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NCJRS Abstract

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NCJ Number: 78711 Add to Shopping cart Find in a Library
Title: Assessing Homogeneity in Cross-classified Proximity Data (From Confirmatory and Exploratory Analysis of the Spatio and Temporal Properties of Crime Data, P 58-79, 1981, by Reginald G Golledge and Lawrence J Hubert - See NCJ-78709)
Author(s): L J Hubert; R G Golledge; C M Costanzo; G D Richardson
Corporate Author: University of California, Santa Barbara
United States of America
Date Published: 1981
Page Count: 22
Sponsoring Agency: National Institute of Justice (NIJ)
Washington, DC 20531
National Science Foundation
Arlington, VA 22230
University of California, Santa Barbara
Santa Barbara, CA 93106
US Dept of Justice NIJ Pub
Washington, DC 20531
Grant Number: 79-NI-AX-0057; SOC-77-28227
Type: Statistics
Format: Document
Language: English
Country: United States of America
Annotation: Assessing homogeneity in cross-classified proximity data is examined as an aspect of confirmatory and exploratory analysis of the spatio-temporal properties of crime data.
Abstract: Given an arbitrary proximity matrix that is cross-classified according to two dimensions, a nonparametric strategy generalizing Friedman's (randomized blocks) analysis-of-variance method is suggested for testing the salience of the dimensions. Straightforward extensions of the approach can be given for more than two dimensions or when only the ordering of the proximity values is of interest. The major contribution of the study is its use of arbitrary proximity measures and the development of a strategy for blocking on the levels of one or more a priori dimensions when evaluating the differences over a second. The strategy proposed is very general, even though the illustration used contains three explicit classification dimensions of space, time, and crime type. Since any two of the dimensions could have been considered the major classification facets of interest, proximity measures could have been obtained between profiles over the cities and the interest in crime type and time. The basic inference principles would remain the same, and the analyses would be conducted as before. The analysis features presented should permit researchers to assess dimensional salience in data sets that are not easily studied by more standard analysis-of-variance schemes because of an unusual proximity measure. Moreover, the possibility of relying on only nonmetric comparisons among proximities should provide a tie-in to the current emphasis on nonmetric clustering and scaling in the social and behavioral sciences. Tabular data, mathematical equations, and eight references are provided. (Author summary modified)
Index Term(s): Crime Statistics; Evaluation; Mathematical modeling; Nonbehavioral correlates of crime; Statistical analysis
Note: Available on microfiche from NCJRS as NCJ-78709.
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