Grid-based crime prediction using geographical features

Ying Lung Lin, Meng Feng Yen, Liang Chih Yu

研究成果: Article同行評審

29 引文 斯高帕斯(Scopus)


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.

期刊ISPRS International Journal of Geo-Information
出版狀態Published - 2018 8月

All Science Journal Classification (ASJC) codes

  • 地理、規劃與發展
  • 地球科學電腦
  • 地球與行星科學(雜項)


深入研究「Grid-based crime prediction using geographical features」主題。共同形成了獨特的指紋。