Module 6: Proportional Symbol and Bivariate Choropleth Mapping

Proportional Symbol Mapping with Positive and Negative Values

Proportional symbol mapping effectively displays positive and negative values by combining circle size for magnitude with color for direction (green for gains, red for losses). To implement this, negative values must be exported as a separate feature class, with a new field created using absolute values. The minimum and maximum size thresholds for both positive and negative symbols must be set to the same values to ensure an accurate visual comparison of magnitudes regardless of whether they represent gains or losses.

Bivariate Choropleth Mapping

The key steps for preparing data for bivariate choropleth mapping include:

1. Data Preparation:
  • Ensure your variables are normalized (in this case, both obesity and physical inactivity are already in percentages)
  • Create new fields in your attribute table to store the classifications (e.g., CLASS_OBESE, CLASS_ACTIVITY, and CLASS_FINAL)
  • Each variable needs to be classified separately before combining them

2. Classification Process:
  • Use a 3-class quantile classification for each variable independently
  • For the first variable (e.g., obesity):
    • Calculate class breaks that divide the data into three equal groups
    • Assign values (e.g., 1, 2, 3 or A, B, C) to represent low, medium, and high
3. Combining Classifications:
  • Create a final classification field that combines both individual classifications
  • Use a concatenation of both codes (e.g., A1, B2, C3)
This results in a 3x3 matrix with 9 possible combinations

4. Color Scheme Development:
  • Choose complementary colors for each variable
  • Create a systematic progression of colors that represents both variables
  • Ensure the color scheme effectively shows the relationship between variables
The result is a map that effectively shows the relationship between two variables simultaneously, as demonstrated in the obesity and physical activity map of the USA. This technique is particularly valuable for revealing spatial patterns and correlations between related variables better.



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