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dc.contributor.authorJohnson, Taylor Christian
dc.date.accessioned2014-03-04T20:03:00Z
dc.date.available2014-03-04T20:03:00Z
dc.date.issued2011-08
dc.identifier.otherjohnson_taylor_c_201108_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/johnson_taylor_c_201108_ms
dc.identifier.urihttp://hdl.handle.net/10724/27490
dc.description.abstractPrevious contextual research relies heavily on data collected by the U.S. Census Bureau due to its widespread availability, but this incorrectly assumes that populations are equally distributed within the arbitrarily-drawn Census boundaries. Areal interpolation research suggests that methods employing ancillary data assist in correcting the problem. This thesis posits that the augmentation of census variables through “Intelligent Dasymetric Mapping,” and the redefinition of neighborhoods as circular or road network-based buffers around interview locations, provides more accurate contextual information for modeling neighborhood effects. This hypothesis is tested through the multivariate regression analysis used to explain individual perceptions of community collective efficacy which is theorized to mediate specific of criminal behavior. Effects of neighborhood size (i.e. buffer distance) are also explored. Results indicate improved model explanatory power compared to that of models using traditional measures of context while significance of model parameters estimates varies by scale.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectIntelligent Dasymetric Mapping
dc.subjectCommunity collective efficacy
dc.subjectAreal interpolation
dc.subjectPopulation
dc.subjectNeighborhood
dc.titleTowards a more contextually-sensitive understanding of community collective efficacy
dc.title.alternativean application of "intelligent dasymetric mapping"
dc.typeThesis
dc.description.degreeMS
dc.description.departmentGeography
dc.description.majorGeography
dc.description.advisorSteven Holloway
dc.description.committeeSteven Holloway
dc.description.committeeLan Mu
dc.description.committeeThomas Jordan


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