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Big Earth Data Volume 5, 2021 - Issue 1: Analytics of Big Geosocial Media and Crowdsourced Data
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Data Article

A spatiotemporal data collection of viral cases for COVID-19 rapid response

Dexuan Shaa NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA;b Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USAORCID Iconhttps://orcid.org/0000-0001-6161-6050View further author information
ORCID Icon,
Yi Liuc Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, USAView further author information
,
Qian Liua NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA;b Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USAORCID Iconhttps://orcid.org/0000-0003-3876-4877View further author information
ORCID Icon,
Yun Lia NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA;b Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USAView further author information
,
Yifei Tiana NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
,
Fayez Beainia NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
,
Cheng Zhonga NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
,
Tao Hud Center for Geographic Analysis, Harvard University, Cambridge, MA, USAView further author information
,
Zifu Wanga NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA;b Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USAView further author information
,
Hai Lana NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
,
You Zhoua NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USAView further author information
,
Zhiran Zhange School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, ChinaView further author information
&
Chaowei Yanga NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA;b Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-7768-4066View further author information
ORCID Icon show all
Pages 90-111 | Received 24 Aug 2020, Accepted 28 Oct 2020, Published online: 30 Nov 2020

Figures & data

Table 1. Access and validation data source by collection scale and region

Table 2. Data source for base maps

Figure 1. Spatiotemporal data cube as a data collection framework

Figure 1. Spatiotemporal data cube as a data collection framework

Figure 2. Data processing workflow

Figure 2. Data processing workflow

Figure 3. Data standardization process

Figure 3. Data standardization process

Figure 4. Unified Modeling Language (UML) of the designed database schema

Figure 4. Unified Modeling Language (UML) of the designed database schema

Figure 5. Conceptual data form of COVID-19 case product

Figure 5. Conceptual data form of COVID-19 case product

Table 3. Metadata of daily report

Table 4. Metadata of summary report

Figure 6. Crowdsourcing-based data validation diagram

Figure 6. Crowdsourcing-based data validation diagram

Figure 7. Evolution of active cases in admin1 regions of Italy from Feb. 24 to Sep. 26

Figure 7. Evolution of active cases in admin1 regions of Italy from Feb. 24 to Sep. 26
Supplemental material

Supplemental Material

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Data availability statement

The data cube described in this article is openly available on GitHub at https://github.com/stccenter/COVID-19-Data and Harvard Dataverse at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN under CC-BY license.

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