Spatial Analysis for Business Site Selection

1. The Framework for Spatial Problem Solving

Most spatial analysis problems in a business context, including site selection, can be broken down into a logical sequence of steps. While the specific tools might change, the overall pattern is often the same:

1. Define the Geography: Establish the key locations and areas for your analysis. This usually involves creating point objects for specific addresses (like stores or competitors) and then generating polygons (like trade areas or drive times) around them.
2. Locate the Targets: Identify the points of interest you want to analyze in relation to your geography. These could be customers, households, assets, or events.
3. Perform the Spatial Match: Determine the relationship between your geography and your targets. This is where you answer questions like, "Which customers are inside my trade area?" or "Which assets are closest to this location?"
4. Aggregate and Analyze: Summarize the results of the spatial match to derive business insights. This could involve counting points within polygons, summing sales data, or, as we'll do today, calculating density.

The workflow below illustrates how these steps come together to solve a customer density problem. This is what we will build in today's lesson.

2. Case Study: Customer Density Analysis

Let's dive into our main task. We'll follow a case study that guides us through building the workflow you just saw.

Now that you understand the goal—to find customer density in a 0-5 mile radius and a 5-8 mile band around an office—let's build the solution.

Task 1: Defining the Analysis Areas

Our first step is to translate the office location into spatial polygons representing our trade areas.

To build this part of the workflow:

1. Use a Text Input tool to enter the store's location data (or use the provided file).
2. Connect a Create Points tool, configuring it to use the longitude (X-Field) and latitude (Y-Field) columns to generate a SpatialObj.
3. Connect a Trade Area tool. In the configuration, specify the values 0-5, 5-8 and ensure the units are set to Miles. This will create two concentric ring polygons.

Task 2: Matching Customers to Areas

With our trade areas defined, we now need to find which customers fall into each one. This requires creating points for all our customers and then using the Spatial Match tool.

To build this part of the workflow:

1. In a separate stream, use an Input Data tool for the customer location data.
2. Add a Create Points tool to generate spatial points for each customer.
3. Add a Spatial Match tool.
   • Connect the customer points to the Target (T) input.
   • Connect the trade area polygons (from the Trade Area tool) to the Universe (U) input.
4. Configure the tool to match where Target is Within Universe. The Matched (M) output anchor will now contain only the customers who are located inside one of our trade areas, with appended data about which area they are in.

Task 3: Calculating Density

We have the raw matches, but our goal is to calculate density. The formula for density is:

$\text{Density}=\frac{\text{Number of Customers}}{\text{Area}}$

To do this, we need to get the area of our polygons and the count of our customers and then combine them.

Here is a summary of the steps to finish the workflow:

1. Get Polygon Area: From your Trade Area tool's output, connect a Spatial Info tool. Configure it to calculate the Area (Square Miles).
2. Count Customers: From the Matched (M) output of your Spatial Match tool, connect a Summarize tool.
   • Group by the customer segment and the RadiusSize (from the trade area).
   • Perform a Count on the customer ID field.
3. Combine Data: Use a Join tool to combine the outputs of the Spatial Info tool and the Summarize tool. Join on the RadiusSize field.
4. Calculate Density: Connect a Formula tool to the Join (J) output of the Join tool. Create a new field, Customer Density, with the expression [Count]/[AreaSqMi].

You have now successfully built a workflow that applies multiple spatial techniques to solve a business problem.

3. A Different Application: Risk Analysis

The pattern of Create Points -> Trade Area -> Spatial Match -> Summarize is incredibly versatile. We used it for customer analysis, but it can be applied to many other scenarios.

For example, the article "Alteryx 101: How to Spatial Analytics" uses this exact pattern for a creative risk analysis problem: identifying which cities are most at risk from meteorite strikes.

• Define Geography: It uses a Trade Area tool to create a 20-mile "danger zone" polygon around each city.
• Locate Targets: It uses Create Points to map the location of historical meteorite strikes.
• Perform Spatial Match: It uses Spatial Match to find which strikes fall within each city's danger zone.
• Aggregate and Analyze: It uses a Summarize tool to count the number of strikes per city, identifying the highest-risk locations.

This demonstrates how mastering a spatial analysis pattern is more powerful than just memorizing what a single tool does.

Conclusion

In this lesson, we consolidated everything you've learned in the Applied Spatial Analytics module. You've seen how to structure a spatial analysis workflow and applied it to a practical site selection problem by combining spatial and standard Alteryx tools.

Key Takeaways:

• Solving spatial business problems involves a logical sequence: Define Geography, Locate Targets, Perform Spatial Match, and Aggregate/Analyze.
• A single workflow often requires a combination of tools from the Spatial, Join, Transform, and Preparation palettes.
• The pattern of creating points, generating polygons around them, and matching other points to those polygons is a fundamental and widely applicable technique in spatial analytics.
• Site selection and customer density analysis are classic use cases for spatial tools in Alteryx.

Preview of the Next Module:

This lesson concludes our exploration of spatial analytics. We've focused on answering "where" questions. In our next module, Fundamentals of Predictive Analytics, we will shift our focus to an entirely new set of questions, such as "what will happen next?" and "which group does this belong to?". We will begin by exploring the standard predictive modeling workflow and learning how to properly prepare data for building models.