12_geospatial_01_osm_download.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download Maps from OSM \n",
    "\n",
    "#### Tutorial from https://automating-gis-processes.github.io/2017/lessons/L7/network-analysis.html#saving-shortest-paths-to-disk\n",
    "\n",
    "This notebook shows how to download maps from OSM through the osmnx package, how to convert OSM graphs to geopandas objects, and how to get basic summary statistics of the road networks by using the OSMNX package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting osmnx\n",
      "  Downloading osmnx-0.16.2-py2.py3-none-any.whl (87 kB)\n",
      "\u001b[K     |████████████████████████████████| 87 kB 6.9 MB/s  eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pyproj>=2.6 in /home/jtl/anaconda3/lib/python3.7/site-packages (from osmnx) (2.6.1.post1)\n",
      "Requirement already satisfied: networkx>=2.5 in /home/jtl/anaconda3/lib/python3.7/site-packages (from osmnx) (2.5)\n",
      "Requirement already satisfied: Rtree>=0.9 in /home/jtl/anaconda3/lib/python3.7/site-packages (from osmnx) (0.9.4)\n",
      "Collecting pandas>=1.1\n",
      "  Downloading pandas-1.1.5-cp37-cp37m-manylinux1_x86_64.whl (9.5 MB)\n",
      "\u001b[K     |████████████████████████████████| 9.5 MB 13.1 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: descartes>=1.1 in /home/jtl/anaconda3/lib/python3.7/site-packages (from osmnx) (1.1.0)\n",
      "Collecting geopandas>=0.8\n",
      "  Downloading geopandas-0.8.1-py2.py3-none-any.whl (962 kB)\n",
      "\u001b[K     |████████████████████████████████| 962 kB 45.7 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: Shapely>=1.7 in /home/jtl/anaconda3/lib/python3.7/site-packages (from osmnx) (1.7.0)\n",
      "Requirement already satisfied: requests>=2.24 in /home/jtl/anaconda3/lib/python3.7/site-packages (from osmnx) (2.24.0)\n",
      "Requirement already satisfied: numpy>=1.19 in /home/jtl/anaconda3/lib/python3.7/site-packages (from osmnx) (1.19.0)\n",
      "Collecting matplotlib>=3.3\n",
      "  Downloading matplotlib-3.3.3-cp37-cp37m-manylinux1_x86_64.whl (11.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 11.6 MB 48.3 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: decorator>=4.3.0 in /home/jtl/anaconda3/lib/python3.7/site-packages (from networkx>=2.5->osmnx) (4.4.2)\n",
      "Requirement already satisfied: setuptools in /home/jtl/anaconda3/lib/python3.7/site-packages (from Rtree>=0.9->osmnx) (47.3.1.post20200622)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/jtl/anaconda3/lib/python3.7/site-packages (from pandas>=1.1->osmnx) (2020.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/jtl/anaconda3/lib/python3.7/site-packages (from pandas>=1.1->osmnx) (2.8.1)\n",
      "Requirement already satisfied: fiona in /home/jtl/anaconda3/lib/python3.7/site-packages (from geopandas>=0.8->osmnx) (1.8.13.post1)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jtl/anaconda3/lib/python3.7/site-packages (from requests>=2.24->osmnx) (1.25.9)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /home/jtl/anaconda3/lib/python3.7/site-packages (from requests>=2.24->osmnx) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/jtl/anaconda3/lib/python3.7/site-packages (from requests>=2.24->osmnx) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/jtl/anaconda3/lib/python3.7/site-packages (from requests>=2.24->osmnx) (2020.6.20)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/jtl/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.3->osmnx) (0.10.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/jtl/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.3->osmnx) (7.1.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/jtl/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.3->osmnx) (1.2.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/jtl/anaconda3/lib/python3.7/site-packages (from matplotlib>=3.3->osmnx) (2.4.7)\n",
      "Requirement already satisfied: six>=1.5 in /home/jtl/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=1.1->osmnx) (1.15.0)\n",
      "Requirement already satisfied: cligj>=0.5 in /home/jtl/anaconda3/lib/python3.7/site-packages (from fiona->geopandas>=0.8->osmnx) (0.5.0)\n",
      "Requirement already satisfied: munch in /home/jtl/anaconda3/lib/python3.7/site-packages (from fiona->geopandas>=0.8->osmnx) (2.5.0)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /home/jtl/anaconda3/lib/python3.7/site-packages (from fiona->geopandas>=0.8->osmnx) (1.1.1)\n",
      "Requirement already satisfied: attrs>=17 in /home/jtl/anaconda3/lib/python3.7/site-packages (from fiona->geopandas>=0.8->osmnx) (19.3.0)\n",
      "Requirement already satisfied: click<8,>=4.0 in /home/jtl/anaconda3/lib/python3.7/site-packages (from fiona->geopandas>=0.8->osmnx) (7.1.2)\n",
