Data Classification Lab

 

🗺️ Understanding the Power of Data Classification in Cartography: A Deep Dive into Miami-Dade Senior Populations

In this lab assignment for Computer Cartography, I explored the world of data classification using ArcGIS Pro. The focus was on symbolizing and interpreting demographic data—specifically, the percentage of residents over age 65 in Miami-Dade County, Florida—using four common classification methods: Equal Interval, Quantile, Standard Deviation, and Natural Breaks.

The goal of this exercise was not just to apply GIS tools, but to understand how different classification methods influence how spatial data is perceived and interpreted. This is a critical skill in cartography and spatial analysis, especially when communicating complex datasets to different audiences.

🖼️ Map Showcase



🧭 About the Lab and Objectives

The assignment guided me through:

  • Working with 2010 U.S. Census data at the census tract level for Miami-Dade County.

  • Using graduated color symbology in ArcGIS Pro to classify and visually present demographic data.

  • Exploring four classification methods to compare how each handles the same dataset.

  • Creating a multi-map layout for visual comparison.

  • Normalizing data (i.e., population count per square mile) to enhance spatial accuracy.

  • Applying thoughtful cartographic design using layout tools, color schemes, and map elements.

📊 Classification Methods Explained

1. Equal Interval
This method divides the range of data into equal-sized classes. It’s simple and consistent but may not effectively reveal important patterns, especially with skewed data. In my map, large areas appeared to have low senior populations, but that’s more due to the distribution of values than the actual spatial pattern.

2. Quantile
Quantile classification ensures that each class contains the same number of features. It often results in uneven class ranges, which can highlight subtle differences, but may also exaggerate small distinctions. This method revealed geographic diversity but introduced visual inconsistency in actual population sizes.

3. Standard Deviation
This method segments the data based on how much it deviates from the mean. It’s useful for highlighting outliers and emphasizing statistical differences. On my map, areas with significantly older populations stood out clearly, but the method grouped the majority near the mean—masking moderate variation.

4. Natural Breaks (Jenks)
My preferred method for this dataset, Natural Breaks finds patterns in the data by minimizing variation within classes and maximizing differences between them. This produced the most intuitive visual pattern, especially for identifying clusters of higher senior populations in specific tracts.

🎯 Key Takeaways

Best for Targeting Senior Populations:
Natural Breaks provided the clearest representation of where senior populations are concentrated. It balanced visibility and accuracy better than the other methods, especially for decision-making and resource planning.

Best for Policy Presentations:
When presenting to stakeholders like the Miami-Dade County Commissioners, I would use normalized senior population data (AGE_65_UP / area in sq mi). Normalizing accounts for tract size and avoids misleading conclusions that could arise from using raw percentages alone, especially in geographically large but sparsely populated tracts.

🧠 Final Thoughts

This lab taught me that the way we classify and present spatial data is just as important as the data itself. A poorly chosen classification method can obscure important patterns or even mislead viewers. As a cartographer and GIS analyst, I now feel more confident in choosing the right technique to match my audience and my message.

Comments

Post a Comment

Popular posts from this blog

About Me Blog Post

  About Me Blog Post For the first assignment of my Computer Cartography course i was tasked with creating a simple story map to serve as an introduction for my classmates. The link is below: https://arcg.is/10OfDL1 I joined this class as part of my MS in GIS Administration which i hope will serve to provide me with the educational accolades and the industry knowledge to be able to one day own and operate my own geospatial consulting company. 
Read more

Module 1: Map Design & Typography

Image
  Making a Busy Map Readable: Labeling Mexico’s Geography For Parts 4 and 5 of this assignment, the goal was to move beyond simply displaying data and focus on how map design choices affect readability , especially as more features and labels are added. The project started with a simple map of Mexico and its major rivers, then expanded to include states and cities, which made labeling strategy much more important. Part 4: Starting Simple In Part 4, the map focused on Mexico’s national boundary and major rivers. Rivers were symbolized using a standard blue line style and labeled dynamically so the text followed the curves of the rivers. Using italic text and halos helped keep river names readable against different background colors. Because the map was relatively simple at this stage, river labels were allowed to repeat along long features and remain visually prominent. Part 5: Adding More Without Adding Clutter Part 5 made the map more complex by adding states and cities. To p...
Read more

Choropleth Mapping Lab

Image
  🎨 Mapping Europe: Population Density and Wine Consumption with ArcGIS Pro In this lab for my computer cartography course, we dove into the world of thematic mapping , specifically focusing on choropleth and proportional symbol mapping using ArcGIS Pro. The assignment centered around visualizing population density across Europe and overlaying wine consumption data using symbol-based mapping techniques. 🗺️ Objective The goal was to build a visually compelling and informative map that communicates population patterns and cultural trends through spatial data. We used a choropleth map to represent population density and then experimented with either graduated or proportional symbols to visualize wine consumption per capita . The combination highlights both the concentration of people and regional wine-drinking habits in an engaging visual format. 🧩 Methods & Techniques Software Used : ArcGIS Pro Key Tasks : Created a choropleth map using the POP_DENSIT attrib...
Read more