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Fast statistical analysis for large spatial datasets with temporal information

Research Project

Project/Area Number 21K13273
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 07030:Economic statistics-related
Research InstitutionKanto Gakuin University

Principal Investigator

Hirano Toshihiro  関東学院大学, 経済学部, 准教授 (10816010)

Project Period (FY) 2021-04-01 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2022: ¥260,000 (Direct Cost: ¥200,000、Indirect Cost: ¥60,000)
Fiscal Year 2021: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Keywords確率場 / 大規模空間データ / 多重解像度近似 / 時空間データ / 状態空間モデル / カルマンフィルタ / データサイエンス / 空間統計学 / 大規模時空間データ / 時空間統計 / クリギング / リモートセンシング
Outline of Research at the Start

単位時間あたりの車両数を表す交通量や気温・降水量といったデータは緯度・経度などの位置情報を伴って観測されており空間データと呼ばれている.計測技術の発展により,センサーを通じて大規模な空間データの入手が容易になったため,これらのデータを高速に統計解析する手法が活発に研究されるようになった.
本研究は,特に時間情報を持つ空間データの研究に重点を置き,高次元性,非定常性,非正規性を持つ大規模空間データに対する新しい高速統計解析手法の提案とその理論的性質の導出を目標とする.

Outline of Final Research Achievements

In this program, I mainly conducted the following two research topics in the fast statistical analysis for large spatial or spatio-temporal datasets. First, I proposed a new fast computation method of the Kalman filter for large spatio-temporal datasets, which was referred to as a multi-resolution filter via linear projection, by using a multi-resolution approximation via linear projection which was proposed in the previous Grant-in-Aid for Early-Career Scientists. Furthermore, I extended the proposed method to nonlinear and non-Gaussian state-space models. Second, I considered a modification of the multi-resolution approximation via linear projection by using the covariance tapering, which can resolve the artificiality in the prediction surface. I evaluated the modified method through the real data analysis. I also conducted other researches related to spatial statistics.

Academic Significance and Societal Importance of the Research Achievements

本研究課題において主に得られた研究成果は,大規模空間データや大規模時空間データに対する新しい高速統計解析手法を提案したものであり,そのうちの一部は統計学における国際学術誌に投稿中である.特に,1つ目の研究成果である「線形射影を用いた多重解像フィルタ」は,時空間データが非ガウス性や非定常性を持つ場合にも適用可能であるだけでなく,大規模空間データが時々刻々と観測される状況でもリアルタイムで高速に統計解析を実行できる.提案手法は地価,交通量,人工衛星から得られる大気中の水蒸気量といった時空間データの統計解析に有用であり,不動産市場の分析,交通渋滞の削減,気候変動問題といった応用と結びついている.

Report

(4 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Research-status Report
  • 2021 Research-status Report
  • Research Products

    (10 results)

All 2023 2022 2021 2020

All Journal Article (2 results) (of which Peer Reviewed: 2 results) Presentation (8 results) (of which Int'l Joint Research: 5 results,  Invited: 1 results)

  • [Journal Article] 非正規確率場におけるCovariance Taperingを用いた最良線形不偏予測量の漸近有効性2022

    • Author(s)
      平野 敏弘
    • Journal Title

      経済系

      Volume: 285 Pages: 38-46

    • Related Report
      2021 Research-status Report
    • Peer Reviewed
  • [Journal Article] A multi-resolution approximation via linear projection for large spatial datasets2020

    • Author(s)
      Hirano Toshihiro
    • Journal Title

      Japanese Journal of Statistics and Data Science

      Volume: - Issue: 1 Pages: 215-256

    • DOI

      10.1007/s42081-020-00092-x

    • NAID

      210000164426

    • Related Report
      2021 Research-status Report
    • Peer Reviewed
  • [Presentation] 非線形・非ガウス状態空間モデルに基づく大規模時空間データに対する高速フィルタリング2023

    • Author(s)
      平野敏弘
    • Organizer
      2023年度 統計関連学会連合大会
    • Related Report
      2023 Annual Research Report
  • [Presentation] Multi-resolution filters via linear projection for large spatio-temporal datasets2023

    • Author(s)
      Toshihiro Hirano
    • Organizer
      Spatial Statistics 2023
    • Related Report
      2023 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Multi-resolution filters via linear projection for large spatio-temporal datasets2023

    • Author(s)
      Toshihiro Hirano
    • Organizer
      ISM Symposium on Environmental Statistics 2023
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research / Invited
  • [Presentation] A multi-resolution approximation by linear projection and covariance tapering for large spatial datasets2022

    • Author(s)
      Toshihiro Hirano
    • Organizer
      The 15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022)
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] 大規模時空間データに対するLinear Projectionを用いた多重解像フィルタ2022

    • Author(s)
      平野 敏弘
    • Organizer
      2022年度 統計関連学会連合大会
    • Related Report
      2022 Research-status Report
  • [Presentation] A multi-resolution approximation via linear projection for large spatial datasets2021

    • Author(s)
      Toshihiro Hirano
    • Organizer
      The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021)
    • Related Report
      2021 Research-status Report
    • Int'l Joint Research
  • [Presentation] Covariance TaperingとLinear Projectionを用いた多重解像度近似について2021

    • Author(s)
      平野 敏弘
    • Organizer
      2021年度 統計関連学会連合大会
    • Related Report
      2021 Research-status Report
  • [Presentation] A multi-resolution approximation via linear projection for large spatial datasets2021

    • Author(s)
      Toshihiro Hirano
    • Organizer
      The XV World Conference of the Spatial Econometrics Association (SEA 2021)
    • Related Report
      2021 Research-status Report
    • Int'l Joint Research

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Published: 2021-04-28   Modified: 2025-01-30  

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