Spatiotemporal Research Agenda: A Living Spatiotemporal I\/UCRC Whitepaper<\/p>\n\n\n\n
We live in a four dimensional world with 3D in space and 1D in time. Studying and Understanding the four dimensions integratively could help us better address many local to global challenges and better prepare our future generations to build a more sustainable living environment on our home planet. The NSF Spatiotemporal I\/UCRC is envisioned to advance such studies in three aspects: 1) improve human intelligence by developing a set of spatiotemporal thinking methodologies built into K-16 curriculum, 2) advance computer science with new spatiotemporal data structure, algorithms, software and tools, 3) improve human capabilities in responding to grand scientific and engineering challenges. This research agenda evolves with relevant advancements to implement the vision and includes the theoretical, technical, infrastructural, applicational, and educational aspects .<\/p>\n\n\n\n
The theoretical aspects will include research on space, time, spatiotemporal integration, their formal description and linkage to other domains and potential information theory adv
ancement<\/p>\n\n\n\n
1) Space: Formulate a theoretical framework for 2D, 2.5D and 3D data models, data structures\/algorithms, relations and linkage to existing knowledge from (Hagerstrand, Christaller, Von Thunen, Schaefer, Bunge, Tobler, Miller, etc)
2) Time:<\/p>\n\n\n\n
a) Represent continuous time using discrete time snapshots and transform between them using mathematical methods
b) Represent events using snapshot (creation, duration, deletion, etc)
c) Time analyses: Markov processes (in RS, land change modeling), Power series, Fourier analysis, a trace of track of data objects, etc.<\/p>\n\n\n\n
3) Spatiotemporal:<\/p>\n\n\n\n
a) The unique aspects of integrated space and time, and how that would change our matured methods, techs, theories
b) Spatiotemporal thinking\/pattern projecting to computing strategies
c) Scale Problem (Clarke and Irmischer 2016): How do we deal with different space-time scales? What are the implications for MAUP (modifiable area unit problem or modifiable volume unit problem) of space-time, and tying these various units to real world use-cases (like sports and weather modeling), could we standardize the scales\/resolutions\/variable extent?
d) Spatiotemporal objects: What are they? How to model them? What is the ontology of space-time? Are they continuous or not? Do we move from studying object to processes?
e) Spatiotemporal processes: wave propagations, rainfall, heat waves, diffusion, aggregation, erosion, creation, merging, splitting that can be modeled, e.g. with CA?
f) Ontology: should we have its own knowledge or the ones in GIScience & other domains would be sufficient?
g) Formal representation: how to integrate space and time using mathematical equations so the previously mentioned aspects can be formalized and used for formal reasoning.<\/p>\n\n\n\n
4) Relationship to other domains\/fields (overlap, complements, shared challenges\/opportunities)<\/p>\n\n\n\n
a) Can ethical, legal, privacy issues in other domains (bio-medical \/ HIPAA) be used to inform ST Research where loss geo-privacy is a concern.
b) Can mathematics, physics, information science inform our treatment of spatiotemporal data (moving from discrete representations of continuous phenomena to mathematical representations)
c) what unique contributions we can make for other domains, geography, giscience, earth science, space time integration?<\/p>\n\n\n\n
5) Are there extensions to existing information theory approaches (Lippitt, Stow and Clarke 2014)<\/p>\n\n\n\n
The technical dimension includes many aspects, such as visualization, analytics, data mining, learning, modeling, simulation, and geocomputation.<\/p>\n\n\n\n
\u25cf Form and convert a formal spatiotemporal description to computable equations and models
\u25cf Spatiotemporal statistics and analyses, e.g.,<\/p>\n\n\n\n
\u25cb Time series analysis
\u25cb Spatiotemporal interpolation? linear? Data scale\/limitation\/error?
\u25cb Validating simulation and prediction with spatiotemporal measurements based on point and satellite
\u25cb Scale and discretization\/interpolation<\/p>\n\n\n\n
\u25cf Simulation and forecasting:<\/p>\n\n\n\n
\u25cb Process modeling, What are the space-time dynamics that we can represent with simulation? What data is required to support these simulations?
