Le total de toutes les pondérations des critères doit être égal à 100 pour cent.
L’influence des critères peut être positive ou inverse.
Lorsque l’option Ideal (Idéale) du paramètre Criteria Properties (Propriétés des critères) est indiquée, le paramètre Ideal Value (Valeur idéale) doit contenir une valeur.
Couche d’analyse d’adéquation qui est utilisée au cours de l’analyse.
Feature Layer; Group Layer
Propriétés des critères
Entités en entrée qui servent à configurer les propriétés des critères.
Colonnes de la table de valeurs :
Criterion—The field, point, or variable that will be used to calculate the suitability score.
Title—The name of the criteria.
Weight—The influence a criteria value has on the overall suitability score. The number must be greater than or equal to 0.
Influence—An example of a positive influence is as follows: You want a site to score higher if it has a greater number of households holding graduate or professional degrees. An example of an inverse influence is as follows: A lower median home value is more desirable, as it is indicative of greater home affordability. An example of an ideal influence is a search for areas within a range of values.
Positive—The higher the criteria value, the higher the suitability score.
Inverse—The lower the criteria value, the higher the suitability score.
Ideal—The closer to the ideal value, the higher the suitability score.
Target—The closer to the target value, the higher the suitability score.
Ideal Value—The closer the criteria value is to the ideal value, the higher the suitability score.
Minimum Value—A numeric value that sets a hard limit for the criteria lower bound.
Maximum Value—A numeric value that sets a hard limit for the criteria upper bound.
Enabled—Specifies whether to include the criteria in the final suitability score.
Value Table
Méthode prédéfinie
(Facultatif)
Précise la méthode de prétraitement et la méthode de combinaison à utiliser lors du calcul du score final.
Combine Values—The sum of scaled values with scores representing the distribution of values for each criterion will be used. This is the default.
Compound Differences—The geometric mean of the scaled values will be used.
Custom—User-defined preprocessing and combination methods will be used.
String
Méthode de prétraitement
(Facultatif)
Indique la méthode utilisée pour convertir les variables en entrée dans une échelle normalisée.
Minimum-maximum—Variables will be scaled between 0 and 1 using the minimum and maximum values of each variable. This is the default.
Percentile—Variables will be converted to percentiles between 0 and 1.
Z-score—Each variable will be standardized by subtracting the mean value and dividing by the standard deviation.
Raw—The values of the variables will be used without change.
String
Méthode de combinaison
(Facultatif)
Indique la méthode à utiliser pour combiner les variables mises à l’échelle en une seule valeur.
Sum—The values will be added. This is the default.
Mean—The arithmetic (additive) mean of the values will be calculated. This is the default.
Product—The values will be multiplied. All scaled values must be greater than or equal to zero.
Geometric Mean—The geometric (multiplicative) mean of the values will be calculated. All scaled values must be greater than or equal to zero.
String
Mise à l’échelle du score final
(Facultatif)
Indique la méthode utilisée pour mettre à l’échelle le score combiné. Ce paramètre détermine le score final.
Method 0-1—The final score will be calculated with the lowest value of 0 and the highest value of 1.
Method 0-100—The final score will be calculated with the lowest value of 0 and the highest value of 100.
None—The data will not be scaled. This is the default.
String
Sortie dérivée
Etiqueter
Explication
Type de données
Couche d’analyse d’adéquation en sortie
Nom de la couche d’analyse d’adéquation qui est ajoutée à la carte.
Entités en entrée qui servent à configurer les propriétés des critères.
Colonnes de la table de valeurs :
Criterion—The field, point, or variable that will be used to calculate the suitability score.
Title—The name of the criteria.
Weight—The influence a criteria value has on the overall suitability score. The number must be greater than or equal to 0.
Influence—An example of a positive influence is as follows: You want a site to score higher if it has a greater number of households holding graduate or professional degrees. An example of an inverse influence is as follows: A lower median home value is more desirable, as it is indicative of greater home affordability. An example of an ideal influence is a search for areas within a range of values.
POSITIVE—The higher the criteria value, the higher the suitability score.
INVERSE—The lower the criteria value, the higher the suitability score.
IDEAL—The closer to the ideal value, the higher the suitability score.
TARGET—The closer to the target value, the higher the suitability score.
Ideal Value—The closer the criteria value is to the ideal value, the higher the suitability score.
Minimum Value—A numeric value that sets a hard limit for the criteria lower bound.
Maximum Value—A numeric value that sets a hard limit for the criteria upper bound.
Enabled—Specifies whether to include the criteria in the final suitability score.
Value Table
criteria_score_preset
(Facultatif)
Précise la méthode de prétraitement et la méthode de combinaison à utiliser lors du calcul du score final.
SUM_SCALED—The sum of scaled values with scores representing the distribution of values for each criterion will be used. This is the default.
GEOMEAN_SCALED—The geometric mean of the scaled values will be used.
CUSTOM—User-defined preprocessing and combination methods will be used.
String
preprocessing
(Facultatif)
Indique la méthode utilisée pour convertir les variables en entrée dans une échelle normalisée.
MINMAX—Variables will be scaled between 0 and 1 using the minimum and maximum values of each variable. This is the default.
PERCENTILE—Variables will be converted to percentiles between 0 and 1.
ZSCORE—Each variable will be standardized by subtracting the mean value and dividing by the standard deviation.
RAW—The values of the variables will be used without change.
String
criteria_score_method
(Facultatif)
Indique la méthode à utiliser pour combiner les variables mises à l’échelle en une seule valeur.
SUM—The values will be added. This is the default.
MEAN—The arithmetic (additive) mean of the values will be calculated. This is the default.
PRODUCT—The values will be multiplied. All scaled values must be greater than or equal to zero.
GEOMETRIC_MEAN—The geometric (multiplicative) mean of the values will be calculated. All scaled values must be greater than or equal to zero.
String
final_score_method
(Facultatif)
Indique la méthode utilisée pour mettre à l’échelle le score combiné. Ce paramètre détermine le score final.
METHOD_0_1—The final score will be calculated with the lowest value of 0 and the highest value of 1.
METHOD_0_100—The final score will be calculated with the lowest value of 0 and the highest value of 100.
NONE—The data will not be scaled. This is the default.
String
Sortie dérivée
Nom
Explication
Type de données
out_analysis_layer
Nom de la couche d’analyse d’adéquation qui est ajoutée à la carte.
Feature Layer; Group Layer
Exemple de code
Exemple d’utilisation de la fonction SetCriteriaProperties (fenêtre Python)
Le script de la fenêtre Python ci-après illustre l’utilisation de la fonction SetCriteriaProperties.