Skip to main content

GIS-MCDA for Group Decision Making

  • Chapter
  • First Online:

Part of the book series: Advances in Geographic Information Science ((AGIS))

Abstract

This chapter provides a discussion of the most often used GIS-MCDA approaches for group decision making It focuses on two distinctive classes of GIS-MCDA procedures for groups: conventional methods for aggregating preferences and geosimulation-based modeling. The former includes conventional GIS-MCDA methods that have been adapted for tackling conflicting preferences in a group decision-making setting. This class of methods is based on the traditional notion of decision makers and tends to focus on prescriptive-constructive modeling. Unlike the conventional approaches, geosimulation involves the concept of decision-making agents and descriptive-normative modeling. It provides a platform for spatially explicit analysis of multicriteria decision problems.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  • Andrienko, G. L., Andrienko, N. V., & Jankowski, P. (2003). Building spatial decision support tools for individuals and groups. Journal of Decision Systems, 12(2), 193–208.

    Article  Google Scholar 

  • Armstrong, M. P., & Densham, P. J. (2008). Cartographic support for locational problem-solving by groups. International Journal of Geographical Information Science, 22(7), 721–749.

    Article  Google Scholar 

  • Arrow, K. J. (1951). Social choice and individual values. New York: Wiley.

    Google Scholar 

  • Arrow, K. J., & Raynaud, H. (1986). Social choice and multicriterion decision making. Cambridge: MIT Press.

    Google Scholar 

  • Arsanjani, J. J., Helbich, M., & Vaz, E. (2013). Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran. Cities, 32, 33–42.

    Article  Google Scholar 

  • Batty, M., & Xie, Y. (1994). Modelling inside GIS: Part 1. Model structures, exploratory spatial data analysis and aggregation. International Journal of Geographical Information Systems, 8(3), 291–307.

    Article  Google Scholar 

  • Bone, C., & Dragićević, S. (2009). GIS and intelligent agents for multiobjective natural resource allocation: A reinforcement learning approach. Transactions in GIS, 13(3), 253–272.

    Article  Google Scholar 

  • Bone, C., & Dragićević, S. (2010). Simulation and validation of a reinforcement learning agent-based model for multi-stakeholder forest management. Computers, Environment and Urban Systems, 34(2), 162–174.

    Article  Google Scholar 

  • Bone, C., Dragićević, S., & White, R. (2011). Modeling-in-the-middle: Bridging the gap between agent-based modeling and multi-objective decision making for land use change. International Journal of Geographical Information Science, 25(5), 717–737.

    Article  Google Scholar 

  • Boroushaki, S., & Malczewski, J. (2010a). Measuring consensus for collaborative decision-making: AGIS-based approach. Computers, Environment and Urban Systems, 34(4), 322–332.

    Article  Google Scholar 

  • Boroushaki, S., & Malczewski, J. (2010b). ParticipatoryGIS: A web-based collaborative GIS and multicriteria decision analysis. URISA Journal, 22(1), 23–32.

    Google Scholar 

  • Boroushaki, S., & Malczewski, J. (2010c). Using the fuzzy majority approach for GIS-based multicriteria group decision-making. Computers & Geosciences, 36(3), 302–312.

    Article  Google Scholar 

  • Cao, K., Huang, B., Li, M., & Li, W. (2014). Calibrating a cellular automata model for understanding rural–urban land conversion: A Pareto front-based multi-objective optimization approach. International Journal of Geographical Information Science, 28(5), 1028–1046.

    Article  Google Scholar 

  • Carver, S. (1999). Developing web-based GIS/MCE: Improving access to data and spatial decision support tools. In J. C. Thill (Ed.), Spatial multicriteria decision-making and analysis (pp. 49–75). Aldershot: Ashgate.

    Google Scholar 

  • Castella, J. C., Kam, S. P., Quang, D. D., Verburg, P. H., & Hoanh, C. T. (2007). Combining top-down and bottom-up modelling approaches of land use/cover change to support public policies: Application to sustainable management of natural resources in northern Vietnam. Land Use Policy, 24(3), 531–545.

    Article  Google Scholar 

  • Chen, K., Blong, R., & Jacobson, C. (2001). MCE-RISK: Integrating multicriteria evaluation and GIS for risk decision-making in natural hazards. Environmental Modelling and Software, 16(4), 387–397.

    Article  Google Scholar 

  • Chen, Y., Li, X., Liu, X., & Liu, Y. (2010). An agent-based model for optimal land allocation (AgentLA) with a contiguity constraint. International Journal of Geographical Information Science, 24(8), 1269–1288.

