AMBER CHEN

Zion National Park Static Map

This map is a reproduction of NPS’s official park map, with adjusted symbology and labeling to improve the accessibility of information. My map’s main color scheme consists of shades of red, brown, and orange to match Zion’s natural landscape. To improve readability, I diversified the map’s point symbology, using intuitively understood symbols to further enhance clarity.

Previously, the parking lot icon for NPS maps was a small half-circle that was barely visible among all of the other features. To make it more prominent, I changed the icon to a white square with the letter “P,” which is commonly understood to be the parking symbol. This makes parking information easily accessible at a glance.

I also decided to make trailheads more prominent, by symbolizing them with brown diamonds. This makes them easy to spot for visitors seeking hiking routes, while preserving visual hierarchy. The diamonds are larger than the small, half-circle dots in the NPS map, but their muted brown color prevents them from overpowering other features.

For Zion’s surrounding areas, I symbolized towns with semi-transparent polygons to show their extents, offering more geographic context than the NPS point symbols. I omitted town elevation labels, which felt unnecessary for this map’s purpose. For significant elevation points, I replaced NPS’ small dots with triangles, a shape more intuitively associated with mountains.

On the NPS map, the big, bold title “Zion National Park” dominates the center, with small features squeezed between its wide kerning and line spacing. I took a different approach by moving my title outside the park boundary. Though it still remains the largest label, it sits in a corner where it doesn’t compete with smaller features. This freed up space for additional labels, such as Ivins Mountain, giving visitors more useful information. Adopting this labeling strategy in future NPS maps could help keep the focus on the park itself, instead of the title.

Some other labels I adjusted were those of towns, elevation points, and rivers. I set town labels in uppercase to emphasize them as area features and align them with their polygon symbology. For natural features like rivers and mountains, I chose serif fonts to distinguish them from other labels. I gave river labels semi-transparent halos to enhance their readability against the hillshade background. However, I chose to leave geographic features like plateaus and canyons in their original sans-serif, capitalized fonts to preserve clarity and neatness.

Overall, I believe NPS could implement these stylistic adjustments in future maps to make information more immediately readable and accessible to visitors. This way, their maps can allow visitors to understand key details at a glance, and continue on their merry ways to explore the beautiful parks!

Zion National Park Interactive Map

Check out an interactive version of my Zion National Park map here!

Zoom in and out, pan to neighboring towns, and click on park features to learn more about them! All features have been updated individually to show pertinent information in their pop-ups.

To view my map legend, click the info box in the top left corner. To view map layers and explore more, click the pop-out icon in the bottom right corner to take you to ESRI’s map viewer.

San Diego Median Housing Values Map

Equal Interval divides the range of prices into evenly sized value intervals. While this method is easy to understand, it glosses over some of the smaller variations within my data and ignores pricing clusters that otherwise fall in unequal categories. That being said, this distribution still does a good job of illustrating the most general patterns within the data, such as where counties with more high-value homes are located (e.g. La Jolla and Rancho Bernardo, which lies east of Del Mar).

Quantile ensures an equal number of tracts in each class, which evenly distributes colors across the map. However, one downside is that it may place some tracts with similar values into different classes (or vice versa – place tracts with different values into the same class), which can distort and exaggerate our perception of actual price differences. In this instance, the quantile classification decreases the price range of the class with the second-lowest values just to even out the number of tracts in that class in comparison to the other classes. This potentially misleads our interpretation of that class.

Natural Breaks groups tracts by natural gaps in the data, producing classes that reflect real clusters of values. Its grouping method captures meaningful differences between housing prices, thus avoiding the issues that Equal Interval and Quantile introduce. I think Natural Breaks best represents my dataset because the classes accurately represent real housing value patterns and allow us to spatially visualize overall high and lower value areas, without sacrificing accurate representation of the variation within the data.

Standard Deviation measures how far values deviate from the mean, so I chose a diverging color scheme to better illustrate this method’s purpose. This method strongly emphasizes a few extremely high-value tracts but compresses the differences among the majority of tracts, which can visually exaggerate high-value outliers. Also, since I chose to stick with 5 classes for consistency across my maps, the standard deviation values were a little hard to read.