Using spatial scan statistics and geographic information systems to detect monthly human mobility clusters and analyze cluster area characteristics
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DOI[10.31662/jmaj.2023-0208]to the data of the same series
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- Material Type
- 記事
- Author/Editor
- Ryo HoriikeTomoya ItataniHisao NakaiDaisuke NishiokaAoi KataokaYuri Ito
- Publication, Distribution, etc.
- Publication Date
- 2024-07-16
- Publication Date (W3CDTF)
- 2024-07-16
- Periodical title
- JMA Journal
- No. or year of volume/issue
- 7(3)
- Volume
- 7(3)
- ISSN (Periodical Title)
- 2433-3298
- ISSN-L (Periodical Title)
- 2433-328X
- Text Language Code
- eng
- DOI
- 10.31662/jmaj.2023-0208
- Persistent ID (NDL)
- info:ndljp/pid/14495142
- Collection
- Collection (Materials For Handicapped People:1)
- Collection (particular)
- 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
- Acquisition Basis
- インターネット資料収集保存事業（WARP）
- Date Accepted (W3CDTF)
- 2025-10-21T09:04:40+09:00
- Date Captured (W3CDTF)
- 2024-09-26
- Format (IMT)
- application/pdf
- Access Restrictions
- インターネット公開
- Availability of remote photoduplication service
- 不可
- Periodical Title (URI)
- Periodical Title (Persistent ID (NDL))
- info:ndljp/pid/14495137
- Data Provider (Database)
- 国立国会図書館 : 国立国会図書館デジタルコレクション
- Summary, etc.
- <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 km<sup>2</sup>, 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
- Access Restrictions
- インターネット公開
- Data Provider (Database)
- 科学技術振興機構 : J-STAGE
- Summary, etc.
- <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 km<sup>2</sup>, 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
- Access Restrictions
- インターネット公開
- Related Material (URI)
- Is Referenced By
- 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
- Data Provider (Database)
- 国立情報学研究所 : CiNii Research
- Original Data Provider (Database)
- Japan Link Center雑誌記事索引データベースCrossref科学研究費助成事業データベース科学研究費助成事業データベースCrossref
- Bibliographic ID (NDL)
- 14495142