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Open Streets Collision Summary.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "Open Streets Collision Summary.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/zhik/3f1602678026834c6f54470bdd8fcb16/open-streets-collision-summary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "tP7894NRG8-v", | |
| "outputId": "af58a0e1-7c79-4b68-dc5f-1ef51cdd2392" | |
| }, | |
| "source": [ | |
| "!pip install pandas geopandas seaborn mapclassify folium" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.1.5)\n", | |
| "Requirement already satisfied: geopandas in /usr/local/lib/python3.7/dist-packages (0.10.2)\n", | |
| "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (0.11.2)\n", | |
| "Requirement already satisfied: mapclassify in /usr/local/lib/python3.7/dist-packages (2.4.3)\n", | |
| "Requirement already satisfied: folium in /usr/local/lib/python3.7/dist-packages (0.8.3)\n", | |
| "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n", | |
| "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n", | |
| "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.19.5)\n", | |
| "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n", | |
| "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (3.2.1)\n", | |
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| "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n", | |
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| "Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2.5.0)\n", | |
| "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n", | |
| "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2021.5.30)\n", | |
| "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.7/dist-packages (from seaborn) (3.2.2)\n", | |
| "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.4.1)\n", | |
| "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n", | |
| "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n", | |
| "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n", | |
| "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from mapclassify) (0.22.2.post1)\n", | |
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| "Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from folium) (0.4.2)\n", | |
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| "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->folium) (1.24.3)\n", | |
| "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->mapclassify) (1.0.1)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "g_b_49QvEEb9", | |
| "outputId": "5f072417-d64b-409c-fc46-20be2c68efa6" | |
| }, | |
| "source": [ | |
| "from google.colab import drive\n", | |
| "drive.mount('/content/drive')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "izZDQwKnHWMK" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import geopandas as gpd\n", | |
| "import seaborn as sns\n", | |
| "from pathlib import Path\n", | |
| "from datetime import datetime, timedelta\n", | |
| "from matplotlib.patches import Rectangle\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import matplotlib.dates as mdates" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Krj3cG40Xopc" | |
| }, | |
| "source": [ | |
| "## Import Our Datasets\n", | |
| "\n", | |
| "- [Open Streets Locations](https://data.cityofnewyork.us/Health/Open-Streets-Locations/uiay-nctu/data) - geocoded\n", | |
| "- [Motor Vehicle Collisions-Crashes](https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95/data)\n", | |
| "- [Motor Vehicle Collisions-Vehicles](https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Vehicles/bm4k-52h4) (one to many on Crashes dataset)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 366 | |
| }, | |
| "id": "UHVyAAtsHZts", | |
| "outputId": "4c94ea15-573b-4611-aaa4-c7dcb8155376" | |
| }, | |
| "source": [ | |
| "#set a basePath to my google drive\n", | |
