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Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable

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Abstract

This paper proposes a method to quantify the goodness-of-fit of a land change projection along a gradient of an explanatory variable, by classifying pixels as one of four types: null successes, false alarms, hits, and misses. The method shows: (1) how the correctness and error of a land change projection are distributed along the gradient of an explanatory variable, (2) how the gradient of the explanatory variable relates to the stationarity of the land transition processes, and (3) how to use the insights from the previous two points to search for additional explanatory variables. The paper illustrates the method through a case study that applies the model Geomod in Central Massachusetts, USA. Results reveal that the model predicts more than the observed amount of change on flat slopes and less than the observed amount of change on steep slopes. One reason for these types of errors is that the land change process during the calibration interval is different than the process during the prediction interval with respect to slope. The method allows modelers to use the validation step as a diagnostic tool to search for potentially influential missing variables and to gain insight into land transition processes. The technique is designed to be applicable to a variety of types of land change models.

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References

  • Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. Professional paper 964. US Geological Survey, Reston, VA

    Google Scholar 

  • Batty M, Torrens PM (2005) Modeling and prediction in a complex world. Futures 37(7):745–766

    Article  Google Scholar 

  • Chen H, Pontius RG Jr (2010) Sensitivity of a land change model to pixel resolution and precision of the independent variable. Environ Model Assess. doi:10.1007/s10666-010-9233-3

  • Clarke KC, Gaydos L (1998) Loose-coupling a cellular automaton model and GIS: long-term 599 urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci 12:699–714

    Article  CAS  PubMed  Google Scholar 

  • Dietzel CK, Clarke KC (2004) Spatial differences in multi-resolution urban automata modeling. Trans GIS 8:479–492

    Article  Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–49

    Article  Google Scholar 

  • Hilferink M, Rietveld P (1999) Land use scanner: an integrated GIS based model for long term projections of land use in urban and rural areas. J Geogr Syst 1(2):155–177

    Article  Google Scholar 

  • McConnell W, Sweeney SP, Mulley B (2004) Physical and social access to land: spatio-temporal patterns of agricultural expansion in Madagascar. Agric Ecosyst Environ 101(2–3):171–184

    Article  Google Scholar 

  • Meyer WB, Turner BL II (1992) Human population growth and global land-use/cover change. Annu Rev Ecol Syst 23:39–61

    Article  Google Scholar 

  • Monserud RA, Leemans R (1992) Comparing global vegetation maps with the Kappa statistic. Ecol Modell 62(4):275–293

    Article  Google Scholar 

  • Overmars KP, Verburg PH, Veldkamp TA (2007) Comparision of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model. Land Use Policy 24:584–599

    Article  Google Scholar 

  • Overmas KP, de Groot WT, Huigen MGA (2007) Comparing inductive and deductive modeling of land use decisions: principles, a model and an illustration from the Philippines. Hum Ecol 35:439–452

    Article  Google Scholar 

  • Pijanowski BC, Stuart HG, Long DT (2000) A land transformation model: integrating policy, socioeconomics and environmental drivers using a geographic information system. In: Harris L, Sanderson J (eds) Landscape ecology: a top down approach. CRC Press, Boca Raton, FL, pp 183–198

    Google Scholar 

  • Pijanowski BC, Brown DG, Manik G, Shellito B (2002) Using neural nets and GIS to forecast land use changes: a land transformation model. Comput Environ Urban Syst 26(6):553–575

    Article  Google Scholar 

  • Pontius RG Jr (2000) Quantification error versus location error in the comparison of categorical maps. Photogramm Eng Remote Sens 66(8):1011–1016

    Google Scholar 

  • Pontius RG Jr (2002) Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogramm Eng Remote Sens 68(10):1041–1049

    Google Scholar 

  • Pontius RG Jr, Chen H (2006) GEOMOD modeling. Idrisi 15: The Andes edition. Clark Labs, Worcester, MA

