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| - | Pontius (2002) realized that we only need to take into account the cells that have changed, instead of comparing the whole maps. His more flexible approach allows to explicitly separate errors of quantity and of location and to use fuzzy classification. Others like Jantz and Goetz (2005) did also address geometric porperties of the land use patterns like number and shape of clusters and length of edges. Some of these metrices are already implemented in TerraME but have not been used for testing. | + | Pontius (2002) realized that we only need to take into account the cells that have changed, instead of comparing the whole maps. His more flexible approach allows to explicitly separate errors of quantity and of location and to use fuzzy classification. Later (Pontius et al. 2008) he refines his technique to compare real changes and predicted changes. Others like Jantz and Goetz (2005) did also address geometric porperties of the land use patterns like number and shape of clusters and length of edges. Some of these metrices are already implemented in TerraME but have not been used for testing. |
| 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. | ||
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| 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) 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. | ||
| - | The diversity of LUCC models may require different calibration and validation methods. An overview over current LUCC models is given by Agarwal et al. whereas Parker et al. (2003) focus on multi-agent models. | + | 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. |
| =====Topics of the proposed Thesis and Questions to be answered in each work package===== | =====Topics of the proposed Thesis and Questions to be answered in each work package===== | ||
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| PONTIUS, R. G. {{encontros_e_eventos:inpeifgi2009:pontius_2002_pers.pdf|Statistical Methods to Partition Effects of Quantity and Location During Comparison of Categorical Maps at Multiple Resolutions}}. Photogrammetric Engineering & Remote Sensing | PONTIUS, R. G. {{encontros_e_eventos:inpeifgi2009:pontius_2002_pers.pdf|Statistical Methods to Partition Effects of Quantity and Location During Comparison of Categorical Maps at Multiple Resolutions}}. Photogrammetric Engineering & Remote Sensing | ||
| Vol. 68, No. 10, October 2002, pp. 1041–1049 | Vol. 68, No. 10, October 2002, pp. 1041–1049 | ||
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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. | ||
| 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, | ||
