Dataset Joins – SpatialKey Support

⬅ Knowledge Base Home

Do you have another dataset that could be used to enhance your existing dataset? Do you want to create thematic maps by a shapefile of your choice? Is your shapefile too complex for simple “filter my data by shape” actions in your dashboard?

… if you answered yes to any of these questions, creating data joins may be right for you!

Joining datasets together is very easy with SpatialKey and can be done if you are the Creator/Editor or Contributor of a dataset. This page will walk you through the types of joins that you can create and the various ways in which you can set them up. Once you have joins setup, you can use them in your dashboard to further enhance your analytics.

Types of joins

Joining two point datasets

Two datasets can be joined to allow data from a secondary dataset to be displayed along with data from the first as if the secondary data was part of the primary. When joining the datasets you select a field from each dataset to be used to align and merge the datasets – these two fields must be the same data type (i.e. text, number, date). For the join to work successfully, the selected field must contain only unique values in each dataset to avoid a many-to-many join relationship. Neither dataset is permanently altered after a join is setup, but it will be possible to perform analysis using the correlated data.

Joining a point dataset with a boundary file (shapefile)

A boundary file contains boundaries like states, counties, postcodes, storm tracks, or even custom polygon shapes. Here are a few examples of boundaries.

You can join a dataset to a boundary file using matches between point locations in the dataset and boundary file. The join will allow you to view your dataset thematically within the shapes of the boundary file. This allows you to visualize the data within the shapes of the boundary file – for example, to show the total amount of sales in each sales territory or the insured value of properties within a storm footprint.

Setting up the join

Through dataset settings

To begin, select the Datasets tab and find your dataset. Click on the gear icon to view data settings and select the “Join a Dataset” option on the left side of the screen. Note: you will only see this option if you are the Creator/Editor or Contributor of the dataset.

Image of the SpatialKey dataset Join Data page explaining why to join datasets. The main content highlights benefits such as adding columns from another dataset and thematic mapping by joining point data with shape data. A blue ‘Join a Dataset’ button appears on the right, while the left navigation shows dataset options including Details, Manage Data, Fields, and Join Data selected.

You’ll be walked through a couple of screens where you will select which data you want to join to your dataset and select how to join them together.

Image of the SpatialKey Join Data workflow with a centered modal titled ‘Select How You Want to Join Your Data.’ The modal presents two join options: using a common field shared between datasets or using the locations of points to determine which shapes they fall within. Each option is illustrated with simple diagrams, while the background shows the Join Data page dimmed to emphasize the selection dialog.

Image of the SpatialKey Join Data workflow with a modal titled ‘Use a Common Field.’ The dialog prompts the user to select matching fields from two datasets to join them, with dropdowns for choosing the common field in each dataset. A Back link and a disabled Continue button appear at the bottom, while the Join Data page remains visible but dimmed in the background.

Once a join has been made with your dataset, it will appear in an “already joined” section of the screen.

Image of the SpatialKey dataset Join Data page showing an ‘Already Joined’ section below the dataset-joining explanation. The table lists an existing joined dataset with its name, matched fields determined by point locations, and a 100% match rate, indicating that a dataset has already been successfully joined to the portfolio. A ‘Join a Dataset’ button remains available above the table, and the left navigation highlights Join Data.

Join Data from Within a Dashboard

What happens if you are already working with a dataset in a dashboard and you realize that you forgot to set up your joins? Don’t worry, SpatialKey has made it easy to set up joins to a boundary file from within a Dashboard. Simply expand the Layer Manager panel on the left side of the map. Find the layer you’d like to join and click “Advanced Options”.

Image of the SpatialKey Analyst map interface with the ‘Advanced’ link highlighted in the left Layers panel under the Sample Portfolio layer. The highlighted Advanced option indicates where users can configure advanced settings, including setting up a dataset join to enable thematic visualization of joined data directly within dashboards.

Next click on the “Thematic Shapes” visualization option. If you already have a shapefile joined to this dataset, SpatialKey will automatically select the first one in the list. To join another file, click the “Boundary” dropdown and select, “Add New Boundary” at the bottom of the list.

Note: you will only see this option if you are the Creator/Editor or Contributor of the dataset.

You’ll be walked through a couple of screens where you will select which data you want to join to your dataset and select how to join them together.

Image of the SpatialKey Analyst Advanced theming panel with the boundary/thematic mapping option highlighted and an ‘Add New Boundary’ button emphasized in the left panel. The interface shows how users can configure a dataset join to visualize data thematically on the map using boundaries such as U.S. states, counties, postal codes, or a joined shape dataset. Yellow thematic shapes appear across the United States on the map, while statistical summaries and a joined data table are visible on the right, illustrating how joined datasets drive dashboard-based thematic views.

Using Joined Columns in Your Dashboard

In addition to viewing your joined data thematically, you can also use joined columns in stat pods, unique value lists, and filters. By default, these joined columns are not visible in your dashboard. To make them available, open the Manage Columns modal by clicking the gear icon next to your dataset in the location panel.

Dashboard view with a map and data table, highlighting the settings (gear) icon next to the dataset name in the Data & Filters panel, which is used to open the Manage Locations Columns modal.

Once you open the Manage Location modal, you’ll first see the visible columns from your primary dataset listed in the Location section at the top. If you collapse that section or scroll down, you’ll find a second section containing columns from your secondary dataset—for example, the Wind Gates section shown below. Simply select the checkbox next to each column you want to use, and it will become available for stats, unique value lists (UVLs), and filtering.

Screenshot of the Manage Locations Columns modal showing expandable column lists, with the Wind Gates section highlighted as an example of secondary dataset columns joined to the primary Location dataset, including selectable fields like Gate Name and Gate Number.

Once a joined column is visible, you can find it at the end of the location columns in the location list view, as well as in any workflow where you can select a column to use in your dashboard.

Was this helpful?

Yes

Thanks for your feedback!


Zip Codes	Police Districts	Supervisor Districts