This dissertation focuses on modeling approach for spatial and spatiotemporal data with epidemiological applications. Chapter one gives the general overview of spatial and spatiotemporal data and challenges in the statistical analysis of spatial and spatiotemporal data, and motivation and objectives of the study. Chapter two describes the regression models commonly used in spatial data analysis. Various types of regression methods such as OLS, GWR and MGWR were used to study the association between diabetes prevalence and socioeconomic and lifestyle factors on county level data of Midwestern United States. A new analysis workflow is purposed for regression analysis of spatial data. Chapter three describes recently developed INLA as an alternative of traditionally used MCMC in Bayesian hierarchical models. INLA method was used to identify the best regression model for the spatiotemporal regression analysis of Lyme disease count data with climatic covariates in county-level data in Minnesota. Chapter four gives the contribution of this dissertation and discusses the direction for the future research.
","abstract_html":"<p>This dissertation focuses on modeling approach for spatial and spatiotemporal data with epidemiological applications. Chapter one gives the general overview of spatial and spatiotemporal data and challenges in the statistical analysis of spatial and spatiotemporal data, and motivation and objectives of the study. Chapter two describes the regression models commonly used in spatial data analysis. Various types of regression methods such as OLS, GWR and MGWR were used to study the association between diabetes prevalence and socioeconomic and lifestyle factors on county level data of Midwestern United States. A new analysis workflow is purposed for regression analysis of spatial data. Chapter three describes recently developed INLA as an alternative of traditionally used MCMC in Bayesian hierarchical models. INLA method was used to identify the best regression model for the spatiotemporal regression analysis of Lyme disease count data with climatic covariates in county-level data in Minnesota. Chapter four gives the contribution of this dissertation and discusses the direction for the future research.</p>","abstract_has_math":false,"creators":["Karki, Laxman"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation - Open Access","degree_discipline":"Mathematics and Statistics","degree_department":null,"school":null,"contributors":["Gary D. Hatfield"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-01-01T08:00:00Z","date_published":"2017-01-01T08:00:00Z","updated_at":"2026-07-24T04:28:43Z","subjects":["diabetes","GWR","INLA","Lyme disease","MGWR","Statistics and Probability"],"languages":["en"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://openprairie.sdstate.edu/etd/1215","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gary D. Hatfield"]},{"key":"dc:creator","label":"Author","values":["Karki, Laxman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-05-11T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics and Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["diabetes","GWR","INLA","Lyme disease","MGWR","Statistics and Probability"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openprairie.sdstate.edu/etd/1215"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation focuses on modeling approach for spatial and spatiotemporal data with epidemiological applications. Chapter one gives the general overview of spatial and spatiotemporal data and challenges in the statistical analysis of spatial and spatiotemporal data, and motivation and objectives of the study. Chapter two describes the regression models commonly used in spatial data analysis. Various types of regression methods such as OLS, GWR and MGWR were used to study the association between diabetes prevalence and socioeconomic and lifestyle factors on county level data of Midwestern United States. A new analysis workflow is purposed for regression analysis of spatial data. Chapter three describes recently developed INLA as an alternative of traditionally used MCMC in Bayesian hierarchical models. INLA method was used to identify the best regression model for the spatiotemporal regression analysis of Lyme disease count data with climatic covariates in county-level data in Minnesota. Chapter four gives the contribution of this dissertation and discusses the direction for the future research.
"]},{"key":"dc:title","label":"Title","values":["Spatial and Spatiotemporal Modeling of Epidemiological Data"]}]}],"canonical_facts":{"dc:contributor":["Gary D. Hatfield"],"dc:creator":["Karki, Laxman"],"dc:date.available":["2017-05-11T07:00:00Z"],"dc:description.abstract":["This dissertation focuses on modeling approach for spatial and spatiotemporal data with epidemiological applications. Chapter one gives the general overview of spatial and spatiotemporal data and challenges in the statistical analysis of spatial and spatiotemporal data, and motivation and objectives of the study. Chapter two describes the regression models commonly used in spatial data analysis. Various types of regression methods such as OLS, GWR and MGWR were used to study the association between diabetes prevalence and socioeconomic and lifestyle factors on county level data of Midwestern United States. A new analysis workflow is purposed for regression analysis of spatial data. Chapter three describes recently developed INLA as an alternative of traditionally used MCMC in Bayesian hierarchical models. INLA method was used to identify the best regression model for the spatiotemporal regression analysis of Lyme disease count data with climatic covariates in county-level data in Minnesota. Chapter four gives the contribution of this dissertation and discusses the direction for the future research.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/1215"],"dc:language":["en"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["diabetes","GWR","INLA","Lyme disease","MGWR","Statistics and Probability"],"dc:title":["Spatial and Spatiotemporal Modeling of Epidemiological Data"],"thesis:degree_discipline":["Mathematics and Statistics"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:28:43Z"}