M6 - Computer Cartography - Isarithmic / Flow Maps

 

The map above illustrates average annual precipitation across Washington State using 30 years of data prepared by the PRISM interpolation method.

This week’s lab focused on isarithmic mapping to visualize continuous raster data, particularly average annual precipitation in Washington over a 30-year period using advanced interpolation methods. Our analysis was based on the Parameter-elevation Regressions on Independent Slopes Model (PRISM), which highlights how elevation impacts climate variables like rainfall and temperature. By gathering data from various weather stations and using inverse distance weighting (IDW), PRISM helps us calculate climate-elevation relationships for each raster cell, showing how elevation affects precipitation patterns.

We created two types of isarithmic maps: continuous tone maps, which display smooth transitions of data across an area, and hypsometric tint maps, which group data into bands with a color gradient. For our precipitation map, we sorted values into ten ranges from ≤10 inches to >180 inches, using a color ramp from red to deep blue. While hypsometric tinting clearly illustrates changes in elevation, it can sometimes mask local variations due to generalization.

The lab also broadened our understanding for different interpolation methods, using 30 years of sensor data to calculate annual averages while considering factors like terrain and rain shadows. Isarithmic maps, including isometric and isopleth types, are key for showing continuous geographic phenomena and rely on the idea that nearby locations tend to share similar characteristics.

Computer Cartography - M5 - Choropleth Mapping & Proportional Symbology

 

This week's lab used ArcGIS Pro to explore key considerations in map design, focusing on the importance of selecting appropriate classification schemes when visualizing population densities and wine consumption across European countries, as well as choosing how to symbolize the wine consumption data using either proportional or graduated symbology.

Europe Albers Equal Area Conic projection is used for this map. This projection is particularly effective for choropleth mapping because it preserves the proportion of each enumeration unit , which is crucial when displaying population densities. Since we're mapping population density per unit area, using a projection optimized for the continental scale ensures that regions maintain their true relative sizes.

I chose the Natural Breaks method to classify the population density data because it effectively reveals clusters and patterns within the dataset. This approach minimizes the differences within each class while highlighting the distinctions between classes, creating clear categories that reflect the natural groupings in the data. By emphasizing areas with similar density levels, Natural Breaks provides a clearer understanding of population distribution across countries, which is essential given the significant variations in population density. This classification method helps the map effectively showcase important differences and trends.

For wine consumption, I decided to use graduated symbology to make it easier to interpret the different classes. I represented these classes with variations of glass symbols, ranging from an almost empty glass for the smallest class to a full glass for the largest class. I aimed for this choice to be intuitive and straightforward for readers.

This lab was a lot of fun, but also frustrating in some ways I won’t go into. I probably spent ten times more time on this map than was needed to deliver the bare minimum for a good grade, which actually made me miss my assignment deadline. I’m happy with the outcome, though, and think this will make a nice addition to my portfolio.

M4 - Computer Cartography - Data Classification

 

Visualizing demographic data is key to making good decisions, especially when it comes making decisions that affect a population. Module 4 introduces us to the various data classification methods and how they can help us understand data patterns, such as senior population density.  Our lab focuses on the senior population of Miami-Dade County, Florida, based on 2010 Census Tracts.

Data classification is vital for creating maps that effectively communicate complex information. Here are four common methods:

• Equal Interval
  
  
• Equal Interval divides the data range into equal-sized classes. For senior population data, it might reveal broad trends across Miami-Dade County but could conceal significant variations in areas with high or low senior densities, leading to classes with few or no tracts.
  
  
• Quantile
  
  
• Quantile classification ensures each class contains an equal number of data points. This method would highlight areas with a high concentration of seniors, making it easier to spot where seniors are densely populated. However, it can obscure differences in actual senior population counts, as similar percentages might end up in different classes.
  
  
• Standard Deviation
  
  
• This method shows how data points deviate from the mean senior population. It effectively highlights areas significantly above or below the average senior density, allowing for the identification of tracts with unusually high or low senior populations. However, if the data is skewed, this method might mask important outliers, such as tracts with a small total population but a high percentage of seniors.
  
  
• Natural Breaks
  
  
• Natural Breaks identifies inherent groupings in the data, maximizing differences between classes. In analyzing senior populations, this method would effectively reveal clusters of high-density senior areas.

