Key Takeaways
- Granular POI attributes unlock deeper insights beyond broad business categories.
- Structured category tags and amenity data make large-scale filtering faster and more reliable.
- Standardized attributes improve consistency across analytics, search, and modeling workflows.
- Detailed place-level features enhance audience targeting and location-based decision-making.
- Rich contextual data enables more accurate AI-driven search and recommendation systems.
Isolating businesses by location specific characteristics is an everlong industry prompt that is riddled with data access challenges. Knowing the general category of a business is a start, but it leaves much to be desired when thousands of businesses share a similar category.
A more narrow set of point of interest (POI) features are required to truly explain differences in business outcomes, customer profiles, etc. Does it offer delivery? Is it family-friendly? Can I pay in cash? These types of questions are critical across user search, real estate site selection, audience creation, and compliance.
To make these details more accessible, SafeGraph revamped its Category Tags feature and introduced Amenity Columns: a structured way to query key attributes about physical places. These updates complement our broader effort to improve location intelligence with richer category and amenity data, making it easier to filter places based on the nuances that matter. Available for all bar, restaurant, cafe, hotel, and retail trade places globally.
Why This Matters
Traditionally, many business characteristics such as “accepts reservations,” “has Wifi,” or “cocktail bar” were buried in free-text fields or inferred from inconsistent metadata, which made them difficult to analyze at scale.
SafeGraph’s POI Category Tags and Amenity Columns solve this by:
- Structuring attributes into logical columns
- Adhering to a normalized set of “reader friendly” string values
- Linked to all other contextual attributes like location name, brand, address and coordinate info, open hours, website, phone number, category, etc.
What’s Included
We’ve organized these rich attributes into eight intuitive columns:
| Column | Answers the question… | Example Values |
|---|---|---|
| category_tags | What words or phrases best describe this place, or what type of products does it sell? | Latin American Food, Mexican Food, Oaxaca Food, Tacos, Sports Bar, Cocktail Lounge, Auto Parts & Accessories |
| accessibility | Can I get there and get around? | Parking, Wheelchair Accessible Restroom |
| activities | What can I do there? | Karaoke, Pool and Billiards, Art Classes |
| amenities | What resources are available? | High Chairs, Wifi, TV, Spa, Outdoor Seating |
| owner_demographic | Who runs the business? | Women-owned, Veteran-owned |
| payment_options | How can I pay? | Accepts Cash, Accepts Credit Cards, Accepts Apple Pay |
| service_options | What kind of service is offered and how? | Accepts Reservations, Breakfast, Delivery, Drive Through, Vegan Options |
| setting | What’s the environment like? | Family Friendly, Moderate Noise, Touristy, Upscale |
All values are consistently formatted and optimized for large-scale querying.
How to Use the Data
These new features aren’t just easier to interpret; they’re designed for direct integration into your existing analytics workflow.
Whether you’re running SQL queries, training ML models, or enriching location search experiences, actionable insights are easy to extract. Below are a few real-world SQL query examples that demonstrate how to combine Category Tags and Amenity Columns to narrow on specific sets of locations:
1. Find sports bars in New York that have a TV, wifi, parking, happy hour, burgers, and accept reservations:
2. Identify cafes in California that offer bagels, delivery, have outdoor seating, and are family friendly with a casual vibe:
3. Identify Department Stores in Texas that sell perfume:
Real World POI Use Cases
Marketing & Advertising
- Build more accurate/custom location based audience segmentations
- Optimize asset selection for OOH campaigns with more precision
- Filter out sensitive locations like ‘Kid Friendly’ or ‘LGBTQ Friendly’
Maps and Generative AI Search & Discovery
- Surface relevant results from narrow, place based search queries (e.g., ‘Quiet’ cafes with ‘Outdoor Seating’ and ‘Wifi’)
- Tailor results by user preferences
- Enhance AI-search capabilities with local intent and better contextual recommendations
Retail & Real Estate
- Prioritize expansion into areas with specific demand attributes (e.g., restaurants with ‘Drive Through’ and ‘Delivery’)
- Understand competitor footprint by absence or presence of specific store attributes
- Identify gaps in service (e.g., ‘Live Music’ venues in high-density zones)
Build with Better Context
With SafeGraph’s POI Category Tags & Amenity Columns, you no longer need to guess what a location offers – you can know.
1. Explore Category Tags & Amenity Columns in our Docs Site
2. Talk to our team to learn more
FAQ’s
1. What are POI category tags?
They are descriptive labels that define the type of business, products, or services a location offers.
2. What is amenity data in geospatial datasets?
Amenity data captures specific features of a place, such as WiFi availability, parking, or payment options.
3. Why are structured attributes important for analysis?
They allow consistent querying and filtering across large datasets without relying on unstructured text.
4. How do category tags and amenities improve marketing?
They help build more precise audience segments and refine location-based targeting strategies.
5. What role do these features play in AI and search systems?
They provide structured context that improves relevance, personalization, and recommendation accuracy.
