Isarithmic Mapping of Precipitation Data in Washington

 

This week, the cartography lab focused on creating an isarithmic map of average annual precipitation in Washington State using a variety of raster symbology techniques. The data came from the PRISM Climate Group and was downloaded from the USDA Geospatial Gateway. There are many methods for interpolating climate data from monitoring stations to grid points. While some perform well in flat terrain, few effectively capture the complex climatic variations found in mountainous areas. PRISM addresses this challenge by incorporating a conceptual framework that accounts for orographic effects such as elevation, aspect, and slope. Precipitation data collected between 1981 and 2010 in Washington was interpolated using the PRISM Model (Parameter-elevation Regressions on Independent Slopes Model), which blends monitoring station data with a digital elevation model (DEM) to generate a climatological average over the 30-year period, to account for topographic variations that occur in mountainous regions.

Figure 1

Continuous Tones

Continuous tone symbology is a method of displaying raster data where values are represented with a smooth gradient of colors, rather than distinct classes or categories. This symbology method is useful for showing continuous data, like precipitation, where values change gradually across space. The initial raster, “precipann_r_wa,” was brought into ArcGIS Pro and symbolized using a continuous tone color ramp specific to precipitation. To enhance the surface visualization, I added a ‘hillshade effect’, which uses elevation to visually emphasize terrain, in ArcGIS Pro via the hillshade function. The result was a smooth and natural-looking precipitation surface.

Hypsometric Tints

To move from continuous to discrete symbology, I used the Int tool to convert the raster’s values into integers. This allowed me to classify the data into 10 equal intervals using the Classify option in the Symbology pane. Each range is a distinct color from the precipitation ramp to create hypsometric tints, a method that divides elevation or continuous data (in this case, precipitation) into visually distinct color bands. The combination of the precipitation symbology and the legend makes the spatial distribution of rainfall easier to interpret at a glance.

Overlay Contours

As hypsometric tins cannot be displayed without contour lines, I then added overlaying contour lines using the Contour List Spatial Analyst tool. Using the “Annual Precipitation (in)” raster dataset for the input layer, I manually added contour elevations in the Contour Value Fields that matched the 10 classes displayed in the hypsometric tint symbology classes creating a “Contours” output dataset. Contours help reinforce the boundaries between precipitation zones and add another visual cue for interpreting variation in the data. With both contours and tints overlaid, the map clearly shows the relationship between precipitation patterns and elevation, especially in areas with steep terrain.

Summary

This lab was a fun exercise in translating complex, continuous climate data into a readable and visually engaging map. By walking through different symbolization techniques (continuous tone, hypsometric tinting, and contours), I gained a better understanding of how to tailor symbology to different data types and mapping goals.

Week 5: Choropleth and Proportional Symbol Mapping

 

This week, I explored the relationship between wine consumption per capita and population density across European countries in 2012. The goal was to visually represent how these two variables are distributed and to choose effective mapping techniques to communicate the data. Using both choropleth and proportional symbol techniques, I created a combined map (Figure 1) that highlights these patterns across the continent.

Figure 1

Using ArcGIS Pro, I began by importing the provided EuroPopulation feature class. I used the quantile classification method to divide population density into five equal groups and applied a graduated blue color scheme, adjusting shades manually for better balance. To avoid skewing the data, I excluded outliers like Monaco and Gibraltar using SQL queries. Once the labels were added, I converted them into annotations, which allowed for precise control over their placement. This process took a few tries but greatly improved the layout’s clarity.

With the population density map in place, I added proportional symbols to represent wine consumption per capita. After testing several classification schemes, I found that the Natural Breaks method worked best for this dataset, effectively grouping countries based on consumption levels. I was less successful in the placement of the symbols as I couldn’t quite figure out how to move or omit them in the layout.

For a creative element, and some extra credit, I imported a wine bottle icon from a free .svg vector file and used it as the symbol for wine consumption. I carefully adjusted the symbol sizes to make sure they weren’t too large or too small, aiming for a proportional and readable display. Interestingly, I found working with the symbols more challenging than managing the country labels! I attempted to strike an aesthetic balance with all the information contained on this map.

