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ISPRS Int. J. Geo-Inf. 2018, 7(8), 298; https://doi.org/10.3390/ijgi7080298

Grid-Based Crime Prediction Using Geographical Features

1
Department of Information Management, Yuan Ze University, 135 Yuan-Tung Road, Taoyuan 32003, Taiwan
2
Department of Accounting and Institute of Finance, National Cheng Kung University, No. 1, Daxue Road, Tainan City 70101, Taiwan
3
Center for Innovative FinTech Business Models, National Cheng Kung University, No. 1, Daxue Road, Tainan City 70101, Taiwan
*
Author to whom correspondence should be addressed.
Received: 24 May 2018 / Revised: 21 June 2018 / Accepted: 23 July 2018 / Published: 25 July 2018
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Abstract

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. View Full-Text
Keywords: crime prevention; crime displacement; machine learning; spatial analysis; feature engineering; crime prediction crime prevention; crime displacement; machine learning; spatial analysis; feature engineering; crime prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lin, Y.-L.; Yen, M.-F.; Yu, L.-C. Grid-Based Crime Prediction Using Geographical Features. ISPRS Int. J. Geo-Inf. 2018, 7, 298.

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