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IPUMS-Terra: integrated big heterogeneous spatiotemporal data analysis system

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Abstract

Big Geo Data promises tremendous benefits to the GIS Science community in particular and the broader scientific community in general, but has been primarily of use to the relatively small body of GIScientists who possess the specialized knowledge and methods necessary for working with this class of data. Much of the greater scientific community is not equipped with the expert knowledge and techniques necessary to fully take advantage of the promise of big spatial data. IPUMS-Terra provides integrated spatiotemporal data to these scholars by simplifying access to thousands of raster and vector datasets, integrating them and providing them in formats that are useable to a broad array of research disciplines. IPUMS-Terra exemplifies a new class of National Spatial Data Infrastructure because it connects a large spatial data repository to advanced computational resources, allowing users to access the needle of information they need from the haystack of big spatial data. The project is trailblazing in its commitment to the open sharing of spatial data and spatial tool development, including describing its architecture, process development workflows, and openly sharing its products for the general use of the scientific community.

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References

  • Abdalla R (2016) Introduction to geospatial information and communication technology (GeoICT). Springer

  • AgMIP (2017) Retrieved from https://mygeohub.org/groups/gabbs/project_page. Accessed 6 Jan 2017

  • Armstrong MP (2000) Geography and computational science. Ann Assoc Am Geogr 90(1):146–156

    Article  Google Scholar 

  • Bédard Y, Merrett T, Han J (2001) Fundamentals of spatial data warehousing for geographic knowledge discovery. Geogr Data Min Knowl Discov 2:53–73

    Article  Google Scholar 

  • Butenuth M, Gösseln GV, Tiedge M, Heipke C, Lipeck U, Sester M (2007) Integration of heterogeneous geospatial data in a federated database. ISPRS J Photogrammetry Remote Sens 62(5):328–346

    Article  Google Scholar 

  • CyberGIS Gateway (2016) http://sandbox.cigi.illinois.edu/home. Accessed 1 Nov 2016

  • Cybergis-toolkit (2017) https://github.com/cybergis/cybergis-toolkit. Accessed 11 Jan 2016

  • DataOne (2014) DataONE. https://www.dataone.org/news/dataone-welcomes-its-25th-member-node-minnesota-population-center. Accessed 1 Aug 2017

  • Deschamps A, Greenlee D, Pultz T, Saper R (2002) Geospatial data integration for applications in flood prediction and management in the Red River Basin. In: IGARSS’02. 2002 IEEE International Geoscience and remote sensing symposium, 2002, vol. 6, pp. 3338–3340. IEEE

  • Di Martino S, Bimonte S, Bertolotto M, Ferrucci F (2009) Integrating Google earth within olap tools for multidimensional exploration and analysis of spatial data. In: International conference on enterprise information systems. Springer, Berlin, pp 940–951

  • Ding Y, Densham PJ (1996) Spatial strategies for parallel spatial modelling. Int J Geogr Inf Syst 10(6):669–698

    Article  Google Scholar 

  • Eldawy A, Mokbel MF (2015) The era of big spatial data. In: 2015 31st IEEE international conference on data engineering workshops (ICDEW). pp 42–49

  • Evangelidis K, Ntouros K, Makridis S, Papatheodorou C (2014) Geospatial services in the Cloud. Comput Geosci 63:116–122. https://doi.org/10.1016/j.cageo.2013.10.007

    Article  Google Scholar 

  • Friis-Christensen A, Schade S, Peedell S (2005) Approaches to solve schema heterogeneity at European Level. In: Proceedings of 11th EC-GI & GIS workshop, ESDI: setting the framework, Alghero, Sardinia, Italy

  • Geospatial Data Analysis Building Blocks (2016) https://purr.purdue.edu/projects/geodibbs. Accessed 15, Sept 2016

  • Haynes D, Ray S, Manson SM, Soni A (2015) High performance analysis of big spatial data. In: 2015 IEEE International Conference on Big Data (Big Data), IEEE, pp 1953–1957

  • Haynes D, Manson S, Shook E (2017) Terra populus’ architecture for integrated big geospatial services. Trans GIS 21(3):546–559. https://doi.org/10.1111/tgis.12286

    Article  Google Scholar 

  • Jaeger E, Altintas I, Zhang J, Ludäscher B, Pennington D, Michener W (2005) A scientific workflow approach to distributed geospatial data processing using web services. In: SSDBM, vol 3, pp 87–90

  • Janowicz K, Schade S, Bröring A, Keßler C, Maué P, Stasch C (2010) Semantic enablement for spatial data infrastructures. Trans GIS 14(2):111–129. https://doi.org/10.1111/j.1467-9671.2010.01186.x

    Article  Google Scholar 

  • Jiang P, Winkley J, Zhao C, Munnoch R, Min G, Yang LT (2016) An intelligent information forwarder for healthcare big data systems with distributed wearable sensors. IEEE Syst J 10(3):1147–1159

    Article  Google Scholar 

  • Kugler TA, Fitch CA (2018) Interoperable and accessible census and survey data from IPUMS. Sci Data 5:180007. https://doi.org/10.1038/sdata.2018.7

    Article  Google Scholar 

  • Kugler TA, Van Riper DC, Manson SM, Haynes DA II, Donato J, Stinebaugh K (2015) Terra Populus: workflows for integrating and harmonizing geospatial population and environmental data. J Map Geogr Libr 11(2):180–206