      "\u001b[31mERROR: snorkel 0.9.6 has requirement networkx<2.4,>=2.2, but you'll have networkx 2.5 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: snorkel 0.9.6 has requirement scikit-learn<0.22.0,>=0.20.2, but you'll have scikit-learn 0.23.2 which is incompatible.\u001b[0m\n",
      "Installing collected packages: pandas, geopandas, matplotlib, osmnx\n",
      "  Attempting uninstall: pandas\n",
      "    Found existing installation: pandas 1.0.5\n",
      "    Uninstalling pandas-1.0.5:\n",
      "      Successfully uninstalled pandas-1.0.5\n",
      "  Attempting uninstall: geopandas\n",
      "    Found existing installation: geopandas 0.7.0\n",
      "    Uninstalling geopandas-0.7.0:\n",
      "      Successfully uninstalled geopandas-0.7.0\n",
      "  Attempting uninstall: matplotlib\n",
      "    Found existing installation: matplotlib 3.2.2\n",
      "    Uninstalling matplotlib-3.2.2:\n",
      "      Successfully uninstalled matplotlib-3.2.2\n",
      "Successfully installed geopandas-0.8.1 matplotlib-3.3.3 osmnx-0.16.2 pandas-1.1.5\n"
     ]
    }
   ],
   "source": [
    "!pip install osmnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                            Version\r\n",
      "---------------------------------- -------------------\r\n",
      "absl-py                            0.9.0\r\n",
      "affine                             2.3.0\r\n",
      "alabaster                          0.7.12\r\n",
      "anaconda-client                    1.7.2\r\n",
      "anaconda-navigator                 1.9.7\r\n",
      "anaconda-project                   0.8.3\r\n",
      "arrow                              0.15.7\r\n",
      "arviz                              0.6.1\r\n",
      "asn1crypto                         1.3.0\r\n",
      "astor                              0.8.1\r\n",
      "astroid                            2.3.3\r\n",
      "astropy                            4.0.1.post1\r\n",
      "atomicwrites                       1.4.0\r\n",
      "attrs                              19.3.0\r\n",
      "Babel                              2.8.0\r\n",
      "backcall                           0.2.0\r\n",
      "backports.functools-lru-cache      1.6.1\r\n",
      "backports.os                       0.1.1\r\n",
      "backports.shutil-get-terminal-size 1.0.0\r\n",
      "backports.tempfile                 1.0\r\n",
      "backports.weakref                  1.0.post1\r\n",
      "beautifulsoup4                     4.9.1\r\n",
      "biogeme                            3.2.6\r\n",
      "bitarray                           1.2.2\r\n",
      "bkcharts                           0.2\r\n",
      "bleach                             3.1.5\r\n",
      "bokeh                              2.1.1\r\n",
      "boto                               2.49.0\r\n",
      "Bottleneck                         1.3.2\r\n",
      "branca                             0.4.1\r\n",
      "brotlipy                           0.7.0\r\n",
      "Cartopy                            0.18.0\r\n",
      "certifi                            2020.6.20\r\n",
      "cffi                               1.14.0\r\n",
      "cftime                             1.1.0\r\n",
      "chardet                            3.0.4\r\n",
      "click                              7.1.2\r\n",
      "click-plugins                      1.1.1\r\n",
      "cligj                              0.5.0\r\n",
      "cloudpickle                        1.4.1\r\n",
      "clyent                             1.2.2\r\n",
      "colorama                           0.4.3\r\n",
      "conda                              4.9.2\r\n",
      "conda-build                        3.18.9\r\n",
      "conda-package-handling             1.7.0\r\n",
      "conda-verify                       3.4.2\r\n",
      "configparser                       5.0.0\r\n",
      "contextily                         1.0.0\r\n",
      "contextlib2                        0.6.0.post1\r\n",
      "cryptography                       2.9.2\r\n",
      "cycler                             0.10.0\r\n",
      "Cython                             0.29.20\r\n",
      "cytoolz                            0.10.1\r\n",
      "dask                               2.19.0\r\n",
      "decorator                          4.4.2\r\n",
      "defusedxml                         0.6.0\r\n",
      "descartes                          1.1.0\r\n",
      "distributed                        2.19.0\r\n",
      "docker-pycreds                     0.4.0\r\n",
      "docutils                           0.16\r\n",
      "dropbox                            10.2.0\r\n",
      "entrypoints                        0.3\r\n",
      "et-xmlfile                         1.0.1\r\n",
      "factor-analyzer                    0.3.2\r\n",
      "fastcache                          1.1.0\r\n",
      "filelock                           3.0.12\r\n",
      "Fiona                              1.8.13.post1\r\n",
      "Flask                              1.1.2\r\n",
