GIS_Distance_calculator_OSM_street_network.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GIS Distance calculator - OSM street network.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNFM+0DtRyOqbLG6QvxtVbC",
      "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/kellytdunn/Distance-Calculator/blob/main/GIS_Distance_calculator_OSM_street_network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepare Data"
      ],
      "metadata": {
        "id": "HxFIfXoF36Jz"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIZNdpneJzyf"
      },
      "source": [
        "from google.colab import files\n",
        "import io"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "S6O2COEfJR2N",
        "outputId": "891645e8-f3e1-406c-da0e-128abaafb7ae"
      },
      "source": [
        "uploaded=files.upload()\n",
        "excel_file = io.BytesIO(uploaded.get('OD data with transformed access.xlsx'))\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1ad5409c-4cd5-4cec-b9fc-7b7fa7dce1a7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1ad5409c-4cd5-4cec-b9fc-7b7fa7dce1a7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving OD data with transformed access.xlsx to OD data with transformed access.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecGPS92OF9uC"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_excel(excel_file, 0)"
      ],
      "metadata": {
        "id": "Ogk0mgPWvGX1"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dscMjADHKTU8"
      },
      "source": [
        "df = df1 #so if I mess up and need to re-load df, I don't have to reimport the whole excel file. "
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert NAs to zero now, before they become the tuples I need. \n",
        "\n",
        "cols_fillna = ['origin_lat','origin_lng','destination_lat','destination_lng','board_stop_lat','board_stop_lon']\n",
        "# replace 'NaN' with zero in these columns\n",
        "for col in cols_fillna:\n",
        "     df[col].fillna(1,inplace=True)\n",
        "\n",
        "#should not see any nans in the output for the columns above\n",
        "#But I think maybe the inputs were already zeroes, so they're not getting switched to 1s here"
      ],
      "metadata": {
        "id": "VxCrqmM_A15Z"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['board_stop_lon'].isnull().sum() #check that there are no NAs in each column. "
      ],
      "metadata": {
        "id": "dscUUMDDK-lM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2003b23f-101f-4e7b-ce68-dde1eec9ae62"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Calculate Origin-Destination Distances"
      ],
      "metadata": {
        "id": "K31btIT6rFKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import json"
      ],
      "metadata": {
        "id": "miWeQMtVxCez"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Accessdistancer(row):\n",
        "  lon_1 = (row['origin_lng'])\n",
        "  lat_1 = (row['origin_lat'])\n",
        "  lon_2 = (row['board_stop_lon'])\n",
        "  lat_2 = (row['board_stop_lat'])\n",
        "  # call the OSMR API\n",
        "  r = requests.get(f\"http://router.project-osrm.org/route/v1/foot/{lon_1},{lat_1};{lon_2},{lat_2}?overview=false\"\"\") #change \"foot\" to \"drive\" or \"transit\" if desired\n",
        "  # then you load the response using the json libray\n",
        "  routes = json.loads(r.content) #returns dictionary which includes `routes` as one key and a list of dictionaries as its value. Diff results if fed a (0,0) point.\n",
        "  if \"routes\" in routes: \n",
        "    route_1 = routes.get(\"routes\")[0] #takes the value from `route`, which is a list of dictionaries, then returns the first (and only) dictionary from that list. \n",
        "  else:\n",
        "    route_1 = {'distance':0}\n",
        "  return (route_1['distance']*0.000621371) #takes just the value of distance from the dictionary and converts meters to miles\n",
        "#http://project-osrm.org/docs/v5.5.1/api/#route-service"
      ],
      "metadata": {
        "id": "PeTfOOGwBIAU"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#free APIs limit the amount of calls you can do per day. So break your file into chunks. \n",
        "#Colab will overwrite excel file last exported, so download your most recent file first.\n",
        "current_chunk = df[0:2501] #end +1 higher than you want\n",
        "access = current_chunk.apply(Accessdistancer, axis = 1) #generate pandas series of access distances\n",
        "access = pd.Series(access, name = \"Street Network Distance\") #give new column a name\n",
        "newdf = df.merge(access, left_index=True, right_index=True) #join series to existing df, based on index. just keep those in the chunk I want.\n",
        "#newdf.loc[newdf['Access Distance'] > 7000, 'Access Distance'] = 0 #optional, if you notice any points are extremely high (which could be because one point was 0,0 and the other point was real)\n",
        "newdf.to_excel('OD dataset with street network distances.xlsx') #export df to excel. In colab, ends up in the file tab at left. "
      ],
      "metadata": {
        "id": "UwSAA7MRlXYE"
      },
      "execution_count": 10,
      "outputs": []
    }
  ]
}