In lecture for this module I learned a great deal about both Flowline and Isarithmic Mapping characteristics and design. However, the focus of this lab exercise was solely on the former. I was given data for the average precipitation in Washington, U.S.A. over the course of a 30 year period, ending in 2010. With this data, I was prompted to explore two different methods of symbolization. First, I displayed the data in ArcGIS Pro and utilized Symbology to represent the data with continuous tones. This method is not used for the every-day audience as each cell in the raster interpolation (the algorithm that displays data based on raw, collected data at control points as continuous, smooth spatial values) is shaded proportionally to its value. This makes it hard for readers to associate location values to the legend on broader scales, like for the state of Washington. However, it was good practice.
This is a very rough, inappropriately oriented and projected layout of the continuous tone data for the precipitation layer. Included is a custom hillshade effect and appropriate legend. Again, this was simply made for practice.
The desired outcome of this lab exercise was an isarithmic map depicting the 30 year precipitation data using hypsometric tints, detailed with a hillshade effect and appropriate contour boundaries, as well as other essential map elements. I was also to incorporate a paragraph-style text blurb briefly describing the map, processes implemented to create it, and how the data itself was derived. Per usual, the final layout would need to adhere to common cartographic design principals such as figure-ground relationship and contrast.
The final map layout pictured above describes much of the intent and methodology involved in this lab exercise within the text blurb. Some things to note are that the legend would normally follow a horizontal color grade for this type of map, however this is not yet supported in ArcGIS Pro so a vertical orientation was used. Also, the contour values represent the highest value of each tint, and are not labeled as this is apparent via the symbology. Classing precipitation range values was tricky to achieve in Symbology, as
representing the required 10 classes is not as straightforward as choosing “Manual
Breaks” as a classification method and selecting 10 classes. There is a workaround I
found in my research in which you can select the desired number of classes
using a different classification method, such as natural breaks, then switching
the classification method to manual and changing the values and labels there.
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