This project gave me valuable experience with classification methods, symbology, SQL filtering, and overall map design. Tools like Data Exclusion were especially helpful in creating a more balanced, readable map, especially when dealing with small countries that had extreme population densities.

Overall, this project was a great opportunity to practice key GIS and cartographic skills. By choosing appropriate mapping techniques for both population density and wine consumption, I was able to create a final product that’s both visually engaging and informative. While there’s still a lot to learn, this assignment helped me better understand how thoughtful design choices and data handling can enhance map readability and impact.

GIS 5007: Data Classification

This week, we explored various data classification methods to effectively represent spatial data. The objective was to visualize the percentage and distribution of senior citizens (aged 65 and older) across census tracts in Miami-Dade County, Florida. I created two map compilations using four classification methods: Equal Interval, Quantile, Standard Deviation, and Natural Breaks. The first map (Figure 1) displays the percentage of individuals aged 65 and above by census tract, while the second map (Figure 2) normalizes the data to show the number of seniors per square mile.

Figure 1

Figure 2

Data Classification Schemes

• The Natural Breaks (Jenks) method looks for natural groupings in the data. Areas with high or low senior populations are easily distinguished in both maps, making it easy to see potential patterns. The only downside? Since the breaks are based entirely on the data itself, it can be tricky to compare this map with others that use different datasets or classifications. Still, it gave one of the clearest pictures overall.
• The Equal Interval method divides the data into equal-sized ranges, making comparisons across areas easier. It works well when the data is evenly distributed, but in this case, the data on both maps are skewed toward the lower end. This happens because areas with higher or lower senior populations are grouped with more average tracts, making it harder to identify patterns.
• Quantile splits the data so that each class contains the same number of census tracts, which helps maintain a balanced and evenly spread map. However, this method doesn’t necessarily reflect actual differences in the data. It simply divides the total number of values into five classes, which can lead to sharp boundaries that don't accurately represent the real distribution on the ground.
• The Standard Deviation method classifies data based on how far each value is from the average, making it useful for identifying outliers and comparing areas to the average. However, it can be difficult for general audiences to interpret, especially if they're not familiar with statistics. As seen in Figure 2, this method can be misleading, making the senior population appear more uniform across the county than it truly is.

I’ve featured a map using the Natural Breaks (Jenks) classification method, which I believe provides the clearest and most intuitive depiction of senior population concentrations. It effectively highlights areas with high senior populations and is accessible for non-technical viewers. However, when considering areas with densely clustered seniors, using the normalized population count provides a more accurate representation. Percentages give a general sense of demographics but don’t account for tract size, which is crucial for understanding density and prioritizing services.

This exercise highlighted the impact of the Modifiable Areal Unit Problem (MAUP), the way data is aggregated and classified can significantly influence interpretation. Both maps demonstrate how different classification methods and normalization approaches can either highlight or obscure spatial patterns, depending on the scale and method used.

GOS 5007: Cartographic Design & Perceptual Organization

For this week's lab, I created a map of Ward 7 in Washington D.C., focusing on Public Schools in the area (Figure 1). The learning objective of the exercise was to apply  Gestalt's Principles of Organization, specifically visual hierarchy, contrast, figure-ground distinction, and balance, to craft a well-designed map. While this might seem simple, the challenge is in the details. 

Figure 1

To implement visual hierarchy, I used red symbols for the schools, adjusting the color saturation with HSV to make them stand out. I also varied the symbol sizes to distinguish between different school types, though these sizes were not weighted values.

For contrast, I chose a gray-scale color scheme, with Ward 7 in a lighter hue to make it stand out against the darker background of Washington, D.C. This created a clear distinction between the area of interest (AOI) and the less important surrounding areas. The use of grayscale helped establish a strong visual structure.

Through trial and error, I established a figure-ground relationship by minimizing distractions. I emphasized the Ward 7 boundary and label, while keeping the background environmental layer in more muted tones. I excluded unnecessary details, like neighborhood boundaries, to keep the focus on the public schools.

For balance, I aimed to distribute map elements evenly, placing features in open areas and avoiding clutter. I didn’t use a legend block at the bottom, but instead placed elements where they felt most balanced within the map's design. The inset map provided additional context while maintaining focus on the AOI.