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Build Balanced Zones

Summary

Creates spatially contiguous zones in your study area using a genetic growth algorithm based on criteria that you specify.

You can create zones that contain an equal number of features, zones that are similar based on a set of attribute values, or both. There are also options to select zones with approximately equal areas, are as compact as possible, and that maintain consistent summary statistics of other variables.

Learn more about how Build Balanced Zones works

Usage

  • Zones can be created by choosing Attribute target, Number of zones and attribute target, or Number of zones in the Zone Creation Method parameter. When Attribute target is chosen, the tool will create zones based on the target values of one or more specified variables. The Number of zones and attribute target option balances the attributes over the specified number of zones. You can also create zones based on feature counts using the Number of zones option.

  • This tool can take input points or polygons.

  • If multiple variables are specified for the Zone Building Criteria parameter, you have the option to provide weights for each criteria. Weights are applied proportionally to the value specified in the weights field.

  • License:
    The Distance to Consider (distance_to_consider) parameter is only available with a Desktop Advanced license.

Syntax

BuildBalancedZones(in_features, output_features, zone_creation_method, {number_of_zones}, {zone_building_criteria_target}, {zone_building_criteria}, {spatial_constraints}, {weights_matrix_file}, {zone_characteristics}, {attribute_to_consider}, {distance_to_consider}, {categorial_variable}, {proportion_method}, {population_size}, {number_generations}, {mutation_factor}, {output_convergence_table})
ParameterExplanationData Type
in_features

The feature class or feature layer that will be aggregated into zones.

Feature Layer
output_features

The output feature class indicating which features are aggregated into each zone. The feature class will be symbolized by the ZONE_ID field and will contain fields displaying the values of each criteria that you specify.

Feature Class
zone_creation_method

Specifies the method that will be used to grow each zone. Zones grow until all specified thresholds are reached.

  • ATTRIBUTE_TARGETZones will be created based on target values of one or multiple variables. If multiple variables are specified, the tool will balance each zone with target values of the input variables.
  • NUMBER_ZONES_AND_ATTRIBUTEThe number of zones specified will be created while homogenizing the values of the specified input attributes.
  • NUMBER_OF_ZONESZones will be created based on feature counts.
String
number_of_zones
(Optional)

The number of zones that will be created.

Long
zone_building_criteria_target
[[variable, sum, weight],...]
(Optional)

Specifies the variables that will be considered, their target values, and optional weights. The default weights are set to 1, and each variable contributes equally unless they are changed.

Value Table
zone_building_criteria
[[variable, weight],...]
(Optional)

Specifies the variables that will be considered and, optionally, weights. The default weights are set to 1, and each variable contributes equally unless changed.

Value Table
spatial_constraints
(Optional)

Specifies how neighbors are defined while the zones grow. Zones can only grow into new features that are neighbors of at least one of the features already in the zone.

  • CONTIGUITY_EDGES_ONLYFor zones containing contiguous polygon features, only polygons that share an edge will be part of the same zone.
  • CONTIGUITY_EDGES_CORNERS For zones containing contiguous polygon features, only polygons that share an edge or a vertex will be part of the same zone.
  • TRIMMED_DELAUNAY_TRIANGULATION Features in the same zone will have at least one natural neighbor in common with another feature in the zone. Natural neighbor relationships are based on a trimmed Delaunay Triangulation. Conceptually, Delaunay Triangulation creates a non-overlapping mesh of triangles from feature centroids. Each feature is a triangle node, and nodes that share edges are considered neighbors. These triangles are then clipped to a convex hull to ensure that features cannot be neighbors with any features outside of the convex hull. This is the default.
  • GET_SPATIAL_WEIGHTS_FROM_FILE Spatial, and, optionally, temporal relationships will be defined by a specified spatial weights file (.swm). Create the spatial weights matrix using the Generate Spatial Weights Matrix tool or the Generate Network Spatial Weights tool. The path to the spatial weights file is specified by the Spatial Weights Matrix File parameter.
String
weights_matrix_file
(Optional)

The path to a file containing spatial weights that define spatial and, optionally, temporal relationships among features.