\u25cb Modeling change & dynamics, uncertainty
\u25cb CA, Markov, ABM.<\/p>\n\n\n\n
\u25cf Data and model fusion\/integration<\/p>\n\n\n\n
\u25cb Multiple scale spatiotemporal data fusion, mining in real time
\u25cb Interoperating and integrating models
\u25cb Combine data\/decision, high dimensional, unified spatiotemporal datum, data quality, uncertainty\/error propagation<\/p>\n\n\n\n
\u25cf Spatiotemporal AI<\/p>\n\n\n\n
\u25cb Would AI be different or easily adopted for spatiotemporal context
\u25cb How to consider spatiotemporal autocorrelation and heterogeneity in AI (machine learning\/deep learning models, machine intelligence etc.).
\u25cb Do spatial lag factors and local methods work for ML? (c.f. Kanevski etc all, CNN-LSTM)
\u25cb How to deal with lack of data problem in spatiotemporal AI\/ML studies? how could industry giants, e.g., facebook, amazon, help?
\u25cb How do we using spatiotemporal methodologies for inference or prediction?<\/p>\n\n\n\n
\u25cf Visualization techniques & tools<\/p>\n\n\n\n
\u25cb Broadly accessible\/distribution\/sharing spatiotemporal simulation & visualization (2D\/3D\/4D\/5D or virtual reality): open source\/crowdsourcing
\u25cb Visualization for public communication (to policy makers, other scientist, yourself) in space-time?
\u25cb Could spatiotemporal visualization help transform science research into policy making?
\u25cb Story telling using spatiotemporal data for communication<\/p>\n\n\n\n
The infrastructure support spatiotemporal research and adoption should be based on computing, data, community and knowledge<\/p>\n\n\n\n
\u25cf Hardware infrastructure (GPU, MIC, FPGA, Cluster, Cloud, Fog, Quantum Computing, etc.)
\u25cf Software Software\/Tools and solutions
\u25cf (Big) spatiotemporal data (sources, collections, standards, fusion, mining, etc.)<\/p>\n\n\n\n
\u25cb Network of data sources, IoT
\u25cb Data infrastructure, sharing, standards, fusion, mining
\u25cb Historical long-tail data treatment
\u25cb Broadly accessible spatiotemporal simulation & visualization (2D\/3D\/4D\/5D or virtual reality)
\u25cb Geoprivacy issues, how to anonymize and mask data, re. bio\/medical domains and social science
\u25cb Bridging government and industry spatiotemporal data in openness, authoritativeness, integrity and uncertainty
\u25cb PII, plagiarism detection of data, information, and knowledge<\/p>\n\n\n\n
\u25cf A framework includes infrastructures of storage, data, models, computing, functional tools, standards in an integrative fashion, e.g., Al Gore\u2019s original Digital Earth concept.
\u25cf How to integrate existing infrastructural components for implementing such a framework so we can leverage one for all in a shared fashion?<\/p>\n\n\n\n
Spatiotemporal applications is key to many science and engineering aspects, such as civil, industrial, environmental, social & behavioral, history and archaeology<\/p>\n\n\n\n
\u25cf Natural Resource and Disaster: Land use model, other natural phenomena or disasters, public health problems, urbanization, etc.
\u25cf Physical phenomena prediction using big spatiotemporal data
\u25cf Spatiotemporal computing in other domains (RS, earth science, climate change, land change\/ urbanization, tools, etc.)
