|
| Authors |
Methods |
Dataset |
Key Findings |
Limitations and Future Work |
|
|
| Almaw & Kadam (2018) |
Random Tree, 1-ensemble, 3-ensemble, Statistical Analysis |
Crime Dataset |
Random Tree: 82.02% accuracy |
More ensemble techniques needed and extend crime trend analysis. |
|
| Feng et.al. |
Stateful LSTM with Keras and Prophet Model |
Crime data (3 years training) |
Compared to conventional neural network models, the Prophet model and Keras LSTM produced superior prediction results, which helped law enforcement allocate resources. |
Further optimization of training dataset sizes and exploration of hybrid deep learning methods. |
|
| Crimes prediction using spatiotemporal data and kernel density estimation et.al. |
Gradient Boosting Machine (GBM) |
Spatiotemporal and zoning datasets |
KDE with zoning district characteristics and smoothing improves model performance; achieved a multiclass logarithmic loss of 2.356104 on validation and 2.35443 on test sets. |
Expand to real-time prediction applications and evaluate generalizability across various cities and regions. |
|
| Kim et.al. |
Enhanced Decision Tree with K-Nearest Neighbour |
Vancouver crime data (15 years) |
The prediction accuracy of KNN and Boosted Decision Tree models varied between 39% and 44%. |
Improve accuracy through advanced preprocessing, feature engineering, and incorporating contextual external data. |
|
| Almaw and Kadam et.al. |
Naive Bayes, J48, and Random Tree |
Experimented dataset |
Random Tree outperformed others with 82.0227% accuracy. Ensemble models showed 81.6073% (1-ensemble) and 79.2353% (3-ensemble). |
Limited focus on computational efficiency and need to explore ensemble models with novel classifiers. |
|
| Sivaranjani, Sivakumari and Aasha et.al. |
K-Means and K-Nearest Neighbor (KNN) |
Crime data visualized on Google Maps |
K-Means clustering visualized with Google Maps enhances usability; KNN used for prediction and evaluated using precision, recall, and F-measure. |
Need to refine spatial accuracy and investigate more advanced algorithms for geospatial clustering and prediction. |
|
