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Abstract Details

Activity Number: 584
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Social Statistics Section
Abstract - #306064
Title: Spatial Biclustering and Nonlinear Modeling of a Complex Data Set
Author(s): Alan Izenman*+
Companies: Temple University
Address: Speakman Hall (006-12), Philadelphia, PA, 19122-6083, United States
Keywords: Spatial biclustering ; Juvenile recidivism ; Plaid ; neural networks ; Nonstationarity ; ProDES

Using a novel database, ProDES, developed by the Crime and Justice Research Center at Temple University, this talk studies the relationship between spatial characteristics and juvenile delinquency and recidivism --- the proportion of delinquents who commit crimes following completion of a court-ordered program --- in Philadelphia, Pennsylvania. ProDES was originally a case-based sample, where the cases had been adjudicated in family court between 1994 and 2004. For our analysis, we focused attention on studying 6,768 juvenile males from the data set. To address the difficult issue of nonstationarity in the data, we apply the plaid biclustering algorithm in which a sequence of subsets ("layers") of both juveniles and variables are extracted from the data one layer at a time, but where the layers are allowed to overlap with each other. This type of "biclustering" is a new way of studying juvenile offense data. We show that the juveniles within each layer can be viewed as spatially clustered. Statistical relationships of the variables and juveniles within each layer are then studied using neural network models.

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