Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Spatial Data Integration

  • Booma Sowkarthiga Balasubramani
  • Isabel F. Cruz
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_218

Synonyms

Definitions

Spatial data integration is a process in which different geospatial datasets, which may or may not have different spatial coverages, are made compatible with one another (Flowerdew 1991). The goal of spatial data integration is to facilitate the analysis, reasoning, querying, or visualization of the integrated spatial data. Figure 1 illustrates the integration of three layers or themes: major streets, hospitals, and police districts of the City of Chicago (Chi 2017).
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References

  1. (2017) Open data portal of the city of Chicago. https://data.cityofchicago.org/. Accessed: 30 Nov 2017
  2. Balasubramani BS, Belingheri O, Boria ES, Cruz IF, Derrible S, Siciliano MD (2017) GUIDES–geospatial Urban infrastructure data engineering solutions. In: ACM SIGSPATIAL international conference on advances in geographic information systemsGoogle Scholar
  3. Batini C, Scannapieco M (2016) Data and information quality: dimensions, principles and techniques. Springer, ChamzbMATHCrossRefGoogle Scholar
  4. Beck AR, Fu G, Cohn AG, Bennett B, Stell JG (2007) A framework for utility data integration in the UK. In: 26th urban data management symposium, Stuttgart, 10–12 Oct 2007. Taylor & Francis, pp 261–276Google Scholar
  5. Beck AR, Cohn AG, Sanderson M, Ramage S, Tagg C, Fu G, Bennett B, Stell JG (2008) UK utility data integration: overcoming schematic heterogeneity. In: International conference on advanced optical materials and devices. International Society for Optics and Photonics, 71431ZGoogle Scholar
  6. Bishr YA (1998) Overcoming the semantic and other barriers to GIS interoperability. Int J Geogr Inf Sci 12(4):229–314CrossRefGoogle Scholar
  7. Cobb MA, Chung MJ, Foley III H, Petry FE, Shaw KB, Miller HV (1998) A rule-based approach for the conflation of attributed vector data. GeoInformatica 2(1):7–35CrossRefGoogle Scholar
  8. Cruz IF, Sunna W (2008) Structural alignment methods with applications to Geospatial ontologies. Trans GIS: Special Issue on Semant Similarity Meas Geospat Appl 12(6):683–711CrossRefGoogle Scholar
  9. Cruz IF, Xiao H (2005) The role of ontologies in data integration. J Eng Intell Syst 13(4): 245–252Google Scholar
  10. Cruz IF, Xiao H (2008) Data integration for querying geospatial sources. Springer, Boston, pp 113–137Google Scholar
  11. Cruz IF, Sunna W, Makar N, Bathala S (2007) A visual tool for ontology alignment to enable geospatial interoperability. J Vis Lang Comput 18(3):230–254CrossRefGoogle Scholar
  12. Cruz IF, Ganesh VR, Caletti C, Reddy P (2013) GIVA: a semantic framework for geospatial and temporal data integration, visualization, and analytics. In: ACM SIGSPATIAL international symposium on advances in geographic information systems (ACM GIS). ACM, pp 544–547Google Scholar
  13. Dare P, Dowman I (2000) A new approach to automatic feature based registration of SAR and SPOT images. Int Arch Photogramm Remote Sens 33(B2):125–130Google Scholar
  14. DeMers MN (2008) Fundamentals of geographic information systems. Wiley, HobokenGoogle Scholar
  15. Devillers R, Jeansoulin R (2006) Fundamentals of spatial data quality. ISTE Publishing Company, London/Newport BeachzbMATHCrossRefGoogle Scholar
  16. Doytsher Y, Filin S, Ezra E (2001) Transformation of datasets in a linear-based map conflation framework. Surv Land Inf Syst 61(3):165–176Google Scholar
  17. Egenhofer M (1993) A model for detailed binary topological relationships. Geomatica 47(3):261–273Google Scholar
  18. Egenhofer MJ (2002) Toward the semantic geospatial Web. In: ACM symposium on advances in geographic information systems. ACM GIS, pp 1–4Google Scholar
  19. Faria D, Pesquita C, Mott I, Martins C, Couto FM, Cruz IF (2017, in press) Tackling the challenges of matching biomedical ontologies. J Biomed Semant 9:1–19Google Scholar
  20. Fellegi IP, Sunter AB (1969) A theory for record linkage. J Am Stat Assoc 64(328):1183–1210zbMATHCrossRefGoogle Scholar
  21. Flowerdew R (1991) Spatial data integration. Geogr Inf Syst 1:375–387Google Scholar
