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STARE into the future of GeoData integrative analysis

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Abstract

Different kinds of observations feature different strengths, e.g. visible-infrared imagery for clouds and radar for precipitation, and, when integrated, better constrain scientific models and hypotheses. Even critical, fundamental operations such as cross-calibrations of related sensors operating on different platforms or orbits, e.g. spacecraft and aircraft, are integrative analyses. The great variety of Earth Science data types and the spatiotemporal irregularity of important low-level (ungridded) data has so far made their integration a customized, tedious process which scales in neither variety nor volume. Generic, higher-level (gridded) data products are easier to use, at the cost of being farther from the original observations and having to settle with grids, interpolation assumptions, and uncertainties that limit their applicability. The root cause of the difficulty in scalably bringing together diverse data is the current rectilinear geo-partitioning of Earth Science data into conventional arrays indexed using consecutive integer indices and then packaged into files. Such indices suffice for archival, search, and retrieval, but lack a common geospatial semantics, which is mitigated by adding on floating-point encoded longitude-latitude information for registration. An alternative to floating-point, the SpatioTemporal Adaptive Resolution Encoding (STARE) provides an integer encoding for geo-spatiotemporal location and neighborhood that transcends the use of files and native array indexing, allowing diverse data to be organized on scalable, distributed computing and storage platforms.

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  1. https://en.wikipedia.org/wiki/Memoization

References

  • Clementini E, Sharma J, Egenhofer MJ (1994) Modelling topological spatial relations: strategies for query processing. Comput Graph 18(6):815–822. https://doi.org/10.1016/0097-8493(94)90007-8

    Article  Google Scholar 

  • Dirmeyer PA, Wu J, Norton HE, Dorigo WA, Quiring SM, Ford TW, Santanello JA, Bosilovich MG, Koster RD (2016) Confronting weather and climate models with observational data from soil moisture networks over the United States. J Hydrometeorol 1049-1067. https://doi.org/10.1175/JHM-D-15-0196.1

  • ERFA (2020) Essential Routines of Fundamental Astronomy derived from the International Astronomical Union’s Standards of Fundamental Astronomy (SOFA) (https://github.com/liberfa/erfa, https://iausofa.org). Accessed 20 Feb 2020

  • Fekete G, Kuo KS (2015) Indexing Earth with Trixels, presented at the 8th XLDB Conference, May 19-20, 2015 Stanford University, CA, USA

  • Gelaro R, McCarty W, Suárez MJ, Todling R, Molod A, Takacs L, Randles CA, Darmenov A, Bosilovich MG, Reichle R, Wargan K, Coy L, Cullather R, Draper C, Akella S, Buchard V, Conaty A, da Silva AM, Gu W, Kim GK, Koster R, Lucchesi R, Merkova D, Nielsen JE, Partyka G, Pawson S, Putman W, Rienecker M, Schubert SD, Sienkiewicz M, Zhao B (2017) The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J Clim 30:5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1

    Article  Google Scholar 

  • GeoData (2020) https://github.com/SpatioTemporal/GeoData. Accessed Jan 2021

  • GeoPandas (2020) https://geopandas.org; https://github.com/geopandas/geopandas. Accessed Feb 2020

  • Gibb R (2019) 19170–1 UML update DGGS DWG, 113th OGC technical committee Toulouse, France, 18 November 2019

  • Gorey C (2017) The volume of data NASA has to manage is mind-boggling, Silicon Republic, 26 Oct 2017, https://www.siliconrepublic.com/enterprise/nasa-data-figures. Accessed 17 Feb 2020

  • Gray J, Szalay AS , Thakar AR, Fekete G, O'Mullane W, Nieto-Santisteban MA, Heber G, Rots AH (2004) “There Goes the Neighborhood: Relational Algebra for Spatial Data Search,” Microsoft Research Technical Report, MSR-TR-2004-32, April 2004. (arXiv.org. pp. arXiv:cs–0408031, Aug-2004)

  • Griessbaum, N, Frew J, Rilee ML, Kuo KS, Gallagher J, Neumiller K (2020) “STARE dataframes for geospatial analysis - a high level STARE interface,” Earth Science Information Partners (ESIP), Winter Meeting, Bethesda, MD. 2–7 January 2020. STAREPandas is available at https://github.com/SpatioTemporal/STAREPandas

