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Description
The current study examines the social structure of local street gangs in Glendale, Arizona. Literature on gang organization has come to different conclusions about gang organization, largely based on the methodology used. One consistent finding from qualitative gang research has been that understanding the social connections between gang members is important for understanding how gangs are organized. The current study examines gang social structure by recreating gang social networks using official police data. Data on documented gang members, arrest records, and field interview cards from a 5-year period from 2006 to 2010 were used. Yearly social networks were constructed going two steps out from documented gang members. The findings indicated that gang networks had high turnover and they consisted of small subgroups. Further, the position of the gang member or associate was a significant predictor of arrest, specifically for those who had high betweenness centrality. At the group level, density and measures of centralization were not predictive of group-level behavior; hybrid groups were more likely to be involved in criminal behavior, however. The implications of these findings for both theory and policy are discussed.
ContributorsFox, Andrew (Author) / Katz, Charles M. (Thesis advisor) / White, Michael D. (Committee member) / Sweeten, Gary (Committee member) / Arizona State University (Publisher)
Created2013
Description
In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive. More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.
This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.
This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.
ContributorsShaabani, Elham (Author) / Shakarian, Paulo (Thesis advisor) / Davulcu, Hasan (Committee member) / Maciejewski, Ross (Committee member) / Decker, Scott (Committee member) / Arizona State University (Publisher)
Created2019