Skip to main content

Spatial Graph Big Data

  • 456 Accesses

Synonyms

Spatial networks big data; Spatiotemporal graphs big data; Spatiotemporal networks big data

Definition

Digital modeling of real-world networks (e.g., road networks, river network) to accurately represent geographic information is done using spatial graphs. Spatial graphs can represent n-ary relationships to model complex relations in the network. They differ from existing geographical models that can only represent binary relationships. Spatial graph is formally defined using the concepts of Xnodes and Xedges and Xgraphs in the following paragraph.

Xnode represents a real-world network feature (e.g., road intersection) that can have scalar or structured values (e.g., an array of scalars). Xedge is a tree of Xnodes that can have scalar or structural values. Further, Xedge can be classified based on the type of network features being modeled (e.g., turn-Xedges) as shown in Fig. 1. Xgraph can be defined as a set of Xnodes and a set of Xedges. Spatial graphis an ensemble of...

This is a preview of subscription content, log in via an institution.

References

  • Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Pearson. ISBN-13: 978-0136175490

    Google Scholar 

  • Barthélemy M (2011) Spatial networks. Phys Rep 499(1):1–101. Elsevier

    Article  MathSciNet  Google Scholar 

  • Batchelor GK (2000) An introduction to fluid dynamics. Cambridge University Press. ISBN 978–0521663960

    Google Scholar 

  • Capps G, Franzese O, Knee B, Lascurain MB, Otaduy P (2008) Class-8 heavy truck duty cycle project final report. ORNL/TM-2008/122

    Google Scholar 

  • Cormen TH (2009) Introduction to algorithms. MIT press. ISBN 978–8120340077. eMarketer, https://goo.gl/TcMzQX

  • Eftelioglu E, Tang X, Shekhar S (2018) Avoidance region discovery: a summary of results. SIAM international conference on data mining (Accepted)

    Google Scholar 

  • Evans MR, Yang K, Kang JM, Shekhar S (2010) A lagrangian approach for storage of spatio-temporal network datasets: a summary of results. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. ACM, New York, pp 212–221

    Google Scholar 

  • George B, Kim S, Shekhar S (2007) Spatio-temporal Network Databases and Routing Algorithms: A Summary of Results. In: Papadias D, Zhang D, Kollios G (eds) Advances in Spatial and Temporal Databases. SSTD 2007. Lecture Notes in Computer Science, vol 4605. Springer, Berlin/Heidelberg

    Google Scholar 

  • Jensen C, Tradišauskas N (2009) Map matching. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, Boston

    Google Scholar 

  • Köhler E, Langkau K, Skutella M (2002) Time-expanded graphs for flow-dependent transit times. In: Möhring R, Raman R (eds) Algorithms – ESA 2002. Lecture notes in computer science, vol 2461. Springer, Berlin/Heidelberg

    Chapter  Google Scholar 

  • Lechner, W., Baumann, S. (2000). Global navigation satellite systems. Comput Electron Agric, 25(1), 67–85. Elsevier

    Article  Google Scholar 

  • Lunden I (2015) 6.1B Smartphone Users Globally By 2020. Overtaking basic fixed phone subscriptions. https://goo.gl/lcMcPK

  • Masumoto Y Global Positioning System (1993) U.S. Patent No. 5,210,540. U.S. Patent and Trademark Office, Washington, DC

    Google Scholar 

  • Pallottino S (1984) Shortest-path methods: complexity, interrelations and new propositions. Networks 14(2):257–267. John Wiley & Sons, Inc

    Article  MathSciNet  Google Scholar 

  • Shekhar S, Vatsavai RR, Ma X, Yoo JS (2004) Navigation systems: A spatial database perspective. as Chapter 3 in Location-Based Services, Agnes Voisard and Jochen Schiller. Elsevier, pp 41–80. ISBN 9781558609297

    Chapter  Google Scholar 

  • Shekhar S, Gunturi V, Evans MR, Yang K (2012) Spatial big-data challenges intersecting mobility and cloud computing. In: Proceedings of the eleventh ACM international workshop on data engineering for wireless and mobile access. ACM, New York, pp 1–6

    Google Scholar 

  • Strano E, Nicosia V, Latora V, Porta S, Barthélemy M (2012) Elementary processes governing the evolution of road networks. Sci Rep 2. Nature.com

  • Yang K, Gunturi VMV, Shekhar S (2012) A Dartboard Network Cut Based Approach to Evacuation Route Planning: A Summary of Results. In: Xiao N, Kwan MP, Goodchild MF, Shekhar S (eds) Geographic Information Science. GIScience 2012. Lecture Notes in Computer Science, vol 7478. Springer, Berlin/Heidelberg

    Google Scholar 

  • Zikopoulos P, Eaton C (2011) Understanding big data: analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jayant Gupta .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Shekhar, S., Gupta, J. (2018). Spatial Graph Big Data. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_222-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63962-8_222-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Spatial Graph Big Data
    Published:
    08 July 2022

    DOI: https://doi.org/10.1007/978-3-319-63962-8_222-2

  2. Original

    Spatial Graph Big Data
    Published:
    05 March 2018

    DOI: https://doi.org/10.1007/978-3-319-63962-8_222-1