| "basePath = Path('/content/drive/MyDrive/Colab Data/Open Streets (10 2021)')\n", | |
| "\n", | |
| "#read and sample open streets\n", | |
| "open_streets = gpd.read_file(basePath / './open-streets.shp')\n", | |
| "open_streets.sample(3)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "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>Location</th>\n", | |
| " <th>Borough</th>\n", | |
| " <th>On_Stree</th>\n", | |
| " <th>From_Str</th>\n", | |
| " <th>To_Stree</th>\n", | |
| " <th>Type</th>\n", | |
| " <th>Open_Dat</th>\n", | |
| " <th>Monday_S</th>\n", | |
| " <th>Monday_E</th>\n", | |
| " <th>Tuesday_</th>\n", | |
| " <th>Wednesda</th>\n", | |
| " <th>Thursday</th>\n", | |
| " <th>Friday_S</th>\n", | |
| " <th>Friday_E</th>\n", | |
| " <th>Saturday</th>\n", | |
| " <th>Sunday_S</th>\n", | |
| " <th>Sunday_E</th>\n", | |
| " <th>Day_of_W</th>\n", | |
| " <th>Cool_Str</th>\n", | |
| " <th>error</th>\n", | |
| " <th>geometry</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>194</th>\n", | |
| " <td>Concrete Safaris</td>\n", | |
| " <td>Manhattan</td>\n", | |
| " <td>East 115th Street</td>\n", | |
| " <td>Park Avenue</td>\n", | |
| " <td>3rd Avenue</td>\n", | |
| " <td>Temporary Limited Local Access</td>\n", | |
| " <td>43965.0</td>\n", | |
| " <td>2:00PM</td>\n", | |
| " <td>5:30PM</td>\n", | |
| " <td>5:30PM</td>\n", | |
| " <td>5:30PM</td>\n", | |
| " <td>5:30PM</td>\n", | |
| " <td>2:00PM</td>\n", | |
| " <td>5:30PM</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>M,T,W,R,F</td>\n", | |
| " <td>None</td>\n", | |
| " <td>None</td>\n", | |
| " <td>LINESTRING (-73.94212 40.79794, -73.94052 40.7...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>318</th>\n", | |
| " <td>Kesher Bridging Communities</td>\n", | |
| " <td>Queens</td>\n", | |
| " <td>Reads Lane</td>\n", | |
| " <td>Empire Avenue</td>\n", | |
| " <td>Jarvis Avenue</td>\n", | |
| " <td>Temporary Limited Local Access</td>\n", | |
| " <td>44007.0</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>12:00PM</td>\n", | |
| " <td>9:00PM</td>\n", | |
| " <td>8:00PM</td>\n", | |
| " <td>12:00PM</td>\n", | |
| " <td>8:00PM</td>\n", | |
| " <td>F,S,U</td>\n", | |
| " <td>None</td>\n", | |
| " <td>None</td>\n", | |
| " <td>LINESTRING (-73.73858 40.59968, -73.73915 40.6...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>576</th>\n", | |
| " <td>Rodeph Sholom School</td>\n", | |
| " <td>Manhattan</td>\n", | |
| " <td>West 78th Street</td>\n", | |
| " <td>Columbus Avenue</td>\n", | |
| " <td>Amsterdam Avenue</td>\n", | |
| " <td>Temporary Full Closure</td>\n", | |
| " <td>44455.0</td>\n", | |
| " <td>10:00AM</td>\n", | |
| " <td>2:45PM</td>\n", | |
| " <td>2:45PM</td>\n", | |
| " <td>2:45PM</td>\n", | |
| " <td>2:45PM</td>\n", | |
| " <td>10:00AM</td>\n", | |
| " <td>2:45PM</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>N/A</td>\n", | |
| " <td>M,T,W,R,F</td>\n", | |
| " <td>None</td>\n", | |
| " <td>None</td>\n", | |
| " <td>LINESTRING (-73.97599 40.78127, -73.97882 40.7...</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Location ... geometry\n", | |
| "194 Concrete Safaris ... LINESTRING (-73.94212 40.79794, -73.94052 40.7...\n", | |
| "318 Kesher Bridging Communities ... LINESTRING (-73.73858 40.59968, -73.73915 40.6...\n", | |
| "576 Rodeph Sholom School ... LINESTRING (-73.97599 40.78127, -73.97882 40.7...\n", | |
| "\n", | |
| "[3 rows x 21 columns]" | |
| ] | |
| }, | |
| "execution_count": 30, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 286 | |
| }, | |
| "id": "zqaB62KKW_rD", | |
| "outputId": "83eeb9fe-0c82-4752-e434-38c77a6ccf59" | |
| }, | |
| "source": [ | |
| "open_streets.plot()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f7bb4ce50>" | |
| ] | |
| }, | |
| "execution_count": 31, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "OFj4cFusYUxN", | |
| "outputId": "d90e64dc-73ed-4c50-8fb2-2303d755b2ba" | |
