Crime Rate Prediction using KNN

Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. Thus, it becomes necessary to study various reasons, factors and relationship between different crimes that are occurring and ?nding the most appropriate methods to control and avoid more crimes. The main objective of this project is to classify clustered crimes based on occurrence frequency during different years. Data mining is used broadly in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. In this work, various clustering approaches of data mining are used to analyze the crime data. The K-Nearest Neighbour (KNN) classi?cation is used for crime prediction. The proposed system can predict regions which have high probability for crime rate and can forecast crime prone areas. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etcit will focuse mainly on crime factors of each day

Countermeasure policy on mining crime under the legal progressive perceptive.

The complexity of mining business governance affects rational actions to tackle mining crime using the criminal policies in a progressive legal dimension. This research focuses on dealing with mining crime using criminal policies and rational efforts in the progressive legal dimension. It was conducted using normative legal research method using secondary data obtained from primary and secondary legal materials. The urgency of this research is to provide guidance towards the application of appropriate rules of mining for the actors in the business. It was also directed to provide references in mining law enforcement through an integral policy. The results showed that criminal policy through penal means in the formulation stage has the ability to regulate licensing crimes, corporate crimes, crimes against reclamation, and criminal obstruction of mining businesses. Moreover, the application stage involves the legal construction of material and formal offenses while the execution stage requires integral law enforcement. It is also important to note that the non-penal means which focuses on prevention maps potential actors with the ability to create the victims while the secondary prevention maps the mining areas with potential conflicts. This means the progressiveness of mining criminal policies rationally in the progressive law dimension enforces certainty and basic ideas underlying the norms

Solution Development to Prevent Crime Rate

Crime is one of the major problems encountered in a society. Thus, there is an urgent need for security agents and agencies to battle and eradicate crime. Preventive are taken to reduce the increasing number of cases of crime. A huge amount of data set is generated every year on the basis of reporting of crime. This data can prove very useful in analysing and predicting crime and help us prevent the crime to some extent. Crime analysis is an area of vital importance in police department. Study of crime data can help us analyse crime pattern, inter-related clues& important hidden relations between the crimes. That is why data mining can be great aid to analyse, visualize and predict crime using crime data set. We analyse data objects using machine learning techniques. Dataset is classified on the basis of tree based algorithm. In this prediction is done using random forest algoritm according to various types of crimes taking place in different states and cities. Crime mapping will help the administration to plan strategies for prevention of crime, further using Random forest algorithm technique data can be predicted and visualized in various form in order using leaflet and shiny package to provide better understanding of crime patterns

Adversarial behaviours knowledge area

The technological advancements witnessed by our society in recent decades have brought improvements in our quality of life, but they have also created a number of opportunities for attackers to cause harm. Before the Internet revolution, most crime and malicious activity generally required a victim and a perpetrator to come into physical contact, and this limited the reach that malicious parties had. Technology has removed the need for physical contact to perform many types of crime, and now attackers can reach victims anywhere in the world, as long as they are connected to the Internet. This has revolutionised the characteristics of crime and warfare, allowing operations that would not have been possible before. In this document, we provide an overview of the malicious operations that are happening on the Internet today. We first provide a taxonomy of malicious activities based on the attacker’s motivations and capabilities, and then move on to the technological and human elements that adversaries require to run a successful operation. We then discuss a number of frameworks that have been proposed to model malicious operations. Since adversarial behaviours are not a purely technical topic, we draw from research in a number of fields (computer science, criminology, war studies). While doing this, we discuss how these frameworks can be used by researchers and practitioners to develop effective mitigations against malicious online operations.Published versio

A platform for discovering and sharing confidential ballistic crime data.

Criminal investigations generate large volumes of complex data that detectives have to analyse and understand. This data tends to be "siloed" within individual jurisdictions and re-using it in other investigations can be difficult. Investigations into trans-national crimes are hampered by the problem of discovering relevant data held by agencies in other countries and of sharing those data. Gun-crimes are one major type of incident that showcases this: guns are easily moved across borders and used in multiple crimes but finding that a weapon was used elsewhere in Europe is difficult. In this paper we report on the Odyssey Project, an EU-funded initiative to mine, manipulate and share data about weapons and crimes. The project demonstrates the automatic combining of data from disparate repositories for cross-correlation and automated analysis. The data arrive from different cultural/domains with multiple reference models using real-time data feeds and historical databases

Mining large-scale human mobility data for long-term crime prediction

Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program