Disrupting resilient criminal networks through data analysis: The case of sicilian mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to LawEnforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs' internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two realworld datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions' frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks

L'identification des rôles dans un trafic de stupéfiants par la géolocalisation des données téléphoniques recueillies au cours de l'enquête

La surveillance téléphonique est un des principaux moyens utilisés dans les enquêtes sur le trafic de stupéfiants afin de reconstituer l’activité délictueuse, identifier les acteurs impliqués, déterminer leurs rôles respectifs et les localiser. À ce jour, les recherches sur l’exploitation de la téléphonie afin d’étudier des groupes criminels portent principalement sur les transcriptions des communications intégrées dans les décisions de justice. Cette recherche exploratoire bénéficie quant à elle d’un accès privilégié aux données de téléphonie provenant de mesures de surveillance en temps réel et rétroactive durant l’enquête. Cette recherche se focalise sur le lien entre le rôle d’un individu impliqué dans le trafic de stupéfiants et les patterns spatiaux détectés dans les traces téléphoniques. La question de recherche posée est : le rôle d’un acteur, à savoir la fonction qu’il remplit et la place qu’il occupe au sein du groupe, peut-il être inféré à partir de ses activités téléphoniques ? Cet article aborde cette question à travers l’analyse spatio-temporelle des contrôles téléphoniques rétroactifs et en temps réel de 20 individus distincts impliqués dans le trafic de stupéfiants. Les individus sélectionnés sont répartis selon trois rôles : livreurs, grossistes ou semi-grossistes et transporteurs. Pour mesurer leur mobilité, la localisation médiane, l’aire de l’enveloppe convexe, et la moyenne des distances parcourues par jour sont notamment calculées pour chaque individu et par type de rôle. Les résultats soutiennent l’hypothèse selon laquelle le rôle de l’utilisateur influence les patterns observés dans ses données téléphoniques. Les transporteurs semblent ainsi pouvoir être différenciés des livreurs et grossistes selon des patterns géographiques identifiables. Les livreurs semblent également pouvoir être différenciés des grossistes. Ces résultats tendent à confirmer l’approche proposée et révèlent un potentiel d’exploration des données de téléphonie pour inférer le rôle des acteurs dans un trafic

Visualization and analysis of mobile phone location data

This thesis investigates the use of passively-collected data from mobile phone networks to map population movements. In Australia, as in most other developed countries, nearly all teenagers and working-age adults carry a mobile phone. When these phones communicate with the network they reveal their location to be within the coverage area of the base station antenna that received their transmission. This location data, if it were collected, could be used to derive movement information for most of the population. Such information does not currently exist. The thesis begins by investigating what information is available within a mobile phone network during normal operations. It looks at how difficult it is to extract this information, how frequently it is generated, and the spatial accuracy when it is used to locate a mobile handset. A new technique is described for estimating the location of a handset within the coverage area of a directional antenna. The theoretical investigation is supplemented by the collection of field data with a GPSequipped smart phone running custom software; by simulating the movement of Australia's mobile phones using census data and a database of base station antenna locations; and by analyzing the mobile phone billing records of an individual who elected to make his data public. Having researched the accuracy and availability of mobile phone location data, the thesis then looks at the feasibility of using it for various applications. These applications include sending alerts to people in the path of a tsunami; predicting the utilization of a new public transport route; tracking the movements of fugitives and missing persons; measuring internal migration within Australia; identifying abnormal population concentrations in real-time; and measuring the population of a region throughout the day/year. Finally, the thesis looks at techniques for visualizing the data. Existing techniques are explored, and a new one is proposed that makes use of clustered velocity vectors. This new approach can display the location, quantity, speed, and direction of large numbers of people at a point in time, and do so efficiently in terms of computational speed. The thesis concludes by summarizing the potential applications of mobile phone location data and suggesting areas of further research

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and the use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to the different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions, including social transportation, recommender systems, and policy implications

Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs’ internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions’ frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks

Dystopian Trademark Revelations

Uncovering dystopian technologies is challenging. Nondisclosure agreements, procurement policies, trade secrets, and strategic obfuscation collude to shield the development and deployment of these technologies from public scrutiny until it is too late to combat them with law or policy. But occasionally, exposing dystopian technologies is simple. Corporations choose technology trademarks inspired by dystopian philosophies and novels or similar elements of real life—all warnings that their potential uses are dystopian as well. That pronouncement is not necessarily trumpeted on social media or corporate websites, however. It is revealed in a more surprising place: trademark registrations at the U.S. Patent and Trademark Office (USPTO). To grant registrations, the USPTO demands detailed disclosures about applied-for trademarks. These include the mark itself as well as information about how the applicant will use the mark, forcing corporations to admit their intent for their technologies. But these details do not always provide the full picture. The public can strategically supplement trademark disclosures with knowledge of the dystopian inspiration for the marks to understand corporations’ plans for their products. This Essay uses the marks PALANTIR for big data analytics, PANOPTO for classroom recording systems, and MECHANICAL TURK for on-demand work to illustrate the power of coupling trademark registrations with underlying namesakes to understand technologies’ dystopian implementations. Dystopian trademarks signal dystopian technologies, and the public is well-positioned to seek them out and develop strategies to combat their entrenchment