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group_modelling:goodnessoffit [2009/05/12 14:57] inpeifgi |
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| With these metrics, it is possible to calibrate or to validate the model. | With these metrics, it is possible to calibrate or to validate the model. | ||
| In fact, the final objective of these goodness-of-fit methods is to point out how to improve the model. | In fact, the final objective of these goodness-of-fit methods is to point out how to improve the model. | ||
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| =====State of the Art===== | =====State of the Art===== | ||
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| Scale (here in terms of resolution and extend) is an important property of LUCC models. Li (2000) investigated the fractal properties which are typical to many land use (change) patterns. Another approach by Jantz and Goetz (2005) compared different goodness-of-fit measures on several resolutions for an urban growth model as land use changes may show varying behaviour on different scales. But also extend can change models a lot as Kok and Veldkamp (2001) showed for national vs. multinational models. | Scale (here in terms of resolution and extend) is an important property of LUCC models. Li (2000) investigated the fractal properties which are typical to many land use (change) patterns. Another approach by Jantz and Goetz (2005) compared different goodness-of-fit measures on several resolutions for an urban growth model as land use changes may show varying behaviour on different scales. But also extend can change models a lot as Kok and Veldkamp (2001) showed for national vs. multinational models. | ||
| - | Calibration of cellular automata or agent-based models is not a trivial task as parameters influence is in most cases non-linear and often the number of parameters is high, making comprehensive evaluation of all combinations unfeasible. Simple approaches like by Clarke et al. (1998) generate lots of simulations to be evaluated by the user, they consider interactive visualization as an important tool. Multi-resolution search of the parameter space as described in Candau (2002) may help to detect important parameter combinations and subsequently to adjust them with feasible computational effort. Still users may not find the most influential parameter combinations. This task was addressed by Miller (1998) who used several robust optimization algorithms to investigate the parameter space. Wu (2002) uses the data to fit a prior distribution to the parameters and updates it according to the results of Monte Carlo simulations for calibrating a cellular automata model. | + | Calibration of cellular automata or agent-based models is not a trivial task as parameters influence is in most cases non-linear and often the number of parameters is high, making comprehensive evaluation of all combinations unfeasible. Simple approaches like by Clarke et al. (1998) generate lots of simulations to be evaluated by the user, they consider interactive visualization as an important tool. Multi-resolution search of the parameter space as described in Candau (2002) or Hakan et.al (2007) may help to detect important parameter combinations and subsequently to adjust them with feasible computational effort. Still users may not find the most influential parameter combinations. This task was addressed by Miller (1998) who used several robust optimization algorithms to investigate the parameter space. Wu (2002) uses the data to fit a prior distribution to the parameters and updates it according to the results of Monte Carlo simulations for calibrating a cellular automata model. Whereas calibration of agent based models is still a domain of econometrics (e.g. Rogers & von Tessin 2004). |
| The diversity of LUCC models may require different calibration and validation methods. An overview over current models is given by Agarwal et al., a comparison of several models by Pontius et al. (2008). Parker et al. (2003) focus on multi-agent models only. | The diversity of LUCC models may require different calibration and validation methods. An overview over current models is given by Agarwal et al., a comparison of several models by Pontius et al. (2008). Parker et al. (2003) focus on multi-agent models only. | ||
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| =====Mobility Measures===== | =====Mobility Measures===== | ||
| The topic is at the overlap of the research at INPE (agent-based and cellular automata models of LUCC) and IFGI (statistics, calibration / validation of models). Therefore the theses should take place as sandwich (exchange: PhD 6-12 months, MSc 2-3 months), starting either at INPE or IFGI. | The topic is at the overlap of the research at INPE (agent-based and cellular automata models of LUCC) and IFGI (statistics, calibration / validation of models). Therefore the theses should take place as sandwich (exchange: PhD 6-12 months, MSc 2-3 months), starting either at INPE or IFGI. | ||
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| COSTANZA, R. {{encontros_e_eventos:inpeifgi2009:costanza_em_1989.pdf|Model Goodness of Fit: A Multiple Resolution Procedure}}. Ecological Modelling 47: 199-215. 1989. | COSTANZA, R. {{encontros_e_eventos:inpeifgi2009:costanza_em_1989.pdf|Model Goodness of Fit: A Multiple Resolution Procedure}}. Ecological Modelling 47: 199-215. 1989. | ||
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| + | HAKAN, O.; KLEIN, A.G.; SRINIVASAN, R. (2007): {{group_modelling:calibration_of_the_sleuth_model_based_on_the_historic_growth_of_houston.pdf|Calibration of the Sleuth Model Based on the Historic Growth of Huston}}. Journal of Applied Sciences 7 (14): 1843 - 1853. | ||
| JANTZ, C. A.; GOETZ, S. J. {{group_modelling:analysis_of_scale_dependencies_in_an_urban_land-use-change_model.pdf|Analysis of scale dependencies in an urban land-use-change model}}. International Journal of Geographical Information Science. Vol. 19, No. 2, February 2005, 217–241. | JANTZ, C. A.; GOETZ, S. J. {{group_modelling:analysis_of_scale_dependencies_in_an_urban_land-use-change_model.pdf|Analysis of scale dependencies in an urban land-use-change model}}. International Journal of Geographical Information Science. Vol. 19, No. 2, February 2005, 217–241. | ||
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| PONTIUS, R.G.; BOERSMA, W.; CASTELLA, J.-CH.; CLARKE, K.; DE NIJS, T.; DIETZEL, CH.; DUAN, Z.; FOTSING, E.; GOLDSTEIN, N.; KOK, K.; KOOMEN, E.; LIPPIT, CH. D.; MCCONNELL, W.; SOOD, A. M.; PIJANOWSKI, B.; PITHADIA, S.; SWEENEY, S.; TRUNG, T. N.; VELDKAMP, A. T. & VERBURG, P. H. {{group_modelling:comparing_the_input_output_and_validation_maps.pdf|Comparing the input, output, and validation maps for several models of land change}}. Ann Reg Sci (2008) 42: 11-37. | PONTIUS, R.G.; BOERSMA, W.; CASTELLA, J.-CH.; CLARKE, K.; DE NIJS, T.; DIETZEL, CH.; DUAN, Z.; FOTSING, E.; GOLDSTEIN, N.; KOK, K.; KOOMEN, E.; LIPPIT, CH. D.; MCCONNELL, W.; SOOD, A. M.; PIJANOWSKI, B.; PITHADIA, S.; SWEENEY, S.; TRUNG, T. N.; VELDKAMP, A. T. & VERBURG, P. H. {{group_modelling:comparing_the_input_output_and_validation_maps.pdf|Comparing the input, output, and validation maps for several models of land change}}. Ann Reg Sci (2008) 42: 11-37. | ||
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| + | ROGERS, A.; VON TESSIN, P. (2004):{{group_modelling:multi-objective_calibration_for_agent_based_models.pdf| Multi-Objective Calibration for Agent-Based Models}}. Proceedings 5th Workshop on Agent-Based Simulation. | ||
| WU, F., 2002, {{group_modelling:calibration_of_stochastic_cellular_automata_the_application_to.pdf|Calibration of stochastic cellular automata: the application to rural-urban land conversions}}. International Journal of Geographical Information Science, 16, | WU, F., 2002, {{group_modelling:calibration_of_stochastic_cellular_automata_the_application_to.pdf|Calibration of stochastic cellular automata: the application to rural-urban land conversions}}. International Journal of Geographical Information Science, 16, | ||
