[1]SHI Tuo,ZHANG Qi,SHI Lei.Prediction model of theft crime based on the dynamic fusion of multiscale perspective characteristics[J].CAAI Transactions on Intelligent Systems,2022,17(6):1104-1112.[doi:10.11992/tis.202203016]
Copy
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 6
Page number:
1104-1112
Column:
学术论文—机器学习
Public date:
2022-11-05
- Title:
-
Prediction model of theft crime based on the dynamic fusion of multiscale perspective characteristics
- Author(s):
-
SHI Tuo1; 2; ZHANG Qi1; 2; SHI Lei3
-
1. Department of Public Security Management, Beijing Police College, Beijing 102202, China;
2. Standard Laboratory of Police Data and Intelligence of Beijing Public Security Bureau, Beijing Police College, Beijing 102202, China;
3. State Key Laboratory of Media Integration and Communication, Communication University of China, Beijing 100024, China
-
- Keywords:
-
crime prediction; self-attention mechanism; multiscale feature fusion; convolutional neural networks; dynamic adaptation; classifier; time series prediction; distributed representation
- CLC:
-
TP391
- DOI:
-
10.11992/tis.202203016
- Abstract:
-
A prediction model combining self-attention and dynamic fusion of multiscale and multiview features is proposed to solve the problems of inaccurate fusion of spatiotemporal prediction features and insufficient temporal dynamic adaptability of theft crime. Initially, data are processed by constructing a method that can match case data with different lengths of time series to an adaptive length by projecting the crime data onto the map grid based on local longitude and latitude information. After word vector mapping, the weather, crime time, and location are used to construct the input vector of multidimensional feature fusion. In addition, a self-attention mechanism is introduced to generate the vector of a dynamic fusion of multiview features. The final step involves encoding the dynamic fusion vector of perspective features and sending it to the classifier to predict the crime situation in each map grid. By validating the method on a real dataset of theft crimes in a city, the proposed model can achieve a maximum prediction precision of 0.899 at three different geographic grid divisions, which is significantly better than other comparable models.