Identification of Crime using Multi Embedding BiLSTM

Crimes pose significant societal challenges with implications for a nation's well-being, economic progress, and reputation. Precisely measuring crime rates, categories, and hotspots from historical patterns presents various computational complexities and opportunities. This study introduces and improves a deep learning approach for predicting crime types with high precision. The system can predict both crime categories and associated risk levels by analyzing concise summaries from criminal case reports. The predictive model is built on a neural network with LSTM and Bi-LSTM components, demonstrating remarkable accuracy in forecasting crime types despite limited training attributes. It is tested on a substantial real-world dataset containing historical urban crime data, offering a deep learning-based solution to enhance public safety in the face of criminal activities

Geospatial-based data and knowledge driven approaches for burglary crime susceptibility mapping in urban areas

The Damansara-Penchala region in Malaysia, is well-known for its high frequency of burglary crime and monetary loss based on the 2011-2016 geospatial burglary data provided by the Polis Diraja Malaysia (PDRM). As such, in order to have a better understanding of the components which influenced the burglary crime incidences in this area, this research aims at developing a geospatial-based burglary crime susceptibility mapping in this urban area. The spatial indicator maps was developed from the burglary data, census data and building footprint data. The initial phase of research focused on the development of the spatial indicators that influence the susceptibility of building towards the burglary crime. The indicators that formed the variable of susceptibility were first enlisted from the literature review. They were later narrowed down to the 18 indicators that were marked as important via the interview sessions with police officers and burglars. The burglary susceptibility mapping was done based on data-driven and knowledge-driven approaches. The data-driven burglary susceptibility maps were developed using bivariate statistics approach of Information Value Modelling (IVM), machine learning approach of Support Vector Machine (SVM) and Artificial Neural Network (ANN). Meanwhile, the knowledge-driven burglary susceptibility maps were developed using Relative Vulnerability Index (RVI) based on the input from experts. In order to obtain the best results, different parameter settings and indicators manipulation were established in the susceptibility modelling process. Both susceptibility modelling approaches were compared and validated with the same independent validation dataset using several accuracy assessment approaches of Area Under Curve - Receiver Operator Characteristic (AUC-ROC curve) and correlation matrix of True Positive and True Negative. The matrix is used to calculate the sensitivity, specificity and accuracy of the models. The performance of ANN and SVM were found to be close to one another with a sensitivity of 91.74% and 88.46%, respectively. However, in terms of specificity, SVM had a higher percentage than ANN at 57.59% and 40.46% respectively. In addition, the error term in classifying high frequency burglary building was also included as part of the measurements in order to decide on the best method. By comparing both classification results with the validation data, it was found that the ANN method has successfully classified buildings with high frequency of burglary cases to the high susceptibility class with no error at all, thus, proving it to be the best method. Meanwhile, the output from IVM had a very moderate percentage of sensitivity and specificity at 54.56% and 46.42% respectively. On the contrary, the knowledge-driven susceptibility map had a high percentage of sensitivity (86.51%) but a very low percentage of specificity (16.4%) which making it the least accurate model as it was not able to classify the high susceptible area correctly as compared to other modelling approaches. In conclusion, the results have indicated that the 18 indicators used in this research could be employed to successfully map the burglary susceptibility in the study area. Furthermore, it was also found that residential areas within the vicinity of Brickfields, Bangsar Baru, Hartamas and Bukit Pantai are consistent to be classified as high susceptible areas, meanwhile areas of Jalan Duta and Taman Tunku are both identified as the least susceptible areas across the modelling methods

Inferência preditiva geoespacial da criminalidade em Porto Alegre : uma abordagem de aprendizado de máquina

Novas estratégias para o enfrentamento da criminalidade no Brasil são necessárias, haja vista o recorde dos índices de crimes violentos registrados nos últimos anos. Dessa forma, o objetivo desta pesquisa é demonstrar o potencial da utilização da inteligência artificial como ferramenta no combate à criminalidade. Foram testados quatro tipos diferentes de modelos na predição de eventos criminosos e do nível de criminalidade em cada localidade, sendo eles: regressão, classificação, redes neurais profundas e long short-term memory. O estudo analisou 351.980 crimes violentos ocorridos na cidade de Porto Alegre/RS entre janeiro de 2005 e outubro de 2019. O desempenho de cada algoritmo construído foi testado prevendo os eventos criminosos diários em diferentes números de clusters no qual a cidade foi subdividida. Os resultados apontam que todos os modelos utilizados tem capacidade significativa na predição de crimes, com destaque para o modelo de classificação construído, que ao utilizar 6 clusters de criminalidade obteve um erro médio absoluto (MAE) de 0.43 e a raiz quadrada do erro médio quadrático (RMSE) de 1.68, para a previsão de crimes diários em cada cluster, obtendo um coeficiente de determinação de 0.94. Quando o objetivo era prever o nível de criminalidade em cada cluster, o mesmo modelo de classificação obteve um R2 de 0.98, MAE de 0.01 e RMSE de 0.07.New strategies for fighting crime in Brazil are required, since the records of violent crimes keep increasing in recent years. The objective of this research is to demonstrate the potential of using artificial intelligence as a tool for crime reduction. Four different types of models were tested in the prediction of criminal events and level of crime in each location, namely: regression, classification, deep neural networks and long shortterm memory. This study analyzed 351,980 violent crimes that occurred in the city of Porto Alegre/RS between January 2005 and October 2019. The accuracy of each algorithm was tested by predicting daily number of crimes in each different cluster of criminal events. Results point that all models have significant capacity in the prediction of crimes, with emphasis on the classification model, that using 6 criminality clusters obtained a MAE of 0.43 and a RMSE of 1.68, forecasting daily crimes in each cluster, in this case the coefficient of determination obtained was 0.94. When the objective was to predict the level of crime in each cluster, the same classification model registered a R2 of 0.98, MAE of 0.01 and a RMSE of 0.07, demonstrating great prediction capacity

Examining Deep Learning Architectures for Crime Classification and Prediction

In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction

Examining Deep Learning Architectures for Crime Classification and Prediction

In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction