Summary
Joins attributes from one feature to another based on the spatial relationship. The target features and the joined attributes from the join features are written to the output feature class.
Usage
A spatial join matches rows from the Join Features to the Target Features based on their relative spatial locations.
By default, all attributes of the join features are appended to attributes of the target features and copied to the output feature class. You can define which attributes will be written to the output by manipulating them in the Field Map of Join Features parameter.
Two new fields, Join_Count and TARGET_FID, are added to the output feature class. Join_Count indicates how many join features match each target feature (TARGET_FID).
Another new field, JOIN_FID, is added to the output when Join one to many (JOIN_ONE_TO_MANY in Python) is specified in the Join Operation (join_operation in Python) parameter .
When the Join Operation parameter is Join one to many, there can be more than one row in the output feature class for each target feature. Use the JOIN_FID field to determine which feature is joined to which target feature (TARGET_FID). A value of -1 for the JOIN_FID field means no feature meets the specified spatial relationship with the target feature.
All input target features are written to the output feature class if both the following apply:
- Join Operation is set to Join one to one.
- Keep All Target Features is checked (join_type = "KEEP_ALL" in Python).
All fields in the output dataset and the contents of those fields can be controlled using the Field map.
- To change the field order, select a field name and drag it to the preferred position.
- The default data type of an output field is the same as the data type of the first input field (of that name) it encounters. You can manually change the data type at any time to any other valid data type.
- The following merge rules are available: first, last, join, sum, mean, median, mode, minimum, maximum, standard deviation, and count.
- When using the Join merge rule, you can specify a delimiter such as a space, comma, period, dash, and so on. To use a space, make sure the pointer is at the start of the input box and press the Spacebar once.
- You can specify the start and end positions of text fields using the format option.
- Do not perform standard deviation on a single input because values cannot be divided by zero, so standard deviation is not a valid option for single inputs.
Merge rules specified in the Field Map of Join Features parameter only apply to attributes from the join features and when more than one feature is matched to a target feature (when Join_Count > 1). For example, if three features with DEPTH attribute values of 15.5, 2.5, and 3.3 are joined, and a merge rule of Mean is applied, the output field will have a value of 6.1. Null values in join fields are ignored for statistic calculation. For example, 15.5, <null>, and 2.5 will result in 9.0 for Mean and 2 for Count.
When the Match Option parameter is set to Closest or Closest geodesic, it is possible that two or more join features are at the same distance from the target feature. When this situation occurs, one of the join features is randomly selected as the matching feature (the join feature's Object ID does not influence this random selection). If you want to find the 2nd, 3rd, or Nth closest feature, use the Generate Near Table tool.
If a join feature has a spatial relationship with multiple target features, it is counted as many times as it is matched with the target feature. For example, if a point is within three polygons, the point is counted three times, once for each polygon.
For more information about using the three-dimensional spatial relationships Intersect 3D and Within a distance 3D see Select by Location: 3D relationships.
Syntax
arcpy.analysis.SpatialJoin(target_features, join_features, out_feature_class, {join_operation}, {join_type}, {field_mapping}, {match_option}, {search_radius}, {distance_field_name})| Parameter | Explanation | Data Type |
target_features | Attributes of the target features and the attributes from the joined features are transferred to the output feature class. However, a subset of attributes can be defined in the field map parameter. | Feature Layer |
join_features | The attributes from the join features are joined to the attributes of the target features. See the explanation of the join_operation parameter for details on how the aggregation of joined attributes are affected by the type of join operation. | Feature Layer |
out_feature_class | A new feature class containing the attributes of the target and join features. By default, all attributes of target features and the attributes of the joined features are written to the output. However, the set of attributes to be transferred can be controlled by the field map parameter. | Feature Class |
join_operation (Optional) | Specifies how joins between the target features and join features will be handled in the output feature class if multiple join features are found that have the same spatial relationship with a single target feature.
| String |
join_type (Optional) | Specifies whether all target features will be maintained in the output feature class (known as outer join) or only those that have the specified spatial relationship with the join features (inner join).
| Boolean |
field_mapping (Optional) | Controls which attribute fields will be in the output. By default, all fields from the inputs will be included. Fields can be added, deleted, renamed, and reordered, and you can change their properties. Merge rules allow you to specify how values from two or more input fields are merged or combined into a single output value. There are several merge rules you can use to determine how the output field will be populated with values.