  • HDF (2020) https://hdfgroup.org. Accessed Feb 2020

  • HDF EOS (2020) Tools and Information Center. https://hdfeos.org. Accessed Feb 2020

  • Herring JR (ed.) (2010) OpenGIS® implementation standard for geographic information - simple feature access - part 1: common architecture. OGC 06-103r4, open geospatial consortium, Inc., https://www.opengeospatial.org/standards/sfa

  • Humphreys P (2008) Computational and conceptual emergence. Philos Sci 75:584–594

    Article  Google Scholar 

  • Klein L, Taaheri A (2016) HDF-EOS5 data model, file format and library, ESDS-RFC-008v1.1, https://cdn.earthdata.nasa.gov/conduit/upload/4880/ESDS-RFC-008-v1.1.pdf. Accessed 31 Jan 2018

  • Kleinhans MG, Buskes CJ, de Regt HW (2010) Philosophy of earth science. In: Allhoff J (ed) Philosophies of the sciences: a guide. Wiley-Blackwell, Oxford, pp 213–286

    Chapter  Google Scholar 

  • Kondor D, Dobos L, Csabai I, Bodor A, Vattay G, Budavári T, Szalay AS (2014) Efficient classification of billions of points into complex geographic regions using hierarchical triangular mesh. In Proceedings of the 26th International Conference on Scientific and Statistical Database Management (SSDBM '14). Association for Computing Machinery, New York, NY, USA, Article 4:1–4. https://doi.org/10.1145/2618243.2618245

  • Konikow LF, Bredehoeft JD (1992) Ground-water models cannot be validated. Adv Water Resour 15:75–83

    Article  Google Scholar 

  • Kunszt PZ, Szalay AS, Thakar AR (2001) The hierarchical triangular mesh. In: Banday A, Zaroubi S, Bartelmann M (eds) Mining the sky. ESO ASTROPHYSICS SYMPOSIA (European Southern Observatory). Springer, Berlin, Heidelberg. https://doi.org/10.1007/10849171_8

  • Kuo KS, Rilee ML (2017) STARE – Toward unprecedented geo-data interoperability, 2017 Conference on Big Data from Space. Toulouse, France. 28–30 November 2017. STARE is available at https://github.com/SpatioTemporal/STARE

  • Kuo KS, Yu H, Pan Y, Rilee ML (2019) Leveraging STARE for Co-aligned Data Locality with netCDF and Python MPI. 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, 2019, pp. 10063-10066. https://doi.org/10.1109/IGARSS.2019.8900423

  • Kuo, KS, Pan Y, Zhu F, Rilee ML, Yu H (2018) A Big Earth Data Platform Exploiting Transparent Multimodal Parallelization. 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 22–27 July 2018, Valencia, Spain 10.1109/IGARSS.2018.8518304

  • Lee HB, Ghia U, Bayyuk S, Oberkampf WL, Roy CJ, Benek JA, Rumsey CL, Powers JM, Bush RH, Mani M (2016) Development and use of engineering standards for computational fluid dynamics for complex aerospace systems, 16th AIAA aviation technology, integration, and operations conference (2016 AIAA aviation); June 13–17, 2016, Washington, DC, United States

  • Modis Characterization Support Team MCST (2012) MODIS level 1B products data dictionary, NASA/Goddard Space Flight Center, Greenbelt, MD MCST Internal Memorandum # M1055-REV D, July 20, 2012. https://mcst.gsfc.nasa.gov/sites/default/files/file_attachments/M1055_PDD_D_072712final.pdf

  • NetCDF (2020) https://www.opengeospatial.org/standards/netcdf. Accessed Feb 2020

  • Nishihama M, Wolfe R, Solomon D, Patt F, Blanchette J, Fleig A, and Masuoka E (1997) MODIS level 1A earth location: algorithm theoretical basis document. Greenbelt, MD: NASA Goddard Space Flight Center. https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf

