# Differences

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simulating_patterns_of_change [2020/07/16 01:39] argemiro [Dinamica's Collection of Spatial Patterns of Change] |
simulating_patterns_of_change [2020/07/16 01:53] (current) argemiro |
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* The capability of the **Dinamica EGO** to reproduce a wide range of spatial patterns of change; | * The capability of the **Dinamica EGO** to reproduce a wide range of spatial patterns of change; | ||

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- | 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 EGO** 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, patch cohesion index, nearest neighbor distance, and mean patch size, of the simulated maps with the ones of the reference landscape. | + | 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 EGO** 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 [[tutorial:landscape_metrics_in_dinamica_ego|landscape metrics]], such as fractal index, patch cohesion index, nearest neighbor distance, and mean patch size, of the simulated maps with the ones of the reference landscape. |

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- | I) The spatial arrangement of the simulated landscape needs to be approximated to the one of the reference landscape by defining the weights of evidence for the modeled transitions and thereby their transition probabilities maps; | + | I) The spatial arrangement of the simulated landscape needs to be approximated to the one of the reference landscape by defining the weights of evidence for the modeled transitions and thereby their transition probabilities maps (See more at [[tutorial:building_a_land-use_and_land-cover_change_simulation_model|Building a land-use and land-cover change simulation model]]); |

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- | The fractal dimension reveals the patch complexity. It is as a function of inner area in relation to the patch edge and varies from 1 to 2. Therefore, the fractal dimension is affected by the patch shape and size (Forman and Godron, 1986). According to McGarical and Marks (1995), the patch cohesion index gives an indication of the level of fragmentation of a landscape and thereby the habitat connectivity, thus large cohesion index indicates less fragmentation. In turn, the nearest neighbor distance shows the dispersion of patches in a landscape. | + | The fractal dimension reveals the patch complexity. It is as a function of inner area in relation to the patch edge and varies from 1 to 2. Therefore, the fractal dimension is affected by the patch shape and size (Forman and Godron, 1986). According to McGarical and Marks (1995), the patch cohesion index gives an indication of the level of fragmentation of a landscape and thereby the habitat connectivity, thus large cohesion index indicates less fragmentation. |

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+ | <note>The nearest neighbor distance shows the dispersion of patches in a landscape.</note> | ||

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**H6:** Transitions occur as a function of the spatial probability. Dinamica sets up a spatial transition probability map for each transition, based on the weights of evidence chosen for specific ranges of each spatial variable stored in the static cube raster dataset. Simulation was run in 1 5 steps, with a rate of 0.005 per step. Only the Patcher function is used with patch mean size of 20 and patch size variance of 0. Patch isometry is equal to 2. | **H6:** Transitions occur as a function of the spatial probability. Dinamica sets up a spatial transition probability map for each transition, based on the weights of evidence chosen for specific ranges of each spatial variable stored in the static cube raster dataset. Simulation was run in 1 5 steps, with a rate of 0.005 per step. Only the Patcher function is used with patch mean size of 20 and patch size variance of 0. Patch isometry is equal to 2. | ||

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- | \\ The next figure depicts the static variable map, the calculated spatial transition probability map, and the simulated landscape. Notice the concentration of changed cells in the higher probability areas at the center of the map. | + | \\ |

+ | The next figure depicts the static variable map, the calculated spatial transition probability map, and the simulated landscape. Notice the concentration of changed cells in the higher probability areas at the center of the map. | ||

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- | **H7:** There is no spatial arrangement and no patch aggregation. Expander percentage is 0, patch mean size is 1 , and patch size variance is 0. Transitions take place randomly only obeying the amounts of change set by the transition matrix. Simulations are run for 1 0 time steps. | + | **H7:** There is no spatial arrangement and no patch aggregation. Expander percentage is 0, patch mean size is 1, and patch size variance is 0. Transitions take place randomly only obeying the amounts of change set by the transition matrix. Simulations are run for 1 0 time steps. |

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=== References === | === References === | ||

- | Soares-Filho et al, 2003. Simulating the spatial patterns of change through the use of the DINAMICA model. Anais XI SBSR. | + | Soares-Filho et al, 2003. Simulating the spatial patterns of change through the use of the DINAMICA model. Anais XI SBSR. \\ |

[[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.9617&rep=rep1&type=pdf]] | [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.9617&rep=rep1&type=pdf]] | ||

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+ | Wu, Jianguo. (2013). Landscape Ecology. 10.1007/978-1-4614-5755-8_11. | ||

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+ | ☞[[:guidebook_start| Back to Guidebook Start]] | ||

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