GeoGeometry started out as a simple side project while I was building a startup in 2012. The key feature I needed at time was a list of geohashes that cover a particular geo shape. This is a nice thing to be able to do if you want to build search engine functionality and want to implement geospatial search. Over time, I added algorithms to solve various geometric problems. Mostly these are well known & documented algorithms of course. But it is nice to have some simple library of implementations for these things. I still maintain this library; especially since I became CTO of FORMATION where we use it with our indoor maps for things like geofences, geo-referencing coordinates from various location providers, etc.
I initially used Java for this and over time added several implementations of common geometry algorithms. In 2019, after not touching this project for years, I ported the entire code base to Kotlin.
This is a Kotlin multi-platform distribution supporting all major compilation targets for Kotlin, including jvm, js, wasm, ios, linux, mac, windows, etc.
We use our own maven repo for distributing this: Add the repository:
repositories {
mavenCentral()
maven { url = uri("https://maven.tryformation.com/releases") }
}and then add the dependency:
implementation("com.github.jillesvangurp:geogeometry:<VERSION>")You can find the latest version in the releases section.
- Simple API, most functions work standalone and use simple input types
- Kotlin Multiplatform
- Currently there are several build targets. More may be added later. I could use some help with mobile targets.
- No runtime dependencies other than the kotlin stdlib
- Adding more targets should 'just work' but has not been tested.
For a few of the algorithms in this code base, I've adapted / been inspired by code from others. This work would not be possible without that and I try to credit any such sources in the code base.
GeoGeometry class with lots of functions that allow you to:
- Calculate distance between two coordinates using the Haversine or Vicenty algorithm
- Calculate perpendicular distance of a point to a line. lineString, and polygon
- Calculate the area of a polygon
- Simplify polygons using the Douglas Peucker - algorithm. Sometimes polygons can be very detailed, which makes handling them very CPU/memory intensive. E.g. some polygons for New Zealand are more than 200MB.
- check bounding box containment for a point
- check polygon containment for a point
- get the center for a polygon
- get bounding box for a polygon
- convert a circle to a polygon
- create a polygon from a point cloud. An algorithm for a convex hull is included and another experimental one for a concave hull.
- translate a wgs84 coordinate by x & y meters along the latitude and longitude
- rotate a point around another point
GeoHashUtils class with methods that allow you to:
- encode and decode geo hashes; this functionality has been adapted from the original Apache Lucene implementation of this class.
- find out the boundingbox of a geohash
- check containment of a point in a geohash (must have the hash as a prefix of its own hash).
- find out neighboring geohashes east, west, south, or north of a geohash.
- get the 32 sub geo hashes for a geohash, or the north/south halves, or the NE, NW, SE, SW quarters.
- cover shapes like lines, paths, polygons, or circles with geo hashes for indexing purposes.
Geojson classes are provided that allow you to easily work with GeoJson, which just like this library uses arrays of doubles as the core primitive. We use kotlinx.serialization for parsing and serializing this so this works across platforms as well!
- sealed class hierarchy to represent the various geometries
- uses type aliases to distinguish the different coordinate arrays
- translate, scaleX, scaleY, and rotate to transform any Geojson Geometry
- calculate a centroid for a shape
I put quite a bit of effort in doing code archeology to combine bits and pieces of various old Java implementations for this. Lots of edge cases and "you just have to know why" bits of code. As a consequence, I have some nice robust tests around this and all seems to work.
I used this amazing Coordinates converter to verify my converters. Also, I use randomized points to ensure round trip conversions end up being close to where they should be. Feedback and pull requests to improve this further are welcome
- extension functions to convert to and from UTM coordinates and UPS coordinates.
- Conversion to and from MGRS / USNG format
A geo hash is a representation of a coordinate that interleaves the bit representations of the latitude and longitude and base32 encodes the result. This string representation has a very useful property: geo hashes of nearby coordinates will have the same prefix. As is observed in this blog post: http://blog.notdot.net/2009/11/Damn-Cool-Algorithms-Spatial-indexing-with-Quadtrees-and-Hilbert-Curves, geo hashes effectively encode the path to a leaf in a quad tree.
Geohashes are super useful if you are building e.g. search engines. Though if you are, there are better data structures such as Quad Trees or BKD Trees that Elasticsearch uses in more recent versions.
Here's a simple example of the hashes for a concave polygon of Berlin rendered on a map (courtesy of geojson.io). These hashes are calculated with the algorithm in GeoHashUtils. You could put these hashes into a database or search engine to implement polygon search.
- Some of the algorithms used have limitations with respect to where you can use them. Generally things should be fine around the date line (if not report bugs). However, the poles are tricky and some of the algorithms get inaccurate or simply fail to exit. Because most data sets lack data for the poles, this should not be a big issue for most. Some of the algorithms now throw an exception if you try this. I'm not currently planning a fix but would appreciate pull requests for this.
- Some of the algorithms have quadratic or exponential complexity and you can easily trigger situations where execution time is going to be substantial and/or you run out of memory. For example covering a circle with a radius of a few hundred kilometers with geohashes of length 10 is probably not a great idea. If you are unsure, use getSuitableHashLength() and add 1 or 2 to get good enough granularity.
- Beware of the
[longitude,latitude]order in geojson arrays vs. the latitude, longitude order when not using arrays. This is based on the (unfortunate) geojson convention of specifying longitude before latitude in arrays. When not using arrays, I use latitude followed by longitude, just like everyone else. - I try to be good about adding tests but test coverage is not perfect and some of the algorithms have 'interesting' edge-cases. Better algorithms may be available. This is merely a best effort from my side and it works well enough for me. I welcome pull requests to improve things
It's a gradle project. So, checking it out and doing a gradle build should do the trick.
Note. this is a kotlin multi-platform build, and currently it produces a JavaScript build as well as a jvm jar. Adding IOS native and other platforms should be straightforward as well. The project has no run time dependencies beyond the standard kotlin library.
Like all my other projects, this project is licensed under the so-called MIT license.
However, GeoHashUtils was copied and adapted from Apache Lucene (a long time ago) and I have retained the Apache License for that file, as required in that license. The rest of this repository is MIT licensed. Both licenses are compatible and should also pose no issues when used with software under most other OSS licenses.
For more details see the LICENSE file