Visualizing Washington’s Precipitation with Isarithmic Mapping

 

Visualizing Washington’s Precipitation with Isarithmic Mapping

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Introduction / Overview

This assignment focused on creating an isarithmic map to represent continuous climate data using GIS techniques. The objective was to explore how interpolation methods and symbology can be used to visualize spatial patterns in precipitation. Using PRISM climate data, the lab emphasized working with raster datasets, applying appropriate classification methods, and designing maps that effectively communicate geographic information.

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Map Explanation

The final map displays the average annual precipitation across Washington State using hypsometric tints and contour lines. The data was derived using the PRISM interpolation method, which integrates weather station observations with terrain variables such as elevation, slope, and coastal proximity to produce a continuous precipitation surface. Hypsometric tinting was used to classify precipitation into meaningful ranges, while contour lines were added to highlight transitions between values. A hillshade effect was incorporated to enhance terrain visualization, helping to illustrate how topography influences precipitation patterns. The map clearly shows higher precipitation in the western mountainous regions and lower values in eastern Washington.

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Key Points / Applications

This type of map is useful in fields such as hydrology, agriculture, and environmental planning, where understanding precipitation patterns is critical. Isarithmic mapping provides a more realistic representation of continuous data compared to discrete mapping methods. By combining classification, contours, and terrain shading, the map communicates both detailed variation and broader spatial trends.

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Reflection

One of the most challenging aspects of this lab was working with the hillshade effect in ArcGIS Pro. I repeatedly encountered error messages when attempting to generate the hillshade layer, which slowed down my progress. After troubleshooting, I determined that the issue was related to the input raster and resolved it by switching back to the original precipann_r_wa dataset as the data source. Once corrected, the hillshade function worked properly and significantly improved the visual quality of the map.

In addition to this challenge, I had to carefully adjust classification, symbology, and layout elements to ensure the map was both accurate and visually balanced. This lab reinforced the importance of attention to detail and problem-solving when working with GIS tools. Overall, the assignment strengthened my understanding of continuous data representation and the role of cartographic design principles such as visual hierarchy, contrast, and balance in creating effective maps.

Population Density and Wine Consumption in Europe

 

Population Density and Wine Consumption in Europe

This assignment focused on creating a choropleth map combined with proportional symbols to visualize population density and wine consumption across Europe. The objective was to apply cartographic principles such as classification, color selection, and symbolization to effectively communicate spatial data. Using ArcGIS Pro, I created a map that displays population density using a graduated color scheme and overlays wine consumption using proportional symbols.

For the choropleth portion of the map, I used population density as the primary variable rather than raw population counts because it allows for a more accurate comparison between countries of different sizes. I applied a sequential light-to-dark blue color ramp, where lighter colors represent lower population densities and darker colors represent higher densities. This choice helps create a clear visual hierarchy and makes it easy for the map reader to interpret patterns across the continent.

For the second dataset, I used proportional symbols to represent wine consumption per capita. I selected proportional symbols instead of graduated symbols because they allow for a more precise representation of continuous data. To enhance the design and visual appeal of the map, I incorporated a custom wine bottle graphic as the symbol. This helped reinforce the theme of the map while still maintaining functionality. Creating and implementing this symbol required additional steps, including working with transparent images and converting files into a format compatible with ArcGIS Pro.

The map also includes an inset to highlight regions with dense clusters of smaller countries, improving readability and reducing clutter on the main map. This ensures that important details are not lost due to overlapping symbols or limited space.

One challenge I encountered during this lab was that some of the instructions did not match the current version of ArcGIS Pro. For example, locating features like “Label view” required additional exploration because the interface has changed. Despite this, I was able to adapt by experimenting with the software and finding updated tools that perform the same functions.

Overall, this lab strengthened my understanding of choropleth mapping, symbolization, and layout design. It also highlighted the importance of flexibility when working with evolving software tools.

Seeing the Difference: How Data Classification Changes the Story

Seeing the Difference: How Data Classification Changes the Story

Figure 1. Comparison of four classification methods (Equal Interval, Quantile, Standard Deviation, and Natural Breaks) used to visualize the percentage of the population aged 65 and older in Miami-Dade County.

For this lab, I explored how different data classification methods affect the way spatial data is interpreted. Using census tract data from Miami-Dade County, I created maps showing the percentage of the population aged 65 and older using four classification methods: Equal Interval, Quantile, Standard Deviation, and Natural Breaks.

The most interesting part of this assignment was seeing how dramatically the same dataset can appear depending on the classification method used. Equal interval created a more uniform distribution but tended to hide variation in the middle ranges. Quantile, on the other hand, made patterns appear more evenly distributed but sometimes exaggerated differences by grouping dissimilar values together. Standard deviation was useful for identifying areas significantly above or below the mean, but it required more interpretation and was less intuitive visually.

Natural breaks stood out as the most effective method for this dataset. It grouped similar values together and clearly highlighted clusters of higher senior populations, making it easier to identify meaningful spatial patterns. This reinforced the idea that classification is not just a technical step—it directly influences how data is understood.

One of the more challenging aspects of this lab was refining the map layout and ensuring that all elements were consistent across the four map frames. Getting the legends, color schemes, and spacing aligned properly required careful attention to detail, especially when working to maintain visual balance and readability across all maps.

Overall, this lab demonstrated how important cartographic decisions are in shaping data interpretation. Even when working with the same dataset, different classification methods can lead to very different conclusions, which has important implications for real-world decision-making.

Mapping Public Schools in Ward 7: A Cartographic Design Approach

 

Mapping Public Schools in Ward 7: A Cartographic Design Approach

 Final map layout showing Ward 7 public schools, symbolized by school type 

with inset map and numbered reference list.

For this lab, I put together a cartographic layout of public schools in Ward 7, Washington, D.C. using ArcGIS Pro. The goal was to actually apply the design concepts we’ve been learning—things like visual hierarchy, contrast, and figure-ground—and see how they play out in a real map.

I organized the schools by type using color and size so they’re easy to tell apart at a glance. High schools stand out the most, then middle, then elementary. I also added neighborhood labels and used curved text to label the Anacostia River, which made the map feel a lot more polished. The layout includes all the required elements like an inset map, legend, scale bar, north arrow, and a numbered school list so everything is clear without overcrowding the map.

The most frustrating (and honestly time-consuming) part was trying to get rid of the basemap source text (“Sources: Esri…”). It’s built into the map frame, so it wouldn’t just delete like normal text. After trying multiple approaches, I ended up covering it with a rectangle and replacing it with my own formatted credits. It worked out in the end and actually made the layout look cleaner.

Overall, this lab really showed me how much design choices matter. Small changes in color, size, and placement can make a huge difference in how easy a map is to read.