Smoothing locational measures in spatial interaction networks

• Published in: Computers, environment and urban systems Vol. 41; pp. 12 - 25
• Main Authors: Koylu, Caglar, Guo, Diansheng
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-09-2013; Elsevier
• Subjects: Air transportation and traffic; Applied sciences; Attraction; Entropy; Exact sciences and technology; Ground, air and sea transportation, marine construction; Mathematical analysis; Migration; Network measure; Networks; Position (location); Smoothing; Spatial interaction; Spatial network; Transportation; United States; North America; America; Spatial interaction; Smoothing; Spatial network; Network measure; Sensitivity analysis; Methodology; Interaction; Migration; Spatial analysis; Modeling; Network; Air transportation; Comparative study
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2013.03.001

•A new approach to smoothing location measures in spatially embedded graphs.•It smoothes the graph around a location and then calculates its graph measure.•It helps discover true patterns that conventional smoothing approaches often miss.•It helps detect patterns in sparse graphs such as migration of a specific age group.•It is a generic framework that can be used to smooth various graph measures.
Spatial interactions such as migration and airline transportation naturally form a location-to-location network (graph) in which a node represents a location (or an area) and a link represents an interaction (flow) between two locations. Locational measures, such as net-flow, centrality, and entropy, are often derived to understand the structural characteristics and the roles of locations in spatial interaction networks. However, due to the small-area problem and the dramatic difference in location sizes (such as population), derived locational measures often exhibit spurious variations, which may conceal the underlying spatial and network structures. This paper introduces a new approach to smoothing locational measures in spatial interaction networks. Different from conventional spatial kernel methods, the new method first smoothes the flows to/from each neighborhood and then calculates its network measure with the smoothed flows. We use county-to-county migration data in the US to demonstrate and evaluate the new smoothing approach. With smoothed net migration rate and entropy measure for each county, we can discover natural regions of attraction (or depletion) and other structural characteristics that the original (unsmoothed) measures fail to reveal. Furthermore, with the new approach, one can also smooth spatial interactions within sub-populations (e.g., different age groups), which are often sparse and impossible to derive meaningful measures if not properly smoothed.