LocationAllocationSolverProperties (arcpy.na)

Resumen

Provides access to analysis properties from a location-allocation network analysis layer. The GetSolverProperties function is used to obtain a LocationAllocationSolverProperties object from a location-allocation network analysis layer.

Debate

The LocationAllocationSolverProperties object provides read and write access to all the analysis properties of a location-allocation network analysis layer. The object can be used to modify the desired analysis properties of the location-allocation layer, and the corresponding layer can be re-solved to determine the appropriate results. A new location-allocation layer can be created using the Make Location-Allocation Layer geoprocessing tool. Obtaining the LocationAllocationSolverProperties object from a new location-allocation layer allows you to reuse the existing layer for subsequent analyses rather than create a new layer for each analysis, which can be slow.

After modifying the properties on the LocationAllocationSolverProperties object, the corresponding layer can be immediately used with other functions and geoprocessing tools. There is no refresh or update of the layer required to honor the changes modified through the object.

Propiedades

Propiedad	Explicación	Tipo de datos
accumulators (Lectura y escritura)	Provides the ability to get or set a list of network cost attributes that are accumulated as part of the analysis. An empty list, [], indicates that no cost attributes are accumulated.	String
attributeParameters (Lectura y escritura)	Provides the ability to get or set the parameterized attributes to be used in the analysis. The property returns a Python dictionary. The dictionary key is a two-value tuple consisting of the attribute name and the parameter name. The value for each item in the dictionary is the parameter value. Parameterized network attributes are used to model some dynamic aspect of an attribute's value. For example, a tunnel with a height restriction of 12 feet can be modeled using a parameter. In this case, the vehicle's height in feet should be specified as the parameter value. If the vehicle is taller than 12 feet, this restriction will then evaluate to true, thereby restricting travel through the tunnel. Similarly, a bridge could have a parameter to specify a weight restriction. Attempting to modify the attributeParameters property in place won't result in updated values. Instead, you should always use a new dictionary object to set values for the property. The following two code blocks demonstrate the difference between these two approaches. #Don't attempt to modify the attributeParameters property in place. #This coding method won't work. solverProps.attributeParameters[('HeightRestriction', 'RestrictionUsage')] = "PROHIBITED" #Modify the attributeParameters property using a new dictionary object. #This coding method works. params = solverProps.attributeParameters params[('HeightRestriction', 'RestrictionUsage')] = "PROHIBITED" solverProps.attributeParameters = params If the network analysis layer does not have parameterized attributes, this property returns None.	Dictionary
defaultCapacity (Lectura y escritura)	Provides the ability to get or set the default capacity of facilities when the location-allocation problemType 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 defaultCapacity parameter for that facility.	Double
facilitiesToFind (Lectura y escritura)	Provides the ability to get or set the number of facilities that the solver should locate. The property value is ignored if the problemType property is set to MINIMIZE_FACILITIES, since the solver determines the minimum number of facilities to locate to maximize coverage. The property value is also ignored if the problemType property is set to TARGET_MARKET_SHARE, because the solver searches for the minimum number of facilities required to capture the specified market share.	Integer
impedance (Lectura y escritura)	Provides the ability to get or set the network cost attribute used as impedance.	String
impedanceCutoff (Lectura y escritura)	Provides the ability to get or set the maximum impedance at which a demand point can be allocated to a facility.	Double
impedanceParameter (Lectura y escritura)	Provides the ability to get or set a parameter value for the equations specified in the impedanceTransformation property. The property value is ignored when the impedanceTransformation property is set to LINEAR. The property value should not be zero.	Double
impedanceTransformation (Lectura y escritura)	Provides the ability to get or set the equation for transforming the network cost between facilities and demand points. This property value, coupled with the impedanceParameter property value, specifies how severely the network impedance between facilities and demand points influences the solver's choice of facilities. The following is a list of possible values: LINEAR —The transformed network impedance between the facility and the demand point is the same as the shortest-path network impedance between them. With this value set, the impedanceParameter property value is always set to one and any value set for impedanceParameter property is ignored. POWER —The transformed network impedance between the facility and the demand point is equal to the shortest-path network impedance raised to the power specified by the impedanceParameter property value. Use this property value with a positive impedanceParameter property value to give higher weight to nearby facilities. EXPONENTIAL —The transformed network impedance between the facility and the demand point is equal to the mathematical constant e raised to the power specified by the shortest-path network impedance multiplied by the impedanceParameter property value. Use this property value with a positive impedanceParameter property value to give a very high weight to nearby facilities.	String
outputPathShape (Lectura y escritura)	Controls whether straight lines are used to represent the results from the location-allocation analysis. The following is a list of possible values: NO_LINES —No shape will be generated for the output of the analysis. This is useful when you have a large number of demand points or facilities and are interested only in the tabular output. STRAIGHT_LINES —Straight lines connecting the solution facilities to their allocated demand points are generated.	String
