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group_modelling:goodnessoffit [2009/03/19 19:02] – inpeifgigroup_modelling:goodnessoffit [2009/03/27 12:03] – inpeifgi
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 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. 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. 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  }} {{  encontros_e_eventos:simulation-real-world-pontius.jpg  }}
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 Goodness-of-fit tests compare the predicted map with the reality at the new time. Goodness-of-fit tests compare the predicted map with the reality at the new time.
  
-Some authors have been proposed ways to calculate metrics trying to inform the quality of the results to the scientist. +Some authors have been proposed ways to calculate metrics trying to inform the quality of the results to the scientist.
 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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 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. 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.
  
-Land use changes may show varying behaviour on different scales. Therefore it can be useful to find the best resolution for a given purpose.  +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. 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 but have not been used for testing.
-Jantz and Goetz (2005) apply several measures for absolute error rate, number and shape of clusters, amount of edges, and exact position to a urban growth 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. 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.
 +
 +The diversity of LUCC models may require different calibration and validation methods. An overview over current 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=====
 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). 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).
- 
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 ====Goodness of fit tests==== ====Goodness of fit tests====
  
-“Qualitatively, these goodness-of-fit measures range from completely non-spatial (the amount of change) to non-spatial metrics (edges and clusters), to explicitly spatial” (Jantz & Goetz 2005). A first task is to compare those tests and to investigate which of them fits best the needs of agent-based models of deforestation in Amazonia. The best test may vary depending on the aim of modelling: a good fit on the averaged deforestation rate needs only a global goodness-of-fit test whereas typical patterns need comparing geometric properties and prediction about the actual areas affected besides need comparison on pixel level.+“Qualitatively, these goodness-of-fit measures range from completely non-spatial (the amount of change) to non-spatial metrics (edges and clusters), to explicitly spatial” (Jantz & Goetz 2005). A first task is to compare those tests and to investigate which of them fits best the needs of agent-based models of deforestation in Amazonia. The best test may vary depending on the aim of modelling: a good fit on the averaged deforestation rate needs only a global goodness-of-fit test whereas typical patterns need comparing geometric properties, and prediction about the actual areas affected besides needs comparison on pixel level.
  
  
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 ===Multi-scale=== ===Multi-scale===
-A current challenge in LUCC is to develop multi-scale models. Human behaviour can only be captured at different levels. Jantz and Goetz (2005) compared goodness-of-fit tests on different resolutions. But they did not address multi-scale models like the partially hierarchical model of Moreira et al. (2009), where the scale below is a finer grid of only a sub-area of the upper scale. +A current challenge in LUCC is to develop multi-scale models. Human behaviour can only be captured at different levels. Jantz and Goetz (2005) compared goodness-of-fit tests on different resolutions. But they did not address multi-scale models like the partially hierarchical model of Moreira et al. (2009), where the scale below is a finer grid of only a sub-area of the upper scale.
  
 {{  encontros_e_eventos:multi-scale-model.jpg?500  }} {{  encontros_e_eventos:multi-scale-model.jpg?500  }}
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 ====Calibration and Validation==== ====Calibration and Validation====
-To complete the design of a model calibration and final validation is needed. Both procedures require goodness-of-fit tests. Calibration should improve the most sensitive and important parameters. Validation finally tests if the calibrated model is overfitting the data or to which extent it is valid.  +To complete the design of a model calibration and final validation is needed. Both procedures require goodness-of-fit tests. Calibration should improve the most sensitive and important parameters. Validation finally tests if the calibrated model is overfitting the data or to which extent it is valid. 
-Up to now, models in TerraME are not calibrated statistically but by expert advice. This expertise could be used together with Monte Carlo simulations to find the sensitive parameters. +Up to now, models in TerraME are not calibrated statistically but by expert advice. This expertise could be used together with Monte Carlo simulations to find the sensitive parameters.
  
 Top-down models are easier to calibrate than bottom-up models as in the former demand (amount of change) and allocation can be separated. Top-down models are easier to calibrate than bottom-up models as in the former demand (amount of change) and allocation can be separated.
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 The first step is to implement Costanza's and Pontiu's models within the TerraME framework. There are some LUCC models developed by INPE's researches that can be used for testing the goodness of fit models. One example is the adaptation of CLUE, a multi-scale model, to a region in the Brazilian Amazonia (Moreira et. al 2009). Another option is the model proposed by Almeida et al. (2005), which is a cellular automata model of city growth in Sao Paulo state. Both models have been developed by INPE researches, although only the first model was already implemented in TerraME framework. The first step is to implement Costanza's and Pontiu's models within the TerraME framework. There are some LUCC models developed by INPE's researches that can be used for testing the goodness of fit models. One example is the adaptation of CLUE, a multi-scale model, to a region in the Brazilian Amazonia (Moreira et. al 2009). Another option is the model proposed by Almeida et al. (2005), which is a cellular automata model of city growth in Sao Paulo state. Both models have been developed by INPE researches, although only the first model was already implemented in TerraME framework.
  
-The results of goodness of fit tests may be simple numbers but often are curves or maps and probability distributions. +The results of goodness of fit tests may be simple numbers but often are curves or maps and probability distributions.
 These results must be communicated e.g. by visualization in a way that supports the usability of the models. The most important properties should have an easy interpretation and access. On the other hand, experts should be able to improve the model by thoroughly investigating the errors. These results must be communicated e.g. by visualization in a way that supports the usability of the models. The most important properties should have an easy interpretation and access. On the other hand, experts should be able to improve the model by thoroughly investigating the errors.
  
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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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 ===== 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.
 +
 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.
 +
 +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)
  
 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.
  
 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 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): 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):
 Problems, Prospects and Research Needs. Banff, Alberta, Canada, September 2 - 8, 2000. Problems, Prospects and Research Needs. Banff, Alberta, Canada, September 2 - 8, 2000.
 +
 +MILLER, J. H. 1998. {{group_modelling:active_nonlinear_tests_ants_of_complex_simulation_models.pdf|Active nonlinear tests (ANTs) of complex
 +simulation models}}. Management Science 44 (6): 820–30.
  
 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 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 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
  
 +
 +==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.)
  
  
group_modelling/goodnessoffit.txt · Última modificação: 2009/06/09 18:00 por inpeifgi