Using Linked Micromaps to Explore Complex Structures in Official Statistics

TL;DR

This paper demonstrates how linked micromaps can enhance understanding of complex statistical data by visualizing geographic and subpopulation variations, aiding exploration and decision-making.

Contribution

It introduces the application of linked micromaps for exploring and visualizing complex statistical structures in official statistics, improving stakeholder comprehension.

Findings

Linked micromaps help visualize geographic and subpopulation differences.

They facilitate exploration of multivariate relationships and patterns.

Potential use in model-building and uncertainty analysis is discussed.

Abstract

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.