🏗️ Architecture Overview

Introduction

PyMapGIS is designed as a modern, modular geospatial toolkit that prioritizes developer experience, performance, and extensibility. This document provides a high-level overview of the system architecture, design principles, and key components.

Design Philosophy

Core Principles

1. Simplicity First - Complex geospatial workflows should be simple to express
2. Performance by Default - Intelligent caching and lazy loading built-in
3. Extensibility - Plugin architecture for custom data sources and operations
4. Standards Compliance - Built on proven geospatial standards and libraries
5. Developer Experience - Fluent APIs, comprehensive documentation, and helpful error messages

Architectural Patterns

• Modular Design - Clear separation of concerns across modules
• Plugin Architecture - Extensible data sources and operations
• Lazy Loading - Components loaded only when needed
• Caching Strategy - Multi-layer caching for performance
• Accessor Pattern - Fluent APIs via pandas/geopandas accessors

System Architecture

High-Level Structure

PyMapGIS Core
├── Universal IO Layer (pmg.read())
├── Data Processing Layer (vector, raster, ml)
├── Visualization Layer (viz, leafmap integration)
├── Service Layer (CLI, web services)
├── Infrastructure Layer (cache, settings, auth)
└── Extension Layer (plugins, integrations)

Module Organization

pymapgis/
├── __init__.py           # Main API surface
├── io/                   # Universal data reading
├── vector/               # Vector operations (GeoPandas)
├── raster/               # Raster operations (xarray)
├── viz/                  # Visualization and mapping
├── serve/                # Web services (FastAPI)
├── cli/                  # Command-line interface
├── cache/                # Caching system
├── settings/             # Configuration management
├── auth/                 # Authentication & security
├── cloud/                # Cloud integrations
├── streaming/            # Real-time data processing
├── ml/                   # Machine learning integration
├── network/              # Network analysis
├── pointcloud/           # Point cloud processing
├── plugins/              # Plugin system
├── deployment/           # Deployment utilities
├── performance/          # Performance optimization
└── testing/              # Testing utilities

Core Components

1. Universal IO System (pymapgis.io)

Purpose: Unified interface for reading geospatial data from any source

Key Features:

• URL-based data source specification
• Automatic format detection
• Built-in caching
• Extensible data source plugins

Architecture:

• DataSourceRegistry - Manages available data sources
• DataSourcePlugin - Base class for custom sources
• CacheManager - Handles intelligent caching
• read() function - Main entry point

2. Vector Operations (pymapgis.vector)

Purpose: Spatial vector operations built on GeoPandas/Shapely

Key Features:

• Core spatial operations (clip, buffer, overlay, spatial_join)
• GeoDataFrame accessor methods (.pmg)
• GeoArrow integration for performance
• Spatial indexing optimization

Architecture:

• Standalone functions in pymapgis.vector namespace
• Accessor methods via .pmg on GeoDataFrames
• Integration with GeoPandas/Shapely ecosystem

3. Raster Processing (pymapgis.raster)

Purpose: Raster data processing built on xarray/rioxarray

Key Features:

• Raster operations (reproject, normalized_difference)
• DataArray accessor methods (.pmg)
• Cloud-optimized formats (COG, Zarr)
• Dask integration for large datasets

Architecture:

• Standalone functions in pymapgis.raster namespace
• Accessor methods via .pmg on DataArrays
• Integration with xarray/rioxarray ecosystem

4. Visualization System (pymapgis.viz)

Purpose: Interactive mapping and visualization

Key Features:

• Leafmap integration for interactive maps
• .map() and .explore() methods
• Customizable styling and symbology
• Export capabilities

Architecture:

• Accessor methods on GeoDataFrames and DataArrays
• Leafmap backend for interactive maps
• Styling engine for cartographic control

5. Web Services (pymapgis.serve)

Purpose: Expose geospatial data as web services

Key Features:

• XYZ tile services
• WMS services
• Vector tiles (MVT)
• FastAPI backend

Architecture:

• serve() function as main entry point
• FastAPI application factory
• Tile generation pipeline
• Service configuration management

Data Flow Architecture

Read Operation Flow

User Request → pmg.read(url) → DataSourceRegistry → 
Plugin Selection → Cache Check → Data Retrieval → 
Format Processing → Return GeoDataFrame/DataArray

Processing Operation Flow

Input Data → Operation Function → 
Validation → Processing → 
Result Caching → Return Processed Data

Visualization Flow

Geospatial Data → .map()/.explore() → 
Style Configuration → Leafmap Integration → 
Interactive Map Rendering

Extension Points

1. Data Source Plugins

• Implement DataSourcePlugin interface
• Register with DataSourceRegistry
• Support custom URL schemes

2. Operation Extensions

• Add functions to vector/raster modules
• Implement accessor methods
• Follow naming conventions

3. Visualization Extensions

• Custom map backends
• Styling engines
• Export formats

4. Service Extensions

• Custom service types
• Authentication providers
• Middleware components

Performance Considerations

Caching Strategy

• L1: In-memory caching for frequently accessed data
• L2: Disk-based caching for downloaded data
• L3: Remote caching for shared environments

Lazy Loading

• Modules loaded on first use
• Optional dependencies handled gracefully
• Minimal import overhead

Optimization Techniques

• Spatial indexing for vector operations
• Chunked processing for large rasters
• Parallel processing where applicable
• Memory-mapped file access

Security Architecture

Authentication

• API key management
• OAuth integration
• Session management
• Role-based access control (RBAC)

Data Security

• Encryption for sensitive data
• Secure token generation
• Password hashing
• Rate limiting

Deployment Architecture

Containerization

• Docker support
• Kubernetes deployment
• Multi-stage builds
• Environment configuration

Cloud Integration

• AWS, GCP, Azure support
• Object storage integration
• Serverless deployment options
• Auto-scaling capabilities

Testing Architecture

Test Categories

• Unit tests for individual functions
• Integration tests for workflows
• Performance tests for optimization
• End-to-end tests for user scenarios

Test Infrastructure

• pytest framework
• Fixtures for test data
• Mocking for external services
• CI/CD integration

Future Architecture Considerations

Scalability

• Distributed processing with Dask
• Streaming data processing
• Microservices architecture
• Event-driven processing

Interoperability

• OGC standards compliance
• STAC integration
• Cloud-native formats
• API standardization

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Next: Package Structure for detailed module breakdown