Geospatially-Intelligent Three-Dimensional Multivariate
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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. In addition to population density, the method has considerable potential in mapping the distribution of other social, economic and demographic variables, such as income level, crime, ethnicity, etc. Another significant gap in the existing studies is the development of three-dimensional dasymetric mapping methods. This study is focused on developing intelligent dasymetric mapping methods to create algorithms for near real-time display demographic and other socio-economic parameters and assess their accuracy and their potential for geovisual analytics. The study is developed and tested in Minneapolis-Saint Paul area, Minnesota, USA as a key study site given the relative diversity of urban areas and the accessibility for field surveys. The goal of this study is to develop and test an effective geospatially-intelligent method and GIS algorithm for the creation of multivariable three-dimensional dasymetric (3DM) geographic visualizations for the Twin Cities Metropolitan area. 2D and 3D binary dasymetric mapping methods, as well as floor fraction and intelligent dasymetric mapping method were used to identify the best performing method in terms of accuracy. The 3D dasymetric mapping method yielded the best accuracy in estimation of population counts in conditions of the given study area. 3D dasymetric mapping method proved to improve the accuracy of population mapping in an urban environment compared to 2D methods. The improvement is more significant at a smaller scale of analysis that reflects a more heterogeneous residential building infrastructure. Finally, the additional socio-economic variables, such as aggregated income and three different types of spending(for food, household supplies, and apparel) were mapped. The study faced the limitations of the inability to obtain data, perfectly synchronized in time between all the spatial layers, non-straightforward nature of the selection of residential/non-residential buildings and low height variance in the study area. The future directions of the study are to integrate the developed methods with the existing web mapping platform, test the dasymetric mapping approach on the extended set of socioeconomic variables and explore the usefulness of the dasymetric mapping approach on the smaller scales of the enumeration units and dasymetric mapping polygons. GEOSPATIALLY-INTELLIGENT THREE-DIMENSIONAL MULTIVARIATE METHODS FOR MULTISCALE DASYMETRIC MAPPING OF URBAN POPULATION: APPLICATION AND PERFORMANCE IN THE MINNEAPOLIS-ST. PAUL METROPOLITAN AREA A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree Master of Arts Nikolay Golosov University of Northern Iowa July 2021 ii This Study by: Nikolay Golosov Entitled: Geospatially-Intelligent Multivariate Three-Dimensional Methods for Multiscale Dasymetric Mapping of Urban Population: Application and Performance in the Minneapolis-St. Paul Metropolitan Area has been approved as meeting the thesis requirement for the Degree of Master of Arts Date Dr. Andrey N. Petrov, Chair, Thesis Committee Member Date Dr. James Dietrich, Thesis Committee member Date Dr. Bingqing Liang, Thesis Committee member Date Mr. John DeGroote, Thesis Committee member Date Dr. Jennifer Waldron, Dean, Graduate College iii TABLE OF CONTENTS PAGE LIST OF TABLES .............................................................................................................. v LIST OF FIGURES ........................................................................................................... vi CHAPTER 1 INTRODUCTION ........................................................................................ 1 Advancing Geospatial Data Science through Dasymetric Mapping............................... 1 Goal, Research Questions, and Objectives ..................................................................... 3 Research Questions ......................................................................................................... 3 Objectives ....................................................................................................................... 3 The Significance of the Outcomes .................................................................................. 4 CHAPTER 2 LITERATURE REVIEW ............................................................................. 5 Why Dasymetric Mapping? ............................................................................................ 5 History of Dasymetric Mapping Methods ...................................................................... 7 Current Approaches to 3D Dasymetric Mapping and Geovisualization ........................ 8 CHAPTER 3 METHODS ................................................................................................. 13 Overview of the Methodology ...................................................................................... 13 Study Area .................................................................................................................... 14 Data ............................................................................................................................... 16 Data Preparation............................................................................................................ 22 Objective 1: Identify the Most Effective (Accurate and Precise) Method to Introduce the 3rd Dimension in Dasymetric Maps. ...................................................................... 27 Binary Areametric Method ........................................................................................... 28 Three-Class Method ...................................................................................................... 29 Limiting Variable Method ............................................................................................ 30 Intelligent Dasymetric Mapping (IDM) ........................................................................ 30 iv Volume Dasymetric method(VDM) ............................................................................. 31 Floor Fraction Method .................................................................................................. 34 Comparing Dasymetric Mapping Methods at Two Scales ........................................... 37 Dasymetic Methods Methods Accuracy Assessment and Comparisons ...................... 38 CHAPTER 4: RESULTS ................................................................................................. 42 What is the Most Accurate Method to Introduce the 3rd Dimension (3D) in Dasymetric Maps? ............................................................................................................................ 42 What is the Difference Between the Standard (2D) and 3D Dasymetric Mapping Methods at Different Scales and Levels of Urban Heterogeneity? ............................... 51 What can Additional Socio-Economic Variables Be Mapped Using the 3D Dasymetric Method? ........................................................................................................................ 53 CHAPTER 5: DISCUSSION ............................................................................................ 58 CONCLUSION ................................................................................................................. 63 REFERENCES ................................................................................................................. 68 APPENDIX 1 DASYMETRIC MAPS OF THE POPULATION DENSITY AND DISAGGREGATED SOCIOECONOMIC VARIABLES ............................................... 72 APPENDIX 2 RESULTS OF THE T-TEST TO COMPARE POPULATION MEANS BETWEEN 3DDM AND 2DDM