Visualizing Spatial Economic Supply Chains to Enhance Sustainability and Resilience

Yicheol Han,Stephan J Goetz,Claudia Schmidt

doi:10.3390/su13031512

Yicheol Han, Stephan J Goetz + Show 1 more

Open Access

https://doi.org/10.3390/su13031512

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Abstract

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.

Highlights

Like no other recent crisis, the coronavirus disease 2019 (COVID-19) pandemic has raised public awareness of both the importance and vulnerability of national and global supply chains, including those for food supplies, e.g., [1,2]
Knowing where different food processors are located relative to COVID-19 hotspots, an earthquake zone, or a tornado belt could be helpful for targeting prevention resources, medical personnel, or even considering lockdowns and similar
Each local economy consists of a network of firms within their respective industries, forming a local IO network in which local industries represent nodes and transactions among the industries measured in dollars represent edges

Summary

Introduction

Like no other recent crisis, the coronavirus disease 2019 (COVID-19) pandemic has raised public awareness of both the importance and vulnerability of national and global supply chains, including those for food supplies, e.g., [1,2]. While rudimentary spatial supply chains could be visualized by mapping where different establishments are located using public information, such industry location maps would not reveal flows of inputs and outputs between firms. We used a systems approach in this study to demonstrate how secondary public data can be used under a limited set of assumptions to construct, model, and visualize spatial supply chains for all businesses engaged in an industrial sector. Knowing where different food processors are located relative to COVID-19 hotspots, an earthquake zone, or a tornado belt could be helpful for targeting prevention resources, medical personnel, or even considering lockdowns and similar

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