Skip to main content

Different Map Classification Methods

 This week we learned about the different map data classification methods: equal intervals, quantile, standard deviation, and natural breaks. Utilizing these different methods, I analyzed how these methods highlight and possible skew different aspects of data while also comparing these same methods to normalized data tracts by area. 

1. Equal Interval: In this classification method, you divide your max value by your desired number of categories. When analyzing our data, this method causes a huge skew in the data where most all of the data landed in the first category.

2. Quantile: This method looks to create an equal number of observations within the number of desired categories. This method helped to display the data in the most diverse way possible.

3. Standard Deviation: This method uses the bell curve and standard deviation to create 5 categories with +/- .5, 1.5, >/< 1.5 standard deviation from the mean. This method helps to visually see how the data various from the mean in one direction or the other. This method demonstrated that there was a pretty good bell curve with not much data skewed either way.

4. Natural Break: this method seeks to group together like values to accentuate the inter-class variance. This method closely represented the Quantile method as well, but this method did not have as much observations in the upper range, with most of the data in the lower two ranges. This showed that most of the population fell beneath the 12.9% category.

"Which classification method do you think best displays the data for an audience looking to target the senior citizen population? Explain why."

I would recommend the Natural Breaks method, as this shows the biggest natural differences/jumps between percentage groups. This will help them to accurately pinpoint areas with high levels of seniors.

Figure 1: Senior Population Distribution of Seniors in Miami Dade County, Florida by percent of age 65+. 

Figure 2: Senior Population Distribution of Seniors in Miami Dade County, Florida by normalized area tracts, per sq mile. 

"If you were presenting data to the Miami Dade County Commissioners regarding the distribution of senior citizens, which data presentation do you think more accurately depicts the distribution?—the percent above 65 or the population count normalized by area?"

I would recommend the normalized data by area, because without actively comparing the percentage to the size it covers, one makes a decision without being fully informed. For example, 1% of 10 sq miles is a much greater number than 5% of 1000 sq miles. If you were looking at just the percentages in the percent above 65 models, you would think 5% is greater. 


Labels:

Comments

Post a Comment

Popular posts from this blog

Positional Accuracy: NSSDA

 In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures. The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection.  Map 1: City of Albuquerque city map data.  Map 2: City of Alburquerque SteetMap data When selecting a random intersection to place the points within each quadrant, I choose a location that had data f...
Post a Comment
Read more

Utilizing ERDAS Imagine to Analyze Map Features

 This week we learned how to utilize histograms and different bands to highlight different features in a map. On the following map that we worked on, dark bodies of water caused high peaks on the left of histograms while snow-peaked mountains were small blips on the far right. These simple distinctions help to quickly identify map features on a graph, that you can then utilize as a stepping stone to finding them on the image. I found it incredibly interesting how the different band layers highlighted different features on the map. Figure 1 below depicts three different features we found on the image.  Figure 1: Distinct features found on an image using ERDAS Imagine. Feature 1: Large body of water. Feature 2: Snow-capped mountains transitioning to thick vegetation. Feature 3: Shallow turbulent body of water near urbanized land, transitioning to deep calm body of water. 
Post a Comment
Read more

Choropleth and Dot Mapping

 This week we explored choropleth and dot mapping. Choropleth is a thematic form of mapping that focuses on color units, whose color intensity is proportional to its corresponding data value. Dot mapping is also thematic. It uses either a proportional or graduated thematic symbol (like a circle), whose size increases due to its data value. Using ArcGIS pro, I analyzed the population densities of countries in Europe (person per square kilometer), as well as their wine consumption (liters per capita) to determine if there was a correlation between the two. In my choropleth map, I decided to use a natural breaks classification. I chose not to use Equal Interval because only 2 classes (with slight 3 rd class) were represented in the map, and it looked like almost just one color in the lower range. The standard deviation classification appeared to be more diverse at first glance but was actually skewed to the top ranges. I was then between Quantile and Natural Breaks. While both t...
Post a Comment
Read more