M4: Data Classification

 Mapping the Ages: Understanding Miami Dade's Senior Demographics

Introduction & Overview

In this week's lab assignment, we dove into the demographics of Miami-Dade County, focusing on the senior citizen population. The objective of the assignment is to apply and analyze four different data classification methods using ArcGIS Pro to visualize and understand the distribution of the population over 65 in Miami Dade County based on the Census data.

Classification Methods Explored

The exploration involved four distinct classification methods:
  • Equal Interval: Dividing data into equal ranges highlighted uniform data distribution, but it potentially masked the subtleties of clustered populations.
  • Quantile: By distributing an equal number of tracts into each class, this method showcased how tracts compared to each other but at the cost of concealing the true range of values.
  • Standard Deviation: Focusing on deviations from the mean, this method emphasized areas with unusual concentrations of senior citizens, yet it could overlook the actual number of individuals affected.
  • Natural Breaks (Jenks): This method was particularly insightful as it identified natural groupings in the data, providing a more intuitive understanding of demographic clusters.
Each method offered unique insights, with Natural Breaks standing out for its ability to articulate visually the senior population's inherent clustering.

Percentage vs. Normalized by Area Maps


Map using Age Data Classified by Percentage: This map would represent the proportion of the population over 65 in each census tract as a percentage of the total population in that tract. It provides a relative measure, showing areas with a higher or lower concentration of seniors relative to their local populations.

Map using Age Data Normalized by Area: This map would depict the count of seniors but adjusted (normalized) by the area of each census tract (square mile), effectively showing the density of the senior population. It reflects the number of seniors per square mile, revealing areas with higher or lower senior population density.






Comments

Post a Comment

Popular posts from this blog

M1 - Python Environments & Flowcharts

Image
  Python Module 1 Recap: From Scripts to Flowcharts and Zen Principles Converting 3 Radians to Degrees To convert 3 radians to degrees, we use a simple formula: degrees = radians * 180 / pi Below is the flowchart illustrating the conversion process: Running the calculations in IDLE gives us:  ≈ 171.887 Interpretation of the Zen of Python The "Zen of Python" showcases the philosophy and guiding principles of Python programming. It emphasizes simplicity, readability, and clarity in code. In other words, “Zen of Python” promotes straightforward and elegant solutions over complicated ones. By valuing explicitness over implicitness, the basis of Python encourages clear and understandable code. Moreover, The principles highlight the importance of practicality and avoiding unnecessary complexity— why use 4 lines of code when 1 will do the same thing? This mentality helps to balance between purity and practicality. Overall, the “Zen of Python” advocates for writing code that is easy
Read more

GIS Map Projections

Image
  This week's lab exercise looked at the importance of map projections in GIS, especially when using multiple datasets. The map above shows four counties in Florida that are displayed using the Albers Conical Equal Area, NAD 1983 UTM Zone 16N, and NAD 1983 HARN State Plane Florida North FIPS 0903 (US Feet) map projections. As the table within the map shows, the areas of the counties vary depending on the map projections. The best map projections are ones that shows the least distortion between map features. For example, The UTM projection is best when used the map features fit within one UTM zone, which is not the case for showing the entire state of Florida. The same can be said for the Florida State Plane that is dependent on the region of interest. Therefore, the map projection would need to be Albers since it minimizes distortion between its standard parallels.  Overall, this lab illustrates how important it is to use accurate map projections since it can cause distortions betw
Read more

ArcGIS Pro Orientation

Image
  Module 1 Lab Assignment This first lab assignment's objective is to prepare and show what is expected for future lab assignments. The primary learning outcomes involved: Getting familiar with accessing the ArcGIS software through Argo Apps. Assessing data storage drives. Preparing project folders for each project. The map shown above is a visualization of the Cities and World Countries data files found in the OverviewArcGIS file in the R drive. In order to view the shapefiles in a way that is informative, we had to add a Folder Connection to the data we downloaded as well as update the symbology to show graduated colors for the field POP_2007 with Natural Breaks and 7 classes. It is important to use a single color from light to dark when using sequential data. While I wish this had been a smooth and uncomplicated process, I struggled with learning UWF's Argo Apps since that is how we access ArcGIS Pro without downloading it to our personal computers. While I am familiar with
Read more