Abstract
We present the design rationale underlying a language for spatial computing and sketch a prototypical implementation in Python. The goal of this work is to provide a high-level language for spatial computing that is executable on existing commercial and open source spatial computing platforms, particularly Geographic Information Systems (GIS). The key idea of the approach is to target an abstraction level higher than that of GIS commands and data formats, yet meaningful within and across application domains. The paper describes the underlying theory of spatial information and shows its evolving formal specification. An embedding in Python exemplifies access to commonly available implementations of spatial computations.
“One of the main reasons why software projects fail is the lack of communication between the business users, who actually know the problem domain, and the developers who design and implement the software model.”
(Ghosh 2011).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Industrial products include ModelBuilder in ArcGIS and the Workflow Designer in Autodesk Map 3D.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
Created by the Open Source Geospatial Foundation, the Geospatial Data Abstraction Library (GDAL, http://www.gdal.org) is a software package that provides an interoperability layer between a variety of raster and vector data formats.
- 9.
References
Albrecht, J. (1998). Universal analytical GIS operations: A task-oriented systematization of data structure-independent GIS functionality. In H. Onsrud & M. Craglia (Eds.), Geographic information research: Transatlantic perspectives (pp. 577–591). London: Taylor & Francis.
Baumann, P. (2010). The OGC web coverage processing service (WCPS) standard. Geoinformatica, 14(4), 447–479.
Burrough, P. A., & Frank, A. U. (1996). Geographic objects with indeterminate boundaries. London: Taylor & Francis.
Burrough, P. A., & McDonnell, R. (1998). Principles of geographical information systems. Oxford, UK: Oxford University Press.
Camara, G., Egenhofer, M. J., Ferreira, K., Andrade, P., Queiroz, G., Sanchez, A., et al. (2014). Fields as a generic data type for big spatial data. In Geographic Information Science (pp. 159–172). Berlin: Springer.
Couclelis, H. (1992). People manipulate objects (but cultivate fields): Beyond the raster-vector debate in GIS. In A. U. Frank, I. Campari, & U. Formentini (Eds.), Theories and methods of spatio-temporal reasoning in geographic space (pp. 65–77). Berlin: Springer.
Degbelo, A., & Kuhn, W. (2012). A Conceptual Analysis of Resolution. In GeoInfo—XIII Brazilian Symposium on GeoInformatics, November 25–28 2012, Campos do Jordão, Brasil (pp. 11–22).
Donnelly, M. (2005). Relative Places. Applied Ontology, 1, 55–75.
Egenhofer, M. J., & Kuhn, W. (1999). Interacting with Geographic Information Systems. In M. F. Goodchild, D. J. Maguire, D. W. Rhind, & P. Longley (Eds.), Geographical Information Systems: Principles, techniques, applications, and management (2nd ed., Vol. 1, pp. 401–412). New York: Wiley.
Galton, A. (2004). Fields and objects in space, time, and space-time. Spatial Cognition & Computation, 4(1), 39–68.
Ghosh, D. (2011). DSL for the uninitiated. Communications of the ACM, 54(7), 44.
Golledge, R. G. (1995). Primitives of spatial knowledge. In T. L. Nyerges, D. M. Mark, R. Laurini, & M. J. Egenhofer (Eds.), Cognitive aspects of human-computer interaction for geographic information systems (pp. 29–44). Berlin: Springer.
Goodchild, M. F., Yuan, M., & Cova, T. J. (2007). Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, 21(3), 239–260.
Janelle, D. G., & Goodchild, M. F. (2011). Concepts, principles, tools, and challenges in spatially integrated social science. SAGE Publications: In the SAGE Handbook of GIS and Society.
Kuhn, W. (2012). Core concepts of spatial information for transdisciplinary research. International Journal of Geographical Information Science, 26(12), 2267–2276 (Special Issue in honor of Michael Goodchild).
Newman, M. E. J. (2010). Networks. Oxford: Oxford University Press.
Norman, D. A. (1986). Cognitive Engineering. In D. Norman & S. Draper (Eds.), User centered system design (pp. 31–61). Hillsdale, NJ: Lawrence Erlbaum Associates.
Rosenfeld, A. (1986). “Continuous” functions on digital pictures. Pattern Recognition Letters, 4(3), 177–184.
Talmy, L. (1983). How language structures space. In H. L. Pick & L. P. Acredolo (Eds.), Spatial Orientation (pp. 225–282). New York/London: Plenum Press.
Acknowledgments
We gratefully acknowledge contributions to the Python embedding and testing from Michel Zimmer, Marc Tim Thiemann, and Eric Ahlgren as well as funding from the UCSB Center for Spatial Studies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Kuhn, W., Ballatore, A. (2015). Designing a Language for Spatial Computing. In: Bacao, F., Santos, M., Painho, M. (eds) AGILE 2015. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-16787-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-16787-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16786-2
Online ISBN: 978-3-319-16787-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)