You've loaded this page without map support, so map tools will not work. Open this page with map instead.
A bivariate map is a type of thematic map that simultaneously displays two different variables across the same geographic area, allowing users to identify relationships, patterns, and spatial correlations between the two datasets.
Basic
Introduction
Key Characteristics of a Bivariate Map:
Two Variables Represented Together: Each geographic feature is symbolized based on two separate data attributes.
Color or Symbol Combinations: Uses color blending, dual-symbol styles, or shading techniques to visualize both datasets simultaneously.
Reveals Spatial Relationships: Helps in understanding correlations or contrasts between different data types.
Explanation
Common Methods for Bivariate Mapping:
Bivariate Choropleth Map: Uses a color grid where two colors blend to represent the combination of two attributes (e.g., income level and education rate).
Bivariate Dot Density Map: Uses different colored dots to represent two different attributes within the same area.
Bivariate Symbol Map: Uses variations in symbol size, shape, or color to represent two variables (e.g., population size vs. median age).
Bivariate Hexbin Map: Uses hexagonal bins with dual coloring to show two spatial datasets in overlapping areas.
Examples
Example Use Cases:
Income vs. Education Levels: Identifies correlations between economic status and educational attainment.
Rainfall vs. Temperature: Helps in climate pattern analysis.
Population Density vs. Crime Rate: Shows how crime patterns relate to population concentration.
Forest Cover vs. Urbanization: Reveals the impact of urban expansion on green spaces.