Getting Started¶

Welcome to Boost.Spatial - Sparse Spatial Hash! This section will help you get up and running quickly.

What You'll Learn¶

In this getting started guide, you'll learn how to:

1. Install the library in your project
2. Quick Start with a working example in 5 minutes
3. Tutorial - comprehensive step-by-step guide

Prerequisites¶

Before you begin, ensure you have:

• C++20 Compiler: GCC 10+, Clang 12+, or MSVC 2019+
• CMake (optional): 3.15+ for build system integration
• Standard Library: No external dependencies required

Learning Path¶

New to Spatial Indexing?¶

If you're new to spatial indexing concepts, we recommend:

1. Start with Core Concepts to understand spatial hashing
2. Try the Quick Start example
3. Follow the complete Tutorial
4. Explore Common Patterns

Experienced with Spatial Data Structures?¶

If you're familiar with R-trees, octrees, or other spatial structures:

1. Check out the Performance Comparison
2. Jump to Installation
3. Review the API Reference
4. See Advanced Usage

Quick Links¶

What is a Sparse Spatial Hash?¶

A sparse spatial hash grid is a data structure that divides space into a grid of cells and uses a hash map to store only the occupied cells. This makes it:

• Memory efficient: Only occupied cells use memory
• Fast: O(1) insertion and cell lookup
• Dynamic: Handles moving entities efficiently
• Simple: Easier to implement than trees

When to Use It?¶

Sparse spatial hashing excels when you have:

• Large, sparse worlds (entities scattered across space)
• Dynamic scenes (entities frequently moving)
• Uniform distribution (entities spread relatively evenly)
• Real-time requirements (games, simulations needing <16ms frame times)

When to Use Alternatives?¶

Consider other data structures if you have:

• Dense, small worlds → Use dense grid
• Static data → Use R-tree or kd-tree
• Hierarchical queries → Use octree or quadtree
• Non-uniform clustering → Use R-tree

Compare Data Structures

Common Use Cases¶

Game Development¶

// Collision detection
grid.for_each_pair(entities, collision_radius,
    [](size_t i, size_t j) {
        resolve_collision(entities[i], entities[j]);
    });

Physics Simulation¶

// Force calculations
for (auto idx : grid.query_radius(cutoff, particle.position)) {
    compute_forces(particle, particles[idx]);
}

AI and Pathfinding¶

// Find nearby entities for awareness
auto nearby = grid.query_radius(vision_radius, agent.position);
for (auto idx : nearby) {
    process_awareness(agent, entities[idx]);
}

Help and Support¶

Need help? Here's where to go:

• API Questions: See API Reference
• Performance Questions: See Performance Guide
• Bug Reports: GitHub Issues
• Discussions: GitHub Discussions

Ready to Begin?¶

Install the Library View Quick Start