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https://github.com/thegeekyasian/geo-assist
Geo Assist is a spatial library to manage spatial data in-memory.
https://github.com/thegeekyasian/geo-assist
data-structures geemap geo-spatial geospatial gis java k-d-tree kd-tree leafmap location mapping mapping-algorithms mapping-services spatial-data spatial-indexing tree tree-structure trees
Last synced: about 2 months ago
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Geo Assist is a spatial library to manage spatial data in-memory.
- Host: GitHub
- URL: https://github.com/thegeekyasian/geo-assist
- Owner: thegeekyasian
- License: apache-2.0
- Created: 2023-01-28T22:13:17.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-05-02T07:31:57.000Z (over 1 year ago)
- Last Synced: 2024-05-17T16:32:05.321Z (4 months ago)
- Topics: data-structures, geemap, geo-spatial, geospatial, gis, java, k-d-tree, kd-tree, leafmap, location, mapping, mapping-algorithms, mapping-services, spatial-data, spatial-indexing, tree, tree-structure, trees
- Language: Java
- Homepage:
- Size: 77.1 KB
- Stars: 207
- Watchers: 5
- Forks: 20
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Geo Assist
Manage and query your geo-spatial data efficiently.
Report a Bug
·
Request a Feature
## ⚡️ What is it?
Geo Assist is an open-source Java library designed to simplify the process of working with spatial data. With an implementation of KD Trees, users can efficiently store and query spatial data such as latitude/longitude coordinates.
By providing a streamlined interface for complex geospatial operations, Geo Assist enables developers to build powerful and accurate search algorithms for applications such as geospatial analysis, location-based services, and more.
The project aims to enable the use of complex search algorithms, by tweaking them for geospatial operations.
## 📖 How to?
### Install:
Geo-assist is available on maven repository and can be imported to your project.
```xml
com.thegeekyasian
geo-assist
1.0.4
```
### 🌳 K-d Tree:
K-d Tree, formally called K-Dimensional Trees, are one of the best options when storing and retrieving objects based on geospatial parameters.
I have provided an implementation of storing objects in a K-d tree using the coordinates and searching nearest neighbors for the provided location (latitude/longitude) and the distance.
#### Insert
Here is how to initialize your data:
``` java
KDTree kdTree = new KDTree<>();
kdTree.insert(new KDTreeObject.Builder()
.id(5)
.latitude(25.2002450)
.longitude(55.2734184)
.build());
```
#### Find Nearest Neighbors
Once you have inserted your object(s) in the tree, here is how you can search for the nearest neighbors for a provided location:
``` java
Point point = new Point.Builder()
.latitude(25.2012544)
.longitude(55.2569389)
.build();
List> nearestNeighbors =
kdTree.findNearestNeighbor(point, 2); // 2 kilometers based on haversine distance.
```
#### Find The Nearest-Most Neighbor
Another feature provided allows you to find the nearest most object. From the objects that you can find in using the `findNearestNeighbor` feature, this method allows you to get the closest one, based on the provided location and distance.
The method is called `findNearest`, and returns a wrapper that holds the closes `KDTreeObject` and its `distance` from the provided location.
The API can be invoked as below:
``` java
Point point = new Point.Builder()
.latitude(25.2012544)
.longitude(55.2569389)
.build();
KDTreeNearestNeighbor nearestNeighbor =
this.kdTree.findNearest(point, 2); // 2 kilometers based on haversine distance.
```
#### Find in Bounding Box (range)
You can also find of objects in a bounding box for the provided range.
The `findInRange` method searches the k-d tree for all nodes whose coordinates fall within a given bounding box. This is useful for finding all points within a specific geographic region or for performing spatial queries on a set of points. The method takes in a BoundingBox object that defines the range to search within, and returns a list of KDTreeObject objects whose coordinates fall within the bounding box.
Here is how you can use `find in range`:
``` java
BoundingBox boundingBox = new BoundingBox.Builder()
.lowerPoint(new Point.Builder()
.latitude(24.836135)
.longitude(66.976089)
.build())
.upperPoint(new Point.Builder()
.latitude(24.951953)
.longitude(67.157364)
.build())
.build();
List> objects = kdTree.findInRange(boundingBox);
```
#### Delete
You can delete the object based on the custom identifier `ID`:
``` java
boolean ok = kdTree.delete(5);
```
This is how simple it has been made to query your geo-spatial data.
## ⭐️ Project assistance
If you want to say **thank you** or/and support active development of `Geo Assist`:
- Add a [GitHub Star](https://github.com/thegeekyasian/geo-assist) to the project.
- Tweet about project [on your Twitter](https://twitter.com/intent/tweet?text=Manage%20and%20query%20your%20%23geospatial%20data%20efficiently%20with%20%23GeoAssist%0A%0A%23java%20%23programming%20%23gis%20%23opensource%20%23coding&url=https%3A%2F%2Fgithub.com%2Fthegeekyasian%2Fgeo-assist%2F).
- Write interesting articles about project on [Dev.to](https://dev.to/), [Medium](https://medium.com/) or personal blog.
- [Create an issue](https://github.com/thegeekyasian/geo-assist/issues/new) to open discussion threads or new feature requests.
- Contribute to the project for any new features.
Together, we can make this project **better** every day! ❤️
For any questions, discussions or support you can join the [Geo Assist Discord Server](https://discord.gg/8Xe2Ds4BWj).