AN ENHANCEMENT OF GROUP-THEORETIC SPECTRUM ANALYSIS FOR DETECTING SPATIAL AGGLOMERATION

• ONDA Mikihisa

  東北大学 大学院工学研究科

• MURAKAMI Daisuke

  統計数理研究所 データ科学研究系

• IKEDA Kiyohiro

  東北大学 大学院工学研究科

• TAKAYAMA Yuki

  金沢大学 理工研究域

• OSAWA Minoru

  東北大学 大学院工学研究科

• KOGURE Yosuke

  東北大学 大学院工学研究科

Bibliographic Information

Other Title

• 群論的スペクトル解析による空間集積抽出手法の高度化

Abstract

 Central place theory¹⁾ envisaged that economic agglomerations form regular hexagonal patterns. A long-standing issue is a lack of quantitative method for assessing emergence of such regularities in the actual data. Ikeda et al.²⁾ introduced a group-theoretic spectrum analysis as the first attempt to develop a quantitative approach to detect hexagonal patterns. However, there has been several technical issues to be settled in their method: (i) How to select domain that crucially affect the result? (ii) How to select predominant spectrum? To settle these issues, we employ several methodologies: an optimization formulation for the choice of a domain, an eigenvector-based criterion for noise reduction, and a permutation test for a significance test of spectrum. The effectiveness of the proposed method is demonstrated by an analysis of the actual population data of Southern Germany and Eastern USA.

Journal

• Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management)

  Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 74 (4), 398-410, 2018

  Japan Society of Civil Engineers

Keywords

• central place theory
• group theory
• genetic algorithm
• permutation test
• population distribution

Details

• CRID: 1390845713034317312
• NII Article ID: 130007536257
• DOI: 10.2208/jscejipm.74.398
• ISSN: 21856540
• Web Site: https://www.jstage.jst.go.jp/article/jscejipm/74/4/74_398/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed