osm2gmns_quickstart_for_Dubai.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "KbmkUUHXWpXS"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/asu-trans-ai-lab/OSM2GMNS/blob/master/osm2gmns_quickstart_for_Dubai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# __OSM2GMNS Quickstart Notebook__\n",
        "\n",
        "OSM2GMNS is a Python package that helps generate high-fidelity mesoscopic transportation network models from OpenStreetMap data. This enables even the smallest transportation authorities to begin analysis with a high quality network model for future analysis. \n"
      ],
      "metadata": {
        "id": "KbmkUUHXWpXS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Data Collection\n",
        "We rely on a few open-source projects to gather the raw data to start our analysis. Export data from your area of interest from the following sources.\n",
        " * __OpenStreetMap base map__: https://www.openstreetmap.org/export\n",
        "   * __Geofabrik__: Alternative for non-rectangular Areas of Interest: http://download.geofabrik.de/\n",
        " * __Bike Transport Info__ (Optional):  https://extract.bbbike.org\n",
        "\n",
        "For this demonstration, we'll use sample data from the ASU Transit AI Lab Github\n",
        "\n",
        "osm2gmns also enables users to download OSM data within the region of interest using a built-in function. A region can be a state, city, or even university. On OpenStreetMap homepage, search the region name to get its unique relation id. The following example shows how to download Tempe city OSM data using function downloadOSMData.\n",
        "\n",
        "https://www.openstreetmap.org/#map=18/33.42141/-111.93472&layers=DG "
      ],
      "metadata": {
        "id": "9URrMF7xXE-g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Install the osm2gmns package"
      ],
      "metadata": {
        "id": "nqUaVegsbTO_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install osm2gmns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0L0kgefhbZaB",
        "outputId": "25528fa2-d3c7-435b-f67a-d74aa785252a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting osm2gmns\n",
            "  Downloading osm2gmns-0.7.3-py3-none-any.whl (59 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.8/59.8 KB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: shapely>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from osm2gmns) (2.0.1)\n",
            "Collecting osmium>=3.1.3\n",
            "  Downloading osmium-3.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from osm2gmns) (1.21.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from osmium>=3.1.3->osm2gmns) (2.25.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->osmium>=3.1.3->osm2gmns) (2.10)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->osmium>=3.1.3->osm2gmns) (4.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->osmium>=3.1.3->osm2gmns) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->osmium>=3.1.3->osm2gmns) (1.24.3)\n",
            "Installing collected packages: osmium, osm2gmns\n",
            "Successfully installed osm2gmns-0.7.3 osmium-3.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Create a network model from OpenStreetMap exports"
      ],
      "metadata": {
        "id": "WobI3HSQbqsw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import osm2gmns with the alias as \"og\" (easier to type)\n",
        "import osm2gmns as og\n",
        "# relation id for Manhattan  is 8398124\n",
        "# relation id for City of Tempe is 110833.\n",
        "# relation id for City of Dubai  is 3766483\n",
        "og.downloadOSMData(3766483, 'map.osm')\n",
        "# Generate a net from the Tempe, AZ OpenStreetMap export and include Points of Interest (POI=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ila8lUreb0TB",
        "outputId": "d12da168-f2a7-4192-c4a8-5167224ef408"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "osm2gmns, 0.7.3\n",
            "valid reponses got from API server.\n",
            "receving data...\n",
            "map data has been written to map.osm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_file = 'map.osm'\n",
        "net = og.getNetFromFile(input_file, link_types=('motorway','trunk','primary')) \n",
        "# ,'secondary'\n",
        "# Use og.show(net) to visualize the network model\n",
        "og.show(net)"
      ],
      "metadata": {
        "id": "O4RJqNGWVtxk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a9f577cd-9349-47d4-b3e0-683cca546585"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "arguments used for network parsing:\n",
            "  filename: map.osm\n",
            "  network_types: ('auto',)\n",
            "  link_types: ('motorway', 'trunk', 'primary')\n",
            "  POI: False\n",
            "  POI_sampling_ratio: 1.0\n",
            "  strict_mode: True\n",
            "  offset: no\n",
            "  min_nodes: 1\n",
            "  combine: False\n",
            "  bbox: None\n",
            "  default_lanes: False\n",
            "  default_speed: False\n",
            "  default_capacity: False\n",
            "  start_node_id: 0\n",
            "  start_link_id: 0\n",
            "\n",
            "Building Network from OSM file\n",
            "  reading osm file\n",
            "  parsing osm network\n",
            "    generating nodes and links\n",
            "  number of nodes: 8137, number of links: 10084, number of pois: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x871.548 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**bold text**#4. Export transportation network model\n",
        "\n",
        "We can export our transportation network model to a CSV format for ease of use in further analysis with other systems. \n",
        "\n",
        "og.outputNetToCSV(...) will generate two files: node.csv representing nodes, and link.csv representing links (edges) between nodes."