\u25cf Social Sciences<\/p>\n\n\n\n
\u25cb Ways to incorporate human\u2019s behaviors\/movement
\u25cb Analysis of trajectory bundles from IoT \/ digital exhaust data
\u25cb Use of cell phone\/mobile positioning data for profiling ST Research
\u25cb Human movement \u2013 migration, employment, journey to work, recreation, etc. Specialist actions e.g., journey to crime; model human behavior
\u25cb anonymization \u2013 privacy, ethics, legal, durable anonymization that leaves data useful (PII), health records, plagiarism,
\u25cb What can we do to correct the biases in social media, and are there ways to connect social media dynamics with well-established social science (e.g. economic models) that tether subset of social media authors to the general population? Do they adhere to supply\/demand and reflect the general theories. How can we link social media to the invisible population they represent (i.e. represent the Census)? Can we lay out best practices in this domain? Can we have a few concrete sets of methodologies? We could try to characterize convenience sample? We could send a survey to the people whose tweets we use? Can we establish infrastructure for social media data? An archive of social media for us that is legal and reusable?<\/p>\n\n\n\n
Use-cases and demonstrations for practical problems could help drive the understanding, building coherence, and set up roadmap milestones.<\/p>\n\n\n\n
\u25cf Stories and exemplars of good ST research and applications areas for ST research. Process of identifying scenarios and and data needs to support ST problem solving.
\u25cf Planetary Defense<\/p>\n\n\n\n
\u25cb Smart search and discovery
\u25cb A PD knowledge base to capture the historical knowledge
\u25cb A visual analytical tool as a decision support system<\/p>\n\n\n\n
\u25cf illegal poaching of animals (South Africa) Prevention of extinctions<\/p>\n\n\n\n
\u25cb video streams
\u25cb geolocation and temporal
\u25cb analyze in real time<\/p>\n\n\n\n
\u25cf migration of populations<\/p>\n\n\n\n
\u25cb urbanization
\u25cb aggregation of historical data
\u25cb integrating real-time data
\u25cb regional or global security problems resulting from this<\/p>\n\n\n\n
\u25cf Tracking history of geographic objects
\u25cf urbanization & security<\/p>\n\n\n\n
\u25cb smart cities & secondary cities
\u25cb population and land use change around cities
\u25cb population migration
\u25cb security<\/p>\n\n\n\n
\u25a0 civil
\u25a0 crime, law enforcement
\u25a0 terrorist threat
\u25a0 health and emerging diseases
\u25a0 natural resource security (food, water, ecological)
\u25a0 emerging threats from global climate change<\/p>\n\n\n\n
\u25cb tools & data, infrastructure to support urbanization & security<\/p>\n\n\n\n
\u25a0 digital earth
\u25a0 smart cities
\u25a0 SDI
\u25a0 API, IoT data, usable\/actionable information,
\u25a0 HPC & cloud
\u25a0 open data & public participation
\u25a0 social media
\u25a0 open source hardware, software, sensor network,
\u25a0 mobile computing, wearable computing,
\u25a0 geomesh, s-t hashing, etc.
\u25a0 sentiment analysis, hotspot,
\u25a0 data fusion & integration
\u25a0 recommendation systems<\/p>\n\n\n\n
\u25cf Education<\/p>\n\n\n\n
\u25cb curriculum & curriculum change and development<\/p>\n\n\n\n
\u25a0 introduce the concepts of spatiotemporal to high school students, perhaps through summer school\/campu and RET
\u25a0 How to integrate concepts into new and existing college level curriculum<\/p>\n\n\n\n
\u25cb Short courses\/summer school
\u25cb K-12 education
\u25cb Where is our body of knowledge for spatio-temporal research, or what subset of that? Might be useful to check out gisbok.ucgis.org. (Note: BoK revision is under way)<\/p>\n\n\n\n
\u25cf Scholarship (journals, texts, conferences, etc.)
\u25cf Job Market (employment) How does it relate to similar \u201ctrends\u201d e.g. Data science?