In Python, you can use the FieldMappings class to define this parameter. | Field Mappings |
match_option (Optional) | Specifies the criteria used to match rows.
| String |
search_radius (Optional) | Join features within this distance of a target feature will be considered for the spatial join. A search radius is only valid when the spatial relationship is specified (match_option) is set to INTERSECT, WITHIN_A_DISTANCE, WITHIN_A_DISTANCE_GEODESIC, HAVE_THEIR_CENTER_IN, CLOSEST, or CLOSEST_GEODESIC). For example, using a search radius of 100 meters with the spatial relationship WITHIN_A_DISTANCE will join feature within 100 meters of a target feature. For the three WITHIN_A_DISTANCE relationships, if no value is specified for search_radius, a distance of 0 is used. | Linear Unit |
distance_field_name (Optional) | The name of a field to be added to the output feature class that contains the distance between the target feature and the closest join feature. This parameter is only valid when the spatial relationship is specified (match_option is set to CLOSEST or CLOSEST_GEODESIC. The value of this field is -1 if no feature is matched within a search radius. If no field name is specified, the field will not be added to the output feature class. | String |
Code sample
The following script demonstrates how to use the SpatialJoin function in a Python window.
import arcpy
target_features = "C:/data/usa.gdb/states"
join_features = "C:/data/usa.gdb/cities"
out_feature_class = "C:/data/usa.gdb/states_cities"
arcpy.SpatialJoin_analysis(target_features, join_features, out_feature_class)The following stand-alone script demonstrates how to use the SpatialJoin function to join attributes of cities to states.
# Name: SpatialJoin_Example2.py
# Description: Join attributes of cities to states based on spatial relationships.
# Requirements: os module
# Import system modules
import arcpy
import os
# Set local variables
workspace = r"C:\gpqa\mytools\spatialjoin\usa.gdb"
outWorkspace = r"C:\gpqa\mytools\spatialjoin\output.gdb"
# Want to join USA cities to states and calculate the mean city population
# for each state
targetFeatures = os.path.join(workspace, "states")
joinFeatures = os.path.join(workspace, "cities")
# Output will be the target features, states, with a mean city population field (mcp)
outfc = os.path.join(outWorkspace, "states_mcp2")
# Create a new fieldmappings and add the two input feature classes.
fieldmappings = arcpy.FieldMappings()
fieldmappings.addTable(targetFeatures)
fieldmappings.addTable(joinFeatures)
# First get the POP1990 fieldmap. POP1990 is a field in the cities feature class.
# The output will have the states with the attributes of the cities. Setting the
# field's merge rule to mean will aggregate the values for all of the cities for
# each state into an average value. The field is also renamed to be more appropriate
# for the output.
pop1990FieldIndex = fieldmappings.findFieldMapIndex("POP1990")
fieldmap = fieldmappings.getFieldMap(pop1990FieldIndex)
# Get the output field's properties as a field object
field = fieldmap.outputField
# Rename the field and pass the updated field object back into the field map
field.name = "mean_city_pop"
field.aliasName = "mean_city_pop"
fieldmap.outputField = field
# Set the merge rule to mean and then replace the old fieldmap in the mappings object
# with the updated one
fieldmap.mergeRule = "mean"
fieldmappings.replaceFieldMap(pop1990FieldIndex, fieldmap)
# Delete fields that are no longer applicable, such as city CITY_NAME and CITY_FIPS
# as only the first value will be used by default
x = fieldmappings.findFieldMapIndex("CITY_NAME")
fieldmappings.removeFieldMap(x)
y = fieldmappings.findFieldMapIndex("CITY_FIPS")
fieldmappings.removeFieldMap(y)
#Run the Spatial Join tool, using the defaults for the join operation and join type
arcpy.SpatialJoin_analysis(targetFeatures, joinFeatures, outfc, "#", "#", fieldmappings)Environments
Licensing information
- Basic: Yes
- Standard: Yes
- Advanced: Yes