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| 1 | 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 |
| 2 | A comparison and evaluation of map construction algorithms using vehicle tracking data显示文摘 | ANMED M KARAGIORGOU S PFOSER D | 2015 | GeoInformatica2015，19，3： | 1 |
| 3 | Incremental join of time-oriented data显示文摘 | Christian S Jensen | 1999 | Scientific and Statistical Database Management1999，，： | 1 |
| 4 | Indexing the trajectories of moving objects 显示文摘 | Pfoser D | 2000 | IEEE Data Engineering Bulletin2000，25，2： | 1 |
| 5 | Trajectory Indexing Using Movement Constraint显示文摘 | PFOSER D JENSEN C S | 2005 | Geoinformatica2005，9，2： | 1 |
| 6 | Indexing the trajectories of moving Objects 显示文摘 | Dieter Pfoser | 2002 | IEEE Data Engineering Bulletin2002，25，2： | 1 |
| 7 | Indexing the trajectories of moving objects显示文摘 | Pfoser D | 2002 | IEEE Data Engineering Bulleetin2002，25，2： | 1 |
| 8 | Indexing the Trajectories of Moving Objects显示文摘 | Pfoser D | 2002 | IEEE Data Engineering Bulletin2002，25，2： | 1 |
| 9 | Generating semantics-based trajectories of moving objects显示文摘 | Pfoser D Theodoridis Y | 2003 | Computers Environment and Urban Systems2003，27，3： | 1 |
| 10 | A vocabulary recommendation method for spatiotemporal data discovery based on Bayesian network and ontologies显示文摘In the research field of spatiotemporal data discovery,how to utilize the semantic characteristics of spatiotemporal datasets is an important topic.This paper presented a content-based recommendation method,and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery.The source data of this research was from the MUDROD(Mining and Utilizing Dataset Relevancy from Oceanographic Datasets)search platform.From the historical search log,major keywords were extracted and organized according to ontologies in a hierarchical structure.Using the search history,the posterior probability between each subclass and their super class in the ontologies was calculated,indicating a recommendation likelihood.We created a Bayesian network model for inference based on ontologies.This model can address the following two objectives:(1)Given one class in the ontology,the model can judge which class has the biggest likelihood to be selected for recommendation.(2)Based on the search history of a user,the Bayesian network model can judge which class has the biggest probability to be recommended.Comparison experimentation with existing system and evaluation experimentation with expert knowledge show that this method is specifically helpful for spatiotemporal data discovery. | Kejin Cui Yongyao Jiang Yun Li Dieter Pfoser | 2019 | Big Earth Data2019，3，3： | 0 |
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