University of Northern Iowa UNI ScholarWorks Dissertations and Theses @ UNI Student Work 2021 Geospatially-intelligent three-dimensional multivariate methods for multiscale dasymetric mapping of urban population: Application and performance in the Minneapolis-St. Paul metropolitan area Nikolay Golosov University of Northern Iowa Let us know how access to this document benefits ouy Copyright ©2021 Nikolay Golosov Follow this and additional works at: https://scholarworks.uni.edu/etd Recommended Citation Golosov, Nikolay, "Geospatially-intelligent three-dimensional multivariate methods for multiscale dasymetric mapping of urban population: Application and performance in the Minneapolis-St. Paul metropolitan area" (2021). Dissertations and Theses @ UNI. 1106. https://scholarworks.uni.edu/etd/1106 This Open Access Thesis is brought to you for free and open access by the Student Work at UNI ScholarWorks. It has been accepted for inclusion in Dissertations and Theses @ UNI by an authorized administrator of UNI ScholarWorks. For more information, please contact
[email protected]. Copyright by NIKOLAY GOLOSOV 2021 All Rights Reserved GEOSPATIALLY-INTELLIGENT THREE-DIMENSIONAL MULTIVARIATE METHODS FOR MULTISCALE DASYMETRIC MAPPING OF URBAN POPULATION: APPLICATION AND PERFORMANCE IN THE MINNEAPOLIS-ST. PAUL METROPOLITAN AREA An Abstract of a Thesis Submitted in Partial Fulfillment of the Requirements for the Degree Master of Arts Nikolay Golosov University of Northern Iowa July 2021 ABSTRACT The wide availability of remote sensing data, the development of computer technology, and the accessibility of census data in the digital form created new opportunities for highly accurate population estimates. Of particular scientific interest is the method of dasymetric mapping, which can significantly improve the spatial accuracy of mapping socio-demographic processes.