A Survey on Crime Detection And Prediction Techniques

Crime is a preeminent issue where the main concern has been worried by individual, the local area and government. Wrongdoing forecast utilizes past information and in the wake of investigating information, anticipate the future wrongdoing with area and time. In present days sequential criminal cases quickly happen so it is a provoking assignment to anticipate future wrongdoing precisely with better execution. This paper examines about various wrongdoing expectation and location. A productive wrongdoing forecast framework speeds up the method involved with addressing violations.. Wrongdoing Prediction framework utilizes recorded information and examinations the information utilizing a few dissecting strategies and later can anticipate the examples and patterns of wrongdoing utilizing any of the underneath referenced methodologies. Keywords: Crime Analysis, Data Mining, Classifiaction , Clustering

Uncovering dynamic textual topics that explain crime

Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain. Some of these techniques may provide insights for understanding crime and disorder, especially, via interpreting the weights for the spatial covariates based on regression modelling. However, to date, the use of temporal covariates for the time domain has not played a significant role in the analysis. In this work, we collect time-stamped crime-related news articles, infer crime topics or themes based on the collection and associate the topics with the historical numeric crime counts. We provide a proof-of-concept study, where instead of adopting spatial covariates, we focus on temporal (or dynamic) covariates and assess their utility. We present a novel joint model tailored for the crime articles and counts such that the temporal covariates (latent variables, more generally) are inferred based on the data sources. We apply the model for violent crime in London.

Crime Analysis and Prediction Using Data Mining and Machine Learning Techniques

With the increase in crime rates across the world, it has become important for the Government and crime handling agencies to control the situation as it has put every person in distress. This paper is an attempt to systematically analyze and identify the crime trends across the years, the inter-state relations based on crime rates and categories through the data available, which will help in predicting the crime trends in future and will be instrumental for the Government to take informed actions and improve the country’s situation. This paper applies various data mining techniques in order to analyze the crime records in India. The results of analysis have been compared for various algorithms in the domain of Association Rule Mining, Clustering, Outlier Analysis, Regression and Classification. The paper also attempts to predict the future occurrences of crimes using classification and regression algorithms which use data mining techniques . Keywords: Crime Analysis, Data Mining, Association Rule Mining, Clustering, outlier Analysis, Classification, Regression

Design And Implementation Of A Geospatial Dashboard For Crime Analysis And Prediction

<div>Dashboard has been around for a long time, and many have been developed as a governing and monitoring tool in city management, such as crime monitoring. However, the majority of crime dashboards function as a visualization tool and few of them has been specifically developed for crime analysis and prediction.</div><div>This thesis focuses on the development of geospatially-enabled crime dashboards with spatial analysis capabilities for supporting crime analysis and prediction. A prototype has been designed and implemented to support the understanding of crime events for crime reduction efforts. This dashboard will assist policy makers and leaders in crime fighting by visualizing basic statistical information of crimes, revealing their spatial and temporal patterns, identifying crime clusters, and analyzing relationships between crimes and other factors. Based on the criteria developed in this thesis, the prototype confirmed its ability of enhancing the understanding of crime events.<br></div>