM,Stanley Joel OSHER,Jack XIN.Deep Learning for Real-Time Crime Forecasting and Its Ternarization[J].Chinese Annals of Mathematics B,2019,40(6):949~966 |
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Net amount： 534 |
Authors： |
Bao WANG; Penghang YIN;Andrea Louise BERTOZZI;P. Jeffrey BRANTINGHAM;Stanley Joel OSHER;Jack XIN |
Foundation： |
This work was supported by ONR Grants N00014-16-1-2119,
N000-14-16-1-2157, NSF Grants DMS-1417674, DMS-1522383, DMS-1737770
and IIS-1632935. |
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| Abstract： |
Real-time crime forecasting is important. However, accurate
prediction of when and where the next crime will happen is
difficult. No known physical model provides a reasonable
approximation to such a complex system. Historical crime data are
sparse in both space and time and the signal of interests is weak.
In this work, the authors first present a proper representation of
crime data. The authors then adapt the spatial temporal residual
network on the well represented data to predict the distribution of
crime in Los Angeles at the scale of hours in neighborhood-sized
parcels. These experiments as well as comparisons with several
existing approaches to prediction demonstrate the superiority of the
proposed model in terms of accuracy. Finally, the authors present a
ternarization technique to address the resource consumption issue
for its deployment in real world. This work is an extension of our
short conference proceeding paper [Wang, B., Zhang, D., Zhang, D.
H., et al., Deep learning for real time Crime forecasting, 2017,
arXiv: 1707.03340]. |
Keywords： |
Crime representation, Spatial-temporal deep learning, Real-timeforecasting, Ternarization |
Classification： |
00A69, 65C50 |
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