Satscan regression: a bibliometric study based on text mining procedures

The scan statistic has been a methodology widely applied for spatial and space-time cluster detection. This is because, by identifying areas with a significant concentration of points, we gain evidence for the analysis of underlying phenomena. This article aims to conduct a bibliometric study on spatial scan regression. Spatial scan regression includes correlation between the outcome and potential regressor variables into the cluster detection. The bibliometric study applies a robust literature search methodology, using Web of Science and Scopus as the main databases. Thus, the most relevant articles on the topic (n=458) were selected and analyzed using Bibliometrix, a package developed for the R® statistical programming language, and the VOSViewer software, employing text mining tools. Subsequently, we proceeded with: (i) matrix construction of attributes and (ii) presentation of descriptive analysis of data and network relationships. The sample covers the years from 1993 to 2023 and represents an annual growth rate of 12.36% with an average of 14.77 articles published each year. Despite the Increasing studies on spatial scan regression, having 82% of the publications made since 2013, with emphasis on the period from 2018 to 2023, comprising 255 publications (56% of the total), it is noteworthy that most of them are produced by relatively few countries, with a significant concentration on health-related themes. It is observed that this method has broad potential for application in other areas. Collaboration networks in research still exhibit a structure to be developed, emphasizing the essential need for creating new partnerships between countries and research institutions on this specific topic.