Spatial Join
Spatial join combines attributes from one dataset to another based on their spatial relationship. To perform spatial join, specify geometry columns in both datasets and select the type of spatial join you want to perform. Once the operation is complete, you will receive a new joined dataset.
Perform a Spatial Join
On a dataset with a geometry column, click ⋮ More Options >> Spatial Join to open the spatial join configuration panel.
Spatial Join Configuration
The Spatial Join panel contains three sections:
 Target Dataset  configure the dataset you will join against.
 Join Operation  select a supported join operation between the source and target dataset.
 Join Dataset  select columns and specify aggregation rules (how to aggregate results when joining multiple rows). The supported aggregation rules include: Count, Sum, Mean, Max, Min, Deviation, Variance, Median, Percentile(P05, P25, P50, P75, P90)
Target Dataset
In the Target Dataset section, select a dataset containing a geometry column, select the geometry column, then select/deselect columns to include in the join operation.
Join Dataset
In the Join Dataset section, select a dataset to join, select a geometry column in that dataset, then specify aggregation rules.
Join Operation
Supported spatial join operations in Foursquare Studio:
Join Operation  Description 

Intersects  True when geometries share any portion of space. 
Touches  True when geometries have at least one point in common, but their interiors do not intersect. 
Within  True when geometry A is completely inside geometry B. 
Overlaps  True when geometries share space but are not completely contained by each other. 
Equals  True when geometries represent the same geometry. 
Cross  True when geometries have some, but not all, interior points in common. 
Based on the geometry types you selected in both datasets, Foursquare Studio suggests an appropriate join type. However, you may select any combination of supported operations on the geometries of the datasets provided.
Click the Join Type dropdown to select a Join Type:
Target  Join with  

Intersects  Touches  Within  Overlaps  Equals  Crosses  
Point  Point  ✔️  ✔️  ✔️  
MultiPoint  ✔️  ✔️  
Line/MultiLine  ✔️  ✔️  ✔️  
Polygon/MultiPolygon  ✔️  ✔️  ✔️  
MultiPoint  Point  ✔️  ✔️  
MultiPoint  ✔️  ✔️  ✔️  ✔️  
Line/MultiLine  ✔️  ✔️  ✔️  ✔️  
Polygon/MultiPolygon  ✔️  ✔️  ✔️  ✔️  
Line/MultiLine  Point  ✔️  ✔️  
MultiPoint  ✔️  ✔️  ✔️  
Line/MultiLine  ✔️  ✔️  ✔️  ✔️  ✔️  ✔️  
Polygon/MultiPolygon  ✔️  ✔️  ✔️  ✔️  
Polygon/MultiPolygon  Point  ✔️  ✔️  
MultiPoint  ✔️  ✔️  ✔️  
Line/MultiLine  ✔️  ✔️  ✔️  
Polygon/MultiPolygon  ✔️  ✔️  ✔️  ✔️  ✔️ 
Aggregation Rules
When running any join operation, you must select an aggregation method for each selected column. Available methods depend on the data type of the column.
The following aggregation methods are currently available in Studio:
Aggregation Method  Column Type  Description 

Count  string , datetime , number ,  Counts the number of nonnull values in the specified column. 
Mode  string , datetime , number  Finds the most frequently occurring value(s) in the specified column. 
Max  datetime , number  Returns the maximum value from the specified column. 
Min  datetime , number  Returns the minimum value from the specified column. 
Unique  string , number  Returns the count of distinct values in the specified column. 
Sum  number  Computes the sum of all numeric values in the specified column. 
Mean  number  Calculates the average (mean) value of all numeric values in the specified column. 
Median  number  Returns the middle value in a sorted list of numeric values in the specified column. 
Variance  number  Measures the variability (spread) of numeric values in the specified column from their mean. 
Deviation  number  Calculates the standard deviation of numeric values in the specified column. 
P05  number  Returns the value below which 5% of the data falls in the specified column. 
P25  number  Returns the value below which 25% of the data falls in the specified column. 
P50  number  Returns the value below which 50% of the data falls (same as the median) in the column. 
P75  number  Returns the value below which 75% of the data falls (same as the median) in the column. 
P95  number  Returns the value below which 95% of the data falls in the specified column. 
Spatial Join Examples
Spatial join is one of the most popular geoprocessing tools in GIS. Here are some examples of practical uses of applying a spatial join using Foursquare Studio:
Example 1. Aggregate the parking infractions data for 157 neighborhoods in Philadelphia.
The parking infractions data include millions of points that each one represents the location of a parking violation ticket. Using spatial join, we can aggregate the points to each neighborhood based on the geographic locations.
In this example, we demonstrate how to compute the total dollar amount fined via parking violations for all neighborhoods in Philadelphia:

Select the
Spatial Join
option from the dataset "Neighborhoods_Philadelphia.json". 
In the Spatial Join panel, select the
Join Dataset
: "Parking Violations.csv". Foursquare Studio will automatically detect the geometry type "Point" and geometry column: "lat" and "lon". 
Ensure the
Join Operation
selection isIntersects
, which means all the points within or touch the polygon are considered as valid spatial join. 
Click on the field selector of
fine
column under theAttribute Columns(1)
label, and chooseSum
as the aggregation rule in the popup menu. By doing so, we can compute the sum of thefine
value of all points joined with each neighborhood.
The result of the spatial join is a new dataset with a column COUNT issue_date
, which represents how many data points (tickets) are in each polygon (neighborhood), and a column SUM fine
, which represents the total amount of fine from all data points in each neighborhood. Then, we can create a thematic map using the new column SUM fine
to show the spatial distribution of the infractions data at the neighborhood level (see Figure 3).
Example 2. Spatial join the building footprints with 25 Zip code areas in San Francisco.
In this example, we are going to spatial joining 25 zip code areas with 177,023 building footprints in San Francisco, so we can compute how many buildings and the total area they occupy in each zip code area.
 Select the
Spatial Join
option from the dataset "sfzip.json".  In the Spatial Join panel, we select the
Join Dataset
: "sf.buildings.json". Studio studio will automatically detect the geometry type "Polygon" and geometry column: "_geojson".  Ensure the
Join Operation
isIntersects
, which means all the polygons (buildings) intersect with the zip code area are considered as valid spatial join.  Click on the field selector of
mblr
(San Francisco property key) column under theAttribute Columns(2)
label, and chooseCount
as the aggregation rule in the popup menu. By doing so, we can count how many building footprints intersect with each zip code area.  Click on the field selector of
area
(The area of building in square meters) column under theAttribute Columns(2)
label, and chooseSum
as the aggregation rule in the popup menu. By doing so, we can compute the total area of the building footprints intersect with each zip code area.
The result of the spatial join is a new dataset with a column COUNT mlbr
, which represents how many building footprints in each zip code area, and a column SUM area
, which represents the total area of the building footprints in each zip code area. Then, we can create a thematic map using the new column SUM area
to show the spatial distribution of the total area of building footprints at the zip code level (see Figure 5).
Updated about 1 month ago