Okayama University


Spatio-Temporal Statistics


The detection of problems such as the occurrence of infectious diseases or the mapping of natural disaster hazards is crucial and fundamental. While there are powerful and useful tools like geographical information systems (GISs) available, determining the location of space-time clusters for large quantities of spatial data or extensive time series poses significant challenges. This study aims to establish methods for identifying disease clusters or contaminant clusters, commonly referred to as hotspots, in various types of spatio-temporal data, as well as develop corresponding software.

  • Prof. ISHIOKA Fumio
  • E-mail: ishiok-f@(cc.okayama-u.ac.jp)
  • Spatial statistics, Computational statistics, Echelon analysis, Spatial clusters, Spatial scan statistics, Spatial epidemiology

Directory of Researchers 


Spatial cluster (hotspots) is a scientific approach that evaluates locations indicating locally high or low observed values based on statistical evidence (data). This study aims to uncover the existence of hotspots among COVID-19 positive cases and answer the questions of ‘Do hotspots of COVID-19 positive cases exist?’ and ‘If they exist, when and where do they occur?’ Furthermore, we will discuss the patterns associated with these hotspots.