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Dr. Yicheol Han
Korea Rural Economic Institute

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Research Keywords & Expertise

0 Rural Development
0 Rural Planning
0 Complex Network Analysis
0 Rural Development and Management
0 Complex Networks and Systems

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Journal article
Published: 01 February 2021 in Sustainability
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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.

ACS Style

Yicheol Han; Stephan Goetz; Claudia Schmidt. Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience. Sustainability 2021, 13, 1512 .

AMA Style

Yicheol Han, Stephan Goetz, Claudia Schmidt. Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience. Sustainability. 2021; 13 (3):1512.

Chicago/Turabian Style

Yicheol Han; Stephan Goetz; Claudia Schmidt. 2021. "Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience." Sustainability 13, no. 3: 1512.

Journal article
Published: 09 December 2019 in Research Policy
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We propose a measure of latent innovation in local economies based on spillovers among industries in terms of inter-industry sales and purchases as well as spatial proximity. The proposed measure captures innovation that is not reflected in typical NSF-based statistics such as patents, R&D spending, or science and engineering workers. When the results are mapped for the U.S., regions that one would expect to be highly innovative also show up as such. To determine whether this measure helps to explain economic growth beyond traditional factors such as human capital and agglomeration, and conventional measures of innovation, we estimate simple regression models with income and employment growth as dependent variables. The proposed innovation measure is statistically significant even after we control for rival causes of growth. We suggest that our measure is preferable to conventional innovation indicators for understanding where in the U.S. innovation, more broadly defined, is occurring.

ACS Style

Stephan J. Goetz; Yicheol Han. Latent innovation in local economies. Research Policy 2019, 49, 103909 .

AMA Style

Stephan J. Goetz, Yicheol Han. Latent innovation in local economies. Research Policy. 2019; 49 (2):103909.

Chicago/Turabian Style

Stephan J. Goetz; Yicheol Han. 2019. "Latent innovation in local economies." Research Policy 49, no. 2: 103909.

Research article
Published: 24 July 2019 in PLOS ONE
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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.

ACS Style

Yicheol Han; Stephan J. Goetz. Measuring network rewiring over time. PLOS ONE 2019, 14, e0220295 .

AMA Style

Yicheol Han, Stephan J. Goetz. Measuring network rewiring over time. PLOS ONE. 2019; 14 (7):e0220295.

Chicago/Turabian Style

Yicheol Han; Stephan J. Goetz. 2019. "Measuring network rewiring over time." PLOS ONE 14, no. 7: e0220295.