Skip to main content

Locations

Beyond Coordinates: The Power of Understanding Location for Relevance​

Location data is a powerful contextual signal in many search and recommendation systems. It can represent a user's current position, their home address, the location of a physical store, the venue of an event, a service area boundary, or the origin/destination for shipping. Simply storing coordinates or region names isn't enough; effectively engineering location features allows systems to grasp:

  • Proximity & Local Relevance: What items, stores, or services are physically close to the user right now ("near me")?
  • Regional Preferences: Do users in different cities, states, or countries exhibit distinct tastes or needs?
  • Logistical Constraints: Is this item available for pickup nearby? Can this service be delivered to the user's address? What are the estimated shipping times/costs based on distance?
  • Geo-Targeting: Should specific content or promotions be shown only to users within a certain geographic area?
  • Spatial Relationships: Are these two locations part of the same defined region or delivery zone?

Transforming raw location information—whether precise coordinates or broad regions—into meaningful signals, or features, that machine learning models can utilize is a vital aspect of feature engineering. Get it right, and you unlock hyperlocal personalization, efficient logistics, and geographically relevant results. Neglect it, and you miss crucial spatial context, potentially showing irrelevant or unavailable options. The standard path involves handling diverse formats, calculating distances, and leveraging spatial indexing techniques.

The Standard Approach: Building Your Own Location Feature Pipeline​

Leveraging location data effectively requires converting various formats into structured inputs suitable for filtering, ranking, and model learning. Doing this yourself typically involves several steps:

Step 1: Gathering and Understanding Formats​

Location data comes in several common forms:

  • Latitude & Longitude (Lat/Lon): A numerical tuple representing precise coordinates on the Earth's surface (e.g., (40.7128, -74.0060)). The primary format for distance calculations.
  • Region Categories (Hierarchical): Categorical labels representing predefined areas, often nested.
    • Examples: Country (US, CA), State/Province (CA, NY, ON), City (San Francisco, Toronto), Postal Code (94107, M5V 2T6), Custom Zones (Delivery Zone A).
  • Addresses: Raw street addresses often need geocoding (converting to Lat/Lon) via external services first.

The Challenge: Data arrives in inconsistent formats. Requires robust parsing and validation (e.g., ensuring Lat/Lon are within valid ranges, standardizing region names). Geocoding addresses adds external dependency and cost.

Step 2: Normalization and Cleaning​

  • Standardization: Convert region names to a consistent format (e.g., using ISO country codes, standard state abbreviations).
  • Validation: Check Lat/Lon ranges. Verify region names against known boundaries if possible.
  • Missing Values: Decide how to handle missing locations (e.g., null category, imputation based on IP address lookup - though often inaccurate, using a default location).

The Challenge: Maintaining consistent and accurate location data across the system. Choosing an appropriate strategy for missing values.

Step 3: Feature Transformation and Creation​

This is where raw location data becomes actionable features.

  • Geohashing: Encodes Lat/Lon coordinates into short alphanumeric strings. Key properties:
    • Proximity: Nearby locations often share common prefixes in their geohash strings. Longer prefixes mean higher precision.
    • Indexing: Excellent for database indexing to quickly find points within a bounding box (by querying string prefixes).
    • Feature: The geohash string itself (at varying precisions) can be used as a categorical feature.
    • Example: dr5ru (lower precision) vs. dr5ru7z (higher precision).
  • Modeling Regions as Categoricals: Treat predefined regions (Country, State, Postal Code) as standard categorical features. Encode using:
    • One-Hot Encoding: For low-cardinality regions (e.g., continent, sometimes country).
    • Learned Embeddings: For higher-cardinality regions (e.g., city, postal code) to capture relationships between nearby or similar areas. Standard approach in deep learning models.
  • Calculating Distance: Compute the distance between two points (e.g., user's location and item's location).
    • Method: Typically using the Haversine formula, which accounts for the Earth's curvature, providing distance "as the crow flies".
    • Context: Often calculated dynamically at request time based on the user's current context (their inferred or provided Lat/Lon).
    • Feature: The calculated distance (e.g., in kilometers or miles) is a powerful numerical feature.
  • Handling Hierarchies: Explicitly model nested regions.
    • Separate Features: Create distinct categorical features for each level (Country, State, City).
    • Combined Features: Concatenate levels (e.g., US_CA_SanFrancisco).
    • Embeddings: Learn embeddings for each level, potentially combining them.

The Challenge: Choosing appropriate geohash precision. Selecting the right encoding for region categories based on cardinality. Performing distance calculations efficiently at scale, especially dynamically at request time. Correctly modeling hierarchical relationships.

