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

The crime drop and the security hypothesis

Major crime drops were experienced in the United States and most other industrialised countries for a decade from the early to mid-1990s. Yet there is little agreement over explanation or lessons for policy. Here it is proposed that change in the quantity and quality of security was a key driver of the crime drop. From evidence relating to vehicle theft in two countries it is concluded that electronic immobilisers and central locking were particularly effective. It is suggested that reduced car theft may have induced drops in other crime including violence. From this platform a broader security hypothesis, linked to routine activity and opportunity theory, is outlined

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Revisiting the Far Right Violent Extremist Threat: Violent Extremist Plot Success From 1948 Through 2017

Far Right violent extremists have successfully executed over 150 violent plots in the United States in just the past decade. This exploratory study analyzed Far Right violent extremist plot success with the plot success of Islamist violent extremists, Far Left violent extremists, and Single Issue violent extremists based on publicly available data from the Profiles of Individual Radicalization in the United States (PIRUS) for the period of 1948 through 2017. By evaluating existing literature on Far Right violent extremism and analyzing the available PIRUS data, it was discovered that while Far Right violent extremists executed more successful violent plots than the other violent ideological extremist groups, Far Left violent extremists proportionally had more successful violent plots. A sample from the PIRUS database was explored, and the analysis demonstrates that the variables of Far Left radicalization, violence against persons and property, and plot preparation are significantly correlated with violent plot success

Leveraging Mobility Flows from Location Technology Platforms to Test Crime Pattern Theory in Large Cities

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 the latter is lacking. The prime reason is that prior studies are limited to aggregated statistics of human visitors rather than mobility flows and, because of that, neglect the temporal dynamics of individual human movements. As a remedy, we provide the first work which studies the ability of granular human mobility in describing and predicting crime concentrations at an hourly scale. For this purpose, we propose the use of data from location technology platforms. This type of data allows us to trace individual transitions and, therefore, we succeed in distinguishing different mobility flows that (i) are incoming or outgoing from a neighborhood, (ii) remain within it, or (iii) refer to transitions where people only pass through the neighborhood. Our evaluation infers mobility flows by leveraging an anonymized dataset from Foursquare that includes almost 14.8 million consecutive check-ins in three major U.S. cities. According to our empirical results, mobility flows are significantly and positively linked to crime. These findings advance our theoretical understanding, as they provide confirmatory evidence for crime pattern theory. Furthermore, our novel use of digital location services data proves to be an effective tool for crime forecasting. It also offers unprecedented granularity when studying the connection between human mobility and crime

The Changing Nature of Homicide and Its Impact on Homicide Clearance Rates: A Quantitative Analysis of Two Trends From 1984-2009

The following analyses uses the Federal Bureau of Investigation’s (FBI) Supplementary Homicide Report (SHR) data from 1984 to 2009 to examine factors that predict whether a homicide will be cleared or not (N=439,542). Two theories inform the current study: 1) Black’s theory of law (discretionary variables) proposes that characteristics of the victim, such as age or race, influence how diligently police work to solve a homicide; and 2) non-discretionary theories propose that characteristics of the homicide act, such as geographic location and weapon use, are more important to the solvability of a homicide. Preliminary analyses of clearance rates indicate decreasing rates from 1984-2004, and increasing rates from 2004-2009; therefore, separate analyses are performed for each trend. Results indicate that firearm use, unknown weapons, males, minority victims, population size, and western regions predict lower clearance rates. However, predicted probabilities analysis provide a more complete picture of the relative importance of each variable. Most variables support non-discretionary theories of crime, where aspects of the homicide itself determine its solvability. Theoretical implications are discussed alongside directions for future research oriented toward more practical analyses of homicide clearance rates and police practices

Peer Cybervictimization ( Pcv ) among University Students in Bangladesh and Institutional Disciplinary Body ( Idb ) under Routine Activity Theory ( Rat )

Research on cybervictimization of Bangladeshi students is scarce, but the research that does exist suggests a high prevalence of victimization followed by low extent of reporting. None of the extant research exclusively studied peer cybervictimization (PCV) in relation to students’ knowledge of the university authority, hereinafter the institutional disciplinary body or IDB. As per the added propositions of Routine Activity Theory (RAT) (Felson, 1995), the IDB can potentially contribute to guardianship building by alerting students about handlers-managers in the university premises and thereby reduce PCV among students. Thus, the knowledge of (the presence of) the IDB can respond to the expected roles of handlers-managers from a cyberspace perspective. The current study seeks to apply RAT to PCV of university students in Bangladesh and assess the relationship between students’ knowledge of the IDB and their perception of PCV. Data were collected by an online survey questionnaire distributed via email to a body of adult university students in Bangladesh under the convenience sampling method. Findings have bearing on the possible policy implications for the higher education institutional administration to combat PCV in Bangladesh