Note: ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights
Authors: 
Rushabh Musthyala, Rudrajit Kargupta, Hritish Jain, Dipanjan ChakrabortyAuthors Info & Claims
Pages 608 - 613
Abstract
Policymakers often make decisions based on GDP, unemployment rate, industrial output, etc. The primary methods to obtain or estimate such information are resource-intensive. In order to make timely and well-informed decisions, it is imperative to come up with proxies for these parameters, which can be sampled quickly and efficiently, especially during disruptive events like the COVID-19 pandemic. We explore the use of remotely sensed data for this task. The data has become cheaper to collect than surveys and can be available in real-time. In this work, we present Regional GDP-NightLight (ReGNL), a neural network trained to predict GDP given the nightlights data and geographical coordinates. Taking the case of 50 US states, we find that ReGNL is disruption-agnostic and can predict the GDP for both normal years (2019) and years with a disruptive event (2020). ReGNL outperforms time-series ARIMA methods for prediction, even during the pandemic.
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Published In
June 2022
710 pages
ISBN:9781450393478
DOI:10.1145/3530190
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Published: 29 June 2022
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COMPASS '22: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies
June 29 - July 1, 2022
WA, Seattle, USA
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