The Simulation of Spatial Change: What Relation Between Knowledge and Modeling? A Proposal and Its Application

  • Silvana Lombardo
  • Massimiliano Petri


The aim of the research is to investigate land use transformations in a territorial area of Albania, analyzing the connections existing between the deep political and socio-economical changes. The tools we adopted, from the field of the KDS, is able to produce IF/THEN type rules, in which the “IF” part describes the observed state, and the “THEN” part identifies the transition to another state. The application included different phases:
  1. a)

    construction of maps referred to different time slices;

  2. b)

    construction of automated procedures within G.I.S. in order to perform various kinds of cartographic analysis such as map overlays, neighbouring analysis, etc;

  3. c)

    construction of automated report tables referred to cartographic analysis, containing all the attributes necessary to describe the territorial structure;

  4. d)

    implementation of an algorithm able to extract land transformation rules;

  5. e)

    analysis of the obtained rules in order to find significant relations between socio-economical and spatial evolution.



Cellular Automaton Multi Agent System Spatial Change Multicriteria Decision Decision Tree Induction 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agrotec S.p.A. Consortium (2003) Land Use Planning Methodology. Technical Report 8. EU Phare Land Use Policy II Project, Agrotec S.p.A., RomeGoogle Scholar
  2. Agrotec S.p.A. Consortium, 2003. Preliminary Land Use Plan for Preza Commune. Technical Report 17. EU Phare Land Use Policy II Project, Agrotec S.p.A., RomeGoogle Scholar
  3. Arentze TA, Timmemrmans HJP (2000) ALBATROSS — A learning-based transportation oriented simulation system. Urban Planning Group/EIRASS, Eindhoven University of Technology, The NederlandsGoogle Scholar
  4. Arrow K (1951) Social Choice and Individual Values. Cowles Foundations and Wiley, New YorkGoogle Scholar
  5. Axtell RL, Epstein JM, Dean JS, Gunerman G, Swedlund A (2002) Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences of the United States of America 99:3: 7275–7279CrossRefGoogle Scholar
  6. Bana e Costa C (1990) Readings in multiple criteria decision aid. Springer, BerlinGoogle Scholar
  7. Ben-Akiva M, Lerman SR (1985) Discrete Choice Analysis Theory and Application to travel demand. The MIT Press, Cambridge, MAGoogle Scholar
  8. Bonchi F, Lombardo S, Pecori S, Santucci A (2004a) Learning about Land Use change in Rome and in Pisa Urban areas. In: Diappi L (ed) Evolving Cities: Geocomputation in Territorial Planning. Ashgate, EnglandGoogle Scholar
  9. Bonchi F, Lombardo S, Pecori S (2004b) Knowledge discovery and data mining to investigate urban and territorial evolution: tools and methodologies. In: Diappi L (ed) Evolving Cities: Geocomputation in Territorial Planning. Ashgate, EnglandGoogle Scholar
  10. Burrough PA, McDonnell RA (1997) Principles of Geographical Information System. Oxford University Press, OxfordGoogle Scholar
  11. Cioffi-Revilla C, Gotts NM (2002) Comparative analysis of agent-based social simulations: GeoSim and FEARLUS models. Paper presented at the Model-2-Model Workshop on Comparing Multi-Agent Based Simulation Models, March 31–April 1 2003, Marseille, FranceGoogle Scholar
  12. Faludi A (1987) A Decision Centered View of Planning. Pion, LondonGoogle Scholar
  13. Fayyad U, Piatetsky-Shapiro G, Smith P, Uturusamy R (1996) Advances in knowledge Discovery and Data Mining. AAAI/MIT Press, Mento Park, CAGoogle Scholar
  14. Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufmann, S.FranciscoGoogle Scholar
  15. Hanisch A, Schultze T (2003) Online simulation of pedestrian, flow in public buildings. In: Chick S, Sanchez D, Ferrin D, Morrice DJ (eds) Proceedings of Winter Simulation Conference, Association for Computing Machinery, New YorkGoogle Scholar
  16. Heppenstall AJ, Evans AJ, Birkin M (2003) A hybrid multi-agent/spatial interaction model system for petrol price setting. Transaction in GIS 9:1: 35–51CrossRefGoogle Scholar
  17. Holsheimer M, Siebes A (1994) Data mining: the search for knowledge in databases. CWI Technical Report CS-R9406, AmsterdamGoogle Scholar
  18. Jager W, Janssen M (2003) The need for development of behaviourally realistic agents. In: Sichman JS, Bousquet F, Davidsson P (eds) Multi-Agent based simulation II. Lecture Notes in Artificial Intelligence, Springer, Berlin, pp 36–49CrossRefGoogle Scholar
  19. Lapucci A, Lombardo S, Petri M, Santucci A (2005) A KDD based multicriteria decision making model for fire risk evaluation. Proceedings of 8th AGILE 2005-Conference on GIScience, 25–29 May, Estoril, PortugalGoogle Scholar
