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Efficient Location-Based Data
Dr Karthik K
May 2025
Spatial Data Structures for
Geo-Distributed Databases
 Uber: matches riders & drivers based on proximity
 Google Maps: Spatial queries across global regions
 Weather Apps: Deliver forecasts by geolocation
 Needs: Location-aware partitioning, Fast proximity queries, Low-
latency access
Real-World Motivation
 Databases deployed across multiple geographic regions
 Why: Reduce latency, Ensure availability, Support regulations
 Challenges: Partitioning, Spatial queries, Consistency vs Performance
What Are Geo-Distributed Databases?
 Organize and index geographic or multidimensional data
 Support fast range and location queries
 Enable efficient partitioning and geo-sharding
What Are Spatial Data Structures?
 Divides space into 4 quadrants recursively
 Ideal for 2D maps and non-uniform data
 Used for map tiles, hotspot indexing
Quadtree – Recursive Spatial
Partitioning
 Nodes represent bounding rectangles
 Efficient for range and nearest neighbor queries
 Used in PostGIS, SQLite, Oracle Spatial
R-Tree – Hierarchical Bounding Boxes
 Converts (lat, lng) into alphanumeric strings
 Preserves locality – nearby locations share prefixes
 Useful for sharding and prefix queries
Geohash – Location to String
Encoding
 Quadtree: Good for visual division, fast queries
 R-Tree: Better for range and spatial containment
 Geohash: Lightweight, perfect for sharding & prefix match
Comparison of Structures
 Partitioning: Geohash zones distributed nodes
→
 Query Routing: Spatial index routes query to the right node
 Load Balancing: Quadtree helps balance spatial load
 Data Locality: Nearby data stays in nearby servers
Applying in Geo-Distributed DBs
 Uber: Geohash for ride matching
 Google Spanner: R-Tree for global spatial queries
 MongoDB Atlas: 2dsphere indexes for sharded queries
Real-World Systems
 Adaptive Quadtree partitioning based on user load
 Hybrid models using Geohash + R-Tree
 Machine learning to optimize spatial indexing
 Latency-aware geo-sharding strategies
Research Opportunities
 Spatial data structures are key for geo-DB performance
 They support fast querying, efficient partitioning
 Enable scalable, location-aware, low-latency systems
 Vital for the future of mobile and IoT systems
Conclusion

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Spatial_Data_Structures_Presentation.pptx

  • 1. Efficient Location-Based Data Dr Karthik K May 2025 Spatial Data Structures for Geo-Distributed Databases
  • 2.  Uber: matches riders & drivers based on proximity  Google Maps: Spatial queries across global regions  Weather Apps: Deliver forecasts by geolocation  Needs: Location-aware partitioning, Fast proximity queries, Low- latency access Real-World Motivation
  • 3.  Databases deployed across multiple geographic regions  Why: Reduce latency, Ensure availability, Support regulations  Challenges: Partitioning, Spatial queries, Consistency vs Performance What Are Geo-Distributed Databases?
  • 4.  Organize and index geographic or multidimensional data  Support fast range and location queries  Enable efficient partitioning and geo-sharding What Are Spatial Data Structures?
  • 5.  Divides space into 4 quadrants recursively  Ideal for 2D maps and non-uniform data  Used for map tiles, hotspot indexing Quadtree – Recursive Spatial Partitioning
  • 6.  Nodes represent bounding rectangles  Efficient for range and nearest neighbor queries  Used in PostGIS, SQLite, Oracle Spatial R-Tree – Hierarchical Bounding Boxes
  • 7.  Converts (lat, lng) into alphanumeric strings  Preserves locality – nearby locations share prefixes  Useful for sharding and prefix queries Geohash – Location to String Encoding
  • 8.  Quadtree: Good for visual division, fast queries  R-Tree: Better for range and spatial containment  Geohash: Lightweight, perfect for sharding & prefix match Comparison of Structures
  • 9.  Partitioning: Geohash zones distributed nodes →  Query Routing: Spatial index routes query to the right node  Load Balancing: Quadtree helps balance spatial load  Data Locality: Nearby data stays in nearby servers Applying in Geo-Distributed DBs
  • 10.  Uber: Geohash for ride matching  Google Spanner: R-Tree for global spatial queries  MongoDB Atlas: 2dsphere indexes for sharded queries Real-World Systems
  • 11.  Adaptive Quadtree partitioning based on user load  Hybrid models using Geohash + R-Tree  Machine learning to optimize spatial indexing  Latency-aware geo-sharding strategies Research Opportunities
  • 12.  Spatial data structures are key for geo-DB performance  They support fast querying, efficient partitioning  Enable scalable, location-aware, low-latency systems  Vital for the future of mobile and IoT systems Conclusion