Bivariate Choropleth Maps: A Comprehensive Guide

What is a Bivariate Map?

A bivariate map is a powerful tool used to visualize data organized within a matrix structure. This type of map allows users to compare two different attributes, visualize their interactions, and analyze how they relate on a spatial basis.

Types of Bivariate Maps

Bivariate maps come in a variety of formats tailored for different applications:

1. Priority: Useful for task management, these maps prioritize tasks by importance, helping teams streamline workflows and focus on high-impact tasks first.

2. Cause-Effect: These are designed to map out causes and effects for specific issues, identifying potential root causes and their impacts.

3. SWOT Analysis: This bivariate evaluates the strengths, weaknesses, opportunities, and threats of a project or organization, offering strategic insights and helping in decision-making.

4. Risk Management: Ideal for identifying and mitigating risks, these maps help to create strategy and plan for potential obstacles, minimizing risk impact.

When to Use a Bivariate Map

Bivariate maps are especially useful when comparing two opposing or correlated characteristics, such as population demographics (e.g., Republicans vs. Democrats) or attributes of new buildings, like apartment counts versus population density. 

For example, a bivariate map could represent the ratio of Republicans and Democrats across states, with different shades indicating varying percentages of each group. 

Alternatively, a bivariate map could compare the number of apartments in new buildings against population density, giving spatial insights into housing needs.

How Bivariate Maps Work in GISCARTA

In GISCARTA, bivariate map styling is available for all geometry types, with primary applications on polygonal data. This styling method involves selecting two numeric attributes, each of which can be divided into three intervals, creating a 3x3 bivariate with a blend of nine colors. Each color represents a unique combination of values, allowing easy visualization of the interaction between the two selected attributes.

Advantages of Using a Bivariate Map

Bivariate map styling highlights the relationship between two indicators, helping to visualize dependencies and trends directly on the map. This approach enhances data analysis, as it reveals connections between different elements and enables pattern identification, ultimately leading to more informed decisions.

How to Use the Bivariate Style in GISCARTA

Step 1: Access Layer Settings
To begin, open the layer properties by clicking on the three dots next to the layer name.

Step 2: Select the Bivariate Map Style
Within the layer settings, navigate to the Styling tab and choose Bivariate Map.

Step 3: Configure the Bivariate Map

Three sections are available for customization:

1. Attributes

• Select Fields for X and Y Axes: Choose the fields from the attribute table that you want displayed along the X and Y axes.
  
  

  

• Highlighting Intervals: Select a method to classify your data within each attribute:
  
  

  

Equal Intervals: This grouping method divides the entire data range into segments of equal size. It’s best suited for data with a fairly consistent distribution across a limited range, as it ensures each interval spans an equal portion of the value range.

Equal Quantity (Quantile): Quantile grouping divides data into equal-sized groups by percentage. This method is ideal for datasets with uneven distribution, as it creates groups with similar characteristics by ensuring an equal number of data points per interval. Care should be taken with group size and count, however, to prevent misleading interpretations.

Natural Breaks (according to Jenkins): This method identifies optimal boundaries for data grouping based on the natural distribution patterns within the dataset. Classes are set where large differences exist between values, making this technique especially effective for revealing clusters and outliers in the data.

Each grouping method has its own strengths, so select one based on your data’s distribution and the goals of your analysis.

2. Labels

Customize the names displayed on the X and Y axes. By default, these labels are the names of the chosen fields, but they can be modified as needed for clarity.

3. Borders

Select two contrasting colors to represent the two extreme values of your bivariate, located at the top left and bottom right of the bivariate grid. Choosing opposite colors enhances readability and allows for quick data interpretation.

Key Takeaways

Bivariate maps provide a unique way to represent two related attributes on a single map layer, showing how one variable correlates with another. In GISCARTA, bivariate map styling allows users to display complex relationships between data points, adding depth and dimension to project visuals. This feature not only enriches the analytical value of maps but also brings a professional layer of detail to spatial data presentations. With bivariate maps, GISCARTA users can make data-rich maps that are visually compelling and insightful.