Towards a Deep Learning Approach for Urban Crime Forecasting

被引：2

作者：

Pirajan, Freddy ^{[1
]}

Fajardo, Andrey ^{[1
]}

Melgarejo, Miguel ^{[1
]}

机构：

[1] Univ Dist Francisco Jose de Caldas, Lab Automat & Computat Intelligence, Bogota, Colombia

来源：

APPLIED COMPUTER SCIENCES IN ENGINEERING (WEA 2019) | 2019年 / 1052卷

关键词：

Deep learning; Convolutional neural networks; Environmental criminology; Crime forecasting;

D O I：

10.1007/978-3-030-31019-6_16

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper presents a deep learning approach for urban crime forecasting. A deep neural network architecture is designed so that it can be trained by using geo-referenced data of criminal activity and road intersections to capture relevant spatial patterns. Preliminary results suggest this model would be able to identify zones with criminal activity in square areas of 500 x 500 m(2) in a weekly scale.

引用

页码：179 / 189

页数：11

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