GIS5007 Module 4: Data Classification

-

Class is in session... Data Classification, that is!

This week focused on different methods of classifying data and how they can affect how data is portrayed on a map and the messages that come across, depending on the use of one classification method over another. My current work doesn't require me to think deeply about data classification to this degree, so this week challenged me and my brain to keep up. However, this was a great refresher on data classification in general.

For the lab, we are located in Miami-Dade County, Florida, this time around. Specifically, we are interested in the elderly population (over 65 years old) across different Miami-Dade census tracts. The four main data classification methods we had to implement in our maps were: Natural Breaks (Jenks), Quantile, Equal Interval, and Standard Deviation. I was able to wrap my brain around them by thinking of each like so...
  • Natural Breaks (Jenks) - Be natural. You accept the data as it is, so to speak. You let the natural groups be as they are — like in the old adage "let the chips fall where they may" — let the breaks be natural. It also minimizes the differences within classes while maximizing differences between them.
  • Quantiles - Think quantity. It evenly distributes your data across classes, ensuring each class has the same number of records. Let's say I have 5 classes and a total of 100 records in my data. I would have 20 records in each of the 5 classes.
  • Equal Interval - Equal + Interval. It's pretty self-explanatory by its own name. Each range or interval for each class is evenly spaced from the preceding class. It's the opposite of quantiles in this way. The classes themselves are properly divided equally, not the records. So, if I wanted a range of ages for my classes, I could ensure they are divided into increments of 100, for example.
  • Standard Deviation - Deviate from the mean/average. Great for identifying outliers in the data. Think bell curves.
Based on this and the lab assignment, we created two different map layouts: a population distribution map showing percentages of the elderly, and another using a normalized version by population per square mile, using the same data classification methods.

I didn't put as much time or extra flair into my maps this time, due to a combination of not having as much extra time and the need to simplify them to cram 4 different map frames into one layout. I chose a blue spectrum for my color scheme for the Natural Breaks, Quantile, and Equal Interval frames, while Standard Deviation used a more red-brown-to-blue-purple diverging color scheme, as recommended in the lab. In the background, I put a dull gray tone to let the colors pop a little more on the layout. I tried to play around with shadows for a bit on Miami-Dade, but even after merging the polygons in the provided shapefile, the smaller floating islands on the east coast did not look great with a shadow gradient applied, so it was removed. Feel free to peruse my final maps below!

Dade & Data Classification. Uses percentages for the elderly.

Dade & Data Classification Part II. Uses normalization by elderly population per square mile.



Comments

Post a Comment

Popular posts from this blog

GIS5050 Final Project: The Bobwhite-Manatee Transmission Line

-
Image
The time has come for the final project to be completed and the class done! It's a bit wild for me to think that much time has passed where my first GIS grad course is over. Throughout the entire final project, I had a lot of fun actually creating these maps -- maybe too much since I ended up losing sleep some days because I was so in a flow state, so into just constantly trying and tweaking ideas of how the basemap should be or how the insets should look, etc. I think the biggest takeaway I have from my first grad course in a long time is that I'm way too much of a perfectionist. I even chuckled at myself how frequently I wanted to redo the recordings of my final presentation just because I didn't like how I said something, stuttered, or so on. At this point, no amount of minor editing will change my grade that drastically -- it's not worth it to mull over to no end. Ultimately, I'm still very proud of the map deliverables I created and are maps I would be content ...
Read more

GIS5007 Module 3: Cartographic Design

-
Image
I'm back from vacation and was itching to start on my layout for this module, because this week's lab is on Cartographic Design. With Cartographic Design, we were heavily advised to keep Gestalt Design Principles in mind, including visual hierarchy, contrast, figure-ground, and visual balance. If one doesn't keep these principles at the core of map-making, then there's a high chance the message you're trying to send will be lost or misinterpreted. Ward 7's Public Schools. We focused on the elementary, middle, and high schools in Washington, D.C.'s Ward 7, with other environmental features, like parks, waterways, and roads, accentuating it. As you can see from the map above, we had to create a map of the public schools located in Ward 7. Due to the awkward shape of Ward 7's polygon/boundary, it was a testament to my confidence when placing my map elements. I moved my elements around so much, slightly tweaking them here and there, that I resorted to scribb...
Read more

GIS5007 Module 2: Typography

-
Image
I'm coming to the realization that I'm becoming more of a GIS/map nerd than I thought I'd ever be. I think a large part of it comes down to my perfectionism, to a fault; however, I also want to believe it's because I long for a creative outlet, especially when I'm stuck making a certain type of map at work. They're not bad maps by any means, but there's a certain formula and structure to them that doesn't allow for much experimentation, which speaking of experimentation... It was so nice to finally get my grubby GIS hands on more creative map-making again! This week's module focused on typography, which made us consider the importance of labels and how we stylize, set, and size them can affect the legibility of features. The lab walked us through the process of building a solid foundation for a map layout that focuses on the major features throughout Florida. These features were rivers, swamps/marshes, cities, and counties. One of my first assignment...
Read more