File
zone_characteristics
[zone_characteristics,...]
(Optional)

Specifies the desired characteristics of the zones that will be created.

  • EQUAL_AREA Zones with total area as similar as possible will be created.
  • COMPACTNESSZones will be created with more closely-packed (compact) features.
  • EQUAL_NUMBER_OF_FEATURESZones with an equal number of features will be created.
String
attribute_to_consider
[[variable, function],...]
(Optional)

Specifies attributes and statistics to consider in the selection of final zones. You can choose to homogenize attributes based on their sum, average, median, or variance. For example, if you are creating zones based on home values and want to balance the average total income within each zone, the solution with the most equal average income across zones will be preferred.

Value Table
distance_to_consider
[distance_to_consider,...]
(Optional)

The feature class that will be used to homogenize the total distance per zone. The distance is calculated from each of the input features to the closest feature provided in this parameter. This distance is then used as an additional attribute constraint when selecting the final zone solution. For example, you can create police patrol districts that are each approximately the same distance from the closest police station.

License:

This optional parameter is not available with a Desktop Basic or Desktop Standard license.

Feature Layer
categorial_variable
(Optional)

The categorical variable to be considered for zone proportions.

Field
proportion_method
(Optional)

Specifies the type of proportion that will be maintained based on the chosen categorical variable.

  • MAINTAIN_WITHIN_PROPORTIONEach zone will maintain the same proportions as the overall study area for the given categorical variable. For example, given a categorical variable that is 60% Type A and 40% Type B, this method will prefer zones that are comprised of approximately 60% Type A features and 40% Type B features.
  • MAINTAIN_OVERALL_PROPORTIONZones will be created so that the overall proportions of category predominance by zone matches the proportions of the given categorical variable for the entire dataset. For example, given a categorical variable that is 60% Type A and 40% Type B, this method will prefer solutions where 60% of the zones are predominantly Type A features and 40% of the zones are predominantly Type B features.
String
population_size
(Optional)

The number of randomly generated initial seeds. For larger datasets, increasing this number will increase the search space and the probability of finding a better solution. The default is 100.

Long
number_generations
(Optional)

The number of times the zone search process is repeated. For larger datasets, increasing the number is recommended in order to find an optimal solution. The default is 50 generations.

Long
mutation_factor
(Optional)

The probability that an individual's seed values will be mutated to a new set of seeds. Mutation increases the search space by introducing variability of the possible solutions in every generation and allows for faster convergence to an optimal solution. The default is 0.1.

Double
output_convergence_table
(Optional)

If specified, a table will be created containing the total fitness score for the best solution found in every generation as well as the fitness score for the individual zone constraints.

Table

Code sample

BuildBalancedZones example 1 (Python window)

The following Python window script demonstrates how to use the BuildBalancedZones tool.

import arcpy
arcpy.env.workspace = r"c:\data\project_data.gdb"
arcpy.stats.BuildBalancedZones("US_Counties", "out_features", 
     "NUMBER_OF_ZONES", 5, None, None, "TRIMMED_DELAUNAY_TRIANGULATION", 
     None, None, None, None, None, '', 100, 50, 0.1)
BuildBalancedZones example 2 (stand-alone script)

The following stand-alone Python script demonstrates how to use the BuildBalancedZones tool.

# Aggregate states into zones that have a target population of approximately
# 250,000 people.  Make the zones of equal area and compact. 
 
import arcpy

# Set the current workspace (to avoid having to specify the full path to
# the feature classes each time)

arcpy.env.workspace = r"c:\data\project_data.gdb"

arcpy.stats.BuildBalancedZones("states", "out_zones", "ATTRIBUTE_TARGET", 
     None, "POPULATION 250000 1", None, "TRIMMED_DELAUNAY_TRIANGULATION", 
     None, "EQUAL_AREA;COMPACTNESS", None, None, None, '', 100, 50, 0.1)

Licensing information

  • Basic: Limited
  • Standard: Limited
  • Advanced: Yes

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