Note: ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights
4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
; Par F180472:608-613, 2022.
Article
in English
| Scopus | ID: covidwho-1950306
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. © 2022 ACM.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
Year:
2022
Document Type:
Article
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