| }, | |
| "source": [ | |
| "collision_vech = pd.read_csv( basePath / './Motor_Vehicle_Collisions_-_Vehicles.csv')\n", | |
| "\n", | |
| "#read and convert to GeoDataFrame\n", | |
| "_df1 = pd.read_csv( basePath / './Motor_Vehicle_Collisions_-_Crashes.csv')\n", | |
| "collision_crashes = gpd.GeoDataFrame(_df1, geometry=gpd.points_from_xy(_df1.LONGITUDE, _df1.LATITUDE), crs = 'EPSG:4326')\n", | |
| "del _df1" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (8) have mixed types.Specify dtype option on import or set low_memory=False.\n", | |
| " interactivity=interactivity, compiler=compiler, result=result)\n", | |
| "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (3) have mixed types.Specify dtype option on import or set low_memory=False.\n", | |
| " interactivity=interactivity, compiler=compiler, result=result)\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "fO8joLeAYk9J" | |
| }, | |
| "source": [ | |
| "#reproject\n", | |
| "collision_crashes = collision_crashes.to_crs(\"EPSG:2263\")" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "mV3EO7H4ZRUy", | |
| "outputId": "9fee77f8-f129-4946-f0b1-6821559b2566" | |
| }, | |
| "source": [ | |
| "collision_crashes.sample(3).transpose()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "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>646649</th>\n", | |
| " <th>1637284</th>\n", | |
| " <th>1645160</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>CRASH DATE</th>\n", | |
| " <td>12/20/2017</td>\n", | |
| " <td>06/17/2013</td>\n", | |
| " <td>05/22/2013</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>CRASH TIME</th>\n", | |
| " <td>11:40</td>\n", | |
| " <td>10:45</td>\n", | |
| " <td>21:12</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>BOROUGH</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>BRONX</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>ZIP CODE</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>10466</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>LATITUDE</th>\n", | |
| " <td>40.6775</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>40.8891</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>LONGITUDE</th>\n", | |
| " <td>-73.7309</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>-73.8355</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>LOCATION</th>\n", | |
| " <td>(40.67753, -73.73085)</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>(40.8891337, -73.835548)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>ON STREET NAME</th>\n", | |
| " <td>LAURELTON PARKWAY</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>AMUNDSON AVENUE</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>CROSS STREET NAME</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>EAST 233 STREET</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>OFF STREET NAME</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>P/L OF 1150 SOUTH AVE</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NUMBER OF PERSONS INJURED</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NUMBER OF PERSONS KILLED</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NUMBER OF PEDESTRIANS INJURED</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NUMBER OF PEDESTRIANS KILLED</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NUMBER OF CYCLIST INJURED</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NUMBER OF CYCLIST KILLED</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NUMBER OF MOTORIST INJURED</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>NUMBER OF MOTORIST KILLED</th>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 1</th>\n", | |
| " <td>Tire Failure/Inadequate</td>\n", | |
| " <td>Backing Unsafely</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 2</th>\n", | |
