Themis.Index

• Themis.Index
  • Overview
• Themis.Index.KdTree
  • Construction
  • Search
  • Balancing
• Themis.Index.QuadTree
  • Construction
  • Search / Query

Overview

This project encompasses the core spatial indexing functionality that allowed for efficient in-memory storage and retrieval of common geospatial datatypes via binary space partioning trees.

Themis.Index.KdTree

This namespace exposes Themis' implementation of a K-D Tree (a binary space partitioning tree) that can be used for fast and efficient radial & nearest neighbour searches of point geometries. The key advantage of a K-D Tree is that it has an average complexity of O(logn) for Insert, Search, and Delete which is ideal for spatial datasets of significant scale and variable dimensionality.

A key note is that we've implemented this structure as KdTree<TKey, TValue> so that consumers can store any desired TValue type as long as they can compose an associated point geometry of form IEnumerable<TKey> to store within the tree.

Further to that point - each KdTree must be given a ITypeMath<TKey> that defines how we should compare the point geometries stored within the KdTree. This is done so that we can support storing point geometries in both Spherical and Euclidean coordinate systems. As of now, we current support the following ITypeMath<TKey> implementations:

• DoubleMath (TypeMath<double> - Euclidean)
• FloatMath (TypeMath<float> - Euclidean)
• GeographicMath (TypeMath<float> - Spherical)

KdTree Construction

When constructing a new KdTree we always have to specify both the dimensionality but also the intended ITypeMath to be applied as follows:

int dimensions = 2; FLoatMath math = new(); var tree = new KdTree<float, string>(dimensions, typeMath);

Now, the KdTree also allows for a few different ways of handling the case when the client attempts to insert a duplicate point geometry into the KdTree. Currently we support the following behaviours:

• AddDuplicateBehaviour.Skip
  • Don't add or change anything, simply skip the 'new' point
• AddDuplicateBehaviour.Error
  • Throw a DuplicateNodeError when attempting to add a duplicate point
• AddDuplicateBehaviour.Update
  • Replace the existing TValue with the new TValue

Here's how to specify the intended behaviour for a given KdTree:

var tree = new KdTree<float, string>(dimensions, typeMath, AddDuplicateBehavior.Skip);

With that construction effort out of the way - you can now actually fill your tree out as follows:

internal record struct PointRecord(float[] Position, string Name); var points = new PointRecord[] { .. }; // Adding points into the tree foreach (var point in points) { tree.Add(point.Position, point.Name); } // Removing an existing point from the tree tree.Remove(point.Position); // Clearing all currently stored nodes tree.Clear();

KdTree Search

So, assuming you've already got a KdTree built and want to actually find something that's stored within it - you've got a few options:

• Search for a specific IEnumerable<TKey> position and return the TValue stored there
• Search for a specific TValue and return its IEnumerable<TKey> position
• Search for N (configurable number) of nearest-neighbours
• Search for points around a given IEnumerable<TKey> position within a specified radial distance

Here's a few examples:

// Attempt to find a value at a given position if (tree.FindValueAt(point.Position, out value)) { Console.WriteLine($"Found value: {value}"); } // Attempt to find a position for a given value if (tree.FindValue(point.Value, out position)) { Console.WriteLine($"Found position: {position.ToArray()}"); } // Perform a radial search for all points w/in 5.0 units of the given position foreach (var point in tree.RadialSearch(point.Position, 5.0)) { .. } // Perform a search for the three nearest neighbours to a given position foreach (var neigh in tree.GetNearestNeighbours(point.Position, 3)) { .. }

KdTree Balancing

Now, our KdTree is not self-balancing during construction and as such can fall victim to becoming un-balanced based on the input data. It does, however, have a simple method that can be called to re-construct the tree in a balanced state as follows:

// Construct the tree var tree = new KdTree<float, string>(dimensions, typeMath); // Adding points into the tree var points = new PointRecord[] { .. }; foreach (var point in points) { tree.Add(point.Position, point.Name); } // Balance the tree tree.Balance();

The call to tree.Balance() will edit the KdTree in-place and will ensure that your searches are as optimized as possible given the input dataset.

Themis.Index.QuadTree

This namespace exposes Themis' implemetation of a QuadTree (a 2D binary space partitioning tree) that can be used for fast and efficient storage and searching of geometries that can be represented by a 2D IBoundingBox. QuadTrees are particularly useful for image processing, mesh generation, and surface collision detection.

This implementation is actually technically a 'Q-Tree' as we're able to specify the maximum capacity of each QuadTreeNode prior to splitting into child nodes - but by default the maximum capacity is 8.

A key note is that our QuadTree<T> implementation allows for storage of any object as long as they can be given an IBoundingBox envelope when inserted into the tree.

QuadTree Construction

Unlike the KdTree we don't have to specify anything beyond what type is being stored within the tree and (optionally) how many items are allowed per QuadTreeNode<T> prior to split.

Here's an example:

int maxItems = 16; // Both trees are empty and store Triangles - but TreeB has a maximum node capacity of 16 var TreeA = new QuadTree<Triangle>(); var TreeB = new QuadTree<Triangle>(maxItems);

It's also quite easy to add point geometries into QuadTree<T>'s by generating an IBoundingBox as follows:

var tree = new QuadTree<Vector<double>>(); // Point geometries are supported - just need to compose an IBoundingBox envelope var point = new double[] { .. }.ToVector(); var env = BoundingBox.FromPoint(point[0], point[1], BoundingBox.SinglePointBuffer); // Add to tree tree.Add(point, env);

Now, let's say you've got a collection of Triangle geometries that represent a 3D ground surface & you want to perform efficient collision detection. Here's how you'd build the tree:

var tree = new QuadTree<Triangle>(); // Add each Triangle using the existing IBoundingBox envelope foreach (var triangle in Triangles) { tree.Add(triangle, triangle.Envelope); }

QuadTree Search / Query

The QuadTree<T> has more rudimentary 'search' functionality when compared to the KdTree<TKey, TValue> but is still quite powerful. Consumers can query a given QuadTree<T> as follows:

• Get all contained elements that intersect with a given 2D (X, Y) position
• Get all contained elements that intersect with a given IBoundingBox

Note: Any given element could be stored in a root QuadTreeNode<T> as well as within one or more children QuadTreeNode<T> of that root node. As such, calls to QueryDistinct() are guaranteed to return each element only once, whereas QueryNonDistinct() can contain duplicates.

Building on our above example with the QuadTree<Triangle>, here's how we'd query the tree to find the Triangle that covers a given position:

var tree = new QuadTree<Triangle>(); // Add each Triangle using the existing IBoundingBox envelope foreach (var triangle in Triangles) { tree.Add(triangle, triangle.Envelope); } // The point-of-interest (POI) to check var poi = new double[] { .. }.ToVector(); // Get all candidate Triangles, filter to explicitly covering, then select the first (or default) var covering = tree.QueryDistinct(poi[0], poi[1]) .Where(t => t.Contains(poi)) .FirstOrDefault();

As you can see - there are two steps to consider when querying the QuadTree<T>:

• Getting all potential candidates
• Filtering down to specific target

Now, let's say we instead wanted to get all Triangles within a specified 2D search distance around a given POI:

// The 2D search distance double searchDistance = 50.0; // Assuming tree is built as above - compose the POI and associated AOI var poi = new double[] { .. }.ToVector(); var queryAoi = BoundingBox.FromPoint(poi[0], poi[1], searchDistance); // Get all elements that overlap with the given AOI var candidates = tree.QueryDistinct(queryAoi);