相关论文: Understanding Public Safety Trends in Calgary thro…

The object of this paper is to develop a statistical approach to criminal linkage analysis that discovers and groups crime events that share a common offender and prioritizes suspects for further investigation. Bayes factors are used to…

Crime prediction plays an impactful role in enhancing public security and sustainable development of urban. With recent advances in data collection and integration technologies, a large amount of urban data with rich crime-related…

This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method.

Nowadays, 23% of the world population lives in multi-million cities. In these metropolises, criminal activity is much higher and violent than in either small cities or rural areas. Thus, understanding what factors influence urban crime in…

Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to retrieve open street map (OSM) spatial data. This study also…

Crime has been previously explained by social characteristics of the residential population and, as stipulated by crime pattern theory, might also be linked to human movements of non-residential visitors. Yet a full empirical validation of…

Violent crime in London is an area of increasing interest following policing and community budget cuts in recent years. Understanding the locally-varying demographic factors that drive distribution of violent crime rate in London could be a…

Exploring the darknet can be a daunting task; this paper explores the application of data mining the darknet within a Canadian cybercrime perspective. Measuring activity through marketplace analysis and vendor attribution has proven…

Community detection in social networks is a problem with considerable interest, since, discovering communities reveals hidden information about networks. There exist many algorithms to detect inherent community structures and recently few…

Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between…

Crowd analysis and management is a challenging problem to ensure public safety and security. For this purpose, many techniques have been proposed to cope with various problems. However, the generalization capabilities of these techniques is…

Municipalities are vulnerable to cyberattacks with devastating consequences, but they lack key information to evaluate their own risk and compare their security posture to peers. Using data from 83 municipalities collected via a…

It is quite evident that majority of the population lives in urban area today than in any time of the human history. This trend seems to increase in coming years. A study [5] says that nearly 80.7% of total population in USA stays in urban…

Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its importance in many fields such as biology, social networks or network traffic analysis. The metrics proposed to shape communities…

In this paper, we present a method of crime analysis from open source information. We employed un-supervised methods of data mining to explore the facts regarding the crimes of an area of interest. The analysis is based on well known…

Understanding the community conditions that best support universal access and improved childhood outcomes allows ultimately to improve decision-making in the areas of planning and investment across the early stages of childhood development.…

We revisit the longstanding question of how physical structures in urban landscapes influence crime. Leveraging machine learning-based matching techniques to control for demographic composition, we estimate the effects of several types of…

The growth of mobile sensor technologies have made it possible for city councils to understand peoples' behaviour in urban spaces which could help to reduce stress around the city. We present a quantitative approach to convey a collective…

Linking crimes by modus operandi has long been employed as an effective tool for crime investigation. The standard statistical method that underpins statistical crime linkage has been logistic regression. The simplicity and interpretability…

We consider a community finding problem called Co-located Community Detection (CCD) over geo-social networks, which retrieves communities that satisfy both high structural tightness and spatial closeness constraints. To provide a solution…