| " <td>Other Vehicular</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 3</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 4</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 5</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>COLLISION_ID</th>\n", | |
| " <td>3814602</td>\n", | |
| " <td>298559</td>\n", | |
| " <td>97504</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>VEHICLE TYPE CODE 1</th>\n", | |
| " <td>Station Wagon/Sport Utility Vehicle</td>\n", | |
| " <td>SPORT UTILITY / STATION WAGON</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>VEHICLE TYPE CODE 2</th>\n", | |
| " <td>Sedan</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>VEHICLE TYPE CODE 3</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>VEHICLE TYPE CODE 4</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>VEHICLE TYPE CODE 5</th>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>geometry</th>\n", | |
| " <td>POINT (1058905.171008306 186233.5535885742)</td>\n", | |
| " <td>POINT (nan nan)</td>\n", | |
| " <td>POINT (1029719.994521396 263256.0132912833)</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " 646649 ... 1645160\n", | |
| "CRASH DATE 12/20/2017 ... 05/22/2013\n", | |
| "CRASH TIME 11:40 ... 21:12\n", | |
| "BOROUGH NaN ... BRONX\n", | |
| "ZIP CODE NaN ... 10466\n", | |
| "LATITUDE 40.6775 ... 40.8891\n", | |
| "LONGITUDE -73.7309 ... -73.8355\n", | |
| "LOCATION (40.67753, -73.73085) ... (40.8891337, -73.835548)\n", | |
| "ON STREET NAME LAURELTON PARKWAY ... AMUNDSON AVENUE \n", | |
| "CROSS STREET NAME NaN ... EAST 233 STREET \n", | |
| "OFF STREET NAME NaN ... NaN\n", | |
| "NUMBER OF PERSONS INJURED 0 ... 0\n", | |
| "NUMBER OF PERSONS KILLED 0 ... 0\n", | |
| "NUMBER OF PEDESTRIANS INJURED 0 ... 0\n", | |
| "NUMBER OF PEDESTRIANS KILLED 0 ... 0\n", | |
| "NUMBER OF CYCLIST INJURED 0 ... 0\n", | |
| "NUMBER OF CYCLIST KILLED 0 ... 0\n", | |
| "NUMBER OF MOTORIST INJURED 0 ... 0\n", | |
| "NUMBER OF MOTORIST KILLED 0 ... 0\n", | |
| "CONTRIBUTING FACTOR VEHICLE 1 Tire Failure/Inadequate ... Unspecified\n", | |
| "CONTRIBUTING FACTOR VEHICLE 2 Other Vehicular ... Unspecified\n", | |
| "CONTRIBUTING FACTOR VEHICLE 3 NaN ... NaN\n", | |
| "CONTRIBUTING FACTOR VEHICLE 4 NaN ... NaN\n", | |
| "CONTRIBUTING FACTOR VEHICLE 5 NaN ... NaN\n", | |
| "COLLISION_ID 3814602 ... 97504\n", | |
| "VEHICLE TYPE CODE 1 Station Wagon/Sport Utility Vehicle ... PASSENGER VEHICLE\n", | |
| "VEHICLE TYPE CODE 2 Sedan ... PASSENGER VEHICLE\n", | |
| "VEHICLE TYPE CODE 3 NaN ... NaN\n", | |
| "VEHICLE TYPE CODE 4 NaN ... NaN\n", | |
| "VEHICLE TYPE CODE 5 NaN ... NaN\n", | |
| "geometry POINT (1058905.171008306 186233.5535885742) ... POINT (1029719.994521396 263256.0132912833)\n", | |
| "\n", | |
| "[30 rows x 3 columns]" | |
| ] | |
| }, | |
| "execution_count": null, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9z3cGVX7gJkf" | |
| }, | |
| "source": [ | |
| "## Filter and Summarize data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "-H8djphifCBg", | |
| "outputId": "ae27ca9f-093e-43b1-eca0-66d5ef5d243e" | |
| }, | |
| "source": [ | |
| "#find some coalitions\n", | |
| "open_streets[open_streets['Location'].str.contains('Coalition')]['Location'].value_counts()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "North Brooklyn Open Streets Community Coalition 29\n", | |
| "34th Ave Open Streets Coalition 26\n", | |
| "Loisaida Open Streets Community Coalition 8\n", | |
| "West 103rd St Open Streets Community Coalition 2\n", | |
| "St Marks Business Coalition 1\n", | |
| "Name: Location, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": null, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "zJPBsjArjB5r", | |
| "outputId": "a346ee61-3c70-4cab-ecca-98f9f39d1fd6" | |
| }, | |
| "source": [ | |
| "#find some coalitions\n", | |
| "open_streets[open_streets['On_Stree'].str.contains('34')]['On_Stree'].value_counts()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "34th Avenue 28\n", | |