    Google Scholar 

  • Pontius RG Jr, Neeti N (2010) Uncertainty in the difference between maps of future land change scenarios. Sustain Sci 5:39–50

    Article  Google Scholar 

  • Pontius RG Jr, Petrova S (2010) Assessing a predictive model of land change using uncertain data. Environ Model Softw 25(3):299–309

    Article  Google Scholar 

  • Pontius RG Jr, Millones M (2008) Problems and solutions for kappa-based indices of agreement. In: Conference proceedings of studying, modeling and sense making of planet Earth, Mytilene, Greece, 8 pp

  • Pontius RG Jr, Peethambaram S, Castella JC (in press) Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam. Ann Assoc Am Geogr

  • Pontius RG Jr, Spencer J (2005) Uncertainty in extrapolations of predictive land change models. Environ Plann B 32:211–230

    Article  Google Scholar 

  • Pontius RG Jr, Cornell J, Hall C (2001) Modeling the spatial pattern of land-use change with GEOMOD2: application and validation for Costa Rica. Agric Ecosyst Environ 85(1–3):191–203

    Article  Google Scholar 

  • Pontius RG Jr, Walker R, Yao-Kumah R, Arima E, Aldrich S, Caldas M, Vergara D (2007) Accuracy assessment for a simulation model of Amazonian deforestation. Ann Assoc Am Geogr 97(4):677–695

    Article  Google Scholar 

  • Pontius RG Jr, Boersma W, Castella JC, Clarke K, de Nijs T, Dietzel C, Zengqiang D, Fotsing E, Goldstein N, Kok K, Koomen E, Lippitt CD, McConnell W, Pijanowski B, Pithadia S, Sood AM, Sweeney S, Trung TN, Veldkamp AT, Verburg PH (2008) Comparing input, output, and validation maps for several models of land change. Ann Reg Sci 42(1):11–47

    Article  Google Scholar 

  • Pontius Jr. RG, Malizia NR (2004) Effect of category aggregation on map comparison. In: Egenhofer MJ, Freksa C, Miller HJ (eds) GIScience2004. Lecture Notes in Computer Science, vol 3234, pp 251–268

  • Silva E, Clarke K (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26:525–552

    Article  Google Scholar 

  • Turner BL II, Meyer WB (1995) The earth transformed: trends, patterns, and processes. In: Johnston RJ, Taylor PJ, Watts M (eds) Geographies of global change: remapping the world in the late twentieth century. Basil Blackwell, Cambridge, MA, pp 302–317

    Google Scholar 

  • Veldkamp T, Fresco L (1996) CLUE-CR: an integrated multi-scale model to simulate land use change scenarios in Costa Rica. Ecol Model 91:231–248

    Article  CAS  Google Scholar 

  • Verburg PH, Soepboer S, Veldkamp TA, Limpiada R, Espaldon V, Sharifah Mastura SA (2002) Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manag 30(3):391–405

    Article  Google Scholar 

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Acknowledgments

The National Science Foundation supported this work via three of its programs: (1) Human-Environment Regional Observatory program via grant 9978052, (2) Long Term Ecological Research via grant OCE-0423565, and (3) Center for Integrated Study of the Human Dimensions of Global Change through a cooperative agreement between Carnegie Mellon University and the National Science Foundation SBR-9521914. Clark Labs facilitated this work by creating the GIS software Idrisi®. We thank anonymous reviewers for constructive comments that improved the quality of this paper.

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  1. Clark Labs/IDRISI Project, Clark University, 950 Main Street, Worcester, MA, 01610-1477, USA

    Hao Chen

  2. Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA, 01610-1477, USA

    Robert Gilmore Pontius Jr.

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  1. Hao Chen
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  2. Robert Gilmore Pontius Jr.
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Correspondence to Hao Chen.

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Chen, H., Pontius, R.G. Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landscape Ecol 25, 1319–1331 (2010). https://doi.org/10.1007/s10980-010-9519-5

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