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| - | Tools for Assessment of Multiple Scale Land Change Models | + | ======Tools for Assessment of Multiple Scale Land Change Models====== |
| Kristina Helle, Pedro Andrade, and Edzer Pebesma | Kristina Helle, Pedro Andrade, and Edzer Pebesma | ||
| - | Introduction | + | =====Introduction===== |
| The complex relations between biophysical and anthropological factors generate the land change patterns of our environment. In order to study this complex phenomena, we have to rely on simulation models, for example cellular automata or agent-based models. LUCC simulation models usually generate a new map given a real world map of land cover classes. In the figure below, the left map shows the real data and the right one the simulated results. | The complex relations between biophysical and anthropological factors generate the land change patterns of our environment. In order to study this complex phenomena, we have to rely on simulation models, for example cellular automata or agent-based models. LUCC simulation models usually generate a new map given a real world map of land cover classes. In the figure below, the left map shows the real data and the right one the simulated results. | ||
| + | {{ encontros_e_eventos:simulation-real-world-pontius.jpg }} | ||
| Figure 1: Example of real world data (left) and a simulation (right). Source: Pontius (2002) | Figure 1: Example of real world data (left) and a simulation (right). Source: Pontius (2002) | ||
| + | \\ | ||
| Considering that "all models are wrong, but some are useful" (Box 1999), model assessment should address the most feasible requirements, such as testing how well the model fits the data and if it is useful for certain purposes. | Considering that "all models are wrong, but some are useful" (Box 1999), model assessment should address the most feasible requirements, such as testing how well the model fits the data and if it is useful for certain purposes. | ||
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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 multi-agent models is given by Parker et al. (2003). | + | 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. |
| =====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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| ===== References ===== | ===== References ===== | ||
| + | AGARWAL, CH.; GREEN, G. M.; GROVE, J. M.; EVANS, T. P. & SCHWEIK, CH. M. [[http://hero.geog.psu.edu/archives/AgarwalEtALInPress.pdf | A Review and Assessment of Land-Use Change Models Dynamics of Space, Time, and Human Choice]]. CIPEC Collaborative Report Series No. 1. | ||
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| C. M. ALMEIDA, A. M. V. MONTEIRO, G. CAMARA, B. S. SOARES-FILHO, G. C. CERQUEIRA, C. L.pENNACHIN, M. BATTY. {{http://www.dpi.inpe.br/gilberto/papers/claudia_ijrs.pdf|GIS and remote sensing as tools for the simulation of urban land-use change}} International Journal of Remote Sensing Vol. 26, No. 4, 20 February 2005, 759–774 | C. M. ALMEIDA, A. M. V. MONTEIRO, G. CAMARA, B. S. SOARES-FILHO, G. C. CERQUEIRA, C. L.pENNACHIN, M. BATTY. {{http://www.dpi.inpe.br/gilberto/papers/claudia_ijrs.pdf|GIS and remote sensing as tools for the simulation of urban land-use change}} International Journal of Remote Sensing Vol. 26, No. 4, 20 February 2005, 759–774 | ||
| BOX, G. E. P. {{http://ecow.engr.wisc.edu/cgi-bin/get/ie/691/barrios/papers/box-1999.pdf|Statistics as a Catalyst to Learning by Scientific Methods Part II - A Discussion}}. XLII Annual Fall Technical Conference of the Chemical and Process Industries Division and Statistics Division of the American Society for Quality and the Section on Physical & Engineering Sciences of the American Statistical Association. 1998. | BOX, G. E. P. {{http://ecow.engr.wisc.edu/cgi-bin/get/ie/691/barrios/papers/box-1999.pdf|Statistics as a Catalyst to Learning by Scientific Methods Part II - A Discussion}}. XLII Annual Fall Technical Conference of the Chemical and Process Industries Division and Statistics Division of the American Society for Quality and the Section on Physical & Engineering Sciences of the American Statistical Association. 1998. | ||
| - | CANDAU, J., 2002, Temporal calibration sensitivity of the SLEUTH urban growth model. | + | CANDAU, J., 2002, {{group_modelling:temporal_calibration_sensitivity_of_the_sleuth_urban_growth_model.pdf|Temporal calibration sensitivity of the SLEUTH urban growth model}}. |
| Masters thesis, Department of Geography, University of California. | Masters thesis, Department of Geography, University of California. | ||
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| 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. | ||
| - | KOK, K. & VELDKAMP, A. Evaluating impact of spatial scales on land use pattern | + | KOK, K. & VELDKAMP, A. {{group_modelling:evaluating_impact_of_spatial_scales_on_land_use_pattern_analysis_in_central_america.pdf|Evaluating impact of spatial scales on land use pattern |
| - | analysis in Central America. Agriculture, Ecosystems and Environment 85 (2001) 205–221. | + | analysis in Central America}}. Agriculture, Ecosystems and Environment 85 (2001) 205–221. |
| LI, B.-L. 2000. {{group_modelling:fractal_geometry_applications_in_description_and_analysis_of_patch_patterns_and_patch_dynamics.pdf|Fractal geometry applications in description and analysis of patch patterns and patch dynamics. }}. Ecological Modelling 132 (1/2): 33–50. | LI, B.-L. 2000. {{group_modelling:fractal_geometry_applications_in_description_and_analysis_of_patch_patterns_and_patch_dynamics.pdf|Fractal geometry applications in description and analysis of patch patterns and patch dynamics. }}. Ecological Modelling 132 (1/2): 33–50. | ||
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| Vol. 68, No. 10, October 2002, pp. 1041–1049 | Vol. 68, No. 10, October 2002, pp. 1041–1049 | ||
| - | WU, F., 2002, Calibration of stochastic cellular automata: the application to rural-urban land | + | 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, |
| - | conversions. International Journal of Geographical Information Science, 16, | + | |
| pp. 795–818. | pp. 795–818. | ||
