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--- group_modelling:goodnessoffit [2009/03/26 15:36]
inpeifgi
+++ group_modelling:goodnessoffit [2009/03/27 11:50]
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-====== Tools for Assessment of Multiple Scale Land Change Models ======
+ Tools for Assessment of Multiple Scale Land Change Models
-Authors: Kristina Helle, Pedro Andrade, 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.
+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.
-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.
@@ Line 27: / Line 24: @@
 \\
+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.
+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.
-Li (2000) investigated the fractal properties which are typical to many land use (change) patterns. Land use changes may show varying behaviour on different scales. Therefore Jantz and Goetz (2005) compared different goodness-of-fit measures on several resolutions for an urban growth model. They do not only address global error rate and exact allocation but also geometric properties of the land use pattern like number and shape of clusters and length of edges. Some of these metrices are already implemented in TerraME.
+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.
-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.
+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).
-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. 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.
+=====Topics of the proposed Thesis and Questions to be answered in each work package=====
-=====Research Agenda=====
 The following open questions can be investigated by a PhD and a Master theses (Supervisors: Prof. Dr. Edzer Pebesma, Prof. Dr.Gilberto Câmara - not confirmed).
@@ Line 91: / Line 85: @@
 =====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.
@@ Line 99: / Line 96: @@
 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.
+Masters thesis, Department of Geography, University of California.
 CLARKE, K.; HOPPEN, S. & GAYDOS, L. (1998): {{http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/clarke_keith/clarkeetal.html|Methods And Techniques for Rigorous Calibration of a Cellular Automaton Model of Urban Growth}}. (accessed 25.03.09)
@@ Line 104: / Line 104: @@
 COSTANZA, R. {{encontros_e_eventos:inpeifgi2009:costanza_em_1989.pdf|Model Goodness of Fit: A Multiple Resolution Procedure}}. Ecological Modelling 47: 199-215. 1989.
-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.
-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
+KOK, K. & VELDKAMP, A. Evaluating impact of spatial scales on land use pattern
-Modelling 132 (1/2): 33–50.
+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.
 MANSON, S. M. {{encontros_e_eventos:inpeifgi2009:agent-based_dynamic_spatial_simulation_of_land-use_cover_change.pdf|Agent-based dynamic spatial simulation of land-use/cover change in the Yucatán peninsula, Mexico}}. 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4):
@@ Line 116: / Line 118: @@
 E. MOREIRA, S. COSTA, A. P. AGUIAR, G. CAMARA, T. CARNEIRO Dynamic coupling of multiscale land change models: interactions and feedbacks across regional and local deforestation models in the Brazilian Amazonia, Ecological Modelling (//submitted//). 2009
+PARKER, D. C.; Manson, S. M.; JANSSEN, M. A.; HOFFMANN, M. J. & DEADMAN, P. 2003 {{group_modelling:multi-agent_systems_for_the_simulation_of_land-use_and_land-cover_change_a_review.pdf|Multi-Agent Systems for the Simulation of Land-Use
+and Land-Cover Change: A Review}}. Annals of the Association of American Geographers 93 (2): 314–337.
 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
+WU, F., 2002, 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==
+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.)
-==not relevant==
-GILES, R. H., Jr. & TRANI, M. K..1999. {{group_modelling:key_elements_of_landscape_pattern_measures.pdf|Key elements of landscape pattern measures}}. Environmental Management 23 (4):477–81.)

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