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It used to be relatively common to overlay barplots on maps, I recall (one of the few times where I think bars might be useful). This only works well if you have a small-ish number of regions and the size disparity is not too large, but it's an option. With the bars, you can of course add confidence intervals. Here's a crude example of what I mean, albeit without error bars: https://www.originlab.com/doc/Origin-Help/Bar-Map

I do also like your other option of adding separate maps showing the lower and upper confidence intervals, though those would suggest a positive spatial correlation in estimates which may or may not be plausible.

Hashing or other texture can also be used to communicate categorical differences. Often this is used to indicate p-values less or greater than 0.05. I'm not sure it would be useful but one might in principle also define bands of confidence interval width and use texture to show that. Transparency could also be used for this with the advantage that it can be used to communicate a continuous response, but I suspect it would be hard to do this effectively.

The best solution is likely to depend heavily on the details of the mapped area and data, so you will likely have to experiment to find the best solution.

It used to be relatively common to overlay barplots on maps, I recall (one of the few times where I think bars might be useful). This only works well if you have a small-ish number of regions and the size disparity is not too large, but it's an option. With the bars, you can of course add confidence intervals. Here's a crude example of what I mean, albeit without error bars: https://www.originlab.com/doc/Origin-Help/Bar-Map

I do also like your other option of adding separate maps showing the lower and upper confidence intervals, though those would suggest a positive spatial correlation in estimates which may or may not be plausible.

Hashing or other texture can also be used to communicate categorical differences. Often this is used to indicate p-values less or greater than 0.05. I'm not sure it would be useful but one might in principle also define bands of confidence interval width and use texture to show that. Transparency could also be used for this with the advantage that it can be used to communicate a continuous response, but I suspect it would be hard to do this effectively and some binning would again be helpful.

The best solution is likely to depend heavily on the details of the mapped area and data, so you will likely have to experiment to find the best solution.

It used to be relatively common to overlay barplots on maps, I recall (one of the few times where I think bars might be useful). This only works well if you have a small-ish number of regions and the size disparity is not too large, but it's an option. With the bars, you can of course add confidence intervals. Here's a crude example of what I mean, albeit without error bars: originlab.com/doc/Origin-Help/Bar-Map

I do also like your other option of adding separate maps showing the lower and upper confidence intervals, though those would suggest a positive spatial correlation in estimates which may or may not be plausible.

Hashing or other texture can also be used to communicate categorical differences. Often this is used to indicate p-values less or greater than 0.05. I'm not sure it would be useful but one might in principle also define bands of confidence interval width and use texture to show that. Transparency could also be used for this with the advantage that it can be used to communicate a continuous response, but I suspect it would be hard to do this effectively.

The best solution is likely to depend heavily on the details of the mapped area and data, so you will likely have to experiment to find the best solution.

It used to be relatively common to overlay barplots on maps, I recall (one of the few times where I think bars might be useful). This only works well if you have a small-ish number of regions and the size disparity is not too large, but it's an option. With the bars, you can of course add confidence intervals. Here's a crude example of what I mean, albeit without error bars: https://www.originlab.com/doc/Origin-Help/Bar-Map

I do also like your other option of adding separate maps showing the lower and upper confidence intervals, though those would suggest a positive spatial correlation in estimates which may or may not be plausible.

Hashing or other texture can also be used to communicate categorical differences. Often this is used to indicate p-values less or greater than 0.05. I'm not sure it would be useful but one might in principle also define bands of confidence interval width and use texture to show that. Transparency could also be used for this with the advantage that it can be used to communicate a continuous response, but I suspect it would be hard to do this effectively.

The best solution is likely to depend heavily on the details of the mapped area and data, so you will likely have to experiment to find the best solution.

It used to be relatively common to overlay barplots on maps, I recall (one of the few times where I think bars might be useful). This only works well if you have a small-ish number of regions and the size disparity is not too large, but it's an option. With the bars, you can of course add confidence intervals. Here's a crude example of what I mean, albeit without error bars: originlab.com/doc/Origin-Help/Bar-Map

I do also like your other option of adding separate maps showing the lower and upper confidence intervals, though those would suggest a positive spatial correlation in estimates which may or may not be plausible.

It used to be relatively common to overlay barplots on maps, I recall (one of the few times where I think bars might be useful). This only works well if you have a small-ish number of regions and the size disparity is not too large, but it's an option. With the bars, you can of course add confidence intervals. Here's a crude example of what I mean, albeit without error bars: originlab.com/doc/Origin-Help/Bar-Map

I do also like your other option of adding separate maps showing the lower and upper confidence intervals, though those would suggest a positive spatial correlation in estimates which may or may not be plausible.

Hashing or other texture can also be used to communicate categorical differences. Often this is used to indicate p-values less or greater than 0.05. I'm not sure it would be useful but one might in principle also define bands of confidence interval width and use texture to show that. Transparency could also be used for this with the advantage that it can be used to communicate a continuous response, but I suspect it would be hard to do this effectively.

The best solution is likely to depend heavily on the details of the mapped area and data, so you will likely have to experiment to find the best solution.