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Data Descriptor

An Environmental Data Collection for COVID-19 Pandemic Research

NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Dougherty Valley High School, San Ramon, CA 94582, USA
Albemarle High School, Charlottesville, VA 22901, USA
School of Geographical Sciences, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
Department of Geography, Dartmouth College, Hanover, NH 03755, USA
University Preparatory Academy, San Jose, CA 95125, USA
Author to whom correspondence should be addressed.
Received: 4 July 2020 / Revised: 24 July 2020 / Accepted: 30 July 2020 / Published: 3 August 2020
(This article belongs to the Special Issue Data-Driven Modelling of Infectious Diseases)
The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection. View Full-Text
Keywords: COVID-19; decision support; rapid response; environmental data; spatiotemporal platform COVID-19; decision support; rapid response; environmental data; spatiotemporal platform
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MDPI and ACS Style

Liu, Q.; Liu, W.; Sha, D.; Kumar, S.; Chang, E.; Arora, V.; Lan, H.; Li, Y.; Wang, Z.; Zhang, Y.; Zhang, Z.; Harris, J.T.; Chinala, S.; Yang, C. An Environmental Data Collection for COVID-19 Pandemic Research. Data 2020, 5, 68.

AMA Style

Liu Q, Liu W, Sha D, Kumar S, Chang E, Arora V, Lan H, Li Y, Wang Z, Zhang Y, Zhang Z, Harris JT, Chinala S, Yang C. An Environmental Data Collection for COVID-19 Pandemic Research. Data. 2020; 5(3):68.

Chicago/Turabian Style

Liu, Qian, Wei Liu, Dexuan Sha, Shubham Kumar, Emily Chang, Vishakh Arora, Hai Lan, Yun Li, Zifu Wang, Yadong Zhang, Zhiran Zhang, Jackson T. Harris, Srikar Chinala, and Chaowei Yang. 2020. "An Environmental Data Collection for COVID-19 Pandemic Research" Data 5, no. 3: 68.

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