Jianting Zhang

@msalazarcgeo Thanks for the bug report. Can you confirm which version you were using? we fixed a related bug for 0.17, which should be used for testing.

It is unclear whether you need Convex Hull code for 3D data or arbitrary dimensional data. It is also unclear the sizes of the problem that you are interested in...

For additional features in your list, perimeter/area/bounding box are embarrassingly parallelizable on both CPUs and GPUs. Computing bounding boxes for polygons has been implemented at https://github.com/rapidsai/cuspatial/blob/branch-22.02/cpp/src/spatial/polygon_bounding_box.cu as part of quadtree-indexed...

It is unclear to me what you meant by "nearest points from a constant radius". If you need all points that fall within circles, it is again embarrassingly parallelizable and...

There is an issue in your first program. According to https://docs.rapids.ai/api/cuspatial/stable/api.html, in the API cuspatial.point_in_polygon(test_points_x, test_points_y, poly_offsets, poly_ring_offsets, poly_points_x, poly_points_y) poly_offsets and poly_ring_offsets were designed to begin with 0. There...

@hakantekbas yes. poly_offsets and poly_ring_offsets arrays were designed to begin with 0. I am trying to repeat your results. Unfortunately I am not a good Python programmer. What are the...

In my previous answer, "poly_offsets and poly_ring_offsets were designed to begin with 0" was not accurate. The API expects values of poly_offsets matches the values of poly_ring_offsets for all the...

@hakantekbas (min_x,min_y,max_x,max_y) (-121.93428802490236, 0.0, 0.0, 60.908756256103516) This is not a good space to apply quadtree indexing using min_size = 50 and max_depth = 4. I would suggest filtering out records...

@hakantekbas point_in_polygon and quadtree_point_in_polygon are completely independent.

> "If I create seperate shape file for every 31 polygons, it is running 10 times faster and results are correct." My result shows that the majority of the time...