Paper
8 March 1996 Exploratory visualization of migration flows
Lin Liu
Author Affiliations +
Proceedings Volume 2656, Visual Data Exploration and Analysis III; (1996) https://doi.org/10.1117/12.234665
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
Regional difference of the society creates movement of people across regions. Conventionally, an interregional migration data set is stored as an origin-destination (O-D) matrix, which has one row for each flow origin and one column for each destination. For example, interregional migration data within fifty states of United States generates a 50 by 50 O-D matrix, and an O- D matrix for county to county migration would have more than 3100 rows and columns. Studying these complex migration flow systems has always been challenging to scientists. This paper reports a methodology that combines scientific visualization, exploratory data analysis, dynamic graphics and projection pursuit methods to explore these migration flow systems. First, a complex migration flow data set is simplified by using a projection pursuit method. These less complex data are then represented in four graphic views: a migration flow view showing direction and magnitude of migration, a choropleth view showing characteristics of O-D regions, a statistical view of flow variables, and a statistical view of O-D attribute variables. These four views are linked by using a dynamic brushing technique, which enables the researcher to explore the relationships between the four views. These relationships can then be used as the basis for understanding the migration flow system.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lin Liu "Exploratory visualization of migration flows", Proc. SPIE 2656, Visual Data Exploration and Analysis III, (8 March 1996); https://doi.org/10.1117/12.234665
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KEYWORDS
Visualization

Matrices

Data analysis

Prototyping

Scientific visualization

Factor analysis

Principal component analysis

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