Avinash S. Bhati

Robust Spatial Analysis of Rare Crimes: An Information-Theoretic Approach

In this paper we describe a semiparametric information-theoretic framework for modeling the determinants of rare events aggregated at intracity areal units while allowing for various forms of error correlation structures. The approach is applied to an examination of the effects of socioeconomic and demographic macro-characteristics of communities on the amount of violence they experience. We investigate and find evidence of some instability in these processes across types of violence and level of areal aggregation. However, we also find evidence of a stable predictor—resource deprivation—for all the types of violence analyzed and at both levels of areal aggregation considered. In addition, we find evidence of a spillover effect of a communitys resource deprivation on the level of violence its neighboring areas can expect. We discuss our findings in light of their substantive, methodological, and practical implications.

A Generalized Cross-Entropy Approach for Modeling Spatially Correlated Counts

This article discusses and applies an information-theoretic framework for incorporating knowledge of the spatial structure in a sample while extracting from it information about processes resulting in count outcomes. The framework, an application of the Generalized Cross-Entropy (GCE) method of estimating count outcome models, allows researchers to incorporate such real-world features as unobserved heterogeneity—with or without spatial clustering—when modeling spatially correlated counts. The information-recovering potential of the approach is investigated using a limited set of simulations. It is then used to study the determinants of counts of homicides recorded in 343 neighborhoods in Chicago, Illinois.

A Process and Outcome Evaluation of an Agricultural Crime Prevention Initiative

Agricultural crime victimization—including theft of farm crops, livestock, pesticides, and equipment—and efforts to address it remain largely ignored despite the potential of such crime to adversely affect the lives and businesses of farmers and increase the costs of food to consumers. The Agricultural Crime, Technology, Information, and Operations Network (ACTION) initiative, located in Californias central valley, was recently developed to combat such crime. The authors present findings from a process and outcome evaluation of this program. The findings are limited by the design of the study but nonetheless suggest that efforts to increase guardianship measures among farmers (e.g., marking equipment and livestock and using surveillance equipment), to “harden targets” (e.g., locking tractors and storing chemicals in locked storage sheds), and to arrest and prosecute offenders may help reduce agricultural crime victimization.

An Information‐Theoretic Approach for Image Reconstruction: The Black and White Case

In this paper an Information‐Theoretic method for reconstructing noisy and blurry images is developed. Basically, the inverse problem is transformed into a generalized moment problem, which is then solved by an information theoretic method. This estimation approach is robust for a whole class of distributions and allows the use of prior information. The resulting method builds on the foundations of information‐theoretic methods, uses minimal distributional assumptions, performs well and uses efficiently all the available information (hard and soft data). This method is computationally efficient. A number of empirical examples are presented.