No / Certain crimes are difficult to be committed by individuals but carefully organised by group of associates and affiliates loosely connected to each other with a single or small group of individuals coordinating the overall actions. A common starting point in understanding the structural organisation of criminal groups is to identify the criminals and their associates. Situations arise in many criminal datasets where there is no direct connection among the criminals. In this paper, we investigate ties and community structure in crime data in order to understand the operations of both traditional and cyber criminals, as well as to predict the existence of organised criminal networks. Our contributions are twofold: we propose a bipartite network model for inferring hidden ties between actors who initiated an illegal interaction and objects affected by the interaction, we then validate the method in two case studies on pharmaceutical crime and underground forum data using standard network algorithms for structural and community analysis. The vertex level metrics and community analysis results obtained indicate the significance of our work in understanding the operations and structure of organised criminal networks which were not immediately obvious in the data. Identifying these groups and mapping their relationship to one another is essential in making more effective disruption strategies in the future.

<p>  </p> <p>My dissertation seeks ways that data systems can be constructed differently in order to focus on improving outcomes for marginalized and vulnerable populations. The cases I study in my dissertation all represent different stakeholders in and types of engagement with crime, violence, and policing in the United States. The three cases are the FBI's crime data system and especially their newer NIBRS and CDE (National Incident Based Reporting System and Crime Data Explorer, respectively) interfaces, the <em>Washington Post</em>'s Fatal Force police brutality database project, and the Urban Indian Health Institute (UIHI)'s <em>Our Bodies, Our Stories</em> reports on the Missing and Murdered Indigenous Women and Girls (MMIWG) crisis. The three cases scaffold onto one another to create a deeply contextual, well-rounded picture of crime data rhetorics. Each case is unique and distinct, but also overlaps onto the other two cases; the UIHI reports, for instance, are an example of community-focused data activism like Fatal Force, but they also co-opt institutional data systems similar to the FBI's database. Similarly, the Fatal Force database explicitly engages issues of social justice and names a gap in institutional reporting; in so doing, Fatal Force includes community reporting and allows private citizens to submit tips, but it also draws on official institutional data as part of its sources. And as a case of institutional data practices that collect crime data from across the country, the FBI's NIBRS case sets up standards that Fatal Force and the UIHI reports look to work both with and against.</p> <p>I describe the methodology I pilot in this study: unblackboxing. I first put unblackboxing in context with current conversations in science and technology studies, information studies, critical data studies, and rhetoric and technical communication. I emphasize the importance of narratives, whether explicit, implicit, or cultural, to unblackboxing, especially when data is the object of study. Then, I enumerate key principles of unblackboxing and offer a heuristic for adapting unblackboxing to studying data systems. This approach helps researchers meet a system on its own terms and work with it rhetorically rather than trying a one-size-fits-all approach. Finally, I describe how I applied unblackboxing in my dissertation research and adapted my preliminary work on unblackboxing in order to study each system fairly and responsibly.</p> <p>Ultimately, I find that each data system is responsive to unique needs and challenges of that system. Strategies that work to make data easier for users to understand in cases like Fatal Force aren’t options in cases like FBI crime data, where the sheer scale of data means relying on automated data visualization that introduces error and uncertainty. But by keeping ethical principles of user-centered design and data justice in mind, I argue, designers and technical communicators can continue to make strides in using data to communicate ethically and effectively.</p>