Using Spatial Scan Statistics and Geographic Information Systems to Detect Monthly Human Mobility Clusters and Analyze Cluster Area Characteristics

• Published in: JMA Journal Vol. 7; no. 3; pp. 319 - 327
• Main Authors: Horiike, Ryo, Itatani, Tomoya, Nakai, Hisao, Nishioka, Daisuke, Kataoka, Aoi, Ito, Yuri
• Format: Journal Article
• Language: English
• Published: Japan Medical Association / The Japanese Associaiton of Medical Sciences 16.07.2024; Japan Medical Association
• Subjects: COVID-19; evidence-based policymaking; geographic information systems; infection disease; Original; spatial scan statistics
• ISSN: 2433-328X; 2433-3298; 2433-3298
• DOI: 10.31662/jmaj.2023-0208

Introduction: 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).Methods: 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.Results: 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.Conclusions: 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.