  20. Lombardo S, Pecori S (2004) Investigating Urban structure and evolution through Artificial Intelligence tools. Studies in Regional & Urban Planning, Special Number, pp 32–52Google Scholar
  21. Lombardo S, Petri M (2005) Un sistema di supporto alle decisioni: integrazione di analisi GIS e Datamining. In: Las Casas G, Pontrandolfi P, Murgente B (eds) Proceedings of the conference URBING 2005, 29th–30th April, Potenza, Italy, pp 15–27Google Scholar
  22. Lombardo S, Petri M, Zotta D (2003) Urban services spatial dynamics in a changing environment simulated by a multi agent system. 13th European Colloquium on Theoretical and Quantitative Geography, vol 1, Lucca, ItalyGoogle Scholar
  23. Lombardo S, Petri M, Zotta D (2004) Intelligent GIS and retail location dynamics. International Journal Computational Science and Its Application (ICCSA 2004), Assisi, Italy, vol 2, pp 1046–1056Google Scholar
  24. Lombardo S, Rabino GA (1983) Some simulations on a Central Place Theory Model. Sistemi urbani 5:2: 315–332Google Scholar
  25. Malczewski J (1999) GIS and Multicriteria Decision Analysis. John Wiley, New YorkGoogle Scholar
  26. Martelli P (1983) La logica della scelta collettiva. Il Saggiatore, MilanoGoogle Scholar
  27. Nonas E, Poulovassilis A (1998) Optimisation of Active Rule Agents Using a Genetic Algorithm Approach. DEXA, pp 332–341Google Scholar
  28. Occelli S (2004) A perspective on MAS approach in urban modelling, IRES-Istituto di Ricerche Economico Sociali del PiemonteGoogle Scholar
  29. Otter HS, van der Veen A, de Vriend HJ (2001) ABLOoM: Location behaviour, spatial patterns, and agent-based modelling. Journal of Artificial Societies and Social Simulation, vol 4, nr 4Google Scholar
  30. Pecori S, Santucci A (2002) The competition for land between urban and rural systems investigated by G.I.S. and Data Mining techniques. Proceedings of the 3rd International Conference of Decision Making in Urban and Civil Engineering, 6–8 November, LondonGoogle Scholar
  31. Piatetsky-Shapiro G, Frawley WJ (1991) Knowledge Discovery in Databases. AAAI/MIT PressGoogle Scholar
  32. Quinlan JR (1986) Induction of decision trees. Machine Learning 1: 81–106Google Scholar
  33. Roy B (1979) De-quelle decision s’agit-il, qui aider et comment?. Document du LAMSADE nr. 4Google Scholar
  34. Roy B (1985) Méthodologie Multicritère d’ Aide à la Décision. Ed. Economica, ParisGoogle Scholar
  35. Schelhorn T, O’Sullivan D, Haklay M, Thurstain-Goodwin M (1999) STREET: an agent-based pedestrian model. Centre for Advanced Spatial Analysis, University College London, Paper 9Google Scholar
  36. Semboloni F, Assfalg J, Armeni S, Gianassi R, Marsoni F (2004) CityDev, an interactive multi-agents model on the web. Computers environment and urban system 4: 45–64CrossRefGoogle Scholar
  37. Tabak V, de Vries B, Dijkstra J (2004) User Behaviour Modelling-Applied in the context of space utilisation. In: von Leeuwen JP, Timmermans HJ (eds) Development in Design and Decision Support System in Architecture and Urban Planning, Eindhoven University of Technology, pp 141–156Google Scholar
  38. Van Delft A, Nijkamp P (1977) Multicriteria Analysis and Regional Decision Making. Martinuns Nijnhof, LeidenGoogle Scholar
  39. Vincke Ph (1981) Multicriteria Analysis: survey and new directions. European Journal of Operation Research 8: 207–218.CrossRefGoogle Scholar
  40. Vincke Ph (1992) Multicriteria Decision Aid. John Wiley, ChichesterGoogle Scholar
  41. Waddel P, Borning A, Noth M, Freier N, Becker M (2003) Microsimulation of Urban Development and Location Choices: Design and Implementation of UrbanSim. University of Washington, SeattleGoogle Scholar
  42. White R, Engelen G (2000) High-resolution integrated modelling of the spatial dynamics of urban and regional systems, Pergamon press, Computers. Environment and Urban Systems 24: 383–400CrossRefGoogle Scholar
  43. Wilson AG (1981) Catastrophe theory and bifurcation: applications to urban and regional systems. Croom Helm, London, University of California Press, Berkeley.Google Scholar
  44. Wilson AG (2000) Complex Spatial System: The modelling foundations of urban and regional analysis. Pearson Education, Harlow, pp 62–77Google Scholar
  45. Witten IH, Frank E (2000) WEKA-Machine Learning Algorithms in Java. Morgan Kaufmann, S. FranciscoGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg and Accademia di Architettura, Mendrisio, Switzerland 2008

Authors and Affiliations

  • Silvana Lombardo
    • 1
  • Massimiliano Petri
    • 1
  1. 1.Department of Civil EngineeringUniversity of PisaItaly

Personalised recommendations