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3-30-300 Analysis in England using Big Spatial Data

Overview

This project implements the 3-30-300 urban forestry rule for England using big spatial data analysis. The 3-30-300 rule states that every citizen should be able to see at least 3 trees from their home, have 30% canopy cover in their neighborhood, and live within 300 meters of a park.

What is the 3-30-300 Rule?

The 3-30-300 rule is an urban forestry guideline that promotes healthy, accessible urban forests:

  • 3 trees visible from home: Ensures every household has immediate access to trees
  • 30% canopy cover: Provides adequate tree coverage for environmental benefits
  • 300 meters to a park: Guarantees easy access to green spaces

Project Components

Core Analysis Modules

Data Infrastructure

Utility Modules

Data Sources

Spatial Data

  • VOM (Vegetation Object Model): High-resolution tree and canopy data from Defra
  • OS (Ordnance Survey): Road networks, buildings, and green spaces
  • ONS (Office for National Statistics): Geographic boundaries and population data
  • Verisk: Building footprints and attributes

Remote Sensing

  • Sentinel-2: Satellite imagery for spectral analysis
  • Google Earth Engine: Cloud-based remote sensing processing

Technology Stack

Distributed Computing

  • Apache Spark: Distributed data processing
  • Apache Sedona: Spatial extensions for Spark
  • PySpark: Python interface for Spark

Spatial Analysis

  • GeoPandas: Spatial data manipulation
  • Rasterio: Raster data processing
  • Shapely: Geometric operations

Remote Sensing

  • Google Earth Engine: Cloud-based geospatial analysis
  • Earth Engine Python API: Programmatic access to GEE

Quick Start

Prerequisites

  1. Java 8+: Required for Apache Spark
  2. Python 3.8+: Core programming language
  3. Google Earth Engine Account: For remote sensing analysis

Installation

# Clone the repository
git clone <repository-url>
cd 3-30-300-analysis

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

Basic Usage

from src.utils.sedona_config import get_spark
from src.t3 import process_geo_code

# Initialize Spark session
sedona = get_spark()

# Process T3 analysis for a geographic area
result = process_geo_code(
    sedona=sedona,
    geo_level="LAD22CD",
    geo_code="E06000001",
    # ... other parameters
)

Analysis Workflow

1. Data Preparation

  • Load and preprocess spatial data
  • Convert to efficient parquet format
  • Set up geographic boundaries and overlays

2. T3 Analysis (Tree Counting)

  • Count trees within building buffers
  • Analyze tree visibility from homes
  • Generate tree density statistics

3. T30 Analysis (Canopy Cover)

  • Process VOM canopy height data
  • Calculate canopy cover percentages
  • Analyze vegetation density

4. T300 Analysis (Park Accessibility)

  • Calculate distances to parks
  • Analyze park accessibility by network
  • Generate accessibility statistics

5. Spectral Analysis

  • Calculate NDVI, NDBI, NDWI indices
  • Analyze environmental conditions
  • Integrate remote sensing data

6. Integration

  • Combine all analysis components
  • Generate comprehensive reports
  • Create final datasets

Output Data

Analysis Results

  • Tree counts: Number of trees per geographic area
  • Canopy cover: Percentage of canopy coverage
  • Park distances: Network and Euclidean distances to parks
  • Spectral indices: Environmental indicators
  • Integrated metrics: Combined 3-30-300 analysis

File Formats

  • Parquet: Efficient columnar storage
  • CSV: Standard tabular format
  • GeoJSON: Spatial data for web applications
  • Shapefile: Traditional GIS format

Performance Considerations

Scalability

  • Distributed processing: Handles large datasets across cluster
  • Spatial partitioning: Optimizes spatial operations
  • Memory management: Efficient memory usage for big data

Optimization

  • Spatial indexing: Accelerates spatial queries
  • Data compression: Reduces storage requirements
  • Parallel processing: Maximizes computational resources

Contributing

Development Setup

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests and documentation
  5. Submit a pull request

Code Standards

  • Follow PEP 8 style guidelines
  • Add comprehensive docstrings
  • Include unit tests for new functions
  • Update documentation for changes

Documentation

This documentation provides comprehensive coverage of:

  • Module Documentation: Detailed API reference for all modules
  • Usage Examples: Practical code examples
  • Configuration Guide: Setup and configuration instructions
  • Performance Tips: Optimization and best practices
  • Troubleshooting: Common issues and solutions