    Article  Google Scholar 

  • Chow, T. E., & Sadler, R. (2010). The Consensus of Local Stakeholders and Outside Experts in Suitability Modeling for Future Camp Development. Landscape and Urban Planning, 94(1), 9–19.

    Article  Google Scholar 

  • Demircan, S., Aydin, M., & Durduran, S. S. (2011). Finding optimum route of electrical energy transmission line using multi-criteria with Q-learning. Expert Systems with Applications: An International Journal, 38(4), 3477–3482.

    Article  Google Scholar 

  • Dyer, R. F., & Forman, E. H. (1992). Group decision support with the analytic hierarchy process. Decision Support Systems, 8(2), 99–124.

    Article  Google Scholar 

  • Engelen, G., White, R., & Uljee, I. (1997). Integrating constrained cellular automata models, GIS and decision support tools for urban planning and policy making. In H. P. J. Timmermans (Ed.), Decision support systems in urban planning (pp. 125–155). London: E. & F.N. Spon.

    Google Scholar 

  • Estoque, R. C. (2012). Analytic hierarchy process in geospatial analysis. In Y. Murayama (Ed.), Progress in geospatial analysis (pp. 157–182). Tokyo: Springer.

    Chapter  Google Scholar 

  • Faber, B. G. (1996). Extending electronic meeting systems for collaborative spatial decision making: Obstacles and opportunities. In Collaborative spatial decision-making: NCGIA Initiative 17. Santa Barbara, CA.: NCGIA. http://www.ncgia.ucsb.edu/research/i17/I-17_home.html.

  • Feick, R. D., & Hall, G. B. (2002). Balancing consensus and conflict with a GIS-based multi-participant, multi-criteria decision support tool. GeoJournal, 53(4), 391–406.

    Article  Google Scholar 

  • Feick, R. D., & Hall, B. G. (2004). A method for examining the spatial dimension of multi-criteria weight sensitivity. International Journal of Geographical Information Science, 18(8), 815–840.

    Article  Google Scholar 

  • Feng, Y., & Liu, Y. (2013). A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing. International Journal of Geographical Information Science, 27(3), 449–466.

    Article  Google Scholar 

  • Ferrand, N. (1996). Modelling and supporting multiactor planning using multiagent systems. In Proceedings of 3rd NCGIA conference on GIS and environmental modeling, Santa Fe. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/ferrand_nils/santafe.html.

  • Forman, E., & Peniwatib, K. (1998). Aggregating individual next term judgments and previous term priorities next term with the analytic hierarchy process. European Journal of Operational Research, 108(1), 165–169.

    Google Scholar 

  • Fotakis, D., & Sidiropoulos, E. (2012). A new multi-objective self-organizing optimization algorithm (MOSOA) for spatial optimization problems. Applied Mathematics and Computation, 218(9), 5168–5180.

    Article  Google Scholar 

  • Gorsevski, P. V., Cathcart, S. C., Mirzaei, G., Jamali, M. M., & Ye, X., Gomezdelcampo, E. (2013). A group-based spatial decision support system for wind farm site selection in Northwest Ohio. Energy Policy, 55(C), 374–385.

    Google Scholar 

  • Hossain, M. S., Chowdhury, S. R., Das, N. G., Sharifuzzaman, S. M., & Sultana, A. (2009). Integration of GIS and multicriteria decision analysis for urban aquaculture development in Bangladesh. Landscape and Urban Planning, 90(3), 119–133.

    Article  Google Scholar 

  • Hwang, C. L., & Lin, M. J. (1987). Group decision making under multiple criteria. Berlin: Springer.

    Book  Google Scholar 

  • Ishizaka, A., & Nemery, P. (2013). A multi-criteria group decision framework for partner grouping when sharing facilities. Group Decision and Negotiation, 22(4), 773–799.

    Article  Google Scholar 

  • Jankowski, P., Andrienko, N., & Andrienko, G. (2001). Map-centred exploratory approach to multiple criteria spatial decision making. International Journal of Geographical Information Science, 15(2), 101–127.

    Article  Google Scholar 

  • Jankowski, P., Ligmann-Zielinska, A., & Swobodzinski, M. (2008). Choice modeler: A web-based spatial multiple criteria evaluation tool. Transactions in GIS, 12(4), 541–561.

    Article  Google Scholar 

  • Jankowski, P., & Nyerges, T. (2001). Geographic information systems for group decision making: Towards a participatory geographic information science. London: Taylor & Francis.

