ogd-at-lab/notebooks/mobility.ipynb

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   "source": [
    "# Urban Mobility\n",
    "\n",
    "[![Binder](http://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/anitagraser/ogd-at-lab/main?urlpath=lab/tree/notebooks/mobility.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hvplot.pandas\n",
    "import movingpandas as mpd\n",
    "from datetime import timedelta\n",
    "from utils.dataaccess import get_uber_movement_gdf, get_osm_traces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uber Movement\n",
    "\n",
    "Source: https://movement.uber.com/explore/vienna/travel-times\n",
    "\n",
    "© 2021 Copyright Uber Technologies. Data made available under the [Creative Commons, Attribution Non-Commercial](https://creativecommons.org/licenses/by-nc/3.0/us/) license"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = get_uber_movement_gdf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_uber(source_id, attribute_col, month, *args, **kwargs):\n",
    "    return ( gdf[(gdf.sourceid==source_id) & (gdf.month==month)].dropna(subset=[attribute_col]).hvplot(\n",
    "            geo=True, tiles='OSM', c=attribute_col, title=f'{attribute_col.title()} - Month: 2020-{month}', *args, **kwargs) * \n",
    "        gdf[gdf.index==source_id].hvplot(geo=True).opts(active_tools=['wheel_zoom']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "COL = 'mean_travel_time'\n",
    "ID = 1\n",
    "plot_uber(ID, COL, 1) + plot_uber(ID, COL, 2) + plot_uber(ID, COL, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan = gdf[gdf.month==1].groupby('sourceid').count()\n",
    "feb = gdf[gdf.month==2].groupby('sourceid').count()\n",
    "mar = gdf[gdf.month==3].groupby('sourceid').count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined = feb.join(mar, lsuffix='_feb', rsuffix='_mar')\n",
    "diff = joined[f'{COL}_mar'] - joined[f'{COL}_feb'] \n",
    "diff.hvplot.hist(title='Histogram of destination area counts (March 2020 - Feb 2020)', xlim=(-300,300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined = jan.join(feb, lsuffix='_jan', rsuffix='_feb')\n",
    "diff = joined[f'{COL}_feb'] - joined[f'{COL}_jan'] \n",
    "diff.hvplot.hist(title='Histogram of destination area counts (Feb 2020 - Jan 2020)', xlim=(-300,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OpenStreetMap Traces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source: https://www.openstreetmap.org/traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = get_osm_traces()\n",
    "osm_traces = mpd.TrajectoryCollection(gdf, 'track_fid')\n",
    "print(f'The OSM traces download contains {len(osm_traces)} tracks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for track in osm_traces: print(f'Track {track.id}: length={track.get_length():.0f}m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "osm_traces = mpd.MinTimeDeltaGeneralizer(osm_traces).generalize(tolerance=timedelta(minutes=1))\n",
    "osm_traces.hvplot(title='OSM Traces', line_width=7, width=700, height=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "osm_traces.get_trajectory(0).hvplot(title='Speed (m/s) along track', c='speed', cmap='RdYlBu',\n",
    "                                    line_width=7, width=700, height=500, tiles='CartoLight', colorbar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive Trajectory Generalization Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import panel as pn\n",
    "from pyproj import CRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_plot(traj, tolerance=0.001, generalizer='DouglasPeuckerGeneralizer'):\n",
    "    if generalizer=='DouglasPeuckerGeneralizer':\n",
    "        generalized = mpd.DouglasPeuckerGeneralizer(traj).generalize(tolerance=tolerance)\n",
    "    else:\n",
    "        generalized = mpd.MinDistanceGeneralizer(traj).generalize(tolerance=tolerance)\n",
    "    generalized.add_speed(overwrite=True)\n",
    "    map_plot = generalized.hvplot(title='Speed along trajectory', c='speed', cmap='Viridis', colorbar=True, clim=(0,20), line_width=10, width=500, height=400)\n",
    "    hist_plot = generalized.df.hvplot.hist('speed', title='Speed histogram', width=300, height=400) \n",
    "    return (map_plot + hist_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traj = osm_traces.get_trajectory(0).to_crs(CRS(31256))\n",
    "kw = dict(traj=traj, tolerance=(0, 1000), generalizer=['DouglasPeuckerGeneralizer', 'MinDistanceGeneralizer'])\n",
    "pn.interact(my_plot, **kw)"
   ]
  }
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