Using Java Topology Suite (JTS) for Spatial Geometry Operations and Fast Point‑in‑Polygon Checks

This article introduces the Java Topology Suite (JTS), explains its core geometry models, WKT format, spatial relations, and operations, and demonstrates how to efficiently determine whether a point lies within service coverage polygons using MBR filtering, spatial indexes such as R‑tree and Quadtree, and geometry repair techniques.

Geometry RepairJTSSpatial Geometry

0 likes · 17 min read

Using Java Topology Suite (JTS) for Spatial Geometry Operations and Fast Point‑in‑Polygon Checks

KooFE Frontend Team

Oct 27, 2021 · Frontend Development

Detect Clicks Inside Polygons on Canvas with Crossing & Winding Numbers

Learn how to capture click events on an HTML5 Canvas, compute the click coordinates, and determine whether the point lies inside a polygon using both the Crossing Number (even‑odd) method and the more robust Winding Number algorithm, complete with JavaScript code examples.

Canvascrossing-numberpoint-in-polygon

0 likes · 7 min read

Detect Clicks Inside Polygons on Canvas with Crossing & Winding Numbers

Alibaba Cloud Developer

Jul 16, 2018 · Big Data

How GeoHash Powers Billion‑Scale Point‑in‑Polygon Matching at Alibaba Xianyu

This article explains how Alibaba Xianyu uses GeoHash encoding and optimized spatial indexing to efficiently match billions of user‑posted GPS points with tens of thousands of market‑area polygons, reducing computation from quadrillions to billions of operations through precise point‑polygon algorithms and fast neighbor lookups.

Algorithm OptimizationAlibabaGeoHash

0 likes · 14 min read

How GeoHash Powers Billion‑Scale Point‑in‑Polygon Matching at Alibaba Xianyu

Alibaba Cloud Developer

Jul 12, 2018 · Fundamentals

How Xianyu Scales Billion‑Item Geo Matching with Fast GeoHash Algorithms

This article explains how Xianyu uses GeoHash‑based spatial indexing, precise and approximate matching, and a rapid neighbor‑search algorithm to efficiently associate billions of GPS‑tagged items with tens of thousands of city‑level business districts, reducing computation from quadrillions to billions of operations.

Algorithm OptimizationGISGeoHash

0 likes · 13 min read

How Xianyu Scales Billion‑Item Geo Matching with Fast GeoHash Algorithms

Xianyu Technology

Jul 9, 2018 · Big Data

Efficient GeoHash-Based Point‑in‑Polygon Matching for Massive Datasets

By encoding billions of GPS points and ten thousand district polygons into GeoHash cells, using exact matches, approximate filtering, neighbor‑cell lookup tables, and a final precise geometry test, the authors cut the required operations from 2×10^20 to about 1.8×10^12, enabling full processing in under a day.

GeoHashSpatial Indexingalgorithm

0 likes · 13 min read

Efficient GeoHash-Based Point‑in‑Polygon Matching for Massive Datasets

Architect

Feb 23, 2016 · Big Data

Efficient Point‑in‑Polygon Determination for Geo‑fencing Using Ray Casting and R‑Tree Indexing

The article explains how geo‑fencing relies on fast point‑in‑polygon checks, compares the basic ray‑casting method with brute‑force performance, and shows how spatial R‑tree indexing—both on polygon bounding boxes and on individual edges—dramatically speeds up queries even for millions of complex polygons.

R-treeSpatial Indexinggeo-fencing

0 likes · 8 min read

Efficient Point‑in‑Polygon Determination for Geo‑fencing Using Ray Casting and R‑Tree Indexing