| "Name: On_Stree, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": null, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "s2-Du1ytZRyW" | |
| }, | |
| "source": [ | |
| "#select a street\n", | |
| "open_street_name = '34th Avenue'\n", | |
| "filter = (open_streets['On_Stree'] == open_street_name) & (open_streets['Borough'] == 'Queens')\n", | |
| "# filter = (open_streets['Location'] == 'North Brooklyn Open Streets Community Coalition')\n", | |
| "\n", | |
| "#filter and convert to 2263 so we can use feet calcuations\n", | |
| "openstr = open_streets[filter].to_crs(\"EPSG:2263\").copy()\n", | |
| "\n", | |
| "#buffer the street by the standard 60 feet\n", | |
| "openstr.geometry = openstr.buffer(60)\n", | |
| "onstr_poly = openstr.dissolve(by='On_Stree')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "vYjxyiR9ZX95" | |
| }, | |
| "source": [ | |
| "#buffer to get the 'near' open street polygon\n", | |
| "nearstr_poly = onstr_poly.buffer(260)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "gAKKglQ0h5Hy", | |
| "outputId": "6ce5b6b1-061f-40f2-f46b-db3d71a61d2c" | |
| }, | |
| "source": [ | |
| "nearstr_poly.plot()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f45000090>" | |
| ] | |
| }, | |
| "execution_count": null, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "lRARWtIugiuP", | |
| "outputId": "6b3f9903-04fe-4eb2-eb5b-0f7ac03fadc4" | |
| }, | |
| "source": [ | |
| "#get collisions on the open street\n", | |
| "onstr_points = collision_crashes[collision_crashes.within(onstr_poly.geometry[0])].copy()\n", | |
| "\n", | |
| "onstr_points.tail()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "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>CRASH DATE</th>\n", | |
| " <th>CRASH TIME</th>\n", | |
| " <th>BOROUGH</th>\n", | |
| " <th>ZIP CODE</th>\n", | |
| " <th>LATITUDE</th>\n", | |
| " <th>LONGITUDE</th>\n", | |
| " <th>LOCATION</th>\n", | |
| " <th>ON STREET NAME</th>\n", | |
| " <th>CROSS STREET NAME</th>\n", | |
| " <th>OFF STREET NAME</th>\n", | |
| " <th>NUMBER OF PERSONS INJURED</th>\n", | |
| " <th>NUMBER OF PERSONS KILLED</th>\n", | |
| " <th>NUMBER OF PEDESTRIANS INJURED</th>\n", | |
| " <th>NUMBER OF PEDESTRIANS KILLED</th>\n", | |
| " <th>NUMBER OF CYCLIST INJURED</th>\n", | |
| " <th>NUMBER OF CYCLIST KILLED</th>\n", | |
| " <th>NUMBER OF MOTORIST INJURED</th>\n", | |
| " <th>NUMBER OF MOTORIST KILLED</th>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 1</th>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 2</th>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 3</th>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 4</th>\n", | |
| " <th>CONTRIBUTING FACTOR VEHICLE 5</th>\n", | |
| " <th>COLLISION_ID</th>\n", | |
| " <th>VEHICLE TYPE CODE 1</th>\n", | |
| " <th>VEHICLE TYPE CODE 2</th>\n", | |
| " <th>VEHICLE TYPE CODE 3</th>\n", | |
| " <th>VEHICLE TYPE CODE 4</th>\n", | |
| " <th>VEHICLE TYPE CODE 5</th>\n", | |
| " <th>geometry</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1826110</th>\n", | |
| " <td>07/03/2012</td>\n", | |
| " <td>18:30</td>\n", | |
| " <td>QUEENS</td>\n", | |
| " <td>11372</td>\n", | |
| " <td>40.753731</td>\n", | |
| " <td>-73.885038</td>\n", | |
| " <td>(40.7537307, -73.8850382)</td>\n", | |
| " <td>82 STREET</td>\n", | |
| " <td>34 AVENUE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>279659</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>SPORT UTILITY / STATION WAGON</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>POINT (1016101.003 213902.002)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1826735</th>\n", | |
| " <td>07/07/2012</td>\n", | |
| " <td>9:07</td>\n", | |
| " <td>QUEENS</td>\n", | |
| " <td>11372</td>\n", | |
| " <td>40.752656</td>\n", | |
| " <td>-73.895258</td>\n", | |
| " <td>(40.7526563, -73.895258)</td>\n", | |
