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Related Experiment Video

Updated: May 13, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Published on: July 1, 2014

Bicomponent Trend Maps: A Multivariate Approach to Visualizing Geographic Time Series.

Jonathan P Schroeder¹

¹Minnesota Population Center, University of Minnesota, 225 19 Avenue South, Minneapolis, MN 55455.

Cartography and Geographic Information Science

|March 19, 2013

Summary

Bicomponent trend mapping visualizes long-term population trends using principal component analysis and bivariate mapping. This method enhances interpretation of spatial-temporal patterns in urban areas.

Keywords:

Spatio-temporal visualization bivariate mapping census mapping temporal mapping

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Area of Science:

Geographic Information Science
Spatial Analysis
Data Visualization

Background:

Traditional temporal mapping methods struggle to represent complex spatio-temporal patterns across diverse regions and timeframes.
Effective visualization of long-term trend variations is crucial for understanding demographic shifts.

Purpose of the Study:

Introduce bicomponent trend mapping as an alternative approach for illustrating spatio-temporal patterns.
Demonstrate the utility of bicomponent trend mapping for analyzing population trends in U.S. urban cores from 1950 to 2000.

Main Methods:

Employ principal component analysis (PCA) to identify key dimensions of trend variations.
Utilize bivariate choropleth mapping to visualize two distinct dimensions of long-term trends.
Develop a bicomponent trend matrix for interpreting trend types and visualizing principal components.

Main Results:

Bicomponent trend mapping effectively illustrates two dimensions of long-term trend variations.
The bicomponent trend matrix serves as a legend and visualization tool for principal components.
Application to U.S. urban population trends reveals interpretable relationships among trend classes.

Conclusions:

Bicomponent trend mapping offers a novel method for visualizing spatio-temporal data, particularly for demographic trends.
While not depicting as wide a variety of properties as other multivariate methods in static displays, it enhances interpretability of trend relationships.
The approach provides unique classification flexibility, beneficial for interactive data exploration environments.