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--- patterns_of_change [2015/09/26 21:00]
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+++ patterns_of_change [2015/09/27 00:56]
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 ====== Spatial Patterns of Changes ======
-As Dinamica EGO was designed to be a general purpose dynamic modeling software, we present a collection of spatial patterns produced by the various combinations of its transition functions. In order to evaluate the wide range of Dinamias’s possibilities, a series of simulation were run using synthetic simplified maps. The results for each model are presented as follows:
+As Dinamica EGO was designed to be a general purpose dynamic modeling software, we present a collection of spatial patterns produced by the various combinations of its transition functions. In order to evaluate the wide range of Dinamicas’s possibilities, a series of simulation were run using synthetic simplified maps. The results for each model are presented as follows:
 H0 to H8 are run, for a sole time step, by using a transition matrix 2x2 that models only one transition – from class 2 to 1 – with a rate of 0.01. The landscape map encompasses a matrix of 132 by 132 cells and all cells have equivalent spatial transition probability, which means that ancillary variables are not used to influence the cell allocation process.
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 |[{{:image140.jpg|Original Landscape Map}}][{{:image139.jpg|Simulated Ladscape}}]|
+H12: There is no spatial arrangement but patch aggregation. Patch mean size is set to 5, and simulation is run for 10 time steps.
+|[{{:image141.jpg}}]|
+H13: There is no spatial arrangement but patch expansion produced by the sole use of the expander function. Patch mean size is set to 1, and simulation is run for 10 time steps. The left figure depicts the result for patch isometry equal to 1 and the right figure for patch isometry equal to 1.2
+|[{{:image142.jpg|Isometry=1}}][{{:image143.jpg|Isometry=1.2}}]|
+H14: There is no patch aggregation but now each transition is influenced by its spatial probability map computed over maps stored in the static raster cube. Expander percentage is 0, patch mean size is 1, and patch variance is 0. Simulations are run for 10 time steps.
+Figures bellow in B&W represent the Weights of Evidence function, respectively, for transitions 1-2, 1-3, 1-4, 1-5, 1-6. The color figure represents the transition probability map for 1-2 computed by integrating the single Weights of Evidence functions. The last two color figures depict the simulated landscape maps after 1 and 10 iterations.
+|[{{:image146.jpg|Weights Of Evidence Function, Transition 1-2}}][{{:image144.jpg|Weights Of Evidence Function, Transition 1-3}}][{{:image147.jpg|Weights Of Evidence Function, Transition 1-4}}][{{:image145.jpg|Weights Of Evidence Function, Transition 1-5}}][{{:image148.jpg|Weights Of Evidence Function, Transition 1-6}}]|
+|[{{:image152.jpg|Probability Map for Transition 1-2}}]|
+|[{{:image149.jpg|Simulated Landscape After 1 Iteration}}][{{:image150.jpg|Simulated Landscape After 10 Iterations}}]|
+In conclusion, the combination of Dinamica’s transition function presents numerous possibilities with respect to the generation and evolvement of spatial patterns of change. As a result, Dinamica can be considered as a potential tool for the replication of dynamic landscape structures. The calibration of a simulated landscape can be achieved by a series of simulation using varying parameters. An approximated solution can be attained comparing landscape metrics, such as fractal index and mean patch size of the simulated maps with the ones of the reference landscape. We intend to incorporate in the next version of Dinamica an automatic calibration procedure aiming at the match of spatial patterns.