First, on making two different timestamps comparable, a good approach would be a delta heat map, where you visualize the change between two timestamps. You’d have to use a different color scheme, something like red to white to green. I don’t know how well this would work on a density heat map; I’ve always seen density heat maps with the rainbow color scheme.

Which brings up the fact that this shouldn’t be a density heat map. The data is representing state-wide data. Density heat maps are best for point-based data (eg: data about cities). Using a density heat map for state data gives the mistaken impression that the syphilis cases are located in specific parts of each state. An area-fill heat map, with the each state filled with the entire color, is a much better approach for this type of data. This would also remove the visual artifact that makes the density dots look like they get bigger and smaller as the color value changes during the animation.

Finally, the data appears to be cyclical, so ideally you want to have some technique that allows you to remove the cyclical nature. A couple ideas here: an option to aggregate values through the year and show year-by-year comparisons; use small multiples to show all the weeks at once (or aggregates of all the months); use a quantized fill to show multiple values per state at once; or use a different visualization entirely to show time-based trends while maintaining some geographic information (horizon graphs grouped by region come to mind).

First, on making two different timestamps comparable, a good approach would be a delta heat map, where you visualize the change between two timestamps. You’d have to use a different color scheme, something like red to white to green. I don’t know how well this would work on a density heat map; I’ve always seen density heat maps with the rainbow color scheme.

Which brings up the fact that this shouldn’t be a density heat map. The data is representing state-wide data. Density heat maps are best for point-based data (eg: data about cities). Using a density heat map for state data gives the mistaken impression that the syphilis cases are located in specific parts of each state. An area-fill heat map, with the each state filled with the entire color, is a much better approach for this type of data. This would also remove the visual artifact that makes the density dots look like they get bigger and smaller as the color value changes during the animation.

Finally, the data appears to be cyclical, so ideally you want to have some technique that allows you to remove the cyclical nature. A couple ideas here: an option to aggregate values through the year and show year-by-year comparisons; use small multiples to show all the weeks at once (or aggregates of all the months); use a quantized fill to show multiple values per state at once; or use a different visualization entirely to show time-based trends while maintaining some geographic information (horizon graphs grouped by region come to mind).

It seems to just follow population… almost. Adjusting for pop. would allow the pattern to pop.

It seems to just follow population… almost. Adjusting for pop. would allow the pattern to pop.