Summary
Makes a location-allocation network analysis layer and sets its analysis properties. A location-allocation analysis layer is useful for choosing a given number of facilities from a set of potential locations such that a demand will be allocated to facilities in an optimal and efficient manner. The layer can be created using a local network dataset or using a service hosted online or in a portal.
Usage
After creating the analysis layer with this tool, you can add network analysis objects to it using the Add Locations tool, solve the analysis using the Solve tool, and save the results on disk using the Save To Layer File tool.
When using this tool in geoprocessing models, if the model is run as a tool, the output network analysis layer must be made a model parameter; otherwise, the output layer is not added to the contents of the map.
In ArcGIS Pro, network analysis layer data is stored on disk in file geodatabase feature classes. When creating a network analysis layer in a project, the layer's data will be created in a new feature dataset in the Current Workspace environment. When creating a network analysis layer in a Python script, you must first explicitly set the workspace environment to a file geodatabase where you want the layer's data to be stored using arcpy.env.workspace = "<path to file gdb>". When the layer is created, a new feature dataset containing the appropriate sublayer feature classes will be added to this file geodatabase.
Syntax
MakeLocationAllocationAnalysisLayer(network_data_source, {layer_name}, {travel_mode}, {travel_direction}, {problem_type}, {cutoff}, {number_of_facilities_to_find}, {decay_function_type}, {decay_function_parameter_value}, {target_market_share}, {capacity}, {time_of_day}, {time_zone}, {line_shape}, {accumulate_attributes})| Parameter | Explanation | Data Type |
network_data_source | The network dataset or service on which the network analysis will be performed. Use the portal URL for a service. | Network Dataset Layer;String |
layer_name (Optional) | The name of the network analysis layer to create. | String |
travel_mode (Optional) | The name of the travel mode to use in the analysis. The travel mode represents a collection of network settings, such as travel restrictions and U-turn policies, that determine how a pedestrian, car, truck, or other medium of transportation moves through the network. Travel modes are defined on your network data source. An arcpy.na.TravelMode object and a string containing the valid JSON representation of a travel mode can also be used as input to the parameter. | String |
travel_direction (Optional) | Specifies the direction of travel between facilities and demand points when calculating the network costs.
Using this option can affect the allocation of the demand points to the facilities on a network with one-way restrictions and different impedances based on direction of travel. For instance, it may take 15 minutes to drive from the demand point to the facility but only 10 minutes when driving from the facility to the demand point. | String |
problem_type (Optional) | The problem type that will be solved. The choice of the problem type depends on the kind of facility being located. Different kinds of facilities have different priorities and constraints.
| String |
cutoff (Optional) | The maximum impedance at which a demand point can be allocated to a facility in the units of the impedance attribute used by your chosen Travel Mode. The maximum impedance is measured by the least-cost path along the network. If a demand point is outside the cutoff, it is left unallocated. This property might be used to model the maximum distance that people are willing to travel to visit your stores or the maximum time that is permitted for a fire department to reach anyone in the community. This cutoff can be overridden on a per-demand-point basis by specifying individual cutoff values in the demand points sublayer in the Cutoff_[Impedance] property. For example, you might find that people in rural areas are willing to travel up to 10 miles to reach a facility while urbanites are only willing to travel up to 2 miles. You can model this behavior by setting the Cutoff value of the analysis layer to 10 and setting the Cutoff_Miles value of each demand point in an urban areas to 2. By default, no cutoff is used for the analysis. | Double |
number_of_facilities_to_find (Optional) | Specifies the number of facilities that the solver should locate. By default, this parameter is set to 1. The facilities with a FacilityType value of Required are always part of the solution when there are more facilities to find than required facilities; any excess facilities to choose are picked from candidate facilities. Any facilities that have a FacilityType value of Chosen before solving are treated as candidate facilities at solve time. The parameter value is not considered for the MINIMIZE_FACILITIES problem type since the solver determines the minimum number of facilities to locate to maximize coverage. The parameter value is overridden for the TARGET_MARKET_SHARE problem type because the solver searches for the minimum number of facilities required to capture the specified market share. | Long |
decay_function_type (Optional) | This sets the equation for transforming the network cost between facilities and demand points. This property, coupled with the Decay Function Parameter Value, specifies how severely the network impedance between facilities and demand points influences the solver's choice of facilities.
