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

NOTE: cuSpatial depends on cuDF and RMM from RAPIDS.

Implemented 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

Future support is planned for the following operations.

1. Temporal window query
2. Temporal point query (year+month+day+hour+minute+second+millisecond)
3. Point-to-polyline nearest neighbor distance
4. Grid-based indexing for points and polygons
5. Quadtree-based indexing for large-scale point data
6. 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:

Install dependencies

Currently, building cuSpatial requires a source installation of cuDF. Install cuDF by following the instructions

The rest of steps assume the environment variable CUDF_HOME points to the root directory of your clone of the cuDF repo, and that the cudf_dev Anaconda environment created in step 3 is active.

Clone, build and install cuSpatial

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

git clone --recurse-submodules https://github.com/rapidsai/cuspatial.git $CUSPATIAL_HOME

3. Compile and install Similar to cuDF (version 0.11), simplely run 'build.sh' diectly under $CUSPATIAL_HOME
   Note that a "build" dir is created automatically under $CUSPATIAL_HOME/cpp

4. Run C++/Python test code
   

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

$CUSPATIAL_HOME/cpp/build/gtests/HAUSDORFF_TEST
$CUSPATIAL_HOME/cpp/build/gtests/POINT_IN_POLYGON_TEST
python python/cuspatial/cuspatial/tests/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_POLYGON_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.