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Many migration studies describe various counties by adopting a priori county typologies, such as the U.S. Department of Agriculture Economic Research Service county typology, which might not be suitable for identifying different age migration patterns of the U.S. counties. This study employs a spatial clustering method that exhaustively compares all U.S. counties on their age migration similarity and spatial proximity to investigate signature age-specific net migration profiles across six decades of U.S. county age-specific net migration data from 1950 to 2010. All of the six-decade data are integrated into a common spatial county boundary on which counties below a population threshold are merged with the nearest county to mitigate the small population problem in net migration rates. As counties are merged by increasing large population thresholds, the Getis-OrdGi* spatial autocorrelation statistic is applied to examine how the spatial migration patterns are affected. It is found that U.S. county age-specific net migration profiles exhibit four signature patterns. Although these patterns are persistent across the past six decades, their spatial distributions have experienced dramatic variation. The small population problem in net migration rates affects the extent and location of the significant spatial migration patterns. [ABSTRACT FROM AUTHOR]

Studies on small-world networks have received intensive interdisciplinary attention during the past several years. It is well-known among researchers that a small-world network is often characterized by high connectivity and clustering, but so far there exist few effective approaches to evaluate small-world properties, especially for spatial networks. This paper proposes a method to examine the small-world properties of spatial networks from the perspective of network autocorrelation. Two network autocorrelation statistics, Moran’s I and Getis–Ord’s G, are used to monitor the structural properties of networks in a process of “rewiring” networks from a regular to a random network. We discovered that Moran’s I and Getis–Ord’s G tend to converge and have relatively low values when properties of small-world networks emerge. Three transportation networks at the national, metropolitan, and intra-city levels are analyzed using this approach. It is found that spatial networks at these three scales possess small-world properties when the correlation lag distances reach certain thresholds, implying that the manifestation of small-world phenomena result from the interplay between the network structure and the dynamics taking place on the network. [ABSTRACT FROM AUTHOR]