    Google Scholar 

  • Jankowski, P., Nyerges, T. L., Smith, A., Moore, T. J., & Horvath, E. (1997). Spatial group choice: A SDSS tool for collaborative spatial decision-making. International Journal of Geographical Information Systems, 11(6), 566–602.

    Google Scholar 

  • Janssen, R., Goosen, H., Verhoeven, M. L., Verhoeven, J. T. A., Omtzigt, A. Q. A., & Maltby, E. (2005). Decision support for integrated wetland management. Environmental Modelling and Software, 20(2), 215–229.

    Article  Google Scholar 

  • Jiao, J., & Boerboom, L. (2006). Transition rule elicitation methods for urban cellular automata models. In J. P. van Leeuwen & H. J. P. Timmermans (Eds.), Innovations in design and decision support systems in architecture and urban planning (pp. 53–68). Berlin: Springer.

    Chapter  Google Scholar 

  • Joerin, F., & Musy, A. (2000). Land management with GIS and multicriteria analysis. International Transactions in Operational Research, 7(1), 67–78.

    Article  Google Scholar 

  • Joerin, F., Theriault, M., & Musy, A. (2001). Using GIS and outranking multi-criteria analysis for land-use suitability assessment. International Journal of Geographical Information Science, 15(2), 153–174.

    Article  Google Scholar 

  • Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe—simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435–447.

    Article  Google Scholar 

  • Kangas, A., Kangas, J., & Kurttila, M. (2008). Decision support for forest management. Berlin: Springer.

    Google Scholar 

  • Kangas, J., Pukkala, T., & Kangas, A. S. (2001). HERO: heuristic optimisation for multi-criteria forestry decision analysis. In D. Schmoldt, J. Kangas, G. A. Mendoza, & M. Pesonen (Eds.), The analytic hierarchy process in natural resource and environmental decision making, managing forest ecosystems 3 (pp. 51–65). Kannus: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. New York: Wiley.

    Google Scholar 

  • Lai, T., Dragićević, S., & Schmidt, M. (2013). Integration of multicriteria evaluation and cellular automata methods for landslide simulation modelling. Geomatics, Natural Hazards and Risk, 4(4), 355–375.

    Article  Google Scholar 

  • Levy, J. K., Hartmann, J., Li, K. W., An, Y., & Asgary, A. (2007). Multi-criteria decision support systems for flood hazard mitigation and emergency response in urban watersheds. Journal of the American Water Resources Association, 43(2), 346–358.

    Article  Google Scholar 

  • Li, X., & Liu, X. (2007). Defining agents’ behaviors to simulate complex residential development using multicriteria evaluation. Journal of Environmental Management, 85(4), 1063–1075.

    Article  Google Scholar 

  • Li, X., Lao, C., Liu, X., & Chen, Y. (2011a). Coupling urban cellular automata with ant colony optimization for zoning protected natural areas under a changing landscape. International Journal of Geographical Information Science, 25(4), 575–593.

    Article  Google Scholar 

  • Li, X., Shi, X., He, J., & Liu, X. (2011b). Coupling simulation and optimization to solve planning problems in a fast-developing area. Annals of the Association of American Geographers, 101(5), 1032–1048.

    Article  Google Scholar 

  • Li, X., & Yeh, A. G. O. (2000). Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14(2), 131–152.

    Article  Google Scholar 

  • Ligmann-Zielinska, A. (2009). The impact of risk-taking attitudes on a land use pattern: an agent based model of residential development. Journal of Land Use Science, 4(4), 215–232.

    Article  Google Scholar 

  • Ligmann-Zielinska, A., & Jankowski, P. (2010). Exploring normative scenarios of land use development decisions with an agent-based simulation laboratory. Computers, Environment and Urban Systems, 34(5), 409–423.

    Article  Google Scholar 

  • Ligtenberg, A., Bregt, A. K., & van Lammeren, R. (2001). Multi-actor-based land use modelling: spatial planning using agents. Landscape and Urban Planning, 56(1–2), 21–33.

    Article  Google Scholar 

  • Ligtenberg, A., Lammeren, R. J. A., & Bregt, A. K. (2000). Cellular automata and multi-agent simulation for dynamic land use planning. In Proceedings of greenwich international symposium digital creativity, Greenwich (pp. 393–402).

    Google Scholar 

  • Ligtenberg, A., Wachowicz, M., Bregt, A. K., Beulens, A., & Kettenis, D. L. (2004). A design and application of a multi-agent system for simulation of multi-actor spatial planning. Journal of Environmental Management, 72(1–2), 43–55.

