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        "author": "Ulf Aslak",
        "author_email": "ulfjensen@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "# Infostop\n*Python package for detecting stop locations in mobility data*\n\nThis package implements the algorithm described in (paper not written yet), for detecting stop locations in time-ordered location data.\n\n## Usage\nGiven a location trace such as:\n\n```Python\n>>> data \narray([[ 55.75259295,  12.34353885 ],\n       [ 55.7525908 ,  12.34353145 ],\n       [ 55.7525876 ,  12.3435386  ],\n       ...,\n       [ 63.40379175,  10.40477095 ],\n       [ 63.4037841 ,  10.40480265 ],\n       [ 63.403787  ,  10.4047871  ]])\n```\n\nA stop location solution can be obtained using:\n\n```Python\n>>> import infostop\n>>> labels = infostop.label_trace(data)\n```\n\nHere, `labels` matches `data` in size, and can easily be mapped back onto `data`:\n\n```Python\n>>> np.hstack([data, labels.reshape(-1, 1)])\narray([[ 55.75259295,  12.34353885,   0.        ],\n       [ 55.7525908 ,  12.34353145,   0.        ],\n       [ 55.7525876 ,  12.3435386 ,   0.        ],\n       ...,\n       [ 63.40379175,  10.40477095, 164.        ],\n       [ 63.4037841 ,  10.40480265, 164.        ],\n       [ 63.403787  ,  10.4047871 , 164.        ]])\n```\n\nPlotting this onto a map:\n\n![img](https://ulfaslak.com/files/infostop_example_map.png)\n\n## Advantages\n* **Simplicity**: At its core, the method works by two steps. (1) Reducing the location trace to the medians of each stationary event and (2) embedding the resulting locations into a network that connects locations that are within a user-defined distance and clustering that network.\n* **Flow based**: Spatial clusters correspond to collections of location points that contain large amounts of flow when represented as a network. This enables the recovery of locations where traces slightly overlap.\n* **Speed**: First the point space is reduced to the median of stationary points, then pairwise distances between these medians are computed using a vectorized implementation of the haversine function, and finally the resulting network at some distance threshold is clustered using the C++ based Infomap implementation. For example, clustering 70.000 location points takes aroung 16 seconds.\n\n## Installation\n`pip install infostop`\n\n\n",
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        "summary": "Detect stop locations in time-ordered (lat, lon) location data",
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