\u25cf Formulation and Promotion of a Research agenda<\/p>\n\n\n\n
\u25cf Training
\u25cf Symposium
\u25cf Workshops
\u25cf Special Issue
\u25cf Book publication
\u25cf Develop a community portal or media to share info<\/p>\n\n\n\n
References<\/strong><\/p>\n\n\n\n 1) Clarke, K. C. and I. J. Irmischer (2016). \u201cOn scale in space, time and space-time\u201d, in: Scale in Remote Sensing and GIScience Applications,, D.A. Quattrochi, E.A. Wentz, N. Lam, and C. Emerson eds. Boca Raton FL: CRC Press.<\/em> Contributors Spatiotemporal Research Agenda: A Living Spatiotemporal I\/UCRC Whitepaper 0. Introduction We live in a four dimensional world with 3D in space and 1D in time. Studying and Understanding the four […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.stcenter.net\/index.php?rest_route=\/wp\/v2\/pages\/6546"}],"collection":[{"href":"https:\/\/www.stcenter.net\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.stcenter.net\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.stcenter.net\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.stcenter.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6546"}],"version-history":[{"count":1,"href":"https:\/\/www.stcenter.net\/index.php?rest_route=\/wp\/v2\/pages\/6546\/revisions"}],"predecessor-version":[{"id":6547,"href":"https:\/\/www.stcenter.net\/index.php?rest_route=\/wp\/v2\/pages\/6546\/revisions\/6547"}],"wp:attachment":[{"href":"https:\/\/www.stcenter.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6546"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
2) Lippitt, C. D.; Stow, D. A. and Clarke, K. C. (2014) On the nature of models for time-sensitive remote sensing. International Journal of Remote Sensing. 35, 18, 6815-6841.<\/em><\/p>\n\n\n\n
Chaowei Yang, cyang3@gmu.edu, Geography & GeoInformation Science, George Mason University
David Mills, dam203@txstate.edu, Geography, Texas State University
Keith Clarke,Geography, kclarke@geog.ucsb.edu UC Santa Barbara.
Robert Stewart, stewartrn@ornl.gov, Oak Ridge National Laboratory
April Morton, mortonam@ornl.gov, Oak Ridge National Laboratory
Jesse Piburn, piburnjo@ornl.gov, Oak Ridge National Laboratory
Alexandre Sorokine, SorokinA@ornl.gov, Oak Ridge National Laboratory
Matt Rice, rice@gmu.edu , Geography & GeoInformation Science, George Mason University
Ben Lewis blewis@cga.harvard.edu Harvard Center for Geographic Analysis
James (Jim) Zollweg, jzollweg@brockport.edu, SUNY Earth Sciences
James Pick james_pick@redlands.edu School of Business, University of Redlands
Qian Liu, qliu6@gmu.edu, Geography & GeoInformation Science, George Mason University
Yun Li, yli38@gmu.edu, Geography & GeoInformation Science, George Mason University
Kejin Cui, kcui2@gmu.edu, Geography & GeoInformation Science, George Mason University
Fikriyah Winata, fwinata2@ilinois.edu, Geography and Geographic Information Science, University of Illinois at Urbana-Champaign
Mei Li\uff0cmli@pku.edu.cn, IRSGIS, Peking University
Sensen Wu, wusensengis@zju.edu.cn, School of Earth Sciences, Zhejiang University
Xiuping Jia, x.jia@adfa.edu.au, The University of New South Wales, Australia
Huayi Wu, wuhuayi@whu.edu.cn, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, China
Zhipeng Gui, zhipeng.gui@whu.edu.cn, School of Remote Sensing and Information Engineering, Wuhan University, China
Mehrdad Koohikamali, Mehrdad_koohikamali@redlands.edu, School of Business, University of Redlands
Lara Kamal, lkamal3@gmu,edu, Computer Science and Mathematics,, George Mason University
Yiqing Guo, Yiqing.Guo@student.adfa.edu.au, The University of New South Wales, Australia
Chang Zhao, chang-zhao@uiowa.edu, Department of Geographical and Sustainability Sciences, University of Iowa
Minrui Zheng, mzheng2@uncc.edu, Center for Applied GIScience and Department of Geography and Earth Sciences, University of North Carolina at Charlotte
Wenwu Tang, WenwuTang@uncc.edu, Center for Applied GIScience and Department of Geography and Earth Sciences, University of North Carolina at Charlotte
Long Chiu, lchiu@gmu.edu, Atmospheric, Oceanic and Earth Sciences Department, George Mason Univeristy
Changjoo Kim, changjoo.kim@uc.edu, Geography & GIS, University of Cincinnati
Xuan Shi, xuanshi@uark.edu, Geosciences, University of Arkansas<\/p>\n","protected":false},"excerpt":{"rendered":"