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dc.contributor.authorChen, Dong
dc.date.accessioned2014-03-04T20:02:07Z
dc.date.available2014-03-04T20:02:07Z
dc.date.issued2011-08
dc.identifier.otherchen_dong_201108_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/chen_dong_201108_ms
dc.identifier.urihttp://hdl.handle.net/10724/27419
dc.description.abstractFeral swine (Sus scrofa) is a very destructive exotic mammal in the United States that carries pathogens of several diseases, endangers the safety of human beings, and disturbs local ecosystems. Although efforts have been made to monitor the distribution of feral swine, little has been done to model and predict the future distribution of feral swine. This project aims at tackling this problem by identifying the relationship between feral swine distribution and a series of environmental and cultural factors based on logistic regression. To assess the effects of spatial autocorrelation in modeling feral swine, autologistic regression was also applied to be compared with the ordinary logistic regression. The results suggest the autologistic regression model is superior to the ordinary counterpart with better performance. In addition, it is strongly recommended that the ordinary logistic regression methods should be employed with caution when spatial autocorrelation exists because they may yield misleading results.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectAutologistic Regression
dc.subjectFeral Swine
dc.subjectHabitat Modeling
dc.subjectLogistic Regression
dc.subjectSpatial Autocorrelation
dc.subjectSpatial Statistics
dc.titleModeling feral swine distribution in Georgia using logistic and autologistic regression
dc.typeThesis
dc.description.degreeMS
dc.description.departmentGeography
dc.description.majorGeography
dc.description.advisorMarguerite Madden
dc.description.committeeMarguerite Madden
dc.description.committeeXiaobai Yao
dc.description.committeeThomas Jordan


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