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A Visual Analytics System for Space–Time Dynamics of Regional Income Distributions Utilizing Animated Flow Maps and Rank‐based Markov Chains
Author(s) -
Rey Sergio,
Han Su Yeon,
Kang Wei,
Knaap Elijah,
Cortes Renan Xavier
Publication year - 2020
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12239
Subject(s) - visual analytics , computer science , visualization , analytics , rank (graph theory) , field (mathematics) , flow map , data visualization , data science , data mining , flow (mathematics) , mathematics , geometry , combinatorics , pure mathematics
Regional income convergence and divergence has been an active field of research for more than 20 years, and research papers in this field are still being produced at a prodigious rate. Despite their importance for the study of dynamics of income distribution, interactive visualization tools revealing spatiotemporal dimensions of the income data have been sparsely developed. This study introduces a visual analytics system for the space–time analysis of income dynamics. We use state‐level US income data from 1929 to 2009 to demonstrate the visual analytics system and its utility for exploring similar data. The system consists of two modules, visualization and analytics. The visualization module, a Web‐based front‐end called Rank‐Path Visualizer (RPV), draws inspiration from the cartographic technique of flow mapping, originally developed by Tobler and embodied in his canonical Flow Mapper application.