      "folium                             0.11.0\r\n",
      "fsspec                             0.7.4\r\n",
      "future                             0.18.2\r\n",
      "gast                               0.2.2\r\n",
      "GDAL                               2.4.2\r\n",
      "geographiclib                      1.50\r\n",
      "geopandas                          0.8.1\r\n",
      "geoplot                            0.4.1\r\n",
      "geopy                              2.0.0\r\n",
      "geos                               0.2.2\r\n",
      "gevent                             20.6.2\r\n",
      "gitdb                              4.0.4\r\n",
      "GitPython                          3.1.1\r\n",
      "glob2                              0.7\r\n",
      "gmpy2                              2.0.8\r\n",
      "google-pasta                       0.1.8\r\n",
      "GPUtil                             1.4.0\r\n",
      "gql                                0.2.0\r\n",
      "graphql-core                       1.1\r\n",
      "greenlet                           0.4.16\r\n",
      "grpcio                             1.27.1\r\n",
      "h5py                               2.10.0\r\n",
      "HeapDict                           1.0.1\r\n",
      "html5lib                           1.1\r\n",
      "hypothesis                         5.18.3\r\n",
      "idna                               2.10\r\n",
      "imageio                            2.8.0\r\n",
      "imagesize                          1.2.0\r\n",
      "importlib-metadata                 1.7.0\r\n",
      "ipykernel                          5.3.0\r\n",
      "ipython                            7.16.1\r\n",
      "ipython-genutils                   0.2.0\r\n",
      "ipywidgets                         7.5.1\r\n",
      "isort                              4.3.21\r\n",
      "itsdangerous                       1.1.0\r\n",
      "jdcal                              1.4.1\r\n",
      "jedi                               0.17.1\r\n",
      "jeepney                            0.4.3\r\n",
      "Jinja2                             2.11.2\r\n",
      "joblib                             0.15.1\r\n",
      "json5                              0.9.5\r\n",
      "jsonschema                         3.2.0\r\n",
      "jupyter                            1.0.0\r\n",
      "jupyter-client                     6.1.3\r\n",
      "jupyter-console                    6.1.0\r\n",
      "jupyter-core                       4.6.3\r\n",
      "jupyterlab                         2.1.5\r\n",
      "jupyterlab-server                  1.1.5\r\n",
      "Keras-Applications                 1.0.8\r\n",
      "Keras-Preprocessing                1.1.0\r\n",
      "keyring                            21.2.1\r\n",
      "kiwisolver                         1.2.0\r\n",
      "lazy-object-proxy                  1.5.0\r\n",
      "libarchive-c                       2.9\r\n",
      "llvmlite                           0.33.0+1.g022ab0f\r\n",
      "locket                             0.2.0\r\n",
      "lxml                               4.5.1\r\n",
      "mapclassify                        2.3.0\r\n",
      "Markdown                           3.2\r\n",
      "MarkupSafe                         1.1.1\r\n",
      "matplotlib                         3.3.3\r\n",
      "mccabe                             0.6.1\r\n",
      "mercantile                         1.1.5\r\n",
      "mistune                            0.8.4\r\n",
      "mkl-fft                            1.1.0\r\n",
      "mkl-random                         1.1.1\r\n",
      "mkl-service                        2.3.0\r\n",
      "mock                               4.0.2\r\n",
      "more-itertools                     8.4.0\r\n",
      "mpmath                             1.1.0\r\n",
      "msgpack                            1.0.0\r\n",
      "multipledispatch                   0.6.0\r\n",
      "munch                              2.5.0\r\n",
      "munkres                            1.1.4\r\n",
      "navigator-updater                  0.2.1\r\n",
      "nbconvert                          5.6.1\r\n",
      "nbformat                           5.0.7\r\n",
      "netCDF4                            1.5.3\r\n",
      "networkx                           2.5\r\n",
      "nltk                               3.5\r\n",
      "nose                               1.3.7\r\n",
      "notebook                           6.0.3\r\n",
      "numba                              0.50.1\r\n",
      "numexpr                            2.7.1\r\n",
      "numpy                              1.19.0\r\n",
      "numpydoc                           1.0.0\r\n",
      "nvidia-ml-py3                      7.352.0\r\n",
      "olefile                            0.46\r\n",
      "opencv-python                      4.2.0.32\r\n",
      "openpyxl                           3.0.3\r\n",
