Welcome to this blog, where we delve into an exciting Alteryx spatial practice following our previous review of Alteryx spatial tools and their diverse use cases. In this blog, we will be exploring the advanced Alteryx spatial challenge “#149: Market Overlap”(Link). If you’re eager to deepen your understanding of Alteryx spatial analysis, you’ve come to the right place.

The chosen challenge provides a fantastic opportunity to harness the power of Alteryx spatial capabilities and expand our knowledge in this domain. By embracing this practice, we will not only enhance our own understanding but also enable you to gain valuable insights into Alteryx spatial tools and the intricacies of spatial analysis.

Objective:

The objective of this challenge is to determine the overlap percentage between the trade area of the retailer’s current location and proposed future locations. Additionally, we aim to identify the percentage of current customers residing within each proposed trade area.

Summary of Data:
  • Proposed Locations Polygon Layer: Contains polygons representing the boundaries of the proposed future locations.

  • Current Location Polygon Layer: Consists of a polygon defining the trade area of the retailer’s existing location.

  • Current Customers: Dataset containing information about the retailer’s existing customers, aiding in understanding customer distribution within proposed trade areas.

 

To determine the overlap percentage between the trade area of the retailer’s current location and proposed future locations, the following steps will be taken:

Workflow:

Step 1: Utilize the Spatial Match tool to match the trade area of the current location with the proposed locations.

Step 2: Employ the Spatial Process tool to create an intersection object (Overlap) between the current location and each proposed location.

Step 3: Use the Spatial Info tool to calculate the area size of the intersection object (Overlap) and the trade area of the retailer’s current location.

Step 4: Employ the Formula tool to calculate the overlap percentage by dividing the area of overlap by the total area of the current location’s trade area.

Result:

 

To identify the percentage of current customers residing within each proposed trade area, the following steps will be undertaken:

Workflow:

Step 1: Utilize the Formula tool to transform the latitude and longitude data format in the Customers table.

Step 2: Utilize the Create Points tool to generate centroid points, allowing for visualization on a map.

Step 3: Employ the Spatial Match tool to match the trade area of the proposed locations with the customer centroid points.

Step 4: Utilize the Summarize tool to group the customer points by the addresses of the proposed locations and calculate the count of customers.

Step 5: Employ another Summarize tool to aggregate the total number of customers.

Step 6: Utilize the Append Fields tool to add the total number of customers to the count of customers in each proposed location. Then, utilize the Formula tool to calculate the percentage of current customers residing within each proposed trade area.

Result:

By following these steps, we can perform a formal analysis using Alteryx spatial tools to derive the overlap percentage and assess the distribution of current customers within the proposed trade areas.

In conclusion, this blog has delved into the Alteryx spatial analysis, focusing on the advanced challenge of “#149 Market Overlap.” Through the outlined steps, we have gained insights into determining the overlap percentage between the trade areas of the retailer’s current and proposed locations. Additionally, we have learned to identify the percentage of current customers residing within each proposed trade area. By leveraging Alteryx spatial tools, retailers can make data-driven decisions about future locations and better understand customer distribution. This Alteryx spatial challenge has been a valuable learning experience to me. I hope this blog has also deepened your knowledge of Alteryx spatial analysis and provided a better understanding of market overlap.

The Data School
Author: The Data School