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determine_weights_of_evidence_continuous_occurrences [2011/08/01 22:02] hermann created |
determine_weights_of_evidence_continuous_occurrences [2015/10/13 19:48] (current) admin |
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| - | ====== Determine Weights Of Evidence Coefficients ====== | + | ====== Determine Weights Of Evidence Continuous Occurrences ====== |
| ===== Description ===== | ===== Description ===== | ||
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| ===== Inputs ===== | ===== Inputs ===== | ||
| - | ^ Name ^ Type ^ Description ^ | + | ^ Name ^ Type ^ Description ^ |
| - | | Occurrences | [[Map Type|Map]] | Map with occurrences of some event. | | + | | Occurrences | [[Map Type]] | Map with occurrences of some event. | |
| - | | Mask | [[Map Type|Map]] | Map defining the area to be analyzed. | | + | | Mask | [[Map Type]] | Map defining the area to be analyzed. | |
| - | | Ranges | [[Weights Type|Weights]] | Pre-defined intervals for continuous gray-tone variable. | | + | | Ranges | [[Weights Type]] | Pre-defined intervals for continuous gray-tone variable. | |
| ===== Optional Inputs ===== | ===== Optional Inputs ===== | ||
| + | |||
| + | ^ Name ^ Type ^ Description ^ Default Value ^ | ||
| + | | Fix Abnormal Weights | [[Boolean Value Type]] | If true, recalculate abnormal weights. Otherwise, assume abnormal values are zero. | False | | ||
| ===== Outputs ===== | ===== Outputs ===== | ||
| - | ^ Name ^ Type ^ Description ^ | + | ^ Name ^ Type ^ Description ^ |
| - | | Weights | [[ Weights Type|Weights]] | Obtained coefficients for selected spatial variables with respect to a transition or set of transitions. | | + | | Weights | [[Weights Type]] | Obtained coefficients for selected spatial variables with respect to a transition or set of transitions. | |
| ===== Group ===== | ===== Group ===== | ||
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| This method can be extended to handle multiple predictive maps, so that each weight of evidence represents the degree of association of a spatial pattern (//B, C, D, ...N//) with the occurrence of (//D//) as follows: | This method can be extended to handle multiple predictive maps, so that each weight of evidence represents the degree of association of a spatial pattern (//B, C, D, ...N//) with the occurrence of (//D//) as follows: | ||
| + | |||
| + | <m>P{delim{lbrace}{D | B inter C inter D cdots inter N}{rbrace}}=ln{D}+{{W_B}^+}+{{W_C}^+}+{{W_D}^+}+ cdots + {{W_N}^+}</m> (8) | ||
| For modeling transition phenomena, in which (//D//) stands for a change from class //i// to //j//, such as deforestation, is necessary to introduce some modifications to this calculation. First, instead of the entire study area that occupied by the class (i) before changes from //i// to //j// take place is used, for example, the former area of forest, as deforestation can only occur in a forested landscape. Second, as we focus on determining the influences of a set of spatial patterns on a modeled transition, we can assume that //O{D}// is equal to 1. Note that the prior probability of a transition is equivalent to its transition rate, in other words, using the example of deforestation, the net deforestation rate calculated by dividing the number of deforestation cells by the number of forest cells prior to deforestation. In this manner, algebraic manipulation of equation (8), replacing the odds ratio by <m>P{delim{lbrace}{D|B}{rbrace}}/{1-P{delim{lbrace}{D|B}{rbrace}}}</m>, leads to the post-probability of a transition //i// to //j//, given a particular combination of spatial patterns in a location (x,y), as follows: | For modeling transition phenomena, in which (//D//) stands for a change from class //i// to //j//, such as deforestation, is necessary to introduce some modifications to this calculation. First, instead of the entire study area that occupied by the class (i) before changes from //i// to //j// take place is used, for example, the former area of forest, as deforestation can only occur in a forested landscape. Second, as we focus on determining the influences of a set of spatial patterns on a modeled transition, we can assume that //O{D}// is equal to 1. Note that the prior probability of a transition is equivalent to its transition rate, in other words, using the example of deforestation, the net deforestation rate calculated by dividing the number of deforestation cells by the number of forest cells prior to deforestation. In this manner, algebraic manipulation of equation (8), replacing the odds ratio by <m>P{delim{lbrace}{D|B}{rbrace}}/{1-P{delim{lbrace}{D|B}{rbrace}}}</m>, leads to the post-probability of a transition //i// to //j//, given a particular combination of spatial patterns in a location (x,y), as follows: | ||