Stochastic Environmental Research and Risk Assessment

, Volume 32, Issue 9, pp 2699–2719 | Cite as

Analysis of the influence of parameter and scale uncertainties on a local multi-criteria land use evaluation model

  • Seda Şalap-Ayça
  • Piotr Jankowski
Original Paper


Land use evaluation involves careful consideration of several environmental factors and their relative importance quantified by factor weights. Local multi-criteria evaluation provides a mechanism for computing factor (criteria) weights within local neighborhoods that capture spatial heterogeneity and contribute to more accurate evaluation results. The accuracy of results, however, is tempered by the potential uncertainty of criteria weights. The paper presents a spatially explicit approach to uncertainty and sensitivity analysis of local criteria weights and modeling scale on the variability of model output. The efficacy of the approach is presented on the example of Environmental Benefit Index (EBI) model used by the U.S. Department of Agriculture Conservation Reserve Program (CRP) to select environmentally sensitive agricultural areas for conservation. The uncertainty analysis resulted in identifying robust areas for CRP selection characterized by high suitability and low uncertainty. The sensitivity analysis focused on the next-best group of candidates characterized by high suitability and high uncertainty. The results show that there is a relationship between spatial heterogeneity, data representation scale, and the level of uncertainty in the results of EBI model. The sensitivity of model output can be attributed to both the uncertainty of criteria weights and the modeling scale. A potential practical value of this approach is the improved analytical support for land suitability evaluation requiring a consideration of sub-optimal land units (high suitability/high uncertainty). Also, this approach can guide modelling effort by allowing the analyst to visualize spatial distribution and patterns of model output uncertainty and focus data collection on influential model input factors.


GIS Local multi-criteria evaluation Uncertainty analysis Global sensitivity analysis Scale effect 



This research was supported in part by the National Science Foundation Geography and Spatial Sciences Program Grant No. BCS-1263071. Any opinion, findings, conclusions, and recommendations expressed in this paper are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.