  22. Fonseca F, Egenhofer M, Davis C, Câmara G (2002) Semantic granularity in ontology-driven geographic information systems. Ann Math Artifi Intell 36(1–2):121–151MathSciNetzbMATHCrossRefGoogle Scholar
  23. Goodchild M, Haining R, Wise S (1992) Integrating GIS and spatial data analysis: problems and possibilities. Int J Geogr Inf Syst 6(5):407–423CrossRefGoogle Scholar
  24. Gotway CA, Young LJ (2002) Combining incompatible spatial data. J Am Stat Assoc 97(458):632–648MathSciNetzbMATHCrossRefGoogle Scholar
  25. Hakimpour F (2003) Using ontologies to resolve semantic heterogeneity for integrating spatial database schemata. Ph.D. thesis, Universität ZürichGoogle Scholar
  26. Kelley RP (1984) Blocking considerations for record linkage under conditions of uncertainty. Technical Report CENSUS/SRD/RR-84/19, U.S. Bureau of the Census, Statistical Research Division, Washington, DC, 709133Google Scholar
  27. Kyzirakos K, Karpathiotakis M, Garbis G, Nikolaou C, Bereta K, Papoutsis I, Herekakis T, Michail D, Koubarakis M, Kontoes C (2014) Wildfire monitoring using satellite images, ontologies and linked geospatial data. J Web Sem 24:18–26CrossRefGoogle Scholar
  28. Longley P (2005) Geographic information systems and science. Wiley, Hoboken, pp 148–149Google Scholar
  29. Longley PA, Goodchild MF, Maguire DJ, Rhind DW (2015) Spatial data analysis, chap 13, 4th edn. Wiley, HobokenGoogle Scholar
  30. Maffini G (1987) Raster versus vector data encoding and handling: a commentary. Photogramm Eng Remote Sens 53(10):1397–1398Google Scholar
  31. Melnik S, Garcia-Molina H, Rahm E (2002) Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: IEEE international conference on data engineering (ICDE), pp 117–128Google Scholar
  32. Mena E, Illarramendi A, Kashyap V, Sheth AP (2000) OBSERVER: an approach for query processing in global information systems based on interoperation across pre-existing ontologies. Distrib Parallel Databases 8(2):223–271CrossRefGoogle Scholar
  33. Mohammadi H, Binns A, Rajabifard A, Williamson IP et al (2006) Spatial data integration. In: 17th UNRCC–AP conference and 12th meeting of the PCGIAP, Bangkok, pp 18–22Google Scholar
  34. Psyllidis A, Bozzon A, Bocconi S, Bolivar CT (2015) A platform for Urban analytics and semantic data integration in city planning. In: International conference on computer–aided architectural design futures. Springer, pp 21–36Google Scholar
  35. Rote G (1991) Computing the minimum hausdorff distance between two point sets on a line under translation. Inf Process Lett 38(3):123–127MathSciNetzbMATHCrossRefGoogle Scholar
  36. Saalfeld A (1988) Conflation: automated map compilation. Int J Geogr Inf Syst 2(3):217–228CrossRefGoogle Scholar
  37. Sehgal V, Getoor L, Viechnicki PD (2006) Entity resolution in geospatial data integration. In: ACM international symposium on advances in geographic information systems. ACM GIS, pp 83–90Google Scholar
  38. Tran BH, Plumejeaud-Perreau C, Bouju A, Bretagnolle V (2015) A semantic mediator for handling heterogeneity of spatio-temporal environment data. In: Metadata and semantics research. Springer, Cham, pp 381–392CrossRefGoogle Scholar
  39. Walker R (1993) AGI standards committee GIS dictionary. Association for Geographic Information, LondonGoogle Scholar
  40. Wiegand N, Patterson D, Zhou N, Ventura S, Cruz IF (2002) Querying heterogeneous land use data: problems and potential. In: National conference for digital government research (dg.o), pp 115–121Google Scholar
  41. Yoon H, Shahabi C (2008) Robust time-referenced segmentation of moving object trajectories. In: IEEE international conference on data mining (ICDM). IEEE, pp 1121–1126Google Scholar
  42. Zhang J, Goodchild MF (2002) Uncertainty in geographical information. CRC Press, LondonCrossRefGoogle Scholar
  43. Zhang J, Atkinson P, Goodchild MF (2014) Scale in spatial information and analysis. CRC Press, Boca RatonCrossRefGoogle Scholar
  44. Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol (TIST) 6(3):29Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Booma Sowkarthiga Balasubramani
    • 1
  • Isabel F. Cruz
    • 1
  1. 1.University of Illinois at ChicagoChicagoUSA