A contiguous area cartogram is a geographic map in which the area of each region is rescaled to be proportional to numerical data (e.g., population size) while keeping neighboring regions connected. Few studies have investigated whether readers can make accurate quantitative assessments using contiguous area cartograms. Therefore, we conducted an experiment to determine the accuracy, speed, and confidence with which readers infer numerical data values for the mapped regions. We investigated whether including an area-to-value legend (in the form of a square symbol next to the value represented by the square's area) makes it easier for map readers to estimate magnitudes. We also evaluated the effectiveness of two additional features: grid lines and an interactive area-to-value legend that allows participants to select the value represented by the square. Without any legends and only informed about the total numerical value represented by the whole cartogram, the distribution of estimates for individual regions was centered near the true value with substantial spread. Selectable legends with grid lines significantly reduced the spread but led to a tendency to underestimate the values. When comparing differences between regions or between cartograms, legends and grid lines made estimation slower but not more accurate. However, legends and grid lines made it more likely that participants completed the tasks. We recommend considering the cartogram's use case and purpose before deciding whether to include grid lines or an interactive legend.

A contiguous area cartogram is a geographic map in which the area of each region is proportional to numerical data (e.g., population size) while keeping neighboring regions connected. In this study, we investigated whether value-to-area legends (square symbols next to the values represented by the squares' areas) and grid lines aid map readers in making better area judgments. We conducted an experiment to determine the accuracy, speed, and confidence with which readers infer numerical data values for the mapped regions. We found that, when only informed about the total numerical value represented by the whole cartogram without any legend, the distribution of estimates for individual regions was centered near the true value with substantial spread. Legends with grid lines significantly reduced the spread but led to a tendency to underestimate the values. Comparing differences between regions or between cartograms revealed that legends and grid lines slowed the estimation without improving accuracy. However, participants were more likely to complete the tasks when legends and grid lines were present, particularly when the area units represented by these features could be interactively selected. We recommend considering the cartogram's use case and purpose before deciding whether to include grid lines or an interactive legend.