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We examine how county characteristics affected voting outcomes in the 2016 presidential election, including the populist component represented by the President Trump to Mitt Romney vote difference. Different voter groups could be harmed or helped by policies proposed during the campaign, such as withdrawal from trade agreements, environmental deregulation, and spending cuts. We investigate whether average county-level voting patterns reflected such proposals. Race and educational attainment had large statistical impacts, but economic factors and religious adherence also mattered. The Trump vote share and Trump-Romney vote margin were larger in counties with more farming and coal employment. [ABSTRACT FROM AUTHOR]

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]

Resilience is defined as a system's ability to initially resist and then recover from a shock. Here we apply this concept to examine the performance of U.S. counties during the Great Recession. The response of local economies to manmade and natural shocks is hypothesized to depend on the centrality of local industries within the economy, or how well connected they are to the other industries. We first calculate a centrality value for each industry using the national Input-Output accounts. We then 'step down' these values to the county level using industry employment data. We then test empirically whether local economies containing more centralized industries were more resilient, using a resilience measure that compares the local employment rebound and decline during the Great Recession. Our results suggest that measures of economic centrality adopted from the study of complex networks provide new insights when applied to the fields of regional science and spatial analysis, and economic growth more generally. [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]

The question of where entrepreneurs obtain information that inspires them to innovate has largely remained unexplored. In order to identify specific actors and organizations that entrepreneurs interact, we employ a novel approach using Twitter data from entrepreneurs participating in ‘One Million Cups’ in Kansas City, a weekly meet-up program where more than 100 entrepreneurs gathered to receive and provide feedback. Our approach allows us, first, to identify specific information sources at the level of entrepreneurs, companies, support programs, media, and key individuals. Second, we find that entrepreneurs relied more heavily on local than on national sources. Third, we find that early-stage entrepreneurs tended to follow more Twitter accounts from wider sources, while more advanced entrepreneurs had sources with specific entrepreneurship-focused organizations, programs, and individuals. The analysis of these specific and local sources can help the public sector identify types of organizations to engage and methods to intervene to promote local entrepreneurial activities. [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]

This article presents a spatial supply network model for estimating and visualizing spatial commodity flows that used data on firm location and employment, an input–output table of inter-industry transactions, and material balance-type equations. Building on earlier work, we proposed a general method for visualizing detailed supply chains across geographic space, applying the preferential attachment rule to gravity equations in the network context; we then provided illustrations for U.S. extractive, manufacturing, and service industries, also highlighting differences in rural–urban interdependencies across these sectors. The resulting visualizations may be helpful for better understanding supply chain geographies, as well as business interconnections and interdependencies, and to anticipate and potentially address vulnerabilities to different types of shocks. [ABSTRACT FROM AUTHOR]