The American Mafia is a network of criminals engaged in drug trafficking, violence and other illegal activities. Here, we analyze a historical spatial social network (SSN) of 680 Mafia members found in a 1960 investigatory dossier compiled by the U.S. Federal Bureau of Narcotics. The dossier includes connections between members who were ‘known criminal associates’ and members are geolocated to a known home address across 15 major U.S. cities.Under an overarching narrative of identifying the network’s proclivities toward security (dispersion) or efficiency (ease of coordination), we pose four research questions related to criminal organizations, power and coordination strategies. We find that the Mafia network is distributed as a portfolio of nearby and distant ties with significant spatial clustering among the Mafia family units.The methods used here differ from former methods that analyze the point pattern locations of individuals and the social network of individuals separately. The research techniques used here contribute to the body of non-planar network analysis methods in GIScience and can be generalized to other types of spatially-embedded social networks.

This paper presents a new methodology and evaluation experiments on the detection of spatial community structure in movements, which can reveal unknown spatial constructs and boundaries. While there are numerous existing approaches for community structure detection in spatial networks using either general-purpose methods or spatially modified extensions, they are usually designed and applied without controlled evaluation and understanding of their robustness in finding the underlying spatial communities. Towards addressing this challenge, we develop a new approach, Spatial Tabu Optimization for Community Structure (STOCS), which transforms trajectory data to a spatial network, integrates different community structure measures (e.g. modularity or edge ratio), and partitions the network into geographic regions to discover spatial communities in movements. We systematically evaluate and compare the new approach with existing methods using synthetic datasets that have known spatial community structures. Evaluation results show that general-purpose (non-spatial) methods are not robust for detecting spatial structures – their outcomes vary dramatically for the same data with different levels of spatial aggregation (resolution), data sampling, or data noise. STOCS is substantially more robust in discovering underlying spatial structures. Last, we present two case studies with animal movements and urban population movements to demonstrate the application of the approach.

This paper presents a new approach to flow mapping that extracts inherent patterns from massive geographic mobility data and constructs effective visual representations of the data for the understanding of complex flow trends. This approach involves a new method for origin-destination flow density estimation and a new method for flow map generalization, which together can remove spurious data variance, normalize flows with control population, and detect high-level patterns that are not discernable with existing approaches. The approach achieves three main objectives in addressing the challenges for analyzing and mapping massive flow data. First, it removes the effect of size differences among spatial units via kernel-based density estimation, which produces a measurement of flow volume between each pair of origin and destination. Second, it extracts major flow patterns in massive flow data through a new flow sampling method, which filters out duplicate information in the smoothed flows. Third, it enables effective flow mapping and allows intuitive perception of flow patterns among origins and destinations without bundling or altering flow paths. The approach can work with both point-based flow data (such as taxi trips with GPS locations) and area-based flow data (such as county-to-county migration). Moreover, the approach can be used to detect and compare flow patterns at different scales or in relatively sparse flow datasets, such as migration for each age group. We evaluate and demonstrate the new approach with case studies of U.S. migration data and experiments with synthetic data.