Spatial Dimensions of Big Data: Application of Geographical Concepts and Spatial Technology to the Internet of Things

Part of the Studies in Computational Intelligence book series (SCI, volume 546)


Geography can be considered an important binding principle in the Internet of Things, as all physical objects and the sensor data they produce have a position, dimension, and orientation in space and time, and spatial relationships exist between them. By applying spatial relationships, functions, and models to the spatial characteristics of smart objects and the sensor data, the flows and behaviour of objects and people in Smart Cities can be more efficiently monitored and orchestrated. In the near future, billions of devices with location—and other sensors and actuators become internet connected, and Spatial Big Data will be created. This will pose a challenge to real-time spatial data management and analysis, but technology is progressing fast, and integration of spatial concepts and technology in the Internet of Things will become a reality.


Smart City Smart environment Internet of things Sensors Actuators Spatial big data Geographical information systems Positioning Georeferencing Spatial context Spatial relationships Spatial functions Spatial models Location based services Service oriented architecture OGC Sensor web enablement Real-time analysis Event stream processing  Complex event processing Pattern recognition Event driven architecture 



Application Domain Extensions


Automatic Number Plate Recognition


Application Programming Interface


Augmented Reality


Complex Event Processing


Event Driven Architecture


Enterprise Service Bus


Event Stream Processing


Extract Transform Load


Global Geospatial Information Management


Geographical Information System


T] Geographical Information Science and Technology


Geography Markup Language


Global Positioning System


Inverse Distance Weighted


Internet of Things


Java Script Object Notation


Location Based Services


Level Of Detail


Near Field Communication


Open Geospatial Consortium


Quick Response


Radio Frequency IDentification


Service Oriented Architecture


Simple Event Processing


Sensor Event Service


Sensor Instance Registry


Sensor Observable Registry


Sensor Observation Service


Sensor Planning Service


Structured Query Language


Sensor Web Enablement


United Nations


Ultra Wide Band



Writing this chapter would not have been possible without the support of our fellow researchers at the SPINlab of the VU University and our collegues at Geodan. We would like to thank them for their inspiring conversations, discussions, and thoughts on this subject. Further, we are grateful to the copyright owners for the use of figures and texts in this chapter. Finally, we are obliged to Laura Till and Patricia Ellman for taking a careful eye and a sharp pencil to review our material.

Author Biographies Erik van der Zee MSc. has a background in Physical Geography and Business Economics. He works as senior Geo-IT consultant and researcher at Geodan ( and is a member of the Geodan Innovation Board. His expertise is in designing and implementing innovative geospatial IT architectures. At SPINlab, he is a researcher on sensor networks, the Internet of Things and Smart Cities. He also supervises PhD students within the EU funded MULTI-POS ( framework, an international initiative with 17 research institutes and associated commercial partners that addresses challenging research topics in the field of location services and technologies.

Prof. Dr. Henk Scholten is head of the SPatial INformation Laboratory (SPINlab, of the VU University Amsterdam. The SPINlab is a world-leading research centre for Geographical Information Science and Technology at the Department of Spatial Economics of the VU University Amsterdam. He is also CEO and founder of Geodan (, one of the largest European companies specialized in geospatial technology and system integration.

Henk Scholten and Erik van der Zee have recently contributed to the UN Global Geospatial Information Management (GGIM) report on the five- to ten-year vision of future trends in geospatial information management.


