What Have We Learned in Five Decades of Neutralization Research?

Neutralization theory, though a popular framework for understanding deviant behavior, remains badly underdeveloped. Few attempts have been made to connect it to narrative and sociocognitive research in psychology and related fields. From this wider perspective, one reason neutralization theory has received only mixed empirical support is that it has been understood as a theory of criminal etiology. This makes little sense (how can one neutralize something before they have done it?) and makes the theory difficult to test. Neutralization should instead be seen as playing a role in persistence in or desistance from criminal behavior. The theory's central premises need to be substantially complicated. The notions that all excuses or justifications are "bad" and that reform involves "accepting complete responsibility" for one's actions are not tenable.

Anomaly Detection for Data with Spatial Attributes

The problem of detecting spatially-coherent groups of data that exhibit anomalous behavior has started to attract attention due to applications across areas such as epidemic analysis and weather forecasting. Earlier efforts from the data mining community have largely focused on finding outliers, individual data objects that display deviant behavior. Such point-based methods are not easy to extend to find groups of data that exhibit anomalous behavior. Scan Statistics are methods from the statistics community that have considered the problem of identifying regions where data objects exhibit a behavior that is atypical of the general dataset. The spatial scan statistic and methods that build upon it mostly adopt the framework of defining a character for regions (e.g., circular or elliptical) of objects and repeatedly sampling regions of such character followed by applying a statistical test for anomaly detection. In the past decade, there have been efforts from the statistics community to enhance efficiency of scan statstics as well as to enable discovery of arbitrarily shaped anomalous regions. On the other hand, the data mining community has started to look at determining anomalous regions that have behavior divergent from their neighborhood.In this chapter,we survey the space of techniques for detecting anomalous regions on spatial data from across the data mining and statistics communities while outlining connections to well-studied problems in clustering and image segmentation. We analyze the techniques systematically by categorizing them appropriately to provide a structured birds eye view of the work on anomalous region detection;we hope that this would encourage better cross-pollination of ideas across communities to help advance the frontier in anomaly detection.