🤖 AI Summary
This study addresses the limitations of traditional linked micromaps—namely, cumbersome data preparation and a lack of intuitive interactivity—that hinder efficient exploration of geostatistical data. To overcome these challenges, the authors introduce, for the first time, a graphical user interface (GUI) for the micromapST package built within the R Shiny framework. This interactive application streamlines data input and visualization workflows, enabling users to rapidly generate statistical graphics such as scatterplots, boxplots, and time series through an accessible visual interface. Crucially, these charts are dynamically linked to arbitrary geographic regions, facilitating coordinated exploration. Validation through real-world case studies demonstrates that the tool substantially lowers the technical barrier to entry and enhances both the efficiency and intuitiveness of geostatistical analysis.
📝 Abstract
The linked micromaps approach was originally developed as an improvement to choropleth maps for displaying statistical summaries connected with spatial areal units, such as countries, states, and counties. Two R packages to create linked micromaps were published in 2015. These are the micromap and micromapST packages. The latter was originally for data indexed to the 50 US states and DC, but the latest version accommodates arbitrary geographies. The micromapST package handles the formatting needed for linked micromaps and offers several options for statistical displays (scatterplots, boxplots, time series plots, and more). The micromapST package is very useful and takes care of most details of the layouts, but it can be problematic specifying the data frames needed to create the desired graphic. Furthermore, exploring data through visualization is easier, faster, and more intuitive using a graphical user interface. This is the motivation behind the R Shiny micromapST app. This paper will serve as a brief tutorial and introduction to micromapST and the Shiny app using real-world data and applications. In this paper, we provide background information on visualizing geographically indexed data and linked micromaps in Section 1. Section 2 discusses the data sets used in two illustrative examples. Sections 3 and 4 describe the application interface and show how it can create linked micromaps. The paper concludes with comments and future work.