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Recent years have seen tremendous advances in the scientific study of networks, as more and larger data sets of relationships among nodes have become available in many different fields. This has led to pathbreaking discoveries of near-universal network behavior over time, including the principle of preferential attachment and the emergence of scaling in complex networks. Missing from the set of network analysis methods to date is a measure that describes for each node how its relationship (or links) with other nodes changes from one period to the next. Conventional measures of network change for the most part show how the degrees of a node change; these are scalar comparisons. Our contribution is to use, for the first time, the cosine similarity to capture not just the change in degrees of a node but its relationship to other nodes. These are vector (or matrix)-based comparisons, rather than scalar, and we refer to them as “rewiring” coefficients. We apply this measure to three different networks over time to show the differences in the two types of measures. In general, bigger increases in our rewiring measure are associated with larger increases in network density, but this is not always the case. [ABSTRACT FROM AUTHOR]

Many urban areas around the world have expanded from monocentric to polycentric regions forming larger hierarchical structures. These regions represent functional regions or labour market areas (LMAs) that are classified as groups of counties representing mutually exclusive, non‐overlapping geographies. In practice, however, a county may belong to multiple overlapping LMAs. To model this overlapping across urban hierarchies, we extend the link community method from network theory and apply it to US commuting data. Our proposed approach reliably identifies the LMAs that we would expect to find as well as core and periphery areas within them. [ABSTRACT FROM AUTHOR]

A U. S. county workplace-to-workplace or latent migration data set is generated from overlapping migration and commuting networks. The latent migration network is the estimated number of movers between places of work, which is then compared with the actual number of migrants between places of residence. This allows both employment-related and amenity-related migration and pull/push factors as causes of migration flows to be identified and contrasted. Certain counties and cities that are not important migration destinations (e.g., with <200,000 net in-migrants between 1995 and 2000) according to official data are in fact important targets when additional in-migrants who commute into surrounding counties also are considered. An econometric analysis is then used to examine whether different regressors have different effects on the residence- versus employment-based migration patterns. This is a first assessment of whether or not the proposed approach has merit. Results are consistent with prior expectations regarding the factors that would motivate latent versus actual migration. [ABSTRACT FROM AUTHOR]

Using the fact that connections between vertices of a network often represent directed and weighted flows, we apply hydraulic principles to develop novel insights into network structure and growth. We develop a network model based on Bernoulli's principle and use it to analyze changes in network properties. Simulation results show that velocity of flow, resistance, fitness and existing connections in a system determine network connections of a vertex as well as overall network structure. We demonstrate how network structure is affected by changes in velocity and resistance, and how one vertex can monopolize connections within a network. Using Bernoulli's principle, we are able to independently reproduce key results in the network literature. [ABSTRACT FROM AUTHOR]

Labor market areas (LMAs) have long been a staple of regional and urban analysis. As commuting patterns have expanded over time, these areas have become larger and more complex, and the dichotomous designation of a county either belonging to an LMA or not may no longer be adequate. We apply recent advances in network science to conduct a more refined analysis of U.S. commuting patterns, and examine their effects on local economic growth. Results show that network degree and entropy measures explain variations in county per capita income growth patterns. Higher in- and out-commuting entropies are associated with lower per capita income growth, but their interaction enhances economic growth in places simultaneously open to both in- and out-commuters. Using these results, common ground may be found for creating new forms of regional governance that better reflect local realities of cross-county border flows of workers and economic activity. [ABSTRACT FROM AUTHOR]

Poor mental health is a concern in the US and world-wide. In this study we examine the effects of socioeconomic and environmental variables on the number of days of poor mental health reported across US counties. The results suggest that educational attainment, employment opportunities including self-employment, and social capital have important benefits in terms of community mental health. Other socio-demographic variables also tend to have predicted effects, as does the amount of sunshine in January, which is our control for Seasonal Affective Disorder. The general conclusion of the study is that living in a non-metro county and adjacent to a metro core, is associated with greater happiness. Mental health days also increase considerably due to natural disasters and they are affected by regional climate variability. For policymakers concerned about reducing the average number of poor mental health days across the nation, our results suggest that reducing poverty is a more powerful strategy than reducing income inequality. [ABSTRACT FROM AUTHOR]