      ],
      "metadata": {
        "id": "1JebB_n6f5Co"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "og.outputNetToCSV(net)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QtMOShGgB2Q",
        "outputId": "3a5a3336-a508-421f-8aac-60b36715417d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outputting Network Files\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Take a peek at the format of the CSVs using the Pandas library\n",
        "Loading the CSV files gives us a DataFrame object which is very heavily used in \n",
        "data science and analysis. \n",
        "\n",
        "For more information on how this data is organized, check out the GMNS specification\n",
        "\n",
        "https://github.com/zephyr-data-specs/GMNS/pull/54\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "node_dataframe = pd.read_csv(\"node.csv\")\n",
        "node_dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "SNYnbMiFgW0h",
        "outputId": "0c2fc6c2-b033-4c9f-8bd7-a6a0217a6eb6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      name  node_id  osm_node_id        osm_highway  zone_id ctrl_type  \\\n",
              "0      NaN        0     90031463                NaN      NaN       NaN   \n",
              "1      NaN        1   1149158725                NaN      NaN       NaN   \n",
              "2      NaN        2  10006732394                NaN      NaN       NaN   \n",
              "3      NaN        3   2330969717  motorway_junction      NaN       NaN   \n",
              "4  Exit 50        4     95110025  motorway_junction      NaN       NaN   \n",
              "\n",
              "   node_type  activity_type  is_boundary    x_coord    y_coord  \\\n",
              "0        NaN            NaN          NaN  55.365407  25.120153   \n",
              "1        NaN            NaN          NaN  55.382421  25.139361   \n",
              "2        NaN            NaN          NaN  55.387390  25.150932   \n",
              "3        NaN            NaN          NaN  55.387625  25.151455   \n",
              "4        NaN            NaN          NaN  55.388342  25.153183   \n",
              "\n",
              "   intersection_id  poi_id  notes  \n",
              "0              NaN     NaN    NaN  \n",
              "1              NaN     NaN    NaN  \n",
              "2              NaN     NaN    NaN  \n",
              "3              NaN     NaN    NaN  \n",
              "4              NaN     NaN    NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-163c4cb3-68e0-452e-8438-303dd0f9772b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>name</th>\n",
              "      <th>node_id</th>\n",
              "      <th>osm_node_id</th>\n",
              "      <th>osm_highway</th>\n",
              "      <th>zone_id</th>\n",
              "      <th>ctrl_type</th>\n",
              "      <th>node_type</th>\n",
              "      <th>activity_type</th>\n",
              "      <th>is_boundary</th>\n",
              "      <th>x_coord</th>\n",
              "      <th>y_coord</th>\n",
              "      <th>intersection_id</th>\n",
              "      <th>poi_id</th>\n",
              "      <th>notes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>90031463</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.365407</td>\n",
              "      <td>25.120153</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1149158725</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.382421</td>\n",
              "      <td>25.139361</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>10006732394</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.387390</td>\n",
              "      <td>25.150932</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>2330969717</td>\n",
              "      <td>motorway_junction</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.387625</td>\n",
              "      <td>25.151455</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Exit 50</td>\n",
              "      <td>4</td>\n",
              "      <td>95110025</td>\n",
              "      <td>motorway_junction</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.388342</td>\n",
              "      <td>25.153183</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-163c4cb3-68e0-452e-8438-303dd0f9772b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-163c4cb3-68e0-452e-8438-303dd0f9772b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-163c4cb3-68e0-452e-8438-303dd0f9772b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "link_dataframe = pd.read_csv(\"link.csv\")\n",
        "link_dataframe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "DEoWxS5DhxrN",
        "outputId": "12a2d372-d751-4e21-9bf4-88b58479833b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      name  link_id  osm_way_id  from_node_id  to_node_id  \\\n",
              "0  شارع الشيخ محمد بن زايد        0     4009554             0           1   \n",
              "1  شارع الشيخ محمد بن زايد        1     4009554             1           2   \n",
              "2  شارع الشيخ محمد بن زايد        2     4009554             2           3   \n",
              "3  شارع الشيخ محمد بن زايد        3     4009554             3           4   \n",
              "4  شارع الشيخ محمد بن زايد        4     4009554             4           5   \n",
              "\n",
              "   dir_flag   length  lanes  free_speed  capacity link_type_name  link_type  \\\n",
              "0         1  2771.09    6.0       110.0       NaN       motorway          1   \n",
              "1         1  1375.79    6.0       110.0       NaN       motorway          1   \n",
              "2         1    62.55    6.0       110.0       NaN       motorway          1   \n",
              "3         1   204.50    6.0       110.0       NaN       motorway          1   \n",
              "4         1  1026.51    6.0       110.0       NaN       motorway          1   \n",
              "\n",
