Using spatial scan statistics and geographic information systems to detect monthly human mobility clusters and analyze cluster area characteristics

• 資料種別: 記事
• タイトル: Using spatial scan statistics and geographic information systems to detect monthly human mobility clusters and analyze cluster area characteristics
• 著者・編者: Ryo Horiike; Tomoya Itatani; Hisao Nakai; Daisuke Nishioka; Aoi Kataoka; Yuri Ito
• 出版事項: 日本 : Japan Medical Association
• 出版年月日等: 2024-07-16
• 出版年（W3CDTF）: 2024-07-16
• タイトル（掲載誌）: JMA Journal
• 巻号年月日等（掲載誌）: 7(3)
• 掲載巻: 7(3)
• ISSN（掲載誌）: 2433-3298
• ISSN-L（掲載誌）: 2433-328X
• 本文の言語コード: eng
• DOI: 10.31662/jmaj.2023-0208; https://doi.org/10.31662/jmaj.2023-0208
• 国立国会図書館永続的識別子: info:ndljp/pid/14495142; https://dl.ndl.go.jp/pid/14495142
• コレクション（共通）: 障害者向け資料
• コレクション（障害者向け資料：レベル1）: テキストデータ
• コレクション（個別）: 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他; https://dl.ndl.go.jp/collections/B00035
• 収集根拠: インターネット資料収集保存事業（WARP）
• 受理日（W3CDTF）: 2025-10-21T09:04:40+09:00
• 保存日（W3CDTF）: 2024-09-26
• 記録形式（IMT）: application/pdf
• オンライン閲覧公開範囲: インターネット公開
• 遠隔複写可否（NDL）: 不可
• 掲載誌（URI）: https://warp.da.ndl.go.jp/info:ndljp/pid/13747952/www.jmaj.jp/detail.php?id=10.31662%2Fjmaj.2023-0208
• 掲載誌（国立国会図書館永続的識別子）: info:ndljp/pid/14495137; https://dl.ndl.go.jp/pid/14495137
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

• 要約等: <p><b>Introduction:</b> This study evaluated the detection of monthly human mobility clusters and characteristics of cluster areas before the coronavirus disease 2019 (COVID-19) outbreak using spatial epidemiological methods, namely, spatial scan statistics and geographic information systems (GIS).</p><p><b>Methods:</b> The research area covers approximately 10.3 km², with a population of about 350,000 people. Analysis was conducted using open data, with the exception of one dataset. Human mobility and population data were used on a 1-km mesh scale, and business location data were used to examine the area characteristics. Data from January to December 2019 were utilized to detect human mobility clusters before the COVID-19 pandemic. Spatial scan statistics were performed using SaTScan to calculate relative risk (RR). The detected clusters and other data were visualized in QGIS to explore the features of the cluster areas.</p><p><b>Results:</b> Spatial scan statistics identified 33 clusters. The detailed analysis focused on clusters with an RR exceeding 1.5. Meshes with an RR over 1.5 included one with clusters for 1 year which is identified in all months of the year, one with clusters for 9 months, three with clusters for 6 months, three with clusters for 3 months, and four with clusters for 1 month. September had the highest number of clusters (eight), followed by April and November (seven each). The remaining months had five or six clusters. Characteristically, the cluster areas included the vicinity of railway stations, densely populated business areas, ball game fields, and large-scale construction sites.</p><p><b>Conclusions:</b> Statistical analysis of human mobility clusters using open data and open-source tools is crucial for the advancement of evidence-based policymaking based on scientific facts, not only for novel infectious diseases but also for existing ones, such as influenza.</p>
• DOI: 10.31662/jmaj.2023-0208
• オンライン閲覧公開範囲: インターネット公開
• 連携機関・データベース: 科学技術振興機構 : J-STAGE; http://www.jstage.jst.go.jp

• 要約等: <p><b>Introduction:</b> This study evaluated the detection of monthly human mobility clusters and characteristics of cluster areas before the coronavirus disease 2019 (COVID-19) outbreak using spatial epidemiological methods, namely, spatial scan statistics and geographic information systems (GIS).</p><p><b>Methods:</b> The research area covers approximately 10.3 km², with a population of about 350,000 people. Analysis was conducted using open data, with the exception of one dataset. Human mobility and population data were used on a 1-km mesh scale, and business location data were used to examine the area characteristics. Data from January to December 2019 were utilized to detect human mobility clusters before the COVID-19 pandemic. Spatial scan statistics were performed using SaTScan to calculate relative risk (RR). The detected clusters and other data were visualized in QGIS to explore the features of the cluster areas.</p><p><b>Results:</b> Spatial scan statistics identified 33 clusters. The detailed analysis focused on clusters with an RR exceeding 1.5. Meshes with an RR over 1.5 included one with clusters for 1 year which is identified in all months of the year, one with clusters for 9 months, three with clusters for 6 months, three with clusters for 3 months, and four with clusters for 1 month. September had the highest number of clusters (eight), followed by April and November (seven each). The remaining months had five or six clusters. Characteristically, the cluster areas included the vicinity of railway stations, densely populated business areas, ball game fields, and large-scale construction sites.</p><p><b>Conclusions:</b> Statistical analysis of human mobility clusters using open data and open-source tools is crucial for the advancement of evidence-based policymaking based on scientific facts, not only for novel infectious diseases but also for existing ones, such as influenza.</p>
• DOI: 10.31662/jmaj.2023-0208; https://doi.org/10.31662/jmaj.2023-0208
• オンライン閲覧公開範囲: インターネット公開
• 関連情報（URI）: http://id.ndl.go.jp/digimeta/14495142; https://dl.ndl.go.jp/pid/14495142; https://www.jmaj.jp/download.php?id=10.31662/jmaj.2023-0208; https://search.jamas.or.jp/link/ui/2025237149
• 参照: 2010年宮崎県口蹄疫を事例とした感染リスク評価指標の構築と防疫支援への応用; https://cir.nii.ac.jp/crid/1390306960925388928
• 連携機関・データベース: 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• 提供元機関・データベース: Japan Link Center; https://japanlinkcenter.org/top; 雑誌記事索引データベース; https://ndlsearch.ndl.go.jp; Crossref; https://www.crossref.org; 科学研究費助成事業データベース; https://kaken.nii.ac.jp/ja
• 書誌ID（NDLBibID）: 14495142; http://id.ndl.go.jp/bib/14495142