Computational and visual analyses of spatial interactions: a case study of the county-to-county migration in the US
Loading...
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of South Carolina
Abstract
Spatial interactions (SI), such as human daily movements, disease spread, and commodity
flows, are among the essential forces that drive many physical and socioeconomic
processes. Spatial interactions are very complex in nature. A normal SI data set often
contains three different data spaces: the geographic space, the graph/network space, and
the multivariate space.
The goal of this research is to address the underutilization and the
underrepresentation of SI data. Currently there is a lack of powerful exploratory analytic
methods that can deal with the complexity of spatial interactions, which often involve: (1)
multiple data spaces, (2) various spatial constraints, (3) many variables for locations and
interactions (flows), and (4) the large data size. It is unlikely that an individual method
alone can fully address these challenges.
This dissertation develops an integrated computational-visual approach to
examining SI data from different perspectives and synthesizing different perspective
views into a holistic understanding. The contribution of this research is two-fold. First, it
develops a graph partitioning method to discover spatially contiguous community
patterns (SI regions). Evaluations with benchmark data indicate that the developed
method is more effective and more computationally efficient than traditional methods.
Second, this research uses SI regions as a data aggregation strategy to summarize
massive spatial flows. It combines the three SI data spaces in data exploration and representation. SI regions, multivariate patterns, and geographic patterns
of SI flows are analyzed simultaneously in a novel and interactive visual analytic system.
A large inter-county migration data set of the U.S. is used to assess the developed
approach and implemented visual analytic system from an application perspective. The
data contains over 700,000 county-to-county migration flows (i.e., origin–destination
pairs). The results demonstrate that the SI regions obtained by analyzing the spatial
information and network connections can unveil real-world structures such as the strong
“core-suburban relationship” from a network perspective.
A focused study on income migration shows that the developed integrative
approach is able to synthesize the various data spaces, address the high-dimensions, and
cope with the large size of SI data. The combination of graph partition, multivariate
visualization, flow mapping, and interactive interfaces creates a flexible, comprehensive,
and efficient environment to explore SI data from different perspectives and obtain
holistic understandings. This reported approach facilitates new and comprehensive
analyses that existing research methodologies cannot support.
Description
Citation
Liao, K. (2011). Computational and visual analyses of spatial interactions: A case study of the county-to-county migration in the US. Retrieved from ProQuest Digital Dissertations. (AAT 3469151)