Locating optimal sites for 15 minute grocery delivery

CARTO Spatial Extension for Snowflake

In New York City, a number of companies such as 1520, Fridge No More, JOKR, and BUYK have started offering local grocery delivery in 15 minutes from ghost grocery stores. As these services expand out into other, less dense cities, they will need strategies for locating optimal delivery locations.

A story map by

In collaboration with

Using CARTO and Snowflake together, we can view how this process might play out in Seattle, using geospatial data and analytics in the CARTO Analytics Toolbox for Snowflake, and how it can work with data that already exists in your Snowflake instance and data from CARTO.

MAP 1 OF 6

As the Expansion Manager, you need to find 5 ideal sites for "ghost grocery stores" in Seattle, WA

When expanding to new markets, the first item to focus on is the suitability of the market, including high-level factors of population density and consumer segments. 

The harder job is on the ground, finding ideal locations for the ghost grocery stores. In this scenario, our Expansion Manager has been handed 7 potential locations by their Real Estate team. How can we find the 5 best locations for maximum coverage and market fit?

MAP 2 OF 6

Each location has a 1-mile delivery radius around it, and spatial analytics can help us narrow in on the right sites

We can analyze each potential location by looking at a number of spatial factors within each delivery zone using the CARTO Data Observatory and Snowflake Data Marketplace including:

• Demographics
• Consumer Segments
• Income 
• Population Density
• Purchasing Patterns
• Housing Units

MAP 3 OF 6

Using a spatial index, the Expansion Manager can find multiple suitable locations across the city for a new ghost store

Using a spatial index, in this case, H3 cells, we can create a grid around the entire city and for each cell, measure multiple spatial data streams. 

We can normalize these measures to create a suitability index to score each cell for all these variables. In this map, darker cells have more of the attributes for a successful location.

MAP 4 OF 6

Each cell is enriched with many different variables from the CARTO Data Observatory using the CARTO Analytics Toolbox for Snowflake

The power of Snowflake and spatial analytics provided by the CARTO Analytics Toolbox allows the Expansion Manager to enrich with hundreds of variables in grids covering every single market nationwide.

This provides complete coverage in every market and granular insights to help find the best possible locations.

MAP 5 OF 6

Next, we need to find the most suitable locations for a ghost store using location-allocation and app signup data

Using location-allocation analysis along with current app sign-ups already stored in Snowflake, we can find the most suitable 6 locations.

We use local only roads, since our drivers will use bikes. The analysis, called the Location Set Covering Problem (LSCP), is the same that is used to locate the best sites for fire stations, for example. It gives us the maximum demand coverage within a minimum service time or distance. 

MAP 6 OF 6

Our Expansion Manager now has the best locations, covering 82% of current app sign ups

We now have a total of six locations that give us the required coverage of one mile from the ghost grocery stores. 

Exactly half of the locations came from our Real Estate team, the other half from our suitability index. We now have target areas for our Real Estate team to explore and find locations, and we can start to lease the other locations right away!

Tap into powerful spatial analytics with CARTO and Snowflake

CARTO Spatial Extension for Snowflake

To get started, you can sign up for a CARTO account and connect your Snowflake instance and start using the CARTO Analytics Toolbox for Snowflake.

You can also access data from the Snowflake Data Marketplace and the CARTO Data Observatory.

A story map by

In collaboration with