{
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  {
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   "source": [
    "# MovingPandas @ SDSC21\n",
    "\n",
    "<img align=\"right\" src=\"https://anitagraser.github.io/movingpandas/pics/movingpandas.png\">\n",
    "\n",
    "[Anita Graser](https://anitagraser.com/about/)\n",
    "\n",
    "* Easy trajectory data handling\n",
    "* Straightforward data exploration\n",
    "\n",
    "\n",
    "Based on GeoPandas and Holoviews.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "import sys\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Loading trajectory data from a GeoPackage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from geopandas import gpd\n",
    "gdf = gpd.read_file('data/demodata_geolife.gpkg')\n",
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "gdf.plot(figsize=(9,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "After reading the trajectory point data from file, we want to construct the trajectories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Creating a TrajectoryCollection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import movingpandas as mpd\n",
    "traj_collection = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')\n",
    "print(traj_collection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "traj_collection.plot(column='trajectory_id', linewidth=5, legend=True, figsize=(9,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exploring movement speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "traj_collection.plot(column='speed', linewidth=5, capstyle='round', legend=True, vmax=20, figsize=(9,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "my_traj = traj_collection.get_trajectory(2)\n",
    "print(my_traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "my_traj.hvplot(c='speed', line_width=7.0, tiles='StamenTonerBackground', cmap='Viridis', colorbar=True, clim=(0,20), width=700, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Detecting stops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "detector = mpd.TrajectoryStopDetector(traj_collection)\n",
    "stop_points = detector.get_stop_points(min_duration=timedelta(seconds=120), max_diameter=100)\n",
    "stop_points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "ax = traj_collection.plot(figsize=(9,5), linewidth=5)\n",
    "stop_points.plot(ax=ax, color='deeppink', markersize=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Splitting at stops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "my_traj = traj_collection.get_trajectory(1)\n",
    "split = mpd.StopSplitter(my_traj).split(min_duration=timedelta(seconds=60), max_diameter=100)\n",
    "split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "ax = split.trajectories[0].plot(figsize=(9,6), linewidth=5)\n",
    "for i, traj in enumerate(split):\n",
    "    traj.plot(ax=ax, color=['deeppink', 'slategray'][i%2], linewidth=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Something more interactive\n",
    "\n",
    "[Holoviews demo](http://localhost:8888/notebooks/Documents/GitHub/anitagraser/movingpandas/tutorials/4-stop-detection.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img align=\"right\" src=\"https://anitagraser.github.io/movingpandas/pics/movingpandas.png\">\n",
    "\n",
    "# More ...\n",
    "\n",
    "Examples: https://github.com/anitagraser/movingpandas-examples\n",
    "\n",
    "Repo: https://github.com/anitagraser/movingpandas\n",
    "\n",
    "Documentation: https://movingpandas.readthedocs.io/en/master/\n"
   ]
  }
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