Grid-Based Crime Prediction Using Geographical Features

Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporat...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2018-08, Vol.7 (8), p.298
Main Authors: Lin, Ying-Lung, Yen, Meng-Feng, Yu, Liang-Chih
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Crime
crime displacement
crime prediction
crime prevention
Criminal statistics
Decision trees
Design
Displacement
feature engineering
Geography
Information systems
Information technology
Learning algorithms
Machine learning
Neural networks
Prediction models
Principal components analysis
spatial analysis
Studies
Support vector machines
Temporal variations
Theft
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Summary:Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design’s baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi7080298