    Article  Google Scholar 

  • Liu, Y. (2009). Modelling urban development with geographical information systems and cellular automata. Boca Raton: CRC Press.

    Google Scholar 

  • Long, Y., & Shen, Z. (2012). Reaching consensus among stakeholders on planned urban form using constrained CA. In Z. Shen (Ed.), Geospatial techniques in urban planning (pp. 91–106). Berlin: Springer.

    Chapter  Google Scholar 

  • Macary, F., Ombredane, D., & Uny, D. (2010). A multicriteria spatial analysis of erosion risk into small watersheds in the low Normandy bocage (France) by ELECTRE III method coupled with a GIS. International Journal of Multicriteria Decision Making, 1(1), 25–48.

    Article  Google Scholar 

  • Macharis, C., Brans, J. P., & Mareschal, B. (1998). The GDSS PROMETHEE procedure: A PROMETHEE-GAIA based procedure for group decision support. Journal of Decision Systems, 7, 283–307.

    Google Scholar 

  • Malczewski, J. (1996). A GIS-based approach to multiple criteria group decision-making. International Journal of Geographical Information Science, 10(8), 955–971.

    Article  Google Scholar 

  • Malczewski, J. (2006). Multicriteria decision analysis for collaborative GIS. In S. Balram & S. Dragićević (Eds.), Collaborative geographic information systems (pp. 167–185). Hershey: Idea Group Publishing.

    Chapter  Google Scholar 

  • Martin, N. J., St Onge, B., & Waaub, J. P. (2003). An integrated decision aid system for the development of Saint Charles River alluvial plain, Quebec, Canada. International Journal of Environment and Pollution, 12(2–3), 264–279.

    Google Scholar 

  • Massam, B. H. (1988). Multi-criteria decision making (MCDM) techniques in planning. Progress in Planning, 30(1), 1–84.

    Article  Google Scholar 

  • Meeks, W. L., & Dasgupta, S. (2004). Geospatial information utility: An estimation of the relevance of geospatial information to users. Decision Support Systems, 38(1), 47–63.

    Article  Google Scholar 

  • Meng, Y., & Malczewski, J. (2010a). Pre-decisional engagement, decision-making outcomes and Web-PPGIS usability: A case study in Canmore, Alberta. International Journal of the Internet and Management, 18(1), 51–60.

    Google Scholar 

  • Meng, Y., & Malczewski, J. (2010b). Web-PPGIS usability and public engagement: A case study in Canmore, Alberta. Journal of URISA, 22(1), 55–64.

    Google Scholar 

  • Moeinaddini, M., Khorasani, N., Danehkar, A., Darvishsefat, A. A., & Zienalyan, M. (2010). Siting MSW land fill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj). Waste Management, 30(5), 912–920.

    Article  Google Scholar 

  • Munda, G. (2008). Social multi-criteria evaluation for a sustainable economy. New York: Springer.

    Book  Google Scholar 

  • Munday, P., Jones, A. P., & Lovett, A. A. (2010). Utilising scenarios to facilitate multi-objective land use modelling for Broadland, UK, to 2100. Transactions in GIS, 14(3), 241–263.

    Article  Google Scholar 

  • Myint, S. W., & Wang, L. (2006). Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing, 32(6), 390–404.

    Article  Google Scholar 

  • Nekhay, O., Arriaza, M., & Boerboom, L. (2009). Evaluation of soil erosion risk using analytic network process and GIS: A case study from Spanish mountain olive plantations. Journal of Environmental Management, 90(10), 3091–3104.

    Article  Google Scholar 

  • Norese, M. F., & Toso, F. (2004). Group decision and distributed technical support. International Transactions in Operational Research, 11(4), 395–417.

    Article  Google Scholar 

  • Nyerges, T. L., & Jankowski, P. (2010). Regional and urban GIS a decision support approach. New York: Guilford.

    Google Scholar 

  • O’Sullivan, D., & Unwin, D. J. (2010). Geographic information analysis. Hoboken, NJ: Wiley.

    Book  Google Scholar 

  • Ozah, A. P., Dami, A., & Adesina, F. A. (2012). A deterministic cellular automata model for simulating rural land use dynamics: A case study of Lake Chad Basin. Journal of Earth Science and Engineering, 2(1), 22–34.

    Google Scholar 

  • Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2), 314–337.

