Retail Catchment Analysis with Overture Maps
🏪 Select. Analyze. Compete. Build an interactive retail catchment analysis tool using real-world POI data - powered by OpenRouteService in Snowflake.
Overview
Build a fully interactive Retail Catchment Analysis application using the OpenRouteService Native App and Carto Overture Maps data.
This quickstart deploys a Streamlit application that performs catchment zone analysis for retail locations. Select any store from real-world POI data, generate isochrone-based catchment areas, identify competitors within the zone, and visualize address density using H3 hexagonal grids.
What You'll Build
🏪 Retail Catchment Analyzer - A Streamlit app that:
- Searches real-world retail locations from Carto Overture Maps (72M+ POIs worldwide)
- Generates travel-time based catchment zones using isochrones
- Identifies competitor stores within the catchment area
- Visualizes address density using H3 hexagonal aggregation
- Supports multiple travel modes (walking and driving)
📊 Catchment Analytics - Real-time insights including:
- Multi-ring catchment zones (configurable 1-5 rings, up to 60 minutes)
- Competitor breakdown by retail category
- Address density heat maps for market potential analysis
- Interactive maps with color-coded catchment boundaries
Prerequisites
IMPORTANT: This demo requires the OpenRouteService Native App to be installed and running. If you haven't built it yet, complete the Build Routing Solution in Snowflake quickstart first.
Required:
- OpenRouteService Native App deployed and activated
- Cortex Code CLI installed and configured
- Active Snowflake connection with ACCOUNTADMIN access
What You'll Learn
- Deploy retail analytics applications using Cortex Code skills
- Work with Carto Overture Maps datasets (Places and Addresses) for real-world location data
- Use isochrone functions to calculate travel-time based catchment zones
- Build H3 hexagonal visualizations for address density analysis
- Perform competitor analysis using spatial queries
- Create multi-layer geospatial visualizations with Pydeck
Deploy the Retail Catchment Demo
Use Cortex Code to deploy the Retail Catchment Analysis solution including Marketplace data and the Streamlit application.
Clone Repository and Deploy Skill
Clone the repository:
git clone https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator cd sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator
In the Cortex Code CLI, type:
$deploy-retail-catchment-demo
NOTE: The skill will first verify that the OpenRouteService Native App is installed. If it's not found, it will provide instructions to install it first.
The skill uses interactive prompting to gather required information:
- Region: San Francisco (default), New York, or custom coordinates
- Retail Categories: Pre-configured set of 17 retail categories
Cortex Code will automatically:
- Verify OpenRouteService Native App is installed and running
- Acquire Marketplace Data - Gets Carto Overture Maps Places and Addresses datasets
- Create Demo Database - Sets up
OPENROUTESERVICE_SETUP.RETAIL_CATCHMENT_DEMOschema - Prepare POI Data - Filters and indexes retail locations for fast querying
- Deploy Streamlit App - Creates the Retail Catchment Analysis application
What Gets Installed
The deploy skill creates the following Snowflake objects:
Marketplace Data
| Component | Name | Description |
|---|---|---|
| Database | OVERTURE_MAPS__PLACES | Carto Overture Maps Places with 72M+ POIs worldwide |
| Database | OVERTURE_MAPS__ADDRESSES | Carto Overture Maps Addresses with 121M+ US addresses |
Demo Database & Infrastructure
| Component | Name | Description |
|---|---|---|
| Database | OPENROUTESERVICE_SETUP | Main demo database |
| Schema | OPENROUTESERVICE_SETUP.RETAIL_CATCHMENT_DEMO | POI data and analytics tables |
| Warehouse | ROUTING_ANALYTICS | Compute warehouse (auto-suspend 60s) |
| Stage | STREAMLIT_STAGE | Stage for Streamlit application files |
Data Tables
| Table | Description |
|---|---|
RETAIL_POIS | Filtered retail POIs with categories, addresses, and geometry |
REGIONAL_ADDRESSES | Address data for H3 density visualization |
CITIES_BY_STATE | City lookup for region filtering |
REGION_CONFIG | Bounding box configuration for the deployed region |
Streamlit Application
| Component | Name | Description |
|---|---|---|
| Streamlit | RETAIL_CATCHMENT_APP | Interactive catchment analysis application |
Explore the Catchment Analyzer
