Why Choose This Project?

Geospatial data is everywhere — from GPS devices, mobile apps, satellite imagery to logistics tracking. This project leverages Google BigQuery’s built-in GIS functions to perform powerful geospatial queries at scale.

It’s perfect for learners aiming to explore location-based analytics, big data querying, and visualization of geographical insights using serverless architecture.

Core Features

• Import geospatial datasets (e.g., latitude/longitude, boundaries, locations)

• Perform spatial joins (e.g., find stores within 5km of users)

• Heatmap generation (e.g., traffic, footfall, delivery density)

• Region-wise aggregation (e.g., sales by city or zip code)

• Distance and proximity analysis

• Time-series geospatial trends

• Interactive dashboards using Google Data Studio or Looker Studio

Technology Stack

Architecture Workflow

1. Upload geospatial datasets (e.g., stores, users, delivery points) to Google Cloud Storage.

2. Load data into BigQuery using Geography data types (GEOGRAPHY, ST_GEOGPOINT, etc.).

3. Perform SQL queries using built-in GIS functions such as:

   • ST_DISTANCE()

   • ST_WITHIN()

   • ST_INTERSECTS()

   • ST_CONTAINS()

4. Aggregate and visualize the results in Google Data Studio or Looker Studio.

5. (Optional) Automate ETL using Cloud Functions or Dataflow for dynamic data.

Example Use Cases

• Retail: Find underserved areas based on user density vs store locations.

• Logistics: Analyze delivery patterns and optimize routes.

• Urban Planning: Traffic heatmaps, public transport usage by zone.

• Marketing: Target ads based on proximity and region performance.

• Agriculture: Analyze crop health zones using coordinates and overlays.

Security Best Practices

• Use IAM roles to restrict access to datasets and queries

• Store source data in private Cloud Storage buckets

• Enable BigQuery logging for query audit and billing monitoring

Google BigQuery GIS Functions Used

Example Dataset Ideas

• GPS data of taxis or delivery agents

• Polygon boundaries of zip codes or districts

• Earthquake/flood-prone zones for risk mapping

• Retail store coordinates and customer distribution

• Wildlife sightings for biodiversity research

Visualization Tips

Use Google Data Studio with BigQuery as the data source and enable:

• Geo maps (choropleths, points, heatmaps)

• Filters by region, time, category

• Drill-down by country → state → district