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        "description": "# Vincenty nearest neighbor search using CUDA\nNearest neighbor search algorithm on Earth's surface that runs on a GPU and uses [Vincenty's formula](https://en.wikipedia.org/wiki/Vincenty%27s_formulae)\n\n## Application\nNearest Neighbour Search is the **key component of location data analysis**:\n- Nearest Neighbour Index is based on measuring distances between points\n- Both **global** pattern analysis algorithms (Global Moran\u2019s I, Getis-Ord General G), as well as **local** pattern analysis algorithms (Anselin Local Moran's I, Getis Ord GI *) with the **k-nn approach** to define neighbours are based on measuring distances between points\n\nUsing Vincenty\u2019s formula allows performing location analysis on **any location using geographic coordinates**.\n\n[Presentation slides](https://docs.google.com/presentation/d/1c0-9hQVlZoordzm1gZ9uKQJVqDyZQ9lZQe-SBEo-lwE/edit?usp=sharing)\n\n## Requirements\n- CUDA-enabled GPU with compute capability 2.0 or above with an up-to-data Nvidia driver.\n- [CUDA toolkit](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html])\n\n## Installation\n```\npip install vincenty-cuda-nns\n```\n\n## Usage example\n```python\nimport geopandas as gpd\nfrom vincenty_cuda_nns import CudaTree\n\ndf = gpd.read_file('points.geojson')\n\n# data is array of points like [longitude, latitude]\ndata = np.stack(df['geometry']).astype(np.float32)\n\n# build tree for the data\ncuda_tree = CudaTree(data, leaf_size=4)\n\n# query over the tree for nearest neighbor (including itself)\ndistances, indices = cuda_tree.query(n_neighbors=2)\n```\n\n\n",
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