One key aspect of this analysis is normalizing data by area instead of just showing percentages of the total population. Normalization gives us clearer insights into senior density, which is crucial for planning services. For instance, knowing the number of seniors per square mile provides more useful information for resource allocation than percentage figures alone.

When it comes to mapping the senior population in Miami-Dade County, the choice of classification method can really impact how effectively we communicate the data. For audiences focused on service delivery, the Natural Breaks method might be the best option, as it emphasizes areas where seniors are concentrated.

I found this module very interesting, especially considering I'm dually enrolled and am taking a statistics class through a different university, and have just been covering these classification methods! 

Computer Cartography - M3 - Cartographic Design

Map illustrating the locations of public schools in Ward 7 of Washington, D.C.  Click here to view in full resolution.

Over the years, I've come across various images and concepts related to the Gestalt Principles of Perception, but I never really took the time to examine them closely.

Before getting into this week’s lab assignment, I want to share a bit of my personal history.

I was a teenager in what I consider a remarkable era—the 90s—when personal computers were advancing rapidly and becoming more accessible. The World Wide Web was just starting to weave itself into our daily lives. During this time, I discovered a chat program called Excite Virtual Places (VP). This innovative platform allowed users to enter chat rooms with a web browser, using any website as the backdrop for conversations. Participants could create and customize avatars that moved freely around the background, engaging with others in a dynamic environment. Excite Virtual Places was one of the early examples of virtual worlds, blending social interaction with the burgeoning internet experience.

A screenshot of Excite Virtual Places

In VP, your avatar represented your identity, so it was natural for users to want a fully customized avatar that reflected their personality. This led to dedicated chat rooms for creating custom avatars, where users could request designs from volunteer designers "on staff." I found this process fun and exciting, sparking my desire to create my own avatars. Funny enough, this was my introduction to graphic design and graphic design software—I started with Paint Shop Pro—which eventually led me to learn basic HTML and web design.

Over the years, I continued to refine my design skills as a hobbyist. I've always been a creative person, and graphic and web design have remained essential outlets for me throughout my life. It wasn’t until 2018 that I began using these skills professionally when I started working for a marketing firm as a social media manager. My role involved creating content, both graphics and copy, for social media posts.  I held this position for about 3 years.

This is a long winded way of saying that I’ve been unknowingly applying the Gestalt Principles of Perception for years.

When I began my GIS journey, I was simply searching for a new career that could provide a decent income. I hadn’t heard of GIS until I made a social media post asking friends for suggestions on career fields that fit my criteria: a skill I could learn without a four-year degree, that didn’t involve physical labor (due to my disability), that involved computers, and could potentially be remote.

It’s purely coincidental that the career path I chose allows me to apply the skills I’ve developed over the years out of pure enjoyment. This is why I’m so excited about cartography! 🙂

Back to the topic at hand....

Module 3 of Computer Cartography builds on what we learned in Module 2 about map design basics like labeling and layout. Now, we’re focusing on how maps are created, using the Gestalt Principles to guide us.

Key ideas for good map design include making maps user-friendly, accurate, and visually appealing. The design process starts with considering how the map will be shared, influencing color schemes and scale. We then classify the data and choose symbols, highlighting important elements while toning down less critical info.

For our lab assignment, I created a thematic map of public schools in Ward 7, DC using ArcGIS Pro. I used bright colors and clear symbols to highlight school locations and included an inset map for context.

To create visual hierarchy, I made the area of interest (AOI) the lightest part of the map and used lighter colors for emphasis. I chose apples as symbols for schools, using their ripening colors—green, yellow, and red—to indicate school levels, making them easily distinguishable.

For the figure-ground relationship, I ensured the AOI stood out against a darker background and used a gradient to soften transitions. To balance the design, I placed the school labels and legend on the right side, countering the visual weight on the left. 

I think I should add that I took inspiration from the interview with graphic designer/self-taught award winning cartographer John Innes of User Friendly Maps in the completion of my map layout.  I used Adobe Illustrator to construct my labeling system.  Please note -- I do have the know how to have completed the entire layout in ArcGIS Pro using lines and shapes tools as well as text.  I just wanted to give Illustrator a try, since it's a piece of design software I've never used. 

This module has been one of my favorite so far in the UWF GIS program.  I am beyond excited to continue to learn how to apply my design skills in a way that fits the highest standards of cartographic design.