Distributed Computing with Apache Sedona
28. Distributed Computing with Apache Sedona
28.2. Learning Objectives
28.3. Installing and Setting Up Apache Sedona
28.3.1. Installation Requirements
28.3.2. Core Imports and Configuration
28.3.3. Creating a Sedona-Enabled Spark Session
28.4. Core Concepts and Data Structures
28.4.1. Understanding Spatial DataFrames
28.4.2. Spatial Data Types
28.4.3. Creating Spatial DataFrames
28.4.4. Working with Real Geospatial Data
28.5. Spatial Operations and Functions
28.5.1. Basic Geometric Properties
28.5.2. Distance Calculations
28.5.3. Spatial Relationships
28.6. Spatial Joins and Indexing
28.6.1. Understanding Spatial Join Types
28.6.3. Spatial Join Example: Cities by Country
28.6.4. Optimizing Spatial Joins with Indexing
28.7. Advanced Spatial Analysis
28.7.1. Spatial Aggregations
28.7.2. Spatial Clustering Analysis
28.8. Working with Raster Data
28.11. Real-World Use Cases
28.11.1. Use Case 1: Urban Heat Island Analysis
28.11.2. Use Case 2: Transportation Network Analysis
28.13. Exercises
28.13.1. Exercise 1: Setting Up Sedona and Basic Spatial Operations
28.13.2. Exercise 2: Working with Real Geospatial Data
28.13.3. Exercise 3: Distance Analysis
28.13.4. Exercise 4: Spatial Joins
28.13.5. Exercise 5: Spatial Aggregation and Clustering
28.13.6. Exercise 6: Buffer Analysis
28.13.7. Exercise 7: Spatial SQL Queries
28.13.9. Exercise 9: Integration with GeoPandas
28.13.10. Exercise 10: Advanced Spatial Analysis
