notebooks/16-download-osm-geospatial-entities.ipynb

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   "source": [
    "# Download any OSM Geospatial Entities with OSMnx\n",
    "\n",
    "Author: [Geoff Boeing](https://geoffboeing.com/)\n",
    "\n",
    "More info:\n",
    "\n",
    "  - [Overview of OSMnx](http://geoffboeing.com/2016/11/osmnx-python-street-networks/)\n",
    "  - [Documentation and install instructions](https://osmnx.readthedocs.io)\n",
    "  - [Examples, demos, tutorials](https://github.com/gboeing/osmnx-examples)\n",
    "  - [Journal article and citation info](http://geoffboeing.com/publications/osmnx-complex-street-networks/)\n",
    "  - [GitHub repo](https://github.com/gboeing/osmnx)\n",
    "  \n",
    "This notebook provides a quick tour of using OSMnx to download any geospatial entites/objects from OpenStreetMap as a geopandas GeoDataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import osmnx as ox\n",
    "\n",
    "%matplotlib inline\n",
    "ox.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `geometries` module to download entities, such as grocery stores, transit stops, points of interest, or building footprints, and turn them into a GeoDataFrame: [see docs](https://osmnx.readthedocs.io/en/stable/osmnx.html#module-osmnx.geometries).\n",
    "\n",
    "To query, pass a `tags` dict where keys are OSM tags. The dict's values can be either:\n",
    "  1. `True` to retrieve all OSM objects with this tag, regardless of its value\n",
    "  2. a single value as a string to retrieve all OSM objects with this tag:value combination\n",
    "  3. a list of string values to retrieve all OSM objects where this tag matches any of these values\n",
    "\n",
    "Pass multiple dict key:value pairs to retrieve the union (not intersection) of these pairs."
   ]
  },
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   "execution_count": null,
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   "outputs": [],
   "source": [
    "# get all building footprints in some neighborhood\n",
    "# `True` means retrieve any object with this tag, regardless of value\n",
    "place = \"Civic Center, Los Angeles, California\"\n",
    "tags = {\"building\": True}\n",
    "gdf = ox.geometries_from_place(place, tags)\n",
    "gdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = ox.plot_footprints(gdf, figsize=(3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all the parks in some neighborhood\n",
    "# constrain acceptable `leisure` tag values to `park`\n",
    "tags = {\"leisure\": \"park\"}\n",
    "gdf = ox.geometries_from_place(place, tags)\n",
    "gdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get everything tagged amenity,\n",
    "# and everything tagged landuse = retail or commercial,\n",
    "# and everything tagged highway = bus_stop\n",
    "tags = {\"amenity\": True, \"landuse\": [\"retail\", \"commercial\"], \"highway\": \"bus_stop\"}\n",
    "gdf = ox.geometries_from_place(\"Piedmont, California, USA\", tags)\n",
    "gdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# view just the banks\n",
    "gdf[gdf[\"amenity\"] == \"bank\"].dropna(axis=1, how=\"any\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# view just the bus stops\n",
    "gdf[gdf[\"highway\"] == \"bus_stop\"].dropna(axis=1, how=\"any\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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