Which visualization is best suited for displaying the measure of the intersection of two dimensions with color coding?

A heat map is particularly effective for visualizing the intersection of two dimensions because it provides a clear representation of data density or intensity across a matrix format. In a heat map, the values associated with the combinations of two variables are displayed in a grid, with the color coding representing the magnitude of the values. This allows for quick visual analysis of how two dimensions interact and where concentrations or contrasts exist.

For example, if you're analyzing two categorical variables, the heat map can display how often combinations of these categories occur, with the color indicating the frequency or count. This makes it easy to identify trends, patterns, or anomalies across the dataset.

Other visualization types, like box plots, scatter plots, and pie charts, serve different purposes. Box plots are mainly used for showing distribution and outliers across a single dimension. Scatter plots can display relationships between two continuous variables but lack the straightforward representation of intensity over a grid. Pie charts are used to show parts of a whole and do not facilitate the visualization of the intersection between two dimensions effectively. Thus, heat maps are the preferred choice for this scenario.