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Diffusion-Based Cartoglobes with Discrete Harmonic Transform and Spherical Finite Elements.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Diffusion-Based Cartoglobes with Discrete Harmonic Transform and Spherical Finite Elements./
作者:
Li, Ziqiang.
面頁冊數:
1 online resource (137 pages)
附註:
Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:
Dissertations Abstracts International84-11B.
標題:
Mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30489183click for full text (PQDT)
ISBN:
9798379558277
Diffusion-Based Cartoglobes with Discrete Harmonic Transform and Spherical Finite Elements.
Li, Ziqiang.
Diffusion-Based Cartoglobes with Discrete Harmonic Transform and Spherical Finite Elements.
- 1 online resource (137 pages)
Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Thesis (Ph.D.)--University of Wyoming, 2023.
Includes bibliographical references
A cartoglobe is a data-homogenizing globe on which the area of regions are proportional to some data. A cartoglobe removes uncorrelated terrestrial bias from a thematic map. To date, a multitude of algorithms have been proposed to construct continuous cartograms, with the poster child being the diffusion-based cartogram. Prior to this work, only a formulation is known for cartoglobes.This work addresses several noticeable gaps in analytical cartography by (i) analyzing the cartographic nuances of a cartogram that is not projected from a cartoglobe, (ii) documenting relevant practical considerations behind the original diffusion-based cartogram by Gastner and Newman, (iii) discussing and improving our equiangular prototype to fully utilize discrete harmonic transform routines self-contained in our Cartosphere library, (iv) designing an experimental finite element space on S2 for spherical triangulation whose gradient is constructible using spherical geometry, and (v) providing a new FEM cartoglobe implementation based on said finite element space.Because the cartoglobe transformation is designed to generate readable topology-preserving cartoglobes, it is illustrated with applications to curated global data from 2012 and 2016, such as worldwide human population, gross domestic product (purchasing power parity), and carbon dioxide emissions. Application to regional data is showcased using the electoral college votes of the United States presidential election of the year 2016.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798379558277Subjects--Topical Terms:
515831
Mathematics.
Subjects--Index Terms:
Applied harmonic analysisIndex Terms--Genre/Form:
542853
Electronic books.
Diffusion-Based Cartoglobes with Discrete Harmonic Transform and Spherical Finite Elements.
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A cartoglobe is a data-homogenizing globe on which the area of regions are proportional to some data. A cartoglobe removes uncorrelated terrestrial bias from a thematic map. To date, a multitude of algorithms have been proposed to construct continuous cartograms, with the poster child being the diffusion-based cartogram. Prior to this work, only a formulation is known for cartoglobes.This work addresses several noticeable gaps in analytical cartography by (i) analyzing the cartographic nuances of a cartogram that is not projected from a cartoglobe, (ii) documenting relevant practical considerations behind the original diffusion-based cartogram by Gastner and Newman, (iii) discussing and improving our equiangular prototype to fully utilize discrete harmonic transform routines self-contained in our Cartosphere library, (iv) designing an experimental finite element space on S2 for spherical triangulation whose gradient is constructible using spherical geometry, and (v) providing a new FEM cartoglobe implementation based on said finite element space.Because the cartoglobe transformation is designed to generate readable topology-preserving cartoglobes, it is illustrated with applications to curated global data from 2012 and 2016, such as worldwide human population, gross domestic product (purchasing power parity), and carbon dioxide emissions. Application to regional data is showcased using the electoral college votes of the United States presidential election of the year 2016.
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