| Title | Comparative Analysis Of Crime Hotspot Detection And Prediction Using Convolutional Neural Network Over Support Vector Machine with Engineered Spatial Features Towards Increase in Classifier Accuracy |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Sravani, T., Suguna, M.Raja |
| Conference Name | 2022 International Conference on Business Analytics for Technology and Security (ICBATS) |
| Date Published | feb |
| Keywords | Classification algorithms, Clustering algorithms, composability, convolutional neural network, Crime hotspot, Engineered Spatial Features, feature extraction, machine learning, Metrics, Novel HDBSCAN Algorithm, Prediction algorithms, pubcrawl, resilience, Resiliency, Spatial databases, supply vector machines, support vector machine, support vector machine classification, Support vector machines |
| Abstract | The major aim of the study is to predict the type of crime that is going to happen based on the crime hotspot detected for the given crime data with engineered spatial features. crime dataset is filtered to have the following 2 crime categories: crime against society, crime against person. Crime hotspots are detected by using the Novel Hierarchical density based Spatial Clustering of Application with Noise (HDBSCAN) Algorithm with the number of clusters optimized using silhouette score. The sample data consists of 501 crime incidents. Future types of crime for the given location are predicted by using the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms (N=5). The accuracy of crime prediction using Support Vector Machine classification algorithm is 94.01% and Convolutional Neural Network algorithm is 79.98% with the significance p-value of 0.033. The Support Vector Machine algorithm is significantly better in accuracy for prediction of type of crime than Convolutional Neural Network (CNN). |
| DOI | 10.1109/ICBATS54253.2022.9759042 |
| Citation Key | sravani_comparative_2022 |
Comparative Analysis Of Crime Hotspot Detection And Prediction Using Convolutional Neural Network Over Support Vector Machine with Engineered Spatial Features Towards Increase in Classifier Accuracy