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| 1 | Big Earth data analytics:a survey显示文摘Big Earth data are produced from satellite observations,Internet-ofThings,model simulations,and other sources.The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding,responding,and addressing challenges of Earth sciences and applications.In the past years,new technologies(such as cloud computing,big data and artificial intelligence)have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically intractable.This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain.We first introduce the concepts of big Earth data.The architecture,various functionalities,and supporting modules are then reviewed from a generic methodology aspect.Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools.The driven questions are exemplified through cutting-edge Earth science researches and applications.A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future. | Chaowei Yang Manzhu Yu Yun Li Fei Hu Yongyao Jiang Qian Liu Dexuan Sha Mengchao Xu Juan Gu | 2019 | Big Earth Data2019，3，2： | 3 |
| 2 | A spatiotemporal data collection of viral cases for COVID-19 rapid response显示文摘Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic.Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(http://gffzz9c54d31c51204187sx59kc6ww00ob6u9x.ffgz.tsg.suse.edu.cn/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(http://gffzz188fe103f8f1460asx59kc6ww00ob6u9x.ffgz.tsg.suse.edu.cn/stccenter/COVID-19-Data). | Dexuan Sha Yi Liu Qian Liu Yun Li Yifei Tian Fayez Beaini Cheng Zhong Tao Hu Zifu Wang Hai Lan You Zhou Zhiran Zhang Chaowei Yang | 2021 | Big Earth Data2021，5，1： | 2 |
| 3 | Taking the pulse of COVID-19:a spatiotemporal perspective显示文摘The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the U.S.The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and Brazil.In response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global citizens.This perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies. | Chaowei Yang Dexuan Sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding | 2020 | International Journal of Digital Earth2020，13，10： | 2 |
| 4 | The spatial dynamics of Ukraine air quality impacted by the war and pandemic显示文摘In recent years,our world has experienced significant disruptions due to the COviD-19 pandemic,and Russia's 2022 invasion of Ukraine,impacting human activities and the global environment.This paper explored air quality changes in Ukraine due to COVID-19,and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approach.During the cOVID-19 pandemic,strict quarantine policies in Ukraine led to a 2%reduction in tropospheric NO_(2) concentration before the lockdown and 4%during the lockdown period.Cities like Kyiv,Donetsk,and Dnipro exhibited reductions of 5%,11%,and 16%,respectively.Total SO_(2) column concentration decreased by 6%before the lockdown and 2.5%during the lockdown period,except in high population density areas.Kyiv showed the highest reduction of 17%in SO_(2) concentration,while Donetsk and Dnipro exhibited an 11%reduction.However,during the Russian invasion,there was a significant increase in tropospheric NO_(2) concentration in heavily destroyed Kharkiv while most eastern regions experienced a reduction.The total SO_(2) column was 48%higher before the war but reduced throughout the country after the war,except for in Kyiv and a few central regions.These findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios. | vizhi Qian Liu Theodore S.Trefonidesc Sina Hasheminassab Jennifer Smith Thomas Huang Kevin M.Marlis Joe T.Roberts Zifu Wang Dexuan Sha Ana Beatriz Moura Pereira Heramb Podar Jacob Cain Chaowei Yang | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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