  1. Alexander ER (1989) Sensitivity analysis in complex decision models. J Am Plan Assoc 55:323–333. CrossRefGoogle Scholar
  2. Babcock BA, Lakshminarayan PG, Wu J (1997) Targeting tools for the purchase of environmental amenities. Land Econ 73:325–339CrossRefGoogle Scholar
  3. Carter B, Rinner C (2014) Locally weighted linear combination in a vector geographic information system. J Geogr Syst 16:343–361CrossRefGoogle Scholar
  4. Chen Y, Yu J, Khan S (2013) The spatial framework for weight sensitivity analysis in AHP-based multi-criteria decision making. Environ Model Softw 48:129–140. CrossRefGoogle Scholar
  5. Chu-Agor ML, Muñoz-Carpena R, Kiker G et al (2011) Exploring vulnerability of coastal habitats to sea level rise through global sensitivity and uncertainty analyses. Environ Model Softw 26:593–604. CrossRefGoogle Scholar
  6. Claessens L, Schoorl JM, Verburg PH et al (2009) Modelling interactions and feedback mechanisms between land use change and landscape processes. Agric Ecosyst Environ 129:157–170. CrossRefGoogle Scholar
  7. Crosetto M, Tarantola S (2001) Uncertainty and sensitivity analysis: tools for GIS-based model implementation. Int J Geogr Inf Sci 15:415–437CrossRefGoogle Scholar
  8. Crosetto M, Tarantola S, Saltelli A (2000) Sensitivity and uncertainty analysis in spatial modelling based on GIS. Agric Ecosyst Environ 81:71–79. CrossRefGoogle Scholar
  9. European Commission—IPSC (2008) SimLab 2.2 reference manualGoogle Scholar
  10. Fischer G (1995) Range sensitivity of attribute weights in multiattribute value models. Organ Behav Hum Decis Process 62:252–266CrossRefGoogle Scholar
  11. Fisher P (1999) Models of uncertainty in spatial data. In: Longley PA, Goodchild MF, Maguire DJ, Rhind DW (eds) Geographical information systems: principles and applications. Wiley, Hoboken, NJ, pp 191–205Google Scholar
  12. Gatelli D, Kucherenko S, Ratto M, Tarantola S (2009) Calculating first-order sensitivity measures: a benchmark of some recent methodologies. Reliab Eng Syst Saf 94:1212–1219. CrossRefGoogle Scholar
  13. Gómez-Delgado M, Tarantola S (2006) GLOBAL sensitivity analysis, GIS and multi-criteria evaluation for a sustainable planning of a hazardous waste disposal site in Spain. Int J Geogr Inf Sci 20:449–466. CrossRefGoogle Scholar
  14. Helton JC, Davis FJ (2002) Illustration of sampling based methods for uncertainty and sensitivity analysis. Risk Anal 22:591–622CrossRefGoogle Scholar
  15. Helton JC, Davis FJ (2003) Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf 81:23–69. CrossRefGoogle Scholar
  16. Helton JC, Johnson JD, Sallaberry CJ, Storlie CB (2006) Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliab Eng Syst Saf 91:1175–1209. CrossRefGoogle Scholar
  17. Homma T, Saltelli A (1996) Importance measures in global sensitivity analysis of nonlinear models. Reliab Eng Syst Saf 52:1–17. CrossRefGoogle Scholar
  18. Jessop A (1999) Entropy in multiattribute problems. J Multi-Criteria Decis Anal 70:61–70CrossRefGoogle Scholar
  19. Joerin F, Thériault M, Musy A (2001) Using GIS and outranking multicriteria analysis for land-use suitability assessment. Int J Geogr Inf Sci 15:153–174CrossRefGoogle Scholar
  20. Keeney RL (1992) Value-focused thinking: a path to creative decisionmaking. Harvard University Press, CambridgeGoogle Scholar
  21. Kucherenko S, Feil B, Shah N, Mauntz W (2011) The identification of model effective dimensions using global sensitivity analysis. Reliab Eng Syst Saf 96:440–449. CrossRefGoogle Scholar
  22. Ligmann-Zielinska A, Jankowski P (2008) A framework for sensitivity analysis in spatial multiple criteria evaluation. In: Cova T, Miller H, Beard K et al (eds) Geographic information science SE—14. Springer, Berlin, pp 217–233CrossRefGoogle Scholar
  23. Ligmann-Zielinska A, Jankowski P (2014) Spatially-explicit integrated uncertainty and sensitivity analysis of criteria weights in multicriteria land suitability evaluation. Environ Model Softw 57:235–247. CrossRefGoogle Scholar
  24. Ligmann-Zielinska A, Jankowski P, Watkins J (2012) Spatial uncertainty and sensitivity analysis for multiple criteria land suitability evaluation. In: GIScience 2012: 7th international conference on geographic information science, pp 2–5Google Scholar
  25. Lilburne L, Tarantola S (2009) Sensitivity analysis of spatial models. Int J Geogr Inf Sci 23:151–168. CrossRefGoogle Scholar
  26. Malczewski J (2004) GIS-based land-use suitability analysis: a critical overview. Prog Plann 62:3–65. CrossRefGoogle Scholar
  27. Malczewski J (2011) Local weighted linear combination. Trans GIS 15:439–455. CrossRefGoogle Scholar
  28. Malczewski J, Liu X (2014) Local ordered weighted averaging in GIS-based multicriteria analysis. Ann GIS 20:117–129. CrossRefGoogle Scholar
  29. Malczewski J, Rinner C (2015) Multicriteria decision analysis in geographic information science. Springer, Berlin, HeidelbergGoogle Scholar
  30. Monat J (2009) The benefits of global scaling in multi-criteria decision analysis. Judgm Decis Mak 4:492–508Google Scholar
