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A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

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International Journal of Digital Earth 2020年第3期13卷 410-428页

作者： Fei Hu Chaowei Yang Yongyao Jiang Yun Li Weiwei Song Daniel Q.Duffy John L.Schnase Tsengdar Lee nsf spatiotemporal innovation center and dept.of geography and geoinformation sciences George Mason UniversityFairfaxVAUSA center for Open-Source Data and AI Technologies IBMSan FranciscoCAUSA Office of Computational and Information sciences and Technology NASA Goddard Space Flight CenterGreenbeltMDUSA NASA center for Climate Simulation Goddard Space Flight CenterGreenbeltMDUSA Earth Science Division NASA HeadquartersWashingtonDCUSA

Earth observations and model simulations are generating big multidimensional array-based raster ***,it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model,distributed physical data storage model,and the data pipeline in distributed computing *** efficiently process big geospatial data,this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System(HDFS)from the following aspects:(1)improve I/O efficiency by adopting the chunking data structure;(2)keep the workload balance and high data locality by building the global index(k-d tree);(3)enable Spark and HDFS to natively support geospatial raster data formats(e.g.,HDF4,NetCDF4,GeoTiff)by building the local index(hash table);(4)index the in-memory data to further improve geospatial data queries;(5)develop a data repartition strategy to tune the query parallelism while keeping high data *** above strategies are implemented by developing the customized RDDs,and evaluated by comparing the performance with that of Spark SQL and *** proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.

关键词： Big data hierarchical indexing multi-dimensional Apache Spark HDFS distributed computing GIS

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

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Big Earth Data 2021年第1期5卷 90-111页

作者： 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 nsf spatiotemporal innovation center George Mason UniversityFairfaxVAUSA Department of geography and geoinformation Science George Mason UniversityFairfaxVAUSA Department of Aerospace and Mechanical Engineering University of Notre DameNotre DameINUSA center for Geographic Analysis Harvard UniversityCambridgeMAUSA School of Resource and Environmental sciences Wuhan UniversityWuhanHubeiChina

Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating *** case data can be jointly analyzed with relevant factors for various applications in the context of the *** COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data *** 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 *** the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city *** 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 *** rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal *** data is available in Harvard Dataverse platform(https://***/***?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://***/stccenter/COVID-19-Data).

关键词： COVID-19 pandemic public health semi-automatic validation spatiotemporal data set

spatiotemporal event detection: a review

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International Journal of Digital Earth 2020年第12期13卷 1339-1365页

作者： Manzhu Yu Myra Bambacus Guido Cervone Keith Clarke Daniel Duffy Qunying Huang Jing Li Wenwen Li Zhenlong Li Qian Liu Bernd Resch Jingchao Yang Chaowei Yang Department of geography Pennsylvania State UniversityUniversity ParkPAUSA NASA Goddard Space Flight center NASA Goddard Space Flight CenterGreenbeltMDUSA Department of geography University of California-Santa BarbaraSanta BarbaraCAUSA Department of geography University of Wisconsin-MadisonMadisonWIUSA Department of geography and the Environment University of DenverDenverCOUSA School of Geographical sciences and Urban Planning Arizona State UniversityTempeAZUSA Department of geography University of South CarolinaColumbiaSCUSA nsf spatiotemporal innovation center&dept.of geography and geoinformation Science George Mason UniversityFairfaxVAUSA Department of Geoinformatics University of SalzburgKapitelgasseSalzburgAustria center for Geographic Analysis Harvard UniversityBostonMAUSA

The advancements of sensing technologies,including remote sensing,in situ sensing,social sensing,and health sensing,have tremendously improved our capability to observe and record natural and social phenomena,such as natural disasters,presidential elections,and infectious *** observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment,urban settings,health and disease propagation,business decisions,and crisis and *** event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant *** paper reviews the literature for different sensing capabilities,spatiotemporal event extraction methods,and categories of applications for the detected *** novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow(from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events)as an agenda for future event detection *** is presented on the current challenges to this research agenda,and future directions are discussed for conducting spatiotemporal event detection in the era of big data,advanced sensing,and artificial intelligence.

关键词： GeoAI geography and geoscience human dynamics digital earth computational challenges cloud computing internet of things

Taking the pulse of COVID-19:a spatiotemporal perspective

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International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： 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 nsf spatiotemporal innovation center George Mason Univ.FairfaxVAUSA Department of geography and geoinformation Science George Mason Univ.FairfaxVAUSA Department of Geographical sciences University of MarylandCollege ParkMDUSA center for Geographic Analysis Harvard UniversityCambridgeMAUSA geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

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 *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** 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 *** 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.

关键词： Big Data Earth system emergency geospatial sciences epidemics applications