Demand points have an ImpedanceTransformation property, which, if set, overrides the Decay Function Parameter Value property of the analysis layer on a per-demand-point basis. You might determine that the decay function should be different for urban and rural residents. You can model this by setting the impedance transformation for the analysis layer to match that of rural residents and setting the impedance transformation for the individual demand points located in urban areas to match that of urbanites. | String |
decay_function_parameter_value (Optional) | Provides a parameter value to the equations specified in the decay_function_type parameter. The parameter value is ignored when the decay function is of type LINEAR. For POWER and EXPONENTIAL decay functions, the value should be nonzero. Demand points have an ImpedanceTransformation property, which, if set, overrides the decay_function_parameter_value property of the analysis layer on a per-demand-point basis. You might determine that the decay function should be different for urban and rural residents. You can model this by setting the impedance transformation for the analysis layer to match that of rural residents and setting the impedance transformation for the individual demand points located in urban areas to match that of urbanites. | Double |
target_market_share (Optional) | Specifies the target market share in percentage to solve for when the problem_type parameter is set to TARGET_MARKET_SHARE. It is the percentage of the total demand weight that you want your solution facilities to capture. The solver chooses the minimum number of facilities required to capture the target market share specified by this numeric value. | Double |
capacity (Optional) | Specifies the default capacity of facilities when the problem_type parameter is set to MAXIMIZE_CAPACITATED_COVERAGE. This parameter is ignored for all other problem types. Facilities have a Capacity property, which, if set to a nonnull value, overrides the capacity parameter for that facility. | Double |
time_of_day (Optional) | Indicates the time and date of departure. The departure time can be from facilities or demand points, depending on whether Travel Direction is from demand to facility or facility to demand. If you have chosen a traffic-based impedance attribute, the solution will be generated given dynamic traffic conditions at the time of day specified here. A date and time can be specified as 5/14/2012 10:30 AM. Instead of using a particular date, a day of the week can be specified using the following dates:
| Date |
time_zone (Optional) | The time zone of the Time of Day parameter.
| String |
line_shape (Optional) |
No matter which output shape type is chosen, the best route is always determined by the network impedance, never Euclidean distance. This means that only the route shapes are different, not the underlying traversal of the network. | String |
accumulate_attributes [accumulate_attributes,...] (Optional) | A list of cost attributes to be accumulated during analysis. These accumulated attributes are for reference only; the solver only uses the cost attribute used by your designated travel mode when solving the analysis. For each cost attribute that is accumulated, a Total_[Impedance] property is populated in the network analysis output features. This parameter is not available if the network data source is an ArcGIS Online service or the network data source is a service on a version of Portal for ArcGIS that does not support accumulation. | String |
Derived Output
| Name | Explanation | Data Type |
| out_network_analysis_layer | The newly created network analysis layer. | Network Analyst Layer |
Code sample
Execute the tool using only the required parameters.
network = "C:/Data/SanFrancisco.gdb/Transportation/Streets_ND"
arcpy.na.MakeLocationAllocationAnalysisLayer(network, "StoreLocations")Execute the tool using all parameters.
network = "C:/Data/SanFrancisco.gdb/Transportation/Streets_ND"
arcpy.na.MakeLocationAllocationAnalysisLayer(network, "NewStores",
"Driving Time", "TO_FACILITIES",
"MAXIMIZE_ATTENDANCE", 3, 5, "POWER", 2, "",
"", "1/1/1900 9:00 AM", "UTC",
"STRAIGHT_LINES", ["TravelTime", "Meters"])The following stand-alone Python script demonstrates how the MakeLocationAllocationAnalysisLayer tool can be used to choose the store locations that would generate the most business for a retail chain.
