Join App

How to use the join app?

Spotzi also offers the advanced function of combining multiple tables. This feature becomes very handy when you want to add a great amount of data to an existing table. For example, if you have bought a postal code boundary map and you want to add your own customer data to certain postal code areas.

What do you need before you start a join?

If you want to import a CSV/Excel file that you eventually want to connect with a boundary file. You have to import the data without adding any geometry. In order to connect the data to these areas on the map you need the Spotzi Join App (Spotzi Basic). We will show how this works in the example below.

In this example we already imported the 2-digit postal code areas of the United States. We will now try to join the following CSV with the 2-digit postal code area and the name of the responsible sales manager. This file looks like this:

PC2 Salesmanager
18 Adam
19 James
20 James

In this example, we will use the 2-digit postal code as the similar value that can be used to join both tables.

Quick tip: Don’t have a postal code list yet? Export your map to a CSV!

If you don’t have a CSV / Excel with postal codes yet, please export your map to CSV. This will give you a full postal code list with all available postal code areas on the map.

Step-by-step explanation

1. Now we are going to start joining both tables together. In this example we already imported the 2-digit postal code boundaries of the United States (us_2_digit_postal_codes) and the CSV with the sales managers (pc2_usa_salesmanagers).

Join Data to Maps

2. To start this process you have to open the Join App in your My Apps.

Join Data to Maps

3. Choose the tables that you want to join. Pick the dataset with the geometry on the left and the CSV / Excel with your own data on the right.

On the screen you will see the columns from your postal code map on the left side. On the right side you have to select the column that contains the mutual value that you want to use to join both dataset with each other. Now press NEXT STEP.

Join Data to Maps

Quick tip: Check your data type!

Make sure that not just your data is matching, but also check whether you join data based on the same data type. So if your 2-digit postal code column is a string type, the 2-digit postal column of your table should be a string too.

4. Now you have to choose which other columns from the dataset you want to join. Now press NEXT STEP.

Join Data to Maps

5. The Mapbuilder will create a new table from your join. Name this new layer. Keep in mind that your original datasets won’t be affected. Now press MERGE DATASETS to start the joining process.

Join Data to Maps

6. After the joining process is finished you will receive a confirmation message.

Join Data to Maps

7. After confirmation your dataset will be imported into My Data. Create a map from your new joined dataset.

Join Data to Maps

8. Name your new map and pres CREATE MAP.

Join Data to Maps

9. Open your map after confirmation.

Join Data to Maps

10. Congratulations! The map from your joined dataset is created. Now you can choose to give your map a styling. In this example I gave the ZIP code areas a styling based on the responsible sales manager.

Join Data to Maps