  31. Morgan MG, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University PressGoogle Scholar
  32. NRCS (2016) Natural resources conservation service. Accessed 11 Feb 2016
  33. Perez KL (2008) The allocation of conservation reserve program acreage: a political economy perspective. Ph.D. dissertation. North Carolina State University, Raleigh, NC. CiteSeerX
  34. Qin X (2013) Local ideal point method for GIS-based multicriteria analysis: a case study in London, OntarioGoogle Scholar
  35. Reichelderfer K, Boggess W (1988) Government decision making and program performance: the case of the conservation reserve program. Am J Agric Econ Agric Appl Econ Assoc 70(1):1–11Google Scholar
  36. Ribaudo MO, Hoag DL, Smith ME, Heimlich R (2001) Environmental indices and the politics of the conservation\rReserve program. Ecol Indic 1:11–20CrossRefGoogle Scholar
  37. Rinner C, Heppleston A (2006) The spatial dimensions of multi-criteria evaluation – case study of a home buyer’s spatial decision support system. In: Raubal M, Miller HJ, Frank AU, Goodchild MF (eds) Geographic information science. GIScience 2006. Lecture Notes in Computer Science, vol 4197. Springer, Berlin, HeidelbergGoogle Scholar
  38. Saint-Geours N, Bailly J-S, Grelot F, Lavergne C (2014) Multi-scale spatial sensitivity analysis of a model for economic appraisal of flood risk management policies. Environ Model Softw 60:153–166. CrossRefGoogle Scholar
  39. Saisana M, Saltelli A, Tarantola S (2005) Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. J R Stat Soc Ser A Stat Soc 168:307–323. CrossRefGoogle Scholar
  40. Şalap-Ayça S, Jankowski P (2016) Integrating local multi-criteria evaluation with spatially explicit uncertainty-sensitivity analysis. Spat Cogn Comput 16:106–132. CrossRefGoogle Scholar
  41. Saltelli A, Tarantola S, Chan K (1999) A role for sensitivity analysis in presenting the results from MCDA studies to decision makers. J Multi-Criteria Decis Anal 145:139–145CrossRefGoogle Scholar
  42. Saltelli A, Chan K, Scott EM (2000) Sensitivity analysis. Wiley, New YorkGoogle Scholar
  43. Saltelli A, Ratto M, Tarantola S, Campolongo F (2006) Sensitivity analysis practices: strategies for model-based inference. Reliab Eng Syst Saf 91:1109–1125. CrossRefGoogle Scholar
  44. Saltelli A, Annoni P, Azzini I et al (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181:259–270. CrossRefGoogle Scholar
  45. Samat N (2006) Characterizing the scale sensitivity of the cellular automata simulated urban growth: a case study of the Seberang Perai Region, Penang State, Malaysia. Comput Environ Urban Syst 30:905–920. CrossRefGoogle Scholar
  46. Seaber PR, Kapinos FP, Knapp GL (1987) Hydrologic unit maps, United States geological survey water-supply paper 2294. USGS, DenverGoogle Scholar
  47. Sobol’ IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55:271–280. CrossRefGoogle Scholar
  48. Sobol’ IM, Turchaninov VI, Levitan YL, Shukhman BV (1992) Quasirandom sequence generators. Keldysh Institute of Applied Mathematics, Russian Academic of Sciences, MoscowGoogle Scholar
  49. Sobol’ IM, Asotsky D, Kreinin A, Kucherenko S (2011) Construction and comparison of high-dimensional sobol’ generators. Wilmott. CrossRefGoogle Scholar
  50. Stewart TR, Ely DW (1984) Range sensitivity: a necessary condition and a test for the validity of weights. In: Multiple Criteria Decision Making Conference, 4–8 June 1978, Cleveland, Ohio, USAGoogle Scholar
  51. Store R, Kangas J (2001) Integrating spatial multi-criteria evaluation and expert knowledge for GIS-based habitat suitability modelling. Landsc Urban Plan 55:79–93. CrossRefGoogle Scholar
  52. Tarantola S, Giglioli N, Jesinghaus J, Saltelli A (2002) Can global sensitivity analysis steer the implementation of models for environmental assessments and decision-making? Stoch Environ Res Risk Assess 16:63–76. CrossRefGoogle Scholar
  53. USDA (2011) Fact sheet—conservation reserve program sign-Up 41 Environmental Benefit Index (EBI)Google Scholar
  54. Von Nitzsch R, Weber M (1993) The effect of attribute ranges on weights in utility multiattribute measurements. Manag Sci 39:937–943CrossRefGoogle Scholar
  55. Voogd H (1983) Multicriteria evaluation for urban and regional planning. Pion, LondonGoogle Scholar
  56. Wei P, Lu Z, Song J (2015) Variable importance analysis: a comprehensive review. Reliab Eng Syst Saf 142:399–432. CrossRefGoogle Scholar
  57. Womach J (2005) Agriculture: a glossary of terms, programs, and laws, 2005 edition, report, June 16, 2005. University of North Texas Libraries, Digital Library, crediting UNT Libraries Government Documents Department, Washington, DC. Accessed 21 Mar 2018
  58. Wong DWS (2009) The Modifiable Areal Unit Problem (MAUP). In: Fotheringham AS, Rogerson PA (eds) The SAGE handbook of spatial analysis, pp 105–124Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of GeographySan Diego State UniversitySan DiegoUSA
  2. 2.Department of GeographyUniversity of California, Santa BarbaraSanta BarbaraUSA
  3. 3.Institute of Geoecology and GeoinformationAdam Mickiewicz UniversityPoznanPoland

Personalised recommendations