problemType (Lectura y escritura)	Provides the ability to get or set 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. The following is a list of possible values: MINIMIZE_IMPEDANCE —This option solves the warehouse location problem. It selects a set of facilities such that the total sum of weighted impedances (demand at a location times the impedance to the closest facility) is minimized. This problem type is often known as the P-Median problem. MAXIMIZE_COVERAGE —This option solves the fire station location problem. It chooses facilities such that all or the greatest amount of demand is within a specified impedance cutoff. MINIMIZE_FACILITIES —This option solves the fire station location problem. It chooses the minimum number of facilities needed to cover all or the greatest amount of demand within a specified impedance cutoff. MAXIMIZE_ATTENDANCE —This option solves the neighborhood store location problem where the proportion of demand allocated to the nearest chosen facility falls with increasing distance. The set of facilities that maximize the total allocated demand is chosen. Demand further than the specified impedance cutoff does not affect the chosen set of facilities. MAXIMIZE_MARKET_SHARE —This option solves the competitive facility location problem. It chooses facilities to maximize market share in the presence of competitive facilities. Gravity model concepts are used to determine the proportion of demand allocated to each facility. The set of facilities that maximizes the total allocated demand is chosen. TARGET_MARKET_SHARE —This option solves the competitive facility location problem. It chooses facilities to reach a specified target market share in the presence of competitive facilities. Gravity model concepts are used to determine the proportion of demand allocated to each facility. The minimum number of facilities needed to reach the specified target market share is chosen.	String
restrictions (Lectura y escritura)	Provides the ability to get or set a list of restriction attributes that are applied for the analysis. An empty list, [], indicates that no restriction attributes are used for the analysis.	String
solverName (Sólo lectura)	Returns the name of the solver being referenced by the Network Analyst layer used to obtain the solver properties object. The property always returns the string value Location-Allocation Solver when accessed from a LocationAllocationSolverProperties object.	String
targetMarketShare (Lectura y escritura)	Provides the ability to get or set the target market share in percentage to solve when the problemType property 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. Any value set for facilitiesToFind property is ignored.	Double
timeOfDay (Lectura y escritura)	Provides the ability to get or set the time and date of departure. The departure can be from facilities or demand points, depending on whether travel is from demand to facility or facility to demand. A value of None can be used to specify that no date and time should be used. Instead of using a particular date, a day of the week can be specified using the following dates: Hoy, 30/12/1899 Domingo, 31/12/1899 Lunes, 1/1/1900 Martes, 2/1/1900 Miércoles, 3/1/1900 Jueves, 4/1/1900 Viernes, 5/1/1900 Sábado, 6/1/1900 For example, to specify that the departure should occur at 8:00 a.m. on Friday, specify the value as datetime.datetime(1900, 1, 5, 8,0,0). The timeZoneUsage parameter specifies whether the date and time refer to UTC or the time zone in which the facilities or demand points are located.	DateTime
timeZoneUsage (Lectura y escritura)	Specifies the time zone of the timeOfDay parameter. GEO_LOCAL —The timeOfDay parameter refers to the time zone in which the facilities or demand points are located. If a time and date is specified in timeOfDay and travelDirection is set to FACILITY_TO_DEMAND, this is the time zone of the facilities. If the same is true, but travelDirection is set to DEMAND_TO_FACILITY, this is the time zone of the facilities. UTC —The timeOfDay parameter refers to Coordinated Universal Time (UTC). Choose this option if you want to solve the analysis for a specific time, such as now, but aren't certain in which time zone the facilities or demand points will be located. When solving a location-allocation analysis that spans across multiple time zones, the following rules apply: All facilities must be in the same time zone when a start time is set and travel is from facility to demand. All demand points must be in the same time zone when a start time is set and travel is from demand point to facility.	String
travelDirection (Lectura y escritura)	Controls the direction of travel between facilities and demand points when calculating the network costs. The following is a list of possible values: FACILITY_TO_DEMAND —Direction of travel is from facilities to demand points. DEMAND_TO_FACILITY —Direction of travel is from demand points to facilities.	String
useHierarchy (Lectura y escritura)	Controls the use of the hierarchy attribute while performing the analysis. The following is a list of possible values: USE_HIERARCHY — Use the hierarchy attribute for the analysis. Using a hierarchy results in the solver preferring higher-order edges to lower-order edges. Hierarchical solves are faster, and they can be used to simulate the preference of a driver who chooses to travel on freeways over local roads when possible—even if that means a longer trip. This option is applicable only if the network dataset referenced by the Network Analyst layer has a hierarchy attribute. A value of True can also be used to specify this option. NO_HIERARCHY —Do not use the hierarchy attribute for the analysis. Not using a hierarchy yields an exact route for the network dataset. A value of False can also be used to specify this option.	String
uTurns (Lectura y escritura)	Provides the ability to get or set the policy that indicates how the U-turns at junctions that could occur during network traversal between stops are being handled by the solver. The following is a list of possible values: ALLOW_UTURNS —U-turns are permitted at junctions with any number of connected edges. NO_UTURNS —U-turns are prohibited at all junctions, regardless of junction valency. Note, however, that U-turns are still permitted at network locations even when this setting is chosen; however, you can set the individual network locations' CurbApproach property to prohibit U-turns there as well. ALLOW_DEAD_ENDS_ONLY —U-turns are prohibited at all junctions, except those that have only one adjacent edge (a dead end). ALLOW_DEAD_ENDS_AND_INTERSECTIONS_ONLY —U-turns are prohibited at junctions where exactly two adjacent edges meet but are permitted at intersections (junctions with three or more adjacent edges) and dead ends (junctions with exactly one adjacent edge). Oftentimes, networks have extraneous junctions in the middle of road segments. This option prevents vehicles from making U-turns at these locations.	String

