TY - JOUR
T1 - Visualizing spatial economic supply chains to enhance sustainability and resilience
AU - Han, Yicheol
AU - Goetz, Stephan J.
AU - Schmidt, Claudia
N1 - Funding Information:
Funding: This research was funded in part by the United States Department of Agriculture, National Institute of Food and Agriculture, under grant no. 2017-51150-27125, and the Pennsylvania State University, Agricultural Experiment Station.
Funding Information:
This research was funded in part by the United States Department of Agriculture, National Institute of Food and Agriculture, under grant no. 2017-51150-27125, and the Pennsylvania State University, Agricultural Experiment Station. Earlier versions of this article were presented at the Western Region Science Association annual conference, Napa Valley, CA, USA, 11 February 2019; the 59th Congress of the European Regional Science Association, Lyon, France, 28 August 2019; the U.S. Economic Development Administration?Indiana University Project Meeting, Bloomington, IN, USA, 26 April 2018; the International Geography Union Mini-Conference on Rural?Urban Linkages for Sustainable Development, Innsbruck, Austria, 19 July 2018; the Agricultural and Applied Economics Association Meeting, Washington, D.C., USA, 7 August 2018. We thank our discussants and attendees at these various workshops for their valuable comments and suggestions, in addition to three anonymous journal reviewers.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - 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 prefer-ential 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.
AB - 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 prefer-ential 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.
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U2 - 10.3390/su13031512
DO - 10.3390/su13031512
M3 - Article
AN - SCOPUS:85100470259
VL - 13
SP - 1
EP - 15
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
SN - 2071-1050
IS - 3
M1 - 1512
ER -