      "opt-einsum                         3.1.0\r\n",
      "osmnx                              0.16.2\r\n",
      "OWSLib                             0.19.2\r\n",
      "packaging                          20.4\r\n",
      "pandas                             1.1.5\r\n",
      "pandocfilters                      1.4.2\r\n",
      "parso                              0.7.0\r\n",
      "partd                              1.1.0\r\n",
      "path.py                            12.0.1\r\n",
      "pathlib2                           2.3.5\r\n",
      "pathtools                          0.1.2\r\n",
      "patsy                              0.5.1\r\n",
      "pep8                               1.7.1\r\n",
      "pexpect                            4.8.0\r\n",
      "pickleshare                        0.7.5\r\n",
      "Pillow                             7.1.2\r\n",
      "pip                                20.1.1\r\n",
      "pkginfo                            1.5.0.1\r\n",
      "pluggy                             0.13.1\r\n",
      "ply                                3.11\r\n",
      "proj                               0.2.0\r\n",
      "prometheus-client                  0.8.0\r\n",
      "promise                            2.3\r\n",
      "prompt-toolkit                     3.0.5\r\n",
      "protobuf                           3.11.3\r\n",
      "psutil                             5.7.0\r\n",
      "psycopg2                           2.8.5\r\n",
      "ptyprocess                         0.6.0\r\n",
      "py                                 1.9.0\r\n",
      "pycodestyle                        2.6.0\r\n",
      "pycosat                            0.6.3\r\n",
      "pycparser                          2.20\r\n",
      "pycrypto                           2.6.1\r\n",
      "pycurl                             7.43.0.5\r\n",
      "pyepsg                             0.4.0\r\n",
      "pyflakes                           2.2.0\r\n",
      "Pygments                           2.6.1\r\n",
      "pykdtree                           1.3.1\r\n",
      "pylint                             2.4.4\r\n",
      "pymc3                              3.8\r\n",
      "pyodbc                             4.0.0-unsupported\r\n",
      "pyOpenSSL                          19.1.0\r\n",
      "pyparsing                          2.4.7\r\n",
      "pyproj                             2.6.1.post1\r\n",
      "pyrsistent                         0.16.0\r\n",
      "pyshp                              2.1.0\r\n",
      "PySocks                            1.7.1\r\n",
      "pytest                             5.4.3\r\n",
      "pytest-arraydiff                   0.3\r\n",
      "pytest-astropy                     0.8.0\r\n",
      "pytest-astropy-header              0.1.2\r\n",
      "pytest-doctestplus                 0.7.0\r\n",
      "pytest-openfiles                   0.5.0\r\n",
      "pytest-remotedata                  0.3.2\r\n",
      "python-dateutil                    2.8.1\r\n",
      "pytz                               2020.1\r\n",
      "PyWavelets                         1.1.1\r\n",
      "PyYAML                             5.3.1\r\n",
      "pyzmq                              19.0.1\r\n",
      "QtAwesome                          0.7.2\r\n",
      "qtconsole                          4.7.5\r\n",
      "QtPy                               1.9.0\r\n",
      "rasterio                           1.1.5\r\n",
      "regex                              2020.6.8\r\n",
      "requests                           2.24.0\r\n",
      "rope                               0.17.0\r\n",
      "Rtree                              0.9.4\r\n",
      "ruamel-yaml                        0.15.87\r\n",
      "scikit-image                       0.16.2\r\n",
      "scikit-learn                       0.23.2\r\n",
      "scipy                              1.5.0\r\n",
      "seaborn                            0.10.1\r\n",
      "SecretStorage                      3.1.2\r\n",
      "Send2Trash                         1.5.0\r\n",
      "sentry-sdk                         0.14.3\r\n",
      "setuptools                         47.3.1.post20200622\r\n",
      "Shapely                            1.7.0\r\n",
      "shortuuid                          1.0.1\r\n",
      "simplegeneric                      0.8.1\r\n",
      "singledispatch                     3.4.0.3\r\n",
      "six                                1.15.0\r\n",
      "smmap                              3.0.2\r\n",
      "snorkel                            0.9.6\r\n",
      "snowballstemmer                    2.0.0\r\n",
      "snuggs                             1.4.7\r\n",
      "sortedcollections                  1.2.1\r\n",
      "sortedcontainers                   2.2.2\r\n",
      "soupsieve                          2.0.1\r\n",
      "Sphinx                             3.1.2\r\n",
      "sphinxcontrib-applehelp            1.0.2\r\n",