  • OGC (2020) Discrete Global Grid Systems SWG, https://www.opengeospatial.org/projects/groups/dggsswg. Accessed Feb 2020

  • Oreskes N, Shrader-Frechette K, Belitz K (1994) Verification, validation, and confirmation of numerical models in the earth sciences. Science 263:641–646. https://doi.org/10.1126/science.263.5147.641

    Article  Google Scholar 

  • Pugh W (1990) Skip lists: a probabilistic alternative to balanced trees. Commun ACM 33:668–676. https://doi.org/10.1145/78973.78977

    Article  Google Scholar 

  • Purss MBJ, Gibb G, Samavati F, Peterson P, Ben J (2016) The OGC® discrete global grid system core standard: a framework for rapid geospatial integration. 2016 IEEE international geoscience and remote sensing symposium (IGARSS) 10–15 July 2016 https://doi.org/10.1109/IGARSS.2016.7729935

  • Rilee M, Kuo KS, Frew J, Griessbaum N, Gallagher J (2020a) STARE towards integrative analysis with minimized data wrangling hassle. 2020 IEEE international geoscience and remote sensing symposium (IGARSS), virtual symposium. Paper TU2.R7.8, 29 September 2020

  • Rilee M, Griessbaum N, Kuo KS, Frew J, Wolfe R (2020b) STARE-based Integrative Analysis of Diverse Data Using Dask Parallel Programming Demo Paper. Proceedings of the 28th International Conference on Advances in Geographic Information Systems. Association for Computing Machinery, New York, NY, USA, 417–420. https://doi.org/10.1145/3397536.3422346

  • Rilee ML, Kuo KS, Clune T, Oloso A, Brown PG, Yu H (2016) Addressing the big-earth-data variety challenge with the hierarchical triangular mesh. 2016 IEEE Int’l. Conf. On Big Data (Big Data, IEEE), 1006–1011. https://www.sugarsync.com/pf/D7103074_07457104_9374790)

  • Ruiz A (2017) The 80/20 Data Science Dilemma. https://www.infoworld.com/article/3228245/the-80-20-data-science-dilemma.html

  • Seaman C (2013) Beginner’s guide to VIIRS imagery data, http://rammb.cira.colostate.edu/projects/npp/Beginner_Guide_to_VIIRS_Imagery_Data.pdf also https://ncc.nesdis.noaa.gov/VIIRS/. Accessed Feb 2020

  • Stanford K (2017) Underdetermination of scientific theory, the Stanford encyclopedia of philosophy (winter 2017 edition). Edward N. Zalta (ed.), https://plato.stanford.edu/archives/win2017/entries/scientific-underdetermination/

  • STARE (2020) https://github.com/SpatioTemporal/STARE. Accessed Feb 2020

  • Stensrud DJ (2007) Parameterization schemes: keys to understanding numerical weather prediction models. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Szalay AS, Gray J, Fekete G, Kunszt PZ, Kukol P, Thakar A (2005) Indexing the sphere with the hierarchical triangular mesh, Micr. Res. Tech. Rpt., MSR-TR-2005-123

  • Wimsatt WC (1997) Aggregativity: reductive heuristics for finding emergence. Philos Sci 64:S372–S384

    Article  Google Scholar 

  • Yu L, Rilee ML, Pan Y, Zhu F, Kuo KS, Yu H (2017) Visual analytics with unparalleled variety scaling for big earth data. In: 2017 IEEE international conference on big data (big data), Boston, MA, pp 514–521 https://ieeexplore.ieee.org/document/8257966/

  • Zarr (2020) https://zarr.readthedocs.io/en/stable/index.html. Accessed Feb 2020

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Acknowledgments

We are grateful for the support provided by the National Aeronautics and Space Administration Advancing Collaborative Connections for Earth System Science (ACCESS-17) program, award ID 80NSSC18M0118. We gratefully acknowledge helpful comments from this paper’s reviewers.

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Correspondence to Michael L. Rilee.

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Rilee, M.L., Kuo, KS., Frew, J. et al. STARE into the future of GeoData integrative analysis. Earth Sci Inform 15, 1495–1512 (2022). https://doi.org/10.1007/s12145-021-00568-8

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