Computational and visual analyses of spatial interactions: a case study of the county-to-county migration in the US

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.