# Name: MakeLocationAllocationAnalysisLayer_Workflow.py
# Description: Choose the store locations that would generate the most business
# for a retail chain. For this scenario, we will perform the
# location-Allocation analysis using the maximize attendance
# problem type.
# Requirements: Network Analyst Extension
#Import system modules
import arcpy
from arcpy import env
import os
try:
#Check out Network Analyst license if available. Fail if the Network Analyst license is not available.
if arcpy.CheckExtension("network") == "Available":
arcpy.CheckOutExtension("network")
else:
raise arcpy.ExecuteError("Network Analyst Extension license is not available.")
#Set environment settings
output_dir = "C:/Data"
#The NA layer's data will be saved to the workspace specified here
env.workspace = os.path.join(output_dir, "Output.gdb")
env.overwriteOutput = True
#Set local variables
input_gdb = "C:/Data/SanFrancisco.gdb"
network = os.path.join(input_gdb, "Transportation", "Streets_ND")
layer_name = "NewStoreLocations"
travel_mode = "Driving Time"
facilities = os.path.join(input_gdb, "Analysis", "CandidateStores")
required_facility = os.path.join(input_gdb, "Analysis", "ExistingStore")
demand_points = os.path.join(input_gdb, "Analysis", "TractCentroids")
output_layer_file = os.path.join(output_dir, layer_name + ".lyrx")
#Create a new location-allocation layer. In this case, the demand travels to
#the facility. We wish to find 3 potential store locations out of all the
#candidate store locations using the maximize attendance model.
result_object = arcpy.na.MakeLocationAllocationAnalysisLayer(network,
layer_name, travel_mode, "TO_FACILITIES",
"MAXIMIZE_ATTENDANCE", cutoff=20,
number_of_facilities_to_find=3)
#Get the layer object from the result object. The location-allocation layer
#can now be referenced using the layer object.
layer_object = result_object.getOutput(0)
#Get the names of all the sublayers within the location-allocation layer.
sublayer_names = arcpy.na.GetNAClassNames(layer_object)
#Stores the layer names that we will use later
facilities_layer_name = sublayer_names["Facilities"]
demand_points_layer_name = sublayer_names["DemandPoints"]
#Load the candidate store locations as facilities using default search
#tolerance and field mappings.
arcpy.na.AddLocations(layer_object, facilities_layer_name, facilities, "",
"")
#Load the existing store location as the required facility. Use the field
#mappings to set the facility type to requried. We need to append this
#required facility to existing facilities.
field_mappings = arcpy.na.NAClassFieldMappings(layer_object,
facilities_layer_name)
field_mappings["FacilityType"].defaultValue = 1
arcpy.na.AddLocations(layer_object, facilities_layer_name,
required_facility, field_mappings, "",
append="APPEND")
#Load the tract centroids as demand points using default search tolerance
#Use the field mappings to map the Weight property from POP2000 field.
demand_field_mappings = arcpy.na.NAClassFieldMappings(layer_object,
demand_points_layer_name)
demand_field_mappings["Weight"].mappedFieldName = "POP2000"
arcpy.na.AddLocations(layer_object, demand_points_layer_name, demand_points,
demand_field_mappings, "")
#Solve the location-allocation layer
arcpy.na.Solve(layer_object)
#Save the solved location-allocation layer as a layer file on disk
layer_object.saveACopy(output_layer_file)
print("Script completed successfully")
except Exception as e:
# If an error occurred, print line number and error message
import traceback, sys
tb = sys.exc_info()[2]
print("An error occurred on line %i" % tb.tb_lineno)
print(str(e))Environments
Licensing information
- Basic: Yes
- Standard: Yes
- Advanced: Yes