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Exposure to crime and violence can harm individuals’ quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to crime prevention. The ...

The number of theft cases is much higher than that of other criminal cases, which frequently occurs in daily life and is seriously destructive to social order. Studying the law of theft cases has a positive impact on social governance ...

It is important but challenging to accurately predict urban crimes. Existing studies rely on domain knowledge specific, pre-defined inter-dependency graphs using extra urban data and have many disadvantages. We propose a novel framework, AGL-STAN, ...

One of the most common types of crime, burglary often results in serious psychological trauma and has financial consequences. Predicting burglaries is a challenging task due to their high degree of randomness. In this study, we propose ...

• A novel attention-based deep learning framework for burglary prediction.
• ...

As one of the most complex social problems around the world, crime may bring the risk of dying or losing property to the public if not handled properly. Crime prediction which aims at predicting crime incidents before they happen is of ...

Crime poses a major threat to human life and property, which has been recognized as one of the most crucial problems in our society. Predicting the number of crime incidents in each region of a city before they happen is of great importance to fight ...

Crime prediction models seek to assist policymakers and law enforcement agencies in the allocation of scarce resources intended to prevent crime occurrences. This paper proposes an extension and two interpretation methods for a novel conditional ...

Crimes sabotage various societal aspects, such as social stability, public safety, economic development, and individuals’ quality of life. To accurately predict crime occurrences can not only bring the peace of mind to individuals but also help ...

Urban crime is an ongoing problem in metropolitan development and attracts general concern from the international community. As an effective means of defending urban safety, crime prediction plays a crucial role in patrol force allocation and public ...

Crime predictors have been sought after by governments and citizens alike for preventing or avoiding crimes. In this paper, we attempt to thoroughly analyze crime predictors from three Web open data sources: Google Street View (GSV), Twitter, and ...

Traditionally, historical crimes and socioeconomic data have been used to understand crime in cities and to build crime prediction models. Nevertheless, the increasing availability of mobility data from cell phones to location-based services, has ...

Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. In this paper, we propose a Deep Temporal Multi-Graph Convolutional Network (DT-MGCN) model which integrates graph ...

Crime is one of the major problems of countries all over the world, and the Philippines is no exception. Crime prediction and prevention strategies are vital for police forces to face inevitable increases in the crime rate as a side effect of the growth ...

Crime is one of the major problems of countries all over the world, and the Philippines is no exception. Crime prediction and prevention strategies are vital for police forces to face inevitable increases in the crime rate as a side effect of the growth ...

For centuries, crime has been viewed as random because it is based on human behavior; even now, it incorporates an excessive number of factors for current machine learning models to forecast accurately. In this work, we tend to discuss the early ...

Violent crime is a critically important community issue. Government has attempted to address this problem in a variety of ways, with varied levels of success. However, there are only a certain number and type of factors that can be addressed by ...

This project investigates the feasibility of using machine learning techniques, specifically neural networks, to make prediction on criminal behavior based on the history of the arrest bookings. The experiment will have to handle imbalanced data ...

Crime rates are expected to increase in the whole world as the growth of many complex factors like: unemployment, poverty, weather, violent ideologies and religion and etc. Obviously crimes have negatively influenced the development of society, economic ...

In recent years we have seen more and more open government and administrative data made available on the Web. Crime data, for example, allows civic organizations and ordinary citizens to obtain safety-related information on their surroundings. In ...

Crime events are known to reveal spatio-temporal patterns, which can be used for predictive modeling and subsequent decision support. While the focus has hitherto been placed on areas with high population density, we address the ...

• Predictive policing reduces crime rates through prediction and decision support.