      "sphinxcontrib-devhelp              1.0.2\r\n",
      "sphinxcontrib-htmlhelp             1.0.3\r\n",
      "sphinxcontrib-jsmath               1.0.1\r\n",
      "sphinxcontrib-qthelp               1.0.3\r\n",
      "sphinxcontrib-serializinghtml      1.1.4\r\n",
      "sphinxcontrib-websupport           1.2.3\r\n",
      "spyder                             3.3.6\r\n",
      "spyder-kernels                     0.5.2\r\n",
      "SQLAlchemy                         1.3.18\r\n",
      "statsmodels                        0.11.1\r\n",
      "subprocess32                       3.5.4\r\n",
      "sympy                              1.6\r\n",
      "tables                             3.6.1\r\n",
      "tabulate                           0.8.6\r\n",
      "tblib                              1.6.0\r\n",
      "tensorboard                        1.15.0\r\n",
      "tensorflow-estimator               1.15.1\r\n",
      "tensorflow-gpu                     1.15.0\r\n",
      "termcolor                          1.1.0\r\n",
      "terminado                          0.8.3\r\n",
      "testpath                           0.4.4\r\n",
      "Theano                             1.0.4\r\n",
      "threadpoolctl                      2.1.0\r\n",
      "toolz                              0.10.0\r\n",
      "torch                              1.4.0\r\n",
      "torchvision                        0.5.0\r\n",
      "tornado                            6.0.4\r\n",
      "tqdm                               4.47.0\r\n",
      "traitlets                          4.3.3\r\n",
      "typing-extensions                  3.7.4.2\r\n",
      "unicodecsv                         0.14.1\r\n",
      "Unidecode                          1.1.1\r\n",
      "urllib3                            1.25.9\r\n",
      "wandb                              0.8.34\r\n",
      "watchdog                           0.10.2\r\n",
      "wcwidth                            0.2.5\r\n",
      "webencodings                       0.5.1\r\n",
      "Werkzeug                           1.0.1\r\n",
      "wheel                              0.34.2\r\n",
      "widgetsnbextension                 3.5.1\r\n",
      "wrapt                              1.12.1\r\n",
      "wurlitzer                          2.0.0\r\n",
      "xarray                             0.15.0\r\n",
      "xlrd                               1.2.0\r\n",
      "XlsxWriter                         1.2.9\r\n",
      "xlwt                               1.3.0\r\n",
      "zict                               2.0.0\r\n",
      "zipp                               3.1.0\r\n",
      "zope.event                         4.4\r\n",
      "zope.interface                     4.7.1\r\n"
     ]
    }
   ],
   "source": [
    "!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# packages\n",
    "import osmnx as ox\n",
    "import networkx as nx\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# retrive a road network\n",
    "place_name = 'Kamppi, Helsinki, Finland'\n",
    "graph = ox.graph_from_place(place_name, network_type='drive')\n",
    "fig, ax = ox.plot_graph(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['osmid', 'oneway', 'lanes', 'name', 'highway', 'maxspeed', 'length',\n",
      "       'geometry', 'junction', 'bridge', 'access', 'u', 'v', 'key'],\n",
      "      dtype='object')\n",
      "                                               osmid  oneway   lanes  \\\n",
      "0                                           23856784    True       2   \n",
      "1                               [29977177, 30470347]    True       3   \n",
      "2                               [372440330, 8135861]    True       2   \n",
      "3          [25514547, 677423564, 30288797, 30288799]    True  [2, 3]   \n",
      "4  [30568275, 36729015, 316590744, 316590745, 316...    True     NaN   \n",
      "\n",
      "             name   highway maxspeed   length  \\\n",
      "0  Mechelininkatu   primary       40   40.885   \n",
      "1  Mechelininkatu   primary       40   16.601   \n",
      "2             NaN   primary       40   25.190   \n",
      "3  Mechelininkatu   primary       40  242.476   \n",
      "4   Fredrikinkatu  tertiary       30  139.090   \n",
      "\n",
      "                                            geometry junction bridge access  \\\n",
      "0  LINESTRING (24.92106 60.16479, 24.92095 60.164...      NaN    NaN    NaN   \n",
      "1  LINESTRING (24.92103 60.16366, 24.92104 60.163...      NaN    NaN    NaN   \n",
      "2  LINESTRING (24.92129 60.16463, 24.92127 60.164...      NaN    NaN    NaN   \n",
      "3  LINESTRING (24.92129 60.16463, 24.92136 60.164...      NaN    NaN    NaN   \n",
      "4  LINESTRING (24.93702 60.16433, 24.93700 60.164...      NaN    NaN    NaN   \n",
      "\n",
      "          u           v  key  \n",
      "0  25216594  1372425714    0  \n",