Ejemplo de código

LocationAllocationSolverProperties example 1 (Python window)

The script shows how to update the problem type of a location-allocation network analysis layer to Minimize Facilities and set a power impedance transformation with an impedance parameter of 2. It assumes that a location-allocation layer called Stores Coverage has been created in a new map document based on the tutorial network dataset for San Francisco region.

#Get the location-allocation layer object from a layer named "Stores Coverage" in
#the table of contents
laLayer = arcpy.mapping.Layer("Stores Coverage")

#Get the solver properties object from the location-allocation layer
solverProps = arcpy.na.GetSolverProperties(laLayer)

#Update the properties for the location-allocation layer using the solver properties
#object
solverProps.problemType = "MINIMIZE_FACILITIES"
solverProps.impedanceTransformation = "POWER"
solverProps.impedanceParameter = 2

LocationAllocationSolverProperties example 2 (workflow)

The script shows how to choose optimal store locations that would generate the most business for a retail chain using location-allocation analysis. The script first creates a new location-allocation layer with appropriate analysis settings. As a next step, the candidate store locations and the block group centroids are loaded as facilities and demand points, respectively. The analysis is solved and saved to a layer file. Two subsequent analyses are performed by modifying the analysis properties using the LocationAllocationSolverProperties object. After each solve, the layer is stored as a layer file. The script uses the tutorial data for the San Francisco region. The detailed description of the scenario is available as part of exercise 9 in the ArcGIS 10.1 Network Analyst tutorial. While the tutorial walks you through this scenario using the ArcMap user interface, the script provides an example of how the same scenario can be automated using a Python script.

import arcpy

#Set up the environment
arcpy.env.overwriteOutput = True
arcpy.env.workspace = "C:/data/SanFrancisco.gdb"
arcpy.CheckOutExtension("network")

#Set up variables
networkDataset = "Transportation/Streets_ND"
outNALayerName = "NewStoreLocations"
inFacilities = "Analysis/CandidateStores"
requiredFacility = "Analysis/ExistingStore"
competitorFacility = "Analysis/CompetitorStores"
inDemandPoints = "Analysis/TractCentroids"
outputFolder = "C:/data/output/"