Once deployment completes, navigate to the Retail Catchment application:
- Go to Projects > Streamlits in Snowsight
- Click on RETAIL_CATCHMENT_APP
Configure Your Analysis
Use the sidebar to configure your analysis:
Retail Category
- Select from 17 pre-configured categories:
- Coffee Shop, Fast Food Restaurant, Restaurant, Casual Eatery
- Grocery Store, Convenience Store, Supermarket
- Gas Station, Pharmacy
- Clothing Store, Electronics Store, Specialty Store
- Gym/Fitness, Beauty Salon, Hair Salon, Bakery, Bar
Store Selection
- Filter by city (based on ORS coverage)
- Search by store name with real-time filtering
- View store address and coordinates
Travel Mode
- Walking - Pedestrian catchment analysis
- Driving - Vehicle-based catchment zones
Catchment Zones
- Number of rings (1-5 concentric zones)
- Maximum travel time (5-60 minutes)
Display Options
- Show/hide catchment boundaries
- Show/hide competitor POIs
- Show/hide address density (H3 hexagons)
- Adjust H3 resolution (7-10)
Analyze Catchment Zones
Click Analyze Catchment to generate the analysis:
- Catchment Boundaries - Color-coded isochrone polygons showing travel-time zones
- Selected Store - Red marker indicating the analysis center point
- Competitor Locations - Color-coded markers for competing retail locations
- Address Density - H3 hexagons showing residential/commercial address concentration
View Analytics
The analysis provides key metrics:
- Selected Store - Name and address of the analyzed location
- Travel Time - Maximum catchment zone travel time
- Competitors - Total number of competing POIs within the catchment
- Addresses in Zone - Total address count for market potential estimation
Competitor Breakdown
View competitor analysis by category:
- Breakdown of competitors by retail type
- Count of each category within the catchment zone
- Sorted by frequency for quick competitive insights
Understanding the Data
Carto Overture Maps Places
The demo uses the Carto Overture Maps Places dataset from Snowflake Marketplace:
| Dataset | Records | Coverage |
|---|---|---|
| Places | 72M+ | Worldwide POIs with categories |
| Addresses | 121M+ | US addresses with geocoding |
Retail Categories
POIs are filtered using the Overture Maps category taxonomy:
| Category Code | Display Name | Examples |
|---|---|---|
coffee_shop | Coffee Shop | Starbucks, Peet's Coffee |
fast_food_restaurant | Fast Food | McDonald's, Subway |
grocery_store | Grocery Store | Trader Joe's, Whole Foods |
convenience_store | Convenience Store | 7-Eleven, CVS |
pharmacy | Pharmacy | Walgreens, Rite Aid |
gas_station | Gas Station | Shell, Chevron |
gym | Gym / Fitness | 24 Hour Fitness, Planet Fitness |
Isochrone Analysis
Isochrones are generated using the OpenRouteService Native App:
| Profile | Use Case | Speed Assumptions |
|---|---|---|
foot-walking | Pedestrian catchment | ~5 km/h walking speed |
driving-car | Vehicle catchment | Variable based on road network |
H3 Hexagonal Grid
Address density is visualized using Uber's H3 spatial indexing:
| Resolution | Hex Edge | Coverage Area | Best For |
|---|---|---|---|
| 7 | ~1.2 km | ~5 km² | Regional overview |
| 8 | ~460 m | ~0.7 km² | Neighborhood analysis |
| 9 | ~174 m | ~0.1 km² | Block-level detail |
| 10 | ~65 m | ~15,000 m² | Building-level precision |
Customize the Demo
Change Retail Categories
Edit the RETAIL_CATEGORIES list in the Streamlit app to focus on specific POI types:
RETAIL_CATEGORIES = [ 'coffee_shop', 'fast_food_restaurant', 'restaurant', 'casual_eatery', 'grocery_store', 'convenience_store', 'gas_station', 'pharmacy', 'clothing_store', 'electronics_store', 'specialty_store', 'gym', 'beauty_salon', 'hair_salon', 'bakery', 'bar', 'supermarket' ]
Add New Regions
To support additional cities:
-
Update OpenRouteService with the appropriate map region using the Building a Routing Solution quickstart
-
Filter POI data for the new region in the database setup script
-
Update the city selector in the Streamlit app sidebar
Adjust H3 Resolution
The H3 resolution affects visualization granularity:
| Resolution | Use When |
|---|---|
| 7-8 | City-wide catchment analysis |
| 9 | Neighborhood competitive analysis |
| 10 | Site selection at specific locations |
Uninstall the Solution
To remove the Retail Catchment solution execute:
DROP SCHEMA OPENROUTESERVICE_SETUP.RETAIL_CATCHMENT_DEMO;
This will remove the OPENROUTESERVICE_SETUP.RETAIL_CATCHMENT_DEMO schema and its contents.