      "1  25238874  1372425713    0  \n",
      "2  25238944    25216594    0  \n",
      "3  25238944   319896278    0  \n",
      "4  25291537    25291591    0  \n"
     ]
    }
   ],
   "source": [
    "# convert osm to gpd\n",
    "edges = ox.graph_to_gdfs(graph, nodes=False,edges=True)\n",
    "print(edges.columns)\n",
    "print(edges.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "residential      113\n",
       "tertiary          78\n",
       "primary           26\n",
       "secondary         17\n",
       "unclassified      10\n",
       "living_street      4\n",
       "primary_link       1\n",
       "Name: highway, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges['highway'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinate system: epsg:4326\n"
     ]
    }
   ],
   "source": [
    "print(\"Coordinate system:\", edges.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# project the graph and visualize it.\n",
    "graph_proj = ox.project_graph(graph)\n",
    "fig, ax = ox.plot_graph(graph_proj)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_proj, edges_proj = ox.graph_to_gdfs(graph_proj, nodes=True, edges=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinate system: +proj=utm +zone=35 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs\n"
     ]
    }
   ],
   "source": [
    "print(\"Coordinate system:\", edges_proj.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>osmid</th>\n",
       "      <th>oneway</th>\n",
       "      <th>lanes</th>\n",
       "      <th>name</th>\n",
       "      <th>highway</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>length</th>\n",
       "      <th>geometry</th>\n",
       "      <th>bridge</th>\n",
       "      <th>junction</th>\n",
       "      <th>access</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23856784</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>40.885</td>\n",
       "      <td>LINESTRING (384631.322 6671580.071, 384624.750...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25216594</td>\n",
       "      <td>1372425714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[78537378, 8169098, 29081876, 78537375]</td>\n",
       "      <td>True</td>\n",
       "      <td>[2, 3]</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>85.540</td>\n",
       "      <td>LINESTRING (384624.179 6671539.986, 384623.768...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1372425714</td>\n",
       "      <td>25238874</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29066052</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>20.591</td>\n",
       "      <td>LINESTRING (384624.179 6671539.986, 384627.284...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1372425714</td>\n",
       "      <td>1372425718</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[29977177, 30470347]</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>Mechelininkatu</td>\n",
       "      <td>primary</td>\n",
       "      <td>40</td>\n",
       "      <td>16.601</td>\n",
       "      <td>LINESTRING (384625.787 6671454.380, 384626.281...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25238874</td>\n",
       "      <td>1372425713</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57839670</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Itämerenkatu</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>40</td>\n",
       "      <td>20.414</td>\n",
       "      <td>LINESTRING (384627.187 6671437.809, 384646.250...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1372425713</td>\n",
       "      <td>568147264</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     osmid  oneway   lanes            name  \\\n",
       "0                                 23856784    True       2  Mechelininkatu   \n",
       "1  [78537378, 8169098, 29081876, 78537375]    True  [2, 3]  Mechelininkatu   \n",
       "2                                 29066052    True       2             NaN   \n",
       "3                     [29977177, 30470347]    True       3  Mechelininkatu   \n",
       "4                                 57839670    True     NaN    Itämerenkatu   \n",
       "\n",
       "    highway maxspeed  length  \\\n",
       "0   primary       40  40.885   \n",
       "1   primary       40  85.540   \n",
       "2   primary       40  20.591   \n",
       "3   primary       40  16.601   \n",
       "4  tertiary       40  20.414   \n",
       "\n",
       "                                            geometry bridge junction access  \\\n",
       "0  LINESTRING (384631.322 6671580.071, 384624.750...    NaN      NaN    NaN   \n",
       "1  LINESTRING (384624.179 6671539.986, 384623.768...    NaN      NaN    NaN   \n",
       "2  LINESTRING (384624.179 6671539.986, 384627.284...    NaN      NaN    NaN   \n",
       "3  LINESTRING (384625.787 6671454.380, 384626.281...    NaN      NaN    NaN   \n",
