Abstract
Urban planning is confronted with multifaceted complexities, related to the complex nature of phenomena, dynamics and processes it has to deal with. We argue that good tools for planning must be informed by these complexities, and therefore must have specific characteristics, in terms of modularity, flexibility, user-friendliness, generality, adaptability, computational efficiency and cost-effectiveness. In this chapter we present and try to make the case for a multi-agent geosimulation infrastructure framework called MAGI, showing how it delivers as such a tool for planning. The modelling and simulation infrastructure MAGI possesses characteristics, features and computational strategies particularly relevant for strongly geo-spatially oriented agent-based simulations. The infrastructure is composed of a development environment for building and executing simulation models, and a class library based on open source components. Differently from most of the existing tools for geosimulation, both raster and vector representation of simulated entities are allowed and managed with efficiency. This is obtained through the integration of a geometry engine implementing a core set of operations on spatial data through robust geometric algorithms, and an efficient spatial indexing strategy for moving agents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bandini, S., Manzoni, S., Vizzari, G.: Toward a platform for multi-layered multi-agent situated system (MMASS)-based simulations: focusing on field diffusion. Appl. Artif. Intell. 20, 327–351 (2006)
Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: An efficient and robust access method for points and rectangles. In: SIGMOD Conf., pp. 322–331 (1990)
Benenson, I., Torrens, P.M.: Geosimulation: object-based modeling of urban phenomena. Comput. Environ. Urban Syst. 28, 1–8 (2004)
Castle, C., Crooks, A.: Principles and concepts of agent-based modelling for developing geospatial simulations. Working Paper 110, Centre for Advanced Spatial Analysis, University College London, London (2006)
Cecchini, A.: New information and communication technologies (NICT) and the professions of town and regional planners. Comput. Environ. Urban Syst. 23, 543–556 (1999)
Cecchini, A., Trunfio, G.A.: Supporting urban planning with CAGE: a software environment to simulate complex systems. Adv. Complex Syst. 10(2), 309–325 (2007)
Clarke, K., Gaydos, L.: Loose-coupling a cellular automaton model and gis: long-term urban growth predictions for San Francisco and Baltimore. Int. J. Geog. Inf. Sci. 12, 699–714 (1998)
Clarke, K., Hoppen, S., Gaydos, L.: A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay Area. Environ. Plan B 24, 247–261 (1997)
Ferber, J.: Multi-agent systems: an introduction to distributed artificial intelligence. Addison-Wesley, Reading (1999)
GEOS: Geometry Engine - Open Source (2008), http://geos.refractions.net/
Gigerenzer, F., Peter, M.T., et al.: Simple Heuristics that Make Us Smart Oxford University Press (1999)
Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: SIGMOD Conf., pp. 47–57 (1984)
Houlding, S.: XML - an opportunity for <meaningful> data standards in the geosciences. Comput. Geosci. 27, 839–849 (2001)
Kollios, G., Gunopulos, D., Tsotras, V.J.: On indexing mobile objects. In: Proceedings of the Eighteenth ACM SIGACT-SIGMOD-SIGART Symp. Princip. Database Syst., pp. 261–272 (1999)
Kriegel, H.P., Brinkho, T., Schneider, R.: Efficient spatial query processing in geographic database systems. IEEE Data Eng. Bull. 16, 10–15 (1993)
Kwon, D., Lee, S., Lee, S.: Indexing the current positions of moving objects using the lazy update R-tree. In: Proceedings of the Third Int. Conf. Mob. Data Manag., pp. 113–120 (2002)
Kwon, D., Lee, S., Lee, S.: Efficient update method for indexing locations of moving objects. J. Inf. Sci. Eng. 21, 643–658 (2005)
Lee, M.L., Hsu, W., Jensen, C.S., et al.: Supporting frequent updates in r-trees: A bottom-up approach. In: VLDB, pp. 608–619 (2003)
Moulin, B., Chaker, W., Perron, J., et al.: MAGS project: Multi-agent geoSimulation and crowd simulation. In: Kuhn, W., Worboys, M.F., Timpf, S. (eds.) COSIT 2003. LNCS, vol. 2825, pp. 151–168. Springer, Heidelberg (2003)
Open GIS Consortium, OpenGIS simple features specification for SQL revision 1.1 (2007), http://www.opengis.org/techno/specs/99-049.pdf
Roussopoulos, N., Leifker, D.: Direct spatial search on pictorial databases using packed R-trees. In: SIGMOD Conf., pp. 17–31 (1985)
Simon, H.: The Science of the Artificial. MIT Press, Cambridge (1981)
Tayeb, J., Ulusoy, Ä.O., Wolfson, O.: A quadtree-based dynamic attribute indexing method. Comput. J. 41, 185–200 (1998)
Tobler, W.: Cellular geography. In: Gale, S., Olsson, G. (eds.) Philosophy in Geography, pp. 379–386. Reidel, Dordrecht (1979)
Torrens, P., Benenson, I.: Geographic automata systems. Int. J. of Geogr. Inf. Sci. 19, 385–412 (2005)
Wagner, D.: Cellular automata and geographic information systems. Environ. Plan B 24, 219–234 (1997)
White, R., Engelen, G.: Cellular automata and fractal urban form: A cellular modeling approach to the evolution of urban land-use patterns. Environ. Plan 25, 1175–1199 (1993)
Wolfson, O., Xu, B., Chamberlain, S., Jiang, L.: Moving objects databases: Issues and solutions. In: 10th Int. Conf. Sci. Stat. Database Manag., pp. 111–122. IEEE Computer Society, Los Alamitos (1998)
Wu, F.: GIS-based simulation as an exploratory analysis for space-time processes. J. Geogr. Syst. 1, 199–218 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Blecic, I., Cecchini, A., Trunfio, G.A. (2009). A Multi-Agent Geosimulation Infrastructure for Planning. In: Murgante, B., Borruso, G., Lapucci, A. (eds) Geocomputation and Urban Planning. Studies in Computational Intelligence, vol 176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89930-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-89930-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89929-7
Online ISBN: 978-3-540-89930-3
eBook Packages: EngineeringEngineering (R0)