  1. 1.
    Ali, M., Chandramouli, B., Sethu, B., Katibah, R.: Spatio-temporal stream processing in microsoft streaminsight. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering (2010)Google Scholar
  2. 2.
    Beecham Research: Sector map showing segmentation of the M2M Market. Available at: (2013). Accessed 1 Aug 2013
  3. 3.
    Berger, H.E.J.: Flood forecasting for the river Meuse, hydrology for the water management of large river boons. In: Proceedings of the Vienna Symposium. IAHS Publication, Vienna (1991)Google Scholar
  4. 4.
    Botts, M., Percivall, G., Reed, C., Davidson, J.: OGC Sensor Web Enablement: Overview and High Level Architecture, 3rd edn. Open Geospatial Consortium Inc. (2007)Google Scholar
  5. 5.
    Boyd, J: A Discourse on Winning and Losing (1987)Google Scholar
  6. 6.
    Casaleggio Associati: Available at: (2011). Accessed 1 Aug 2013
  7. 7.
    Dias, E.S.: The Added Value of Contextual Information in Natural Areas: Measuring Impacts of Mobile Environmental Information. Vrije Universiteit, Amsterdam (2007)Google Scholar
  8. 8.
    Dias, E.S., et al.: Adding Value and Improving Processes Using Location-Based Services in Protected Areas. 11, pp. 291–302 (2004)Google Scholar
  9. 9.
    Dibiase, D., et al.: Geographic Information Science & Technology Body of Knowledge. Association of American Geographers (AAG), Washington, DC. ISBN- 10: 0–89291-267-7, ISBN-13: 978-0-89291-267-4 (2006)Google Scholar
  10. 10.
    Esri Inc.: Available at: (2013a). Accessed Nov 2013
  11. 11.
    Esri Inc.: Available at: (2013b). Accessed Nov 2013
  12. 12.
    European Commission Digital Agenda: Available at: (2013). Accessed Nov 2013
  13. 13.
    Evans, D.: The Internet of things—how the next evolution of the Internet is changing everything. CISCO Internet Business Solutions Group (IBSG) (2011)Google Scholar
  14. 14.
    Gewin, V.: Mapping opportunities. Nature 427, 376–77 (2004)Google Scholar
  15. 15.
    Gröger, G., Kolbe, T.H., Nagel, C., Häfele, K.H.: OGC 12-019 City Geography Markup Language (CityGML) Encoding Standard Version: 2.0.0. Open Geospatial Consortium (OGC) (2012)Google Scholar
  16. 16.
    GSMA: Guide to Smart Cities—The Opportunity for Mobile Operators. GSMA, London (2013)Google Scholar
  17. 17.
    Hilbert, M., López, P.: The world’s technological capacity to store, communicate, and compute information. Science 332(6025), 60–65 (2011)Google Scholar
  18. 18.
    Huisman, O., De By, R.A.: Principles of Geographic Information Systems—An Introductory Textbook, Educational Textbook Series. International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands. (2009)Google Scholar
  19. 19.
    IoT SWG: The OGC Forms “Sensor Web for IoT” Standards Working Group. Available at: (2013). Accessed Nov 2013
  20. 20.
    ISO: Reference ISO/IEC FDIS 13249-3:2011(E) Information Technology—Database languages—SQL Multimedia and Application Packages—Part 3: Spatial. ISO, Geneva, Switzerland (2011)Google Scholar
  21. 21.
    ITU: ISBN 978-92-61-14071-7 Measuring the Information Society. International Telecommunication Union, Geneva, Switzerland (2012)Google Scholar
  22. 22.
    Jirka, S., Nüst, D: OGC 10-171 OGC Sensor Instance Registry Discussion Paper. Open Geospatial Consortium Inc. ( 2010)Google Scholar
  23. 23.
    Klopfer, M., Simonis, I.: SANY—An Open Architecture for Sensor Networks. SANY Consortium (2009)Google Scholar
  24. 24.
    Libelium: 50 Sensor Applications for a Smarter World. Available at: (2013). Accessed 1 Aug 2013
  25. 25.
    Magerkurth, C.: IoT-A (257521) Internet of Things—Architecture IoT-A Deliverable D1.4—Converged Architectural Reference Model for the IoT v2.0. European Commission. Deliverable within the Seventh Framework Programme (2007–2013), (2012)Google Scholar
  26. 26.
    Michael, K., McNamee, A., Michael, M.G.: The Emerging Ethics of Humancentric GPS Tracking and Monitoring. IEEE Xplore, Copenhagen (2006)Google Scholar
