arXiv subjects

Randall Powers

Publications and source records attributed to Randall Powers.

4 recordsLinked to original sources

arXiv2026

Using Linked Micromaps to Explore Complex Structures in Official Statistics

Over the past decade, researchers have focused increasing levels of attention on the use of survey and non-survey data to inform decision-making by multiple stakeholders. Work with such data generally requires extensive exploration before a statistics practitioner focuses on specific steps in model building and inference. For many of the resulting initial exploratory analyses, crucial issues center on the extent to which empirical results may vary over geography and subpopulations. Such information is usually presented in tabular form, which can be difficult for stakeholders and decision makers to understand and to utilize. To address these issues, this paper uses data from the U.S. Bureau of Labor Statistics to illustrate a suite of tools known as linked micromaps. These applications show how linked micromaps can help stakeholders better understand and view descriptive statistics for populations and subpopulations, explore multivariate relationships and ordinal structure, and discover patterns of heterogeneity across time and space. In addition, this paper comments briefly on the prospective use of linked micromaps in model-building and analysis of multiple components of uncertainty.

stat.AP↗

arXiv2026

A Shiny micromapST App

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.

stat.AP↗

arXiv2024

Using Linked Micromaps for Evidence-Based Policy

Linked micromaps were originally developed to display geographically indexed statistics in an intuitive way by connecting them to a sequence of small maps. The approach integrates several visualization design principles, such as small multiples, discrete color indexing, and ordering. Linked micromaps allow for other types of data displays that are connected to and conditional on geographic areas. Initial applications of micromaps used data from the National Cancer Institute and the Environmental Protection Agency. In this paper, we will show how linked micromaps can be used to better understand and explore relationships and distributions of statistics linked to US states and Washington, DC. We will compare linked micromaps with other popular data displays of geographic data, such as bubble maps, choropleth maps, and bar charts. We will illustrate how linked micromaps can be used for evidence-based decision-making using data from the Bureau of Labor Statistics, the Census Bureau, and the Economic Research Service. The presentations, R scripts, and the data sets used in this article are available here: https://github.com/wlmcensus/Joint-Statistical-Meetings-Presentation-2024. The work discussed in this article was presented at the Joint Statistical Meetings (JSM) 2024 and the American Association for Public Opinion Research (AAPOR) 2024 Annual Conference.

stat.AP↗

arXiv2023

R-Shiny Applications for Local Clustering to be Included in the growclusters for R Package

growclusters for R is a package that estimates a partition structure for multivariate data. It does this by implementing a hierarchical version of k-means clustering that accounts for possible known dependencies in a collection of datasets, where each set draws its cluster means from a single, global partition. Each component data set in the collection corresponds to a known group in the data. This paper focuses on R Shiny applications that implement the clustering methodology and simulate data sets with known group structures. These Shiny applications implement novel ways of visualizing the results of the clustering. These visualizations include scatterplots of individual data sets in the context of the entire collection and cluster distributions versus component (or sub-domain) datasets. Data obtained from a collection of 2000-2013 articles from the Bureau of Labor Statistics (BLS) Monthly Labor Review (MLR) will be used to illustrate the R-Shiny applications. Here, the known grouping in the collection is the year of publication.

cs.MS↗