NOTE: The OpenRouteService Native App remains installed. You can uninstall it separately.
Available Cortex Code Skills
| Skill | Description | Command |
|---|---|---|
| Deploy Demo | Deploy the full Retail Catchment solution | $deploy-retail-catchment-demo$ |
Conclusion and Resources
Conclusion
You've deployed a complete Retail Catchment Analysis application that demonstrates:
- OpenRouteService Native App - Isochrone-based catchment zone generation
- Carto Overture Maps - Real-world POI and address data for authentic analysis
- H3 Hexagonal Indexing - Address density visualization for market potential
- Pydeck Visualization - Interactive multi-layer maps with tooltips
- Spatial Analytics - ST_WITHIN queries for competitor identification
What You Learned
- Deploy retail analytics applications using Cortex Code skills
- Work with Carto Overture Maps datasets for POI and address data
- Generate isochrone-based catchment zones with multiple travel modes
- Visualize address density using H3 hexagonal aggregation
- Perform competitor analysis within geographic boundaries
- Build interactive Streamlit apps with geospatial visualizations
AI-Powered Market Analysis
The application includes an AI-powered market analysis feature that uses Snowflake Cortex LLM functions to generate comprehensive insights:
Store Performance Analysis
- Daily visitor estimates based on location data
- Assessment of store visibility and accessibility
- Performance benchmarking against competitors
Catchment Area Insights
- Analysis of distinct catchment zones by travel time
- Total addressable market (residential addresses count)
- H3 cell analysis for geographic coverage assessment
Competitive Landscape
- Identification of major competitors in the trade area
- Combined competitor footfall estimates
- Market saturation analysis
Demographic Profile
- Population density metrics
- Household income estimates
- Customer profile alignment with retail category
AI-Recommended Store Locations
The AI analysis also provides optimal store location recommendations:
- Generates coordinates for recommended new store placement
- Considers competitor proximity and gaps in coverage
- Visualizes the recommendation on an interactive map
- Provides rationale based on catchment analysis data
Related Quickstarts
- Build Routing Solution in Snowflake with Cortex Code - Build and customize the routing solution (prerequisite for this demo)
- Deploy Route Optimization Demo - Build a visual route optimization simulator
- Deploy Fleet Intelligence Solution for Taxis - Track and analyze taxi fleet operations
- Deploy Snowflake Intelligence Routing Agent - Build an AI-powered route planning assistant that understands natural language locations - powered by OpenRouteService and Snowflake Intelligence
Source Code
- Source Code on GitHub - Skills, scripts, and Streamlit apps
OpenRouteService Resources
- OpenRouteService Official Website - Documentation and API reference
- Isochrones Documentation - Travel-time polygon generation
Cortex Code & Snowflake
- Snowflake Cortex - AI-powered features in Snowflake
- Carto Overture Maps Places - POI data for retail analysis
- H3 in Snowflake - Hexagonal hierarchical geospatial indexing
- Streamlit - Interactive data apps
This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances
