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I'm trying to map (using ArcMap 10.6.1) some climate change data and I'm not entirely sure how to classify the raster datasets. For this work I am focusing on four different climate change scenarios:

-2040 RCP 2.6 -2090 RCP 2.6 -2040 RCP 8.5 -2090 RCP 8.5 (RCP is epresentative Concentration Pathway and relates to greenhouse gas emissions, the lower the number the better....)

I'm focusing on things such as temperature + precipitation changes in any of the above four scenarios.

How should I classify this data?

Should I have the same number and range of classes for any given scenario using Manual classification, or should I use Equal Interval for each of the given scenarios?

My gut feel is that values should remain the same over the four different scenarios, however in the RCP 2040 it then becomes difficult to highlight any changes. I also am wary of adding too many classes (trying to stay below 5, 7 max) as things start looking a bit too complicated the more classes that are added.

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First of all, you need the same threshold for each scenario in order to have a meaningfull visual comparison.

To make sure that there is no "gap" in the represented data, the most usefull way is to use quantiles (each class has the same number of items). However, you have here values at different dates. My suggestion is to combine the values from all dates, to compute the quantiles and to apply the same legend to all layers. This would require some analysis, for instance using extract values to points with, e.g. 5000 points on each raster, then merge each set of point into a single feature class, compute the quantile and set those values manually on each raster.

Alternatively, if you focus on change, you could take the 2040 as a baseline and compute the difference between the other dates and your baseline (using map algebra tool). Then the range will be more obvious and you will be able to show clearly areas with increasing and decreasing values (e.g one class around zero in grey, 3 "degradating" classes in red and 3 "improving" clases in green).

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