  27. 27.
    Mollenkopf, A.: Using ArcGIS GeoEvent Processor for Server to Power Real-Time Applications. Esri International Developer Summit. Esri Inc. (2013)Google Scholar
  28. 28.
    Nebert, D., Whiteside, A., Vretanos, P.: OpenGIS Catalogue Services Specification, vol. 202, 2nd edn. Open Geospatial Consortium Inc. ( 2007)Google Scholar
  29. 29.
    Ng, C.B., Tay, Y.H., Goi, B.-M.: Vision-based human gender recognition: a survey. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012: Trends in Artificial Intelligence (2012)Google Scholar
  30. 30.
    OGC: OGC PUCK Protocol Standard Version 1.4. Open Geospatial Consortium Inc. (2012)Google Scholar
  31. 31.
    OGC: Homepage. Available at: (2013). Accessed 1 Aug 2013
  32. 32.
    Pals, N., De Vries, A., De Jong, A., Broertjes, E.: Remote and in situ Sensing for Dyke Monitoring—te IJKDIJK experience. GeoSpatial Visual Analytics, pp. 465–475. NATO Science for Peace and Security Series C: Environmental Security (2009)Google Scholar
  33. 33.
    Peters, E., Van der Vliet, P.: Digidijk & Alert Solutions: Real Time Monitoring van Civieltechnische Constructies. (Civiele techniek), pp. 14–16. (2010)Google Scholar
  34. 34.
    Pschorr, J., Henson, C., Patni, H., Sheth, A.: Sensor Discovery on Linked Data Technical Report. Dayton, OH, USA: Knoesis Center, Department of Computer Science and Engineering. ( 2010)
  35. 35.
    Roche, S., Kloeck, K., Ratti, C.: Are ‘Smart Cities’ Smart Enough? Chap. 12. pp. 215–236. GSDI Association Press, Needham (2012)Google Scholar
  36. 36.
    Satterthwaite, D.: The transition to a predominantly urban world and its underpinnings. Human Settlements Discussion Paper Series 2007, (September, 2007)Google Scholar
  37. 37.
    Schmeisser R.: Location Based Marketing (2011)Google Scholar
  38. 38.
    Scholten, H.J., Van de Velde, R., Van Manen, N.: Geospatial Technology and the Role of Location in Science, 96th edn. Springer, Berlin (2009)Google Scholar
  39. 39.
    Serbanati, A., Medaglia, C.M., Ceipidor, U.B.: Building Blocks of the Internet of Things: State of the Art and Beyond, Chap. 20. In: Turcu, C. (ed.) Deploying RFID—Challenges, Solutions, and Open Issues. INTECH. doi:10.5772/19997 (2011)Google Scholar
  40. 40.
    Sharma, J.: Complex Event Processing, Oracle Spatial and Fusion Middleware products OBIEE Suite, JDeveloper (2011)Google Scholar
  41. 41.
    Smartbin: Available at: (2013). Accessed Nov 2013
  42. 42.
    United Nations: ST/ESA/SER.A/236 World Population to 2300. United Nations Publication (2004)Google Scholar
  43. 43.
    United Nations: ESA/P/WP/224 World Urbanization Prospects: The 2011 Revision. United Nations Publication (2012)Google Scholar
  44. 44.
    United Nations: ST/ESA/344-E/2013/50/Rev. 1, Sustainable Development Challenges—World Economic and Social Survey 2013. United Nations Publication, ISBN 978-92-1-109167-0, eISBN 978-92-1-056082-5 (2013a)Google Scholar
  45. 45.
    United Nations: D10227 Future Trends in Geospatial Information Management: The Five to Ten Year Vision. United Nations—Global Geospatial Information Management (GGIM) (2013b)Google Scholar
  46. 46.
    Viti, F. et al.: National data warehouse: how the Netherlands is creating a reliable, widespread, accessible data bank for traffic information, monitoring, and road network control. Trans. Res. Record (TRR Journal) 2049, 176–185 (2008)Google Scholar
  47. 47.
    Wikipedia. Thing. Available at: (2013a). Accessed 1 Aug 2013
  48. 48.
    Wikipedia: Object. Available at: (2013b). Accessed 1 Aug 2013
  49. 49.
    Zeimpekis, V., Kourouthanassis, P., Giaglis, G.M.: UNESCO-EOLSS. In: Telecommunication Systems and Technologies, vol. I. UNESCO-EOLSS (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.VU UniversityAmsterdamThe Netherlands
  2. 2.GeodanAmsterdamThe Netherlands

Personalised recommendations