There and Back Again

This was another fun lab. We got to experiment with diferent symbolizations, trying both continuous tone and hypsometric symbology. Here we produced a map of annual rainfall data in Washington State, with contours added to accent the changes. This involved using the Int (Spatial Analysis) and Contours geoprocessing tools. We also learned about the PRISM model, where better phenomena data is derived by weighting information with variables such as elevation to produce more accurate data over spaces.

This was another great lab. Here, I prepared a map of Europe with a choropleth map of population density, with graduated symbols over each country representing annual per capita wine consumption. We were asked to look at the use of map projection and why it was a good or bad fit for this map, and to consider the relative strengths and weaknesses of projections in general. The main focus on this lab was on making stylistic and aesthetic to clearly convey map information to readers, while incorporating previous lessons like Gestalt principles. Map choices included choosing a classification scheme for our data, choosing aesthetically appealing color ramps and symbolism, style choices about the basemap and legend, data hygiene (some country names were in other languages initially), and choosing countries that were too small or too much of an outlier to display well, as well as cleaning up and displaying effectively areas with dense labels. I chose to make a layer beneath my main map w...

In this lab, we experimented with displaying one population dataset in eight different ways -- by natural breaks, equal interval, standard deviation, or quantile classifications, both as a percent of population per census tract area, and normalized to absolute numbers per square mile. This was super interesting, because it displayed different patterns that emerged, and showed what was obscured or highlighted by different classifications. There are inherent tradeoffs in how you present your data, and no individual classification or display style will tell the full story or work in every situation. In the discussion questions, I ruminated on the relative pros and cons of displaying info about seniors as a percent of a census tract or normalized by square mile -- very large tracts could wash out seniors (a modifiable unit area problem), high concentration as a percent could obscure small populations, etc. Also, the population data alone doesn't tell the full story if you're usin...

This was an interesting lab, where we got a map area that produced a lot of empty space around the relevant areas, and had to make a map that utilized Gestalt design principles. Gestalt is a system for understanding human perception, and how human beings summarize the individual parts of what they see into a coherent whole. For instance, the Gestalt principle of closure states that we will fill in the missing parts of an image with enough information. In this map, I tried to utilize the principle of figure-ground , where visual cues indicate something is closer or more important to the viewer. In this map, the locator shows ward 7 within Washington, D.C., while the main map is zoomed on Ward 7, and excess noise in the form of labeling, competing visual information, unimportant map space, etc. is all minimized. Deliberate design choices included not labeling roads and instead suggesting their importance by line weight, not showing surface roads outside Ward 7, using a muted basemap,...

In this lab, we practiced typography choices for labeling rivers, swamps, and cities. We reviewed visual hierarchy, tried to avoid clutter and visual noice, and to make our maps visually appealing. Specific techniques I learned were creating labels, turning labels to annotation, using a SQL command to only label certain features, using halos around lettering, curving lettering to follow river paths, and using Arcade to turn labels into Proper Case. I had a hard time initially filtering labels to only display some categories from an attribute table, and I sank a ton of time into choosing the right color scheme for counties that would look ok with my swamps layer. But ultimately, I learned a lot of extremely useful techniques in ArcGIS.