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--- group_modelling:goodnessoffit [2009/03/27 14:50] – inpeifgi
+++ group_modelling:goodnessoffit [2009/06/09 18:00] (atual) – inpeifgi
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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
-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.
+{{  encontros_e_eventos:simulation-real-world-pontius.jpg  }}
 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.
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 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.
 =====State of the Art=====
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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.
-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 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 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=====
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 =====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.
@@ Linha 93: / Linha 99: @@
 ===== 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.
 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.
-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.
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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.
+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.
-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.
@@ Linha 125: / Linha 135: @@
 Vol. 68, No. 10, October 2002, pp. 1041–1049
-WU, F., 2002, Calibration of stochastic cellular automata: the application to rural-urban land
+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.
-conversions. International Journal of Geographical Information Science, 16,
+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,
 pp. 795–818.
-==not directly relevant==
+==related literature==
 GILES, R. H., Jr. & TRANI, M. K..1999. {{http://www.springerlink.com/content/je6g2v0khpl93gpc/fulltext.pdf|Key elements of landscape pattern measures}}. Environmental Management 23 (4):477–81.)