Set Criteria Properties (Business Analyst)

Disponible con licencia de Business Analyst.

Resumen

Defines parameters for criteria.

Uso

• You can use the Make Suitability Analysis Layer tool to create the analysis layer.

• All of the criteria weights criteria should total 100 percent.

• The influence of the criteria can be positive or inverse.

• When the Criteria Properties parameter's Ideal option is specified, Ideal Value must have an input.

Parámetros

Cuadro de diálogo

Etiqueta	Explicación	Tipo de datos
Input Suitability Analysis Layer	The Suitability Analysis layer that will be used in the analysis.	Feature Layer; Group Layer
Criteria Properties	The input features that will be used to set up the criteria properties. 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—Check to include the criteria in the final suitability score.	Value Table
Preset Method (Opcional)	Specifies the preprocessing and combination method that will be used when calculating the final score. 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
Preprocessing Method (Opcional)	Specifies the method that will be used to convert the input variables to a standardized scale. 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
Combination Method (Opcional)	Specifies the method that will be used to combine the scaled variables into a single value. 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 Scale (Opcional)	Specifies the method that will be used to scale the combined score. This parameter will determine the final score. 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

Salida derivada

Etiqueta	Explicación	Tipo de datos
Output Suitability Analysis Layer	The name of the Suitability Analysis layer that will be added to the map.	Feature Layer; Group Layer

Python

arcpy.ba.SetCriteriaProperties(in_analysis_layer, criteria_properties, {criteria_score_preset}, {preprocessing}, {criteria_score_method}, {final_score_method})

Nombre	Explicación	Tipo de datos
in_analysis_layer	The Suitability Analysis layer that will be used in the analysis.	Feature Layer; Group Layer
criteria_properties [[criterion, title, weight, influence, ideal_value, minimum_value, maximum_value, enabled],...]	The input features that will be used to set up the criteria properties. 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—Use a value of true to include the criteria in the final suitability score.	Value Table
criteria_score_preset (Opcional)	Specifies the preprocessing and combination method that will be used when calculating the final score. 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 (Opcional)	Specifies the method that will be used to convert the input variables to a standardized scale. 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 (Opcional)	Specifies the method that will be used to combine the scaled variables into a single value. 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 (Opcional)	Specifies the method that will be used to scale the combined score. This parameter will determine the final score. 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

Salida derivada

Nombre	Explicación	Tipo de datos
out_analysis_layer	The name of the Suitability Analysis layer that will be added to the map.	Feature Layer; Group Layer

Muestra de código

SetCriteriaProperties example (Python window)

The following Python window script demonstrates how to use the SetCriteriaProperties function.

import arcpy
arcpy.ba.SetCriteriaProperties("Site Suitability001", "wealth_meddi_cy 'Criterion for wealth.meddi_cy variable' 1 # # POSITIVE # true;wealth_mednw_cy 'Criterion for wealth.mednw_cy variable' 1 # # INVERSE # true")

Entornos

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Información de licenciamiento

• Basic: Requiere Business Analyst
• Standard: Requiere Business Analyst
• Advanced: Requiere Business Analyst