       "4  LINESTRING (384627.187 6671437.809, 384646.250...    NaN      NaN    NaN   \n",
       "\n",
       "            u           v  key  \n",
       "0    25216594  1372425714    0  \n",
       "1  1372425714    25238874    0  \n",
       "2  1372425714  1372425718    0  \n",
       "3    25238874  1372425713    0  \n",
       "4  1372425713   568147264    0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(edges_proj))\n",
    "edges_proj.head() # this is a geo df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "      <th>osmid</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>highway</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25216594</th>\n",
       "      <td>6.671580e+06</td>\n",
       "      <td>384631.322372</td>\n",
       "      <td>25216594</td>\n",
       "      <td>24.921057</td>\n",
       "      <td>60.164794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (384631.322 6671580.071)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372425714</th>\n",
       "      <td>6.671540e+06</td>\n",
       "      <td>384624.178763</td>\n",
       "      <td>1372425714</td>\n",
       "      <td>24.920951</td>\n",
       "      <td>60.164432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (384624.179 6671539.986)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25238874</th>\n",
       "      <td>6.671454e+06</td>\n",
       "      <td>384625.787221</td>\n",
       "      <td>25238874</td>\n",
       "      <td>24.921028</td>\n",
       "      <td>60.163665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (384625.787 6671454.380)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372425713</th>\n",
       "      <td>6.671438e+06</td>\n",
       "      <td>384627.187049</td>\n",
       "      <td>1372425713</td>\n",
       "      <td>24.921063</td>\n",
       "      <td>60.163516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (384627.187 6671437.809)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25238944</th>\n",
       "      <td>6.671562e+06</td>\n",
       "      <td>384643.473274</td>\n",
       "      <td>25238944</td>\n",
       "      <td>24.921286</td>\n",
       "      <td>60.164631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (384643.473 6671561.534)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       y              x       osmid        lon        lat  \\\n",
       "25216594    6.671580e+06  384631.322372    25216594  24.921057  60.164794   \n",
       "1372425714  6.671540e+06  384624.178763  1372425714  24.920951  60.164432   \n",
       "25238874    6.671454e+06  384625.787221    25238874  24.921028  60.163665   \n",
       "1372425713  6.671438e+06  384627.187049  1372425713  24.921063  60.163516   \n",
       "25238944    6.671562e+06  384643.473274    25238944  24.921286  60.164631   \n",
       "\n",
       "           highway                        geometry  \n",
       "25216594       NaN  POINT (384631.322 6671580.071)  \n",
       "1372425714     NaN  POINT (384624.179 6671539.986)  \n",
       "25238874       NaN  POINT (384625.787 6671454.380)  \n",
       "1372425713     NaN  POINT (384627.187 6671437.809)  \n",
       "25238944       NaN  POINT (384643.473 6671561.534)  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(nodes_proj))\n",
    "nodes_proj.head() # this is a geo df."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze the network properties with osm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n': 124,\n",
       " 'm': 249,\n",
       " 'k_avg': 4.016129032258065,\n",
       " 'intersection_count': 116,\n",
       " 'streets_per_node_avg': 3.217741935483871,\n",
       " 'streets_per_node_counts': {0: 0, 1: 8, 2: 1, 3: 71, 4: 44},\n",
       " 'streets_per_node_proportion': {0: 0.0,\n",
       "  1: 0.06451612903225806,\n",
       "  2: 0.008064516129032258,\n",
       "  3: 0.5725806451612904,\n",
       "  4: 0.3548387096774194},\n",
       " 'edge_length_total': 19986.840000000015,\n",
       " 'edge_length_avg': 80.26843373493982,\n",
       " 'street_length_total': 13671.708999999999,\n",
       " 'street_length_avg': 74.70879234972676,\n",
       " 'street_segments_count': 183,\n",
       " 'node_density_km': None,\n",
       " 'intersection_density_km': None,\n",
       " 'edge_density_km': None,\n",
       " 'street_density_km': None,\n",
       " 'circuity_avg': 1.2704220169558416e-05,\n",
       " 'self_loop_proportion': 0.0,\n",
       " 'clean_intersection_count': None,\n",
       " 'clean_intersection_density_km': None}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats=ox.basic_stats(graph_proj) # this uses the osmnx package\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "832856.2904615453\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "n                                                                                    124\n",
