AjayThorve / cuspatial

CUDA-accelerated GIS and spatiotemporal algorithms

 cuSpatial - GPU-Accelerated Spatial and Trajectory Data Management and Analytics Library

NOTE: cuSpatial depends on cuDF and RMM from RAPIDS.

Operations

cuSpatial supports the following operations on spatial and trajectory data:

1. Spatial window query
2. Point-in-polygon test
3. Haversine distance
4. Hausdorff distance
5. Deriving trajectories from point location data
6. Computing distance/speed of trajectories
7. Computing spatial bounding boxes of trajectories
8. Quadtree-based indexing for large-scale point data
9. Quadtree-based point-in-polygon spatial join
10. Quadtree-based point-to-linestring nearest neighbor distance
11. Distance computation (point-point, point-linestring, linestring-linestring)
12. Finding nearest points between point and linestring

Future support is planned for the following operations:

1. Temporal window query
2. Temporal point query (year+month+day+hour+minute+second+millisecond)
3. Grid-based indexing for points and polygons
4. R-Tree-based indexing for Polygons/Polylines

Install from Conda

To install via conda:

conda install -c conda-forge -c rapidsai-nightly cuspatial

Install from Source

To build and install cuSpatial from source:

Pre-requisite

• gcc >= 7.5
• cmake >= 3.23
• miniconda

Fetch cuSpatial repository

export `CUSPATIAL_HOME=$(pwd)/cuspatial` && \
git clone https://github.com/rapidsai/cuspatial.git $CUSPATIAL_HOME

Install dependencies

1. export CUSPATIAL_HOME=$(pwd)/cuspatial
2. clone the cuSpatial repo

conda env update --file conda/environments/all_cuda-115_arch-x86_64.yaml 

Build and install cuSpatial

1. Compile and install

   cd $CUSPATIAL_HOME && \
   chmod +x ./build.sh && \
   ./build.sh

2. Run C++/Python test code

   Some tests using inline data can be run directly, e.g.:

   $CUSPATIAL_HOME/cpp/build/gtests/LEGACY_HAUSDORFF_TEST
   $CUSPATIAL_HOME/cpp/build/gtests/POINT_IN_POLYGON_TEST
   python python/cuspatial/cuspatial/tests/legacy/test_hausdorff_distance.py
   python python/cuspatial/cuspatial/tests/test_pip.py

   Some other tests involve I/O from data files under $CUSPATIAL_HOME/test_fixtures. For example, $CUSPATIAL_HOME/cpp/build/gtests/SHAPEFILE_READER_TEST requires three pre-generated polygon shapefiles that contain 0, 1 and 2 polygons, respectively. They are available at $CUSPATIAL_HOME/test_fixtures/shapefiles
   

NOTE: Currently, cuSpatial supports reading point/polyine/polygon data using Structure of Array (SoA) format and a shapefile reader to read polygon data from a shapefile. Alternatively, python users can read any point/polyine/polygon data using existing python packages, e.g., Shapely and Fiona,to generate numpy arrays and feed them to cuSpatial python APIs.