Operationalizing Trumbo’s Principles of Bivariate Choropleth Map Design

Abstract

Trumbo’s (1981) ideas on bivariate choropleth design have been underexplored and underutilized. He noted that effective map design (including color selection) is directly informed by the intended goal or use of the map (i.e., what questions might the map answer), and he identified three common spatial relationships that can be displayed by a bivariate choropleth: inverse relationships, a range of one variable within another, and direct relationships. Each is best suited to answering different map readers’ questions. Trumbo also suggested sample color palettes to focus the map reader’s attention on pertinent data. In consultation with Trumbo, we extended his ideas, first by creating focal models that illustrate his three spatial relationships. We then constructed sample maps to examine each of the focal models, and finally compared each model by mapping the same two data sets (of obesity and inactivity). We investigated the visual differences in each of the resulting maps, and asked spatial questions regarding the relationships between obesity and inactivity. Our work validates Trumbo’s ideas on bivariate choropleth map design, and we hope our focal models guide cartographers towards making color choices by linking their map purpose to the appropriate focal model.

Visualisations for Bayesian Additive Regression Trees

“…The colours for the VSUP heatmap were carefully chosen to be distinguishable, colour-blind friendly, and to aid in highlighting high values, while still making the uncertainty prominent. To achieve this, we follow the advice of Strode et al (2019), who build upon the work of Trumbo (1981), and aim to highlight and focus the reader's attention on the interesting data.…”

Visualizations for Bayesian Additive Regression Trees

“…The colors for the VSUP heatmap were carefully chosen to be distinguishable, color-blind friendly, and to aid in highlighting high values, while still making the uncertainty prominent. To achieve this, we follow the advise of Strode et al (2019), who build upon the work of Trumbo (1981), that aims to highlight the interesting data and leads to design choices that support a statistical graphic's intended purpose. Figure 3 shows a comparison of a heatmap showing the importance and interactions jointly with and without uncertainty.…”

Examining rehabilitation access disparities: an integrated analysis of electronic health record data and population characteristics through bivariate choropleth mapping

“…Specifically, Geographical Information Systems (GIS) offer a promising avenue for robust spatial analyses of health outcomes at the contextual-level [ 16 ]. Further, the capacity to unveil spatial relationships between paired thematic variables such as SES data and health outcomes through bivariate choropleth maps holds the potential to illuminate new findings [ 17 ]. Integrating SES data and GIS technology with EHR data can give deeper insights into the relationships between patients’ social environments, health outcomes, and geographic factors [ 18 – 20 ].…”