Elio Marchione

Spatial, Temporal and Spatio-Temporal Patterns of Maritime Piracy

Objectives: To examine patterns in the timing and location of incidents of maritime piracy to see whether, like many urban crimes, attacks cluster in space and time. Methods: Data for all incidents of maritime piracy worldwide recorded by the National Geospatial Intelligence Agency are analyzed using time-series models and methods originally developed to detect disease contagion. Results: At the macro level, analyses suggest that incidents of pirate attacks are concentrated in five subregions of the earth’s oceans and that the time series for these different subregions differ. At the micro level, analyses suggest that for the last 16 years (or more), pirate attacks appear to cluster in space and time suggesting that patterns are not static but are also not random. Conclusions: Much like other types of crime, pirate attacks cluster in space, and following an attack at one location the risk of others at the same location or nearby is temporarily elevated. The identification of such regularities has implications for the understanding of maritime piracy and for predicting the future locations of attacks.

Knowledge exchange processes in Industrial Districts and the emergence of networks

This paper aims at exploring conditions under which the need for knowledge exchange within a small firms’ cluster generates a structure of links between firms. We focus in particular on small firms’ clusters called Industrial Districts (IDs). Specifically, we analyze IDs with flexible specialization, in which knowledge exchange is driven by the search for complementary knowledge assets. Previous works of the authors proposed an agent-based model of IDs to explore the properties of networks emerging from the interaction of firms prompted by the search and exchange of complementary specialized knowledge. This model showed that limited relational capability, due to the small size, and an exchange mechanism solely based on the barter of complementary knowledge are structural conditions that limit individual firms’ growth in IDs with flexible specialization. This paper presents a new version of this model to analyze the role of embeddedness of relationships among IDs firms in shaping the emergent network structures. The aim of the paper is to answer to the following research questions: Can knowledge complementariness explain the emergence of a stable network of firms within a small firms’ cluster? What are the structural properties of these networks? Which role does the embeddedness of relationships among firms play in shaping the structure of emerging networks?

Analysing Differential School Effectiveness Through Multilevel and Agent-Based Modelling

During the last thirty years education researchers have developed models for judging the comparative performance of schools, in studies of what has become known as “differential school effectiveness†. A great deal of empirical research has been carried out to understand why differences between schools might emerge, with variable-based models being the preferred research tool. The use of more explanatory models such as agent-based models (ABM) has been limited. This paper describes an ABM that addresses this topic, using data from the London Educational Authoritys Junior Project. To compare the results and performance with more traditional modelling techniques, the same data are also fitted to a multilevel model (MLM), one of the preferred variable-based models used in the field. The paper reports the results of both models and compares their performances in terms of predictive and explanatory power. Although the fitted MLM outperforms the proposed ABM, the latter still offers a reasonable fit and provides a causal mechanism to explain differences in the identified school performances that is absent in the MLM. Since MLM and ABM stress different aspects, rather than conflicting they are compatible methods.

Event Networks and the Identification of Crime Pattern Motifs.

In this paper we demonstrate the use of network analysis to characterise patterns of clustering in spatio-temporal events. Such clustering is of both theoretical and practical importance in the study of crime, and forms the basis for a number of preventative strategies. However, existing analytical methods show only that clustering is present in data, while offering little insight into the nature of the patterns present. Here, we show how the classification of pairs of events as close in space and time can be used to define a network, thereby generalising previous approaches. The application of graph-theoretic techniques to these networks can then offer significantly deeper insight into the structure of the data than previously possible. In particular, we focus on the identification of network motifs, which have clear interpretation in terms of spatio-temporal behaviour. Statistical analysis is complicated by the nature of the underlying data, and we provide a method by which appropriate randomised graphs can be generated. Two datasets are used as case studies: maritime piracy at the global scale, and residential burglary in an urban area. In both cases, the same significant 3-vertex motif is found; this result suggests that incidents tend to occur not just in pairs, but in fact in larger groups within a restricted spatio-temporal domain. In the 4-vertex case, different motifs are found to be significant in each case, suggesting that this technique is capable of discriminating between clustering patterns at a finer granularity than previously possible.