              "                                            geometry allowed_uses  from_biway  \\\n",
              "0  LINESTRING (55.3654066 25.1201526, 55.3683512 ...         auto           0   \n",
              "1  LINESTRING (55.3824206 25.1393608, 55.3830336 ...         auto           0   \n",
              "2  LINESTRING (55.3873900 25.1509321, 55.3876249 ...         auto           0   \n",
              "3  LINESTRING (55.3876249 25.1514550, 55.3883417 ...         auto           0   \n",
              "4  LINESTRING (55.3883417 25.1531827, 55.3904814 ...         auto           0   \n",
              "\n",
              "   is_link  VDF_fftt1  VDF_cap1  \n",
              "0        0        NaN       NaN  \n",
              "1        0        NaN       NaN  \n",
              "2        0        NaN       NaN  \n",
              "3        0        NaN       NaN  \n",
              "4        0        NaN       NaN  "
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            "text/html": [
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              "      <div>\n",
              "<style scoped>\n",
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              "      <th>3</th>\n",
              "      <td>شارع الشيخ محمد بن زايد</td>\n",
              "      <td>3</td>\n",
              "      <td>4009554</td>\n",
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              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e510f50a-a9e6-4cbc-87b6-c5b2d357bb28')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e510f50a-a9e6-4cbc-87b6-c5b2d357bb28 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e510f50a-a9e6-4cbc-87b6-c5b2d357bb28');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. Basic analysis with Pandas\n",
        "\n",
        "The node and link files are in CSV format, which can be used with a wide variety of programming languages, such as R, and programs, such as Excel.\n",
        "\n",
        "Here, we show a quick demonstration of some simple analysis using Python"
      ],
      "metadata": {
        "id": "17tTpV4Ah0-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# From the previous cells, we have pandas DataFrame objects representing the node and link data for our model\n",
        "print(node_dataframe.head(), link_dataframe.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z52bpRrVhzM_",
        "outputId": "d67288c7-47db-4024-bd2a-1f2f071bae51"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      name  node_id  osm_node_id        osm_highway  zone_id ctrl_type  \\\n",
            "0      NaN        0     90031463                NaN      NaN       NaN   \n",
            "1      NaN        1   1149158725                NaN      NaN       NaN   \n",
            "2      NaN        2  10006732394                NaN      NaN       NaN   \n",
            "3      NaN        3   2330969717  motorway_junction      NaN       NaN   \n",
            "4  Exit 50        4     95110025  motorway_junction      NaN       NaN   \n",
            "\n",
            "   node_type  activity_type  is_boundary    x_coord    y_coord  \\\n",
            "0        NaN            NaN          NaN  55.365407  25.120153   \n",
            "1        NaN            NaN          NaN  55.382421  25.139361   \n",
            "2        NaN            NaN          NaN  55.387390  25.150932   \n",
            "3        NaN            NaN          NaN  55.387625  25.151455   \n",
            "4        NaN            NaN          NaN  55.388342  25.153183   \n",
            "\n",
            "   intersection_id  poi_id  notes  \n",
            "0              NaN     NaN    NaN  \n",
            "1              NaN     NaN    NaN  \n",
            "2              NaN     NaN    NaN  \n",
            "3              NaN     NaN    NaN  \n",
            "4              NaN     NaN    NaN                         name  link_id  osm_way_id  from_node_id  to_node_id  \\\n",
            "0  شارع الشيخ محمد بن زايد        0     4009554             0           1   \n",
            "1  شارع الشيخ محمد بن زايد        1     4009554             1           2   \n",
            "2  شارع الشيخ محمد بن زايد        2     4009554             2           3   \n",
            "3  شارع الشيخ محمد بن زايد        3     4009554             3           4   \n",
            "4  شارع الشيخ محمد بن زايد        4     4009554             4           5   \n",
            "\n",
            "   dir_flag   length  lanes  free_speed  capacity link_type_name  link_type  \\\n",
            "0         1  2771.09    6.0       110.0       NaN       motorway          1   \n",
            "1         1  1375.79    6.0       110.0       NaN       motorway          1   \n",
            "2         1    62.55    6.0       110.0       NaN       motorway          1   \n",
            "3         1   204.50    6.0       110.0       NaN       motorway          1   \n",
            "4         1  1026.51    6.0       110.0       NaN       motorway          1   \n",
            "\n",
            "                                            geometry allowed_uses  from_biway  \\\n",
            "0  LINESTRING (55.3654066 25.1201526, 55.3683512 ...         auto           0   \n",
            "1  LINESTRING (55.3824206 25.1393608, 55.3830336 ...         auto           0   \n",
            "2  LINESTRING (55.3873900 25.1509321, 55.3876249 ...         auto           0   \n",
            "3  LINESTRING (55.3876249 25.1514550, 55.3883417 ...         auto           0   \n",
            "4  LINESTRING (55.3883417 25.1531827, 55.3904814 ...         auto           0   \n",
            "\n",
            "   is_link  VDF_fftt1  VDF_cap1  \n",
            "0        0        NaN       NaN  \n",
            "1        0        NaN       NaN  \n",
            "2        0        NaN       NaN  \n",
            "3        0        NaN       NaN  \n",
            "4        0        NaN       NaN  \n"
          ]
        }
      ]
    }
  ]
}