| Project/Area Number |
18H01552
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 22050:Civil engineering plan and transportation engineering-related
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| Research Institution | Tohoku University |
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Principal Investigator |
Inoue Ryo 東北大学, 情報科学研究科, 准教授 (60401303)
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| Co-Investigator(Kenkyū-buntansha) |
磯田 弦 東北大学, 理学研究科, 准教授 (70368009)
金森 亮 名古屋大学, 未来社会創造機構, 特任准教授 (40509171)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥17,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥3,990,000)
Fiscal Year 2020: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2019: ¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000)
Fiscal Year 2018: ¥5,980,000 (Direct Cost: ¥4,600,000、Indirect Cost: ¥1,380,000)
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| Keywords | 点事象 / 集積領域検出 / スパースモデリング / 空間的異質性 / 地域分析 / 点事象集積領域検出 / 小地域分析 / Generalized lasso / ラプラス分布 / Fused-MCP / NEG分布 / 空間統計解析 / Fused lasso / Fused MCP / 点事象集積 / 地理空間データの可視化 / ポアソン点過程モデル / 集積検出 / 空間解析 |
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| Outline of Final Research Achievements |
In this study, we developed new analysis methods to detect regions and time periods with high event probabilities that differ from the surrounding areas from point event data, which is a type of geospatial data that records the occurrence points and time points of events. The proposed method is based on sparse modeling, a machine learning technique. We also extended the proposed methods to Bayesian statistical methods and developed methods that can clearly show the reliability of the estimation results, which is not possible with existing methods. The proposed methods are validated by using simulated data, and their usefulness as regional analysis methods is confirmed by applying them to real data such as crime location dataset.
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| Academic Significance and Societal Importance of the Research Achievements |
地域の社会経済活動を記録した地理空間データは，地域現況のモニタリングやエビデンスに基づく政策決定に活用可能な情報を有しており，その解析手法開発は地理情報科学における重要課題の一つである．本研究では，事件・事故の発生や感染症発症など，事象が発生した地点・時点を記録する点事象データから，周辺と異なる高い事象発生確率を有する地域・期間を発見・抽出する手法を開発した．結果の信頼性を明示できる提案手法の分析結果は，科学的妥当性の高い評価を可能にする．本成果は，点事象データに基づく地域の実態把握の高度化に貢献できると期待される．
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