Skip to main content

Defining Spatio-Temporal Granularities for Raster Data

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 6121)


The notion of granularity is used in several areas of computing. In temporal databases, granularity relates to the fact that the time frame associated to an event of interest (e.g., an accident) can be envisaged at several levels of detail (e.g., hour, day, month, etc.). Similarly, granularity in data warehousing is the level of detail at which facts (e.g., sales) are captured in dimensions (e.g., product, store, and day). However, there is no commonly-agreed definition of spatial or spatio-temporal granularities. Sometimes, the term spatial granularity is confounded with multiple resolutions. Further, the few proposals about them are mainly focused on the vector data model. In this paper, we define spatial and spatio-temporal granularities for raster data models. In our framework, relations and operations between spatial and spatio-temporal granularities are also defined.


  • Space Domain
  • Raster Data
  • Poral Granularity
  • Temporal Granularity
  • Time Granule

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Belussi, A., Combi, C., Pozzani, G.: Formal and conceptual modeling of spatio-temporal granularities. In: Proceedings of the International Database Engineering and Applications Symposium, pp. 275–283. ACM (2009)

    Google Scholar 

  2. Camossi, E., Bertolotto, M., Bertino, E.: A multigranular object-oriented framework supporting spatio-temporal granularity conversions. Int. J. Geogr. Inf. Sci. 20(5), 511–534 (2006)

    CrossRef  Google Scholar 

  3. Cattell, R.G.G., Berler, D.K.B.M., Eastman, J., Jordan, D., Russell, C., Schadow, O., Stanienda, T., Velez, F. (eds.): The Object Data Standard: ODMG 3.0. Morgan Kaufmann Publishers Inc., San Francisco (2000)

    Google Scholar 

  4. Erwig, M., Schneider, M.: Partition and Conquer. In: Frank, A.U. (ed.) COSIT 1997. LNCS, vol. 1329, pp. 389–407. Springer, Heidelberg (1997)

    CrossRef  Google Scholar 

  5. Frank, A.U.: Map Algebra Extended with Functors for Temporal Data. In: Akoka, J., Liddle, S.W., Song, I.-Y., Bertolotto, M., Comyn-Wattiau, I., van den Heuvel, W.-J., Kolp, M., Trujillo, J., Kop, C., Mayr, H.C. (eds.) ER Workshops 2005. LNCS, vol. 3770, pp. 194–207. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  6. Güting, R.H., Böhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Trans. Database Syst. 25(1), 1–42 (2000)

    CrossRef  Google Scholar 

  7. Malinowski, E., Zimányi, E.: Advanced data warehouse design: From conventional to spatial and temporal applications. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  8. McKenney, M., Schneider, M.: Spatial Partition Graphs: A Graph Theoretic Model of Maps. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 167–184. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  9. Mennis, J., Tomlin, C.D.: Cubic map algebra functions for spatio-temporal analysis. Cartogr. and Geogr. Inform. 32(1), 17–32 (2005)

    CrossRef  Google Scholar 

  10. Ning, P., Wang, X.S., Jajodia, S.: An algebraic representation of calendars. Ann. Math. Artif. Intel. 36(1-2), 5–38 (2002)

    CrossRef  MATH  Google Scholar 

  11. Shekhar, S., Xiong, H. (eds.): Encyclopedia of GIS. Springer, Heidelberg (2008)

    Google Scholar 

  12. Tomlin, C.D., Berry, J.K.: A mathematical structure for cartographic modeling in environmental analysis. In: Proceedings of the 39th Symposium of the American Congress on Surveying and Mapping, pp. 269–283 (1979)

    Google Scholar 

  13. Wang, S., Liu, D.: Spatio-temporal Database with Multi-granularities. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 137–146. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pozzani, G., Zimányi, E. (2012). Defining Spatio-Temporal Granularities for Raster Data. In: MacKinnon, L.M. (eds) Data Security and Security Data. BNCOD 2010. Lecture Notes in Computer Science, vol 6121. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25703-2

  • Online ISBN: 978-3-642-25704-9

  • eBook Packages: Computer ScienceComputer Science (R0)