I've created a project that provides in-memory Geo-spatial Indexing, with 2-dimensional K-D Tree. I've worked on multiple projects where I used K-D Trees to find the nearest neighbors for provided geo coordinates with efficient results. I used this implementation for applications with ~35m requests per day, with custom modifications and it worked like a charm. Hence I have created a generic implementation, ready to use. I have already shared great feedback from many programmers and working on improvements already to add new features. The project is open to suggestions and contributions as well. Feel free to create any issues on the GitHub repository.
builder patterns are very common now because they make maintainability a lot easier, (i.e. if god forbid someone accidentally swapped vars in list of inputsof the same type), and secondly because you can use an annotation library to generate the boilerplate for you, you don't have to write any of it.
Although agreed it's used when you start to get to more parameters. I didn't check but it's possible there are a lot of null/default values like projection or something.
You can also have cached static instances(assuming immutability), eg Point.fromLonLat(0,0) can always return the same instance, whereas new Point(0,0) must allocate and return a new instance.
now do this in 3d, and forget about the geolocation, move toward establishing location and orientation of limb segments, and establish a set of vectors that will satisfy a required velocity/distance intercept, to coordinate limb function and interaction, then worry about where on the globe, the local instance is with respect to other nearby instances; that would impress me, and would be much more usefull than nearest nieghbours geolocation.
