Source: examples/geoanalytics/README.md — this page is rendered live from that file.

GeoAnalytics Examples¶

This directory contains examples demonstrating the CSA-in-a-Box GeoAnalytics module, which provides open-source geospatial analysis capabilities as an alternative to ArcGIS.

Overview¶

The GeoAnalytics module offers comprehensive spatial analysis tools built on proven open-source libraries:

graph TD
    A[Input Data] --> B{Analysis Path}
    B -->|OSS Path| C[GeoAnalytics Module]
    B -->|Enterprise Path| D[ArcGIS Enterprise]

    C --> E[geopandas + H3]
    C --> F[PostGIS Storage]
    C --> G[Spatial Operations]

    D --> H[ArcGIS Spatial Analyst]
    D --> I[ArcGIS Data Store]
    D --> J[ArcGIS Server]

    E --> K[Analysis Results]
    F --> K
    G --> K

    H --> L[Enterprise Results]
    I --> L
    J --> L

    K --> M[GeoParquet Output]
    L --> N[Feature Services]

    style C fill:#e1f5fe
    style D fill:#fff3e0
    style K fill:#e8f5e8
    style L fill:#fff8e1

Architecture Comparison¶

Prerequisites¶

Install the required geospatial libraries:

# Core geospatial stack
pip install geopandas h3 pyproj shapely

# Database connectivity
pip install sqlalchemy geoalchemy2 psycopg2-binary

# Azure integration
pip install azure-identity azure-storage-blob

# Visualization (optional)
pip install matplotlib folium

# For Databricks/Spark integration
pip install apache-sedona pyspark

Examples¶

1. Environmental Analysis (environmental_analysis.py)¶

Comprehensive spatial analysis demonstrating:

• Data Loading: EPA facility locations and Census county boundaries
• Spatial Joins: Assign facilities to counties using spatial relationships
• H3 Indexing: Create hexagonal grid for pollution density analysis
• Aggregation: Calculate pollution metrics by administrative boundaries
• Visualization: Generate maps and statistical summaries

Usage:

# Basic analysis
python examples/geoanalytics/environmental_analysis.py

# With visualization
python examples/geoanalytics/environmental_analysis.py --plot

# Custom output directory
python examples/geoanalytics/environmental_analysis.py --output-dir ./my_analysis

# Higher resolution H3 grid
python examples/geoanalytics/environmental_analysis.py --h3-resolution 9

Outputs: - epa_facilities_analyzed.parquet - Processed facility data - county_pollution_stats.parquet - County-level aggregations - h3_pollution_grid.parquet - Hexagonal grid with pollution density - analysis_summary.json - Summary statistics - environmental_analysis.png - Visualization plots (with --plot)

Module Components¶

Core Classes¶

• GeoProcessor - Main processing class for data loading, transformation, and analysis
• H3Indexer - Hexagonal spatial indexing using H3 library
• SpatialJoiner - Spatial relationship operations between datasets
• PostGISStore - PostgreSQL/PostGIS database integration

Key Capabilities¶

1. Data Loading
2. GeoParquet, GeoJSON, Shapefiles, CSV with coordinates
3. Azure Data Lake Storage integration

4. Automatic CRS detection and handling

5. Spatial Operations

6. Coordinate system transformations
7. Buffer, clip, union, intersection operations

8. Distance calculations (geodesic and planar)

9. H3 Hexagonal Indexing

10. Point-to-H3 and polygon-to-H3 conversion
11. H3 grid generation for spatial aggregation

12. Multiple resolution levels (0-15)

13. Database Integration

14. PostGIS table read/write operations
15. Spatial indexing and optimization
16. Complex spatial SQL query execution

Configuration¶

The module uses a configuration class for customization:

from csa_platform.geoanalytics import GeoProcessingConfig, GeoProcessor

config = GeoProcessingConfig(
    default_crs="EPSG:4326",              # Default coordinate system
    h3_resolution=8,                       # H3 hexagon resolution
    buffer_distance_meters=1000.0,         # Default buffer distance
    azure_storage_account="mystorageacct", # Azure storage integration
    azure_container="geoanalytics"         # Container for geo data
)

processor = GeoProcessor(config)

Data Sources¶

Common geospatial data sources for CSA applications:

• EPA: Environmental facility data, air quality monitoring
• Census: Administrative boundaries, demographic data
• USGS: Topographic data, water resources
• NOAA: Weather, climate, coastal data
• OpenStreetMap: Road networks, points of interest

Performance Considerations¶

• Large Datasets: Use GeoParquet for efficient storage and loading
• Coordinate Systems: Choose appropriate projected CRS for analysis
• H3 Resolution: Balance granularity with computational cost
• Database Indexing: Create spatial indexes for large PostGIS tables
• Memory Management: Process data in chunks for very large datasets

Integration with Azure¶

The module supports Azure cloud integration:

• Authentication: Uses DefaultAzureCredential for managed identity
• Storage: Direct read/write to Azure Data Lake Storage
• Databases: Azure Database for PostgreSQL with PostGIS extension
• Compute: Compatible with Azure Databricks and Synapse Analytics

Next Steps¶

1. Explore Notebooks: Check notebooks/geoanalytics/ for interactive examples
2. Database Setup: Configure PostGIS for persistent spatial data storage
3. Custom Analysis: Adapt examples for your specific use cases
4. Performance Tuning: Optimize for your data sizes and complexity

Support¶

For questions about GeoAnalytics examples:

1. Check module documentation in csa_platform/geoanalytics/
2. Review test cases for additional usage patterns
3. Consult PostGIS and geopandas documentation for advanced operations