Step 4: Integration & Usage Context​

Location features are used across the relevance stack:

  • At Retrieval Time (Filtering/Candidate Generation):
    • Region Matching: Use categorical region features (country = 'US', city = 'New York').
    • Geohash Prefix Matching: Efficiently find items within an approximate bounding box.
    • Radius Search: Use calculated distance to filter items within X miles/km of the user (often requires spatial database capabilities or efficient pre-filtering). Crucial for "near me".
  • At Scoring Time (Ranking):
    • Feed distance (numerical), region embeddings (dense), or geohash features (categorical/embedding) into the ML ranking model.
    • The model learns user sensitivity to distance, regional preferences, and the importance of locality for different query types.
  • At Ordering Time (Post-Processing):
    • Apply rules like boosting items within a certain distance, filtering out items outside a delivery zone, or prioritizing items with local availability after initial scoring.

The Challenge: Implementing efficient spatial queries for retrieval. Making dynamically calculated distance available to the ranker with low latency. Ensuring consistency between filtering logic and ranking features.

Step 5: Maintenance​

  • Updating Boundaries: Geographic definitions (postal codes, city limits) can change.
  • Geocoding Services: Keep geocoding dependencies up-to-date.
  • Data Freshness: Ensure user location context is reasonably current.

The Challenge: Keeping geographical data accurate and up-to-date. Managing dependencies on external services.

Streamlining Location Feature Engineering​

The DIY path for location features involves complex data handling, spatial algorithms, dynamic calculations, and specialized indexing. Platforms and tools aim to simplify this workflow.

How a Streamlined Approach Can Help:

  1. Automated Parsing & Handling: Intelligently parse various location formats (Lat/Lon tuples, region names). Potentially offer integrations with geocoding services. Enforce standardization.
  2. Built-in Spatial Functions: Provide easy access to geohashing generation and, crucially, efficient dynamic distance calculation (e.g., Haversine) based on request-time user context.
  3. Native Categorical & Embedding Support: Treat region features appropriately, automatically learning embeddings for higher-cardinality regions alongside other features.
  4. Managed Infrastructure & Indexing: Abstract away the complexities of spatial indexing (e.g., using efficient internal representations or integrations with spatial databases) needed for fast retrieval.
  5. Seamless Integration: Natively combine distance features, geohashes, and region embeddings within unified ranking models.

Leveraging Location in a Simplified Workflow​

Imagine using Shaped to streamline location feature engineering:

Goal: Use item location (store Lat/Lon) and user's current location to provide localized recommendations.

1. Ensure Data is Available:
Assume item_metadata (with item_id, store_latitude, store_longitude) and user context providing user_latitude, user_longitude at request time are accessible.

2. Define Model Configuration:

location_model.yaml
model:
  name: location_recs_platform
connectors:
  - name: items
    type: database
    id: items_source
fetch:
  items: |
    SELECT
      item_id, name, category,
      store_latitude,   # <-- Shaped identifies as latitude
      store_longitude   # <-- Shaped identifies as longitude
    FROM items_source

3. Trigger Model Training:
Initiate model training. Shaped sets up to handle dynamic distance calculation during inference.

shaped create-model --file location_model.yaml

# Monitor the model until it reaches the ACTIVE state
shaped view-model --model-name location_recs_platform

4. Use Standard Shaped APIs (with Context):
Call Shaped's rank API, providing the user's current location in the request context.

from shaped import Shaped

# Initialize the Shaped client
shaped_client = Shaped()

# User's current location
user_location_context = {
    "user_latitude": 40.7580,
    "user_longitude": -73.9855
}

# Get localized recommendations
response = shaped_client.rank(
    model_name='location_recs_platform',
    user_id='USER_ABC',
    user_features=user_location_context,
    limit=10
)

# Print the recommendations
if response and response.metadata:
    print("Localized Recommendations:")
    for item in response.metadata:
        print(f"- {item['name']} (Distance: {item['distance_km']} km)")
else:
    print("No recommendations found.")

Conclusion: Put Your Relevance on the Map, Minimize Spatial Pain​

Location data offers invaluable context for delivering relevant, practical, and personalized experiences. However, harnessing its power requires navigating diverse formats, implementing specialized calculations like Haversine distance and geohashing, managing spatial indexing, and handling dynamic user context efficiently.

Streamlined platforms and MLOps tools can significantly ease this burden by automating parsing, providing built-in spatial functions, managing infrastructure, and seamlessly integrating location signals into ranking models. This allows teams to focus on leveraging the where—proximity, regionality, logistics—to improve user satisfaction, without getting lost in the complexities of geospatial engineering.

Ready to streamline your feature engineering process?

Request a demo of Shaped today to see Shaped in action for your feature types. Or, start exploring immediately with our free trial sandbox.