    Article  Google Scholar 

  • Rajabi, M., Mansourian, A., & Bazmani, A. (2012). Susceptibility mapping of visceral leishmaniasis based on fuzzy modelling and group decision-making methods. Geospatial Health, 7(10), 37–50.

    Article  Google Scholar 

  • Rinner, C. (2001). Argumentation maps: GIS-based discussion support for online planning. Environment and Planning B, 28(6), 847–863.

    Article  Google Scholar 

  • Rinner, C., Keßler, C., & Andrulis, S. (2008). The use of Web 2.0 concepts to support deliberation in spatial decision-making. Computers, Environment and Urban Systems, 32(5), 386–395.

    Article  Google Scholar 

  • Roy, B. (1991). The outranking approach and the foundations of ELECTRE methods. Theory and Decision, 31, 49–73.

    Article  Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.

    Google Scholar 

  • Sabri, S., Ludin, A. N. M. M., & Ho, C. S. (2012). Conceptual design for an integrated geosimulation and analytic network process (ANP) in gentrification appraisal. Applied Spatial Analysis and Policy, 5(3), 253–271.

    Article  Google Scholar 

  • Schmoldt, D. L., Peterson, D. L., & Smith, R. L. (1994). The analytic hierarchy process and participatory decision making. In J. M. Power & M. Strome (Eds.), Proceedings of the 17th Annual Geographic Information Seminar on Decision Support—2001, (Vol. 1) Toronto, Ontario, September 12–16, 1994.

    Google Scholar 

  • Shafizadeh Moghadam, H., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40, 140–149.

    Article  Google Scholar 

  • Sharifi, M., Hadidi, M., Vessali, E., Mosstafakhani, P., Taheri, K., Shahoie, S., et al. (2009). Integrating multi-criteria decision analysis for a GIS-based hazardous waste landfill sitting in Kurdistan Province, western Iran. Waste Management, 29(10), 2740–2758.

    Article  Google Scholar 

  • Strager, M. P., & Rosenberger, R. S. (2006). Incorporating stakeholder preferences for land conservation: Weights and measures in spatial MCA. Ecological Economics, 58(1), 79–92.

    Article  Google Scholar 

  • Torrens, P. M. (2002). Cellular automata and multi-agent systems as planning support tools. In S. S. Geertman & J. Stillwell (Eds.), Planning support systems in practice (pp. 205–222). London: Springer.

    Google Scholar 

  • Trunfio, G. A. (2006). Exploiting spatio-temporal data for the multiobjective optimization of cellular automata models. In E. Corchado, H. Yin, V. Botti, C. Fyfe (Eds.), Intelligent data engineering and automated learning—IDEAL 2006. Lecture Notes in Computer Science No. 4224 (pp. 81–89), Burgos, Spain, September 20–23, 2006. Heidelberg: Springer.

    Google Scholar 

  • Vaz, E., Caetano, M., & Nijkamp, P. (2011). Trapped between antiquity and urbanism – a multi-criteria assessment model of the greater Cairo Metropolitan area. Land Use Science, 6(4), 283–299.

    Article  Google Scholar 

  • Ward, D. P., Murray, A. T., & Phinn, S. R. (2003). Integrating spatial optimization and cellular automata for evaluating urban change. The Annals of Regional Science, 37(1), 131–148.

    Article  Google Scholar 

  • Wu, F. (1998). SimLand: A prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules. International Journal of Geographical Information Science, 12(1), 63–82.

    Article  Google Scholar 

  • Wu, F., & Webster, C. J. (1998). Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B, 25(1), 103–126.

    Article  Google Scholar 

  • Yager, R. R. (1996). Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems, 11(1), 49–73.

    Article  Google Scholar 

  • Ying, X., Guang-Ming, Z., Gui-Qiu, C., Lin, T., Ke-Lin, W., & Dao-You, H. (2007). Combining AHP with GIS in synthetic evaluation of eco-environment quality: A case study of Hunan Province, China. Ecological Modeling, 209(2–4), 97–109.

    Article  Google Scholar 

  • Yu, J., Chen, Y., & Wu, J. (2011). Modeling and implementation of classification rule discovery by ant colony optimisation for spatial land-use suitability assessment. Computers, Environment and Urban Systems, 35(4), 308–319.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jacek Malczewski .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this chapter

Cite this chapter

Malczewski, J., Rinner, C. (2015). GIS-MCDA for Group Decision Making. In: Multicriteria Decision Analysis in Geographic Information Science. Advances in Geographic Information Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74757-4_8

Download citation

Publish with us

Policies and ethics