#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.
outNALayer = arcpy.na.MakeLocationAllocationLayer(networkDataset, outNALayerName,
                                                  "TravelTime","DEMAND_TO_FACILITY",
                                                  "MAXIMIZE_ATTENDANCE",3,5,
                                                  "LINEAR")
#Get the layer object from the result object. The location-allocation layer
#can now be referenced using the layer object.
outNALayer = outNALayer.getOutput(0)

#Get the names of all the sublayers within the location-allocation layer.
subLayerNames = arcpy.na.GetNAClassNames(outNALayer)
#Stores the layer names that we will use later
facilitiesLayerName = subLayerNames["Facilities"]
demandPointsLayerName = subLayerNames["DemandPoints"]

#Load the candidate store locations as facilities using default search
#tolerance and field mappings.
arcpy.na.AddLocations(outNALayer, facilitiesLayerName, inFacilities, "", "",
                      exclude_restricted_elements = "EXCLUDE")

#Load the tract centroids as demand points using default search tolerance. Use 
#the field mappings to map the Weight property from POP2000 field.
demandFieldMappings = arcpy.na.NAClassFieldMappings(outNALayer,
                                                    demandPointsLayerName)
demandFieldMappings["Weight"].mappedFieldName = "POP2000"
arcpy.na.AddLocations(outNALayer,demandPointsLayerName ,inDemandPoints,
                      demandFieldMappings, "",
                      exclude_restricted_elements = "EXCLUDE")

#Solve the location-allocation layer
arcpy.na.Solve(outNALayer)
    
#Save the solved location-allocation layer as a layer file on disk with 
#relative paths
outLayerFile = outputFolder + outNALayerName + ".lyr"
arcpy.management.SaveToLayerFile(outNALayer,outLayerFile,"RELATIVE")

#We need to re-solve the previous scenario as a store-expansion scenario, in
#which we will start with an existing store and optimally locate two additional
#stores.
#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.
fieldMappings = arcpy.na.NAClassFieldMappings(outNALayer, facilitiesLayerName)
fieldMappings["FacilityType"].defaultValue = 1
fieldMappings["Name"].mappedFieldName = "Name"
arcpy.na.AddLocations(outNALayer, facilitiesLayerName, requiredFacility,
                      fieldMappings, "", append = "APPEND",
                      exclude_restricted_elements = "EXCLUDE")

#Solve the location-allocation layer
arcpy.na.Solve(outNALayer)
    
#Save the solved location-allocation layer as a layer file on disk with 
#relative paths
updatedNALayerName = "StoreExpansionScenario"
outNALayer.name = updatedNALayerName
outLayerFile = outputFolder + updatedNALayerName + ".lyr"
arcpy.management.SaveToLayerFile(outNALayer,outLayerFile,"RELATIVE")

#We need to resolve the previous scenario and locate new stores to 
#maximize market share in light of competing stores.

#Load the competitor store locations as the competitor facilities. Use the field
#mappings to set the facility type to Competitor. We need to append these
#competitor facilities to existing facilities.
fieldMappings["FacilityType"].defaultValue = 2
arcpy.na.AddLocations(outNALayer, facilitiesLayerName, competitorFacility,
                      fieldMappings, "", append = "APPEND",
                      exclude_restricted_elements = "EXCLUDE")

#Get the LocationAllocationSolverProperties object from the location-allocation 
#layer to modify the analysis settings for the layer.
solverProps = arcpy.na.GetSolverProperties(outNALayer)

#Set the problem type to Maximize Market Share, and impedance transformation to
#Power with an impedance parameter value of 2.
solverProps.problemType = "MAXIMIZE_MARKET_SHARE"
solverProps.impedanceTransformation = "POWER"
solverProps.impedanceParameter = 2

#Solve the location-allocation layer
arcpy.na.Solve(outNALayer)

#print the market share that was obtained
arcpy.AddMessage(arcpy.GetMessage(0))

#Change the name of the NA Layer
updatedNALayerName = "MaximizedMarketShareStoreLocations"
outNALayer.name = updatedNALayerName

#Save the solved location-allocation layer as a layer file on disk with 
#relative paths
outLayerFile = outputFolder + updatedNALayerName + ".lyr"
arcpy.management.SaveToLayerFile(outNALayer,outLayerFile,"RELATIVE")
    
arcpy.AddMessage("Completed")