       "m                                                                                    249\n",
       "k_avg                                                                            4.01613\n",
       "intersection_count                                                                   116\n",
       "streets_per_node_avg                                                             3.21774\n",
       "streets_per_node_counts                                 {0: 0, 1: 8, 2: 1, 3: 71, 4: 44}\n",
       "streets_per_node_proportion            {0: 0.0, 1: 0.06451612903225806, 2: 0.00806451...\n",
       "edge_length_total                                                                19986.8\n",
       "edge_length_avg                                                                  80.2684\n",
       "street_length_total                                                              13671.7\n",
       "street_length_avg                                                                74.7088\n",
       "street_segments_count                                                                183\n",
       "node_density_km                                                                  148.885\n",
       "intersection_density_km                                                           139.28\n",
       "edge_density_km                                                                  23997.9\n",
       "street_density_km                                                                16415.4\n",
       "circuity_avg                                                                 1.27042e-05\n",
       "self_loop_proportion                                                                   0\n",
       "clean_intersection_count                                                            None\n",
       "clean_intersection_density_km                                                       None\n",
       "avg_neighbor_degree                    {25216594: 2.0, 1372425714: 1.0, 25238874: 2.0...\n",
       "avg_neighbor_degree_avg                                                          2.14315\n",
       "avg_weighted_neighbor_degree           {25216594: 0.04891769597651951, 1372425714: 0....\n",
       "avg_weighted_neighbor_degree_avg                                               0.0838883\n",
       "degree_centrality                      {25216594: 0.024390243902439025, 1372425714: 0...\n",
       "degree_centrality_avg                                                          0.0326515\n",
       "clustering_coefficient                 {25216594: 0, 1372425714: 0, 25238874: 0, 1372...\n",
       "clustering_coefficient_avg                                                     0.0954301\n",
       "clustering_coefficient_weighted        {25216594: 0, 1372425714: 0, 25238874: 0, 1372...\n",
       "clustering_coefficient_weighted_avg                                            0.0166188\n",
       "pagerank                               {25216594: 0.00870092873515, 1372425714: 0.008...\n",
       "pagerank_max_node                                                               25416262\n",
       "pagerank_max                                                                   0.0239181\n",
       "pagerank_min_node                                                             1008183915\n",
       "pagerank_min                                                                  0.00142892\n",
       "eccentricity                           {25216594: 1915.0230000000001, 1372425714: 187...\n",
       "diameter                                                                         2153.57\n",
       "radius                                                                           1007.07\n",
       "center                                                                      [1372376937]\n",
       "periphery                                                                    [319896278]\n",
       "closeness_centrality                   {25216594: 0.0009353926092551588, 1372425714: ...\n",
       "closeness_centrality_avg                                                      0.00143689\n",
       "betweenness_centrality                 {25216594: 0.03945088631214181, 1372425714: 0....\n",
       "betweenness_centrality_avg                                                     0.0784176\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return some summary stat for the road network.\n",
    "# note: it depends on the ox package. \n",
    "area = edges_proj.unary_union.convex_hull.area\n",
    "print(area)\n",
    "\n",
    "stats = ox.basic_stats(graph_proj, area=area)\n",
    "\n",
    "extended_stats = ox.extended_stats(graph_proj, ecc=True, bc=True, cc=True) # still ox package.\n",
    "\n",
    "for key, value in extended_stats.items():\n",
    "    stats[key] = value\n",
    "\n",
    "pd.Series(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}