PointsData

PointsData is a generic data abstraction used to store collections of points in a coordinate space. It typically represents points in two or three dimensions, with each point defined by coordinates such as (x, y) or (x, y, z). In enhanced versions, points may carry attributes like color, intensity, or timestamp. It is used in mathematics, computer graphics, GIS, and data analysis as a basis for spatial computations and visualization.

A PointsData instance can be implemented in various forms, including separate arrays for each coordinate dimension, an array of coordinate tuples, or an array of objects with labeled fields. Common interchange formats include CSV, JSON, and binary formats such as PLY for 3D meshes or LAS for lidar point clouds. Some libraries also support streaming points for large datasets.

Typical operations on PointsData include adding or removing points, querying a subset by attribute, and transforming coordinates via translation, rotation, and scaling. Proximity and neighborhood queries, such as nearest neighbor search, are supported using spatial indices like k-d trees or ball trees. Analytical functions compute bounding boxes, centroids, covariances, and hulls.

PointsData is central to applications ranging from scientific visualization and geographic information systems to 3D modeling, computer vision, and machine learning on point clouds. Performance considerations include memory footprint and data access patterns, especially for large samples, making efficient indexing, streaming, and parallel processing important design aspects.