Show simple item record

dc.contributor.advisorWhitaker, Lyn R.
dc.contributor.advisorAppleget, Jeffrey A.
dc.contributor.authorHousley, Jimmy L.
dc.dateJun-17
dc.date.accessioned2017-08-14T16:48:36Z
dc.date.available2017-08-14T16:48:36Z
dc.date.issued2017-06
dc.identifier.urihttp://hdl.handle.net/10945/55623
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractMegacities are characterized by large populations (at least 10 million) and interdependent infrastructure, demographic, economic, and government networks (the four pillars). To be successful in future operations, the military must expand its understanding of megacities and their networks. In particular the Joint Warfare Analysis Center (JWAC) is interested in these megacity networks and their implications for potential urban operations. We develop a methodology to group like megacities into five clusters. With 33 variables describing the four pillars, we construct a data set using over 90 data sources for 41 large urban areas. This work greatly expands previous work in both the number of cities studied and the number of variables used. We also study clustering sensitivity to missing values by generating an ensemble of 5,000 clusterings based on randomly imputed missing values. We compare these to clustering without imputation, the ensemble consensus or average clustering, and clusterings from previous studies in addition to identifying which cities are sensitive to missing values. Our work not only informs JWAC of the similarities and differences between the 41 cities studied, it provides a method to identify for which cities, more data collection is warranted, and it provides a blueprint for future work in this area.en_US
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleMegacity analysis: A clustering approach to classificationen_US
dc.typeThesisen_US
dc.contributor.secondreaderBurks, Robert
dc.contributor.departmentOperations Research (OR)
dc.subject.authormegacityen_US
dc.subject.authorurbanen_US
dc.subject.authorclusteren_US
dc.subject.authorunsupervised learningen_US
dc.description.serviceCaptain, United States Marine Corpsen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record