    Article  Google Scholar 

  • Kugler TA, Manson SM, Donato JR (2017) Spatiotemporal aggregation for temporally extensive international microdata. Comput Environ Urban Syst 63:26–37. https://doi.org/10.1016/j.compenvurbsys.2016.07.007

    Article  Google Scholar 

  • Laney D (2001) 3D data management: controlling data volume, velocity, and variety. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed 21 Aug 2017

  • Lieberman J, Goad C (2008) Geosemantic web standards for the spatial information infrastructure. In: van Oosterom P, Zlatanova S (eds) Creating spatial information infrastructures: towards the spatial semantic web. CRC Press, Boca Raton, pp 119–128

    Google Scholar 

  • Maguire DJ, Longley PA (2005) The emergence of geoportals and their role in spatial data infrastructures. Comput Environ Urban Syst 29(1):3–14

    Article  Google Scholar 

  • Minnesota Population Center (2011) National historic geographic information system (Version 2.0). Minneapolis, MN: University of Minnesota. http://www.nhgis.org. Accessed 6 Jan 2017

  • Minnesota Population Center (2017) Integrated public use microdata seris, international (Version 6.5). Minneapolis, MN: University of Minnesota. http://www.nhgis.org, https://international.ipums.org/international/. Accessed 6 Jan 2017

  • National Research council M.S. (1993) Toward a coordinated spatial data infrastructure for the nation. National Academies Press, Washington

    Google Scholar 

  • Olasz A, Thai BN, Kristóf D (2016) A new initiative for tiling, stitching and processing geospatial big data in distributed computing environments. In: ISPRS annals of photogrammetry, remote sensing and spatial information sciences, vol 3(4)

  • Percivall G (2010) Progress in OGC web services interoperability development. In: Di L, Ramapriyan HK (eds) Standard-based data and information systems for earth observation. Springer, Berlin, pp 37–61. https://doi.org/10.1007/978-3-540-88264-0_4

    Chapter  Google Scholar 

  • Reitsma F, Laxton J, Ballard S, Kuhn W, Abdelmoty A (2009) Semantics, ontologies and eScience for the geosciences. Comput Geosci 35(4):706–709. https://doi.org/10.1016/j.cageo.2008.03.014

    Article  Google Scholar 

  • Rivest S, Bédard Y, Marchand P (2001) Toward better support for spatial decision making: defining the characteristics of spatial on-line analytical processing (SOLAP). Geomatica-Ottawa 55(4):539–555

    Google Scholar 

  • Rivest S, Bédard Y, Proulx M-J, Nadeau M (2003) SOLAP: a new type of user interface to support spatio-temporal multidimensional data exploration and analysis. In: Proceedings of the ISPRS joint workshop on spatial, temporal and multi-dimensional data modelling and analysis, Quebec, Canada, pp. 2–3

  • Shekhar S, Lu C, Tan X, Chawla S, Vatsavai R (2001) A visualization tool for spatial data warehouses. Geogr Data Min Knowl Discov 73:73–108

    Google Scholar 

  • Sohn G, Dowman I (2007) Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction. ISPRS J Photogrammetry Remote Sens 62(1):43–63

    Article  Google Scholar 

  • Vaccari L, Shvaiko P, Marchese M (2009) A geo-service semantic integration in spatial data infrastructures. IJSDIR 4:24–51

    Google Scholar 

  • Viswanathan G, Schneider M (2011) On the requirements for user-centric spatial data warehousing and SOLAP. In: International conference on database systems for advanced applications. Springer, Berlin, pp 144–155

  • Yue P, Gong J, Di L, Yuan J, Sun L, Sun Z, Wang Q (2010) GeoPW: laying blocks for the geospatial processing web. Trans GIS 14(6):755–772. https://doi.org/10.1111/j.1467-9671.2010.01232.x

    Article  Google Scholar 

  • Yue P, Zhou H, Gong J, Hu L (2013) Geoprocessing in Cloud Computing platforms—a comparative analysis. Int J Dig Earth 6(4):404–425. https://doi.org/10.1080/17538947.2012.748847

    Article  Google Scholar 

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Acknowledgements

We would like to thank the editor and peer reviewers for providing feedback on this article. Additionally, we would like to thank members of the Institute of Social Research and Data Innovation and the data project teams IPUMS-I and IPUMS-Terra for their help in this process. Special thanks to the IT-Core for assisting with this development process. The research in this article is supported by National Institutes of Health Award 5T32CA163184.

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Authors and Affiliations

  1. Program in Health Disparities, 717 Delaware Street SE, Suite #166, Minneapolis, MN, 55414, USA

    David Haynes

  2. Institute for Social Research and Data Innovation, University of Minnesota, 225 19th Avenue South, Minneapolis, MN, 55455, USA

    Alex Jokela

  3. Department of Geography, Environment, and Society, University of Minnesota, 267 19th Avenue South, Minneapolis, MN, 55455, USA

    Steven Manson

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  1. David Haynes
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  2. Alex Jokela
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  3. Steven Manson
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Correspondence to David Haynes.

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Haynes, D., Jokela, A. & Manson, S. IPUMS-Terra: integrated big heterogeneous spatiotemporal data analysis system. J Geogr Syst 20, 343–361 (2018). https://doi.org/10.1007/s10109-018-0277-2

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