| " <td>34 AVENUE</td>\n", | |
| " <td>71 STREET</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>279695</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>POINT (1013270.002 213507.012)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1827324</th>\n", | |
| " <td>07/01/2012</td>\n", | |
| " <td>20:45</td>\n", | |
| " <td>QUEENS</td>\n", | |
| " <td>11372</td>\n", | |
| " <td>40.754416</td>\n", | |
| " <td>-73.878529</td>\n", | |
| " <td>(40.7544157, -73.8785293)</td>\n", | |
| " <td>89 STREET</td>\n", | |
| " <td>34 AVENUE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>0</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>279644</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>POINT (1017903.994 214154.003)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1827673</th>\n", | |
| " <td>07/09/2012</td>\n", | |
| " <td>9:05</td>\n", | |
| " <td>QUEENS</td>\n", | |
| " <td>11372</td>\n", | |
| " <td>40.753339</td>\n", | |
| " <td>-73.888756</td>\n", | |
| " <td>(40.7533391, -73.8887565)</td>\n", | |
| " <td>34 AVENUE</td>\n", | |
| " <td>78 STREET</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>279708</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>POINT (1015071.003 213757.999)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1829067</th>\n", | |
| " <td>07/01/2012</td>\n", | |
| " <td>18:00</td>\n", | |
| " <td>QUEENS</td>\n", | |
| " <td>11372</td>\n", | |
| " <td>40.753731</td>\n", | |
| " <td>-73.885038</td>\n", | |
| " <td>(40.7537307, -73.8850382)</td>\n", | |
| " <td>82 STREET</td>\n", | |
| " <td>34 AVENUE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>Unspecified</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>279643</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>PASSENGER VEHICLE</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>POINT (1016101.003 213902.002)</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " CRASH DATE ... geometry\n", | |
| "1826110 07/03/2012 ... POINT (1016101.003 213902.002)\n", | |
| "1826735 07/07/2012 ... POINT (1013270.002 213507.012)\n", | |
| "1827324 07/01/2012 ... POINT (1017903.994 214154.003)\n", | |
| "1827673 07/09/2012 ... POINT (1015071.003 213757.999)\n", | |
| "1829067 07/01/2012 ... POINT (1016101.003 213902.002)\n", | |
| "\n", | |
| "[5 rows x 30 columns]" | |
| ] | |
| }, | |
| "execution_count": null, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "sCX6AMY4jopE", | |
| "outputId": "5ec9f696-8489-408a-d0e9-b9441db0c701" | |
| }, | |
| "source": [ | |
| "#get number of crashes after \n", | |
| "onstr_points['DATE'] = pd.to_datetime(onstr_points['CRASH DATE'], format= \"%m/%d/%Y\") #.dt.to_period('M')\n", | |
| "len(onstr_points[onstr_points['DATE'] > datetime(2020, 4 ,1)])" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "93" | |
| ] | |
| }, | |
| "execution_count": null, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ABgs-3qcjwUW" | |
| }, | |
| "source": [ | |
| "#join with collision vech\n", | |
| "t = pd.merge(onstr_points, collision_vech, on = 'COLLISION_ID', how = 'left')\n", | |
| "\n", | |
| "t['Year'] = t['DATE'].dt.year\n", | |
| "t['DATE'] = pd.to_datetime(t['DATE'])\n", | |
| "\n", | |
| "#get summary for each year\n", | |
| "for year in (2018,2019,2020,2021):\n", | |
| " t2 = t[t['DATE'].dt.year == year]\n", | |
| " print('Yearly Collisions:',len(t2), '-', year)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "8WA_GBzIGDYo" | |
| }, | |
| "source": [ | |
| "### Bar Cart" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "JOQAMT3ulryv" | |
| }, | |
| "source": [ | |
| "#melt table\n", | |
| "keep_cols = ['UNIQUE_ID','Year','NUMBER OF MOTORIST INJURED','NUMBER OF CYCLIST INJURED','NUMBER OF PEDESTRIANS INJURED']\n", | |
| "\n", | |
| "table_2 = t[t.Year >=2018][keep_cols].melt(id_vars=['UNIQUE_ID','Year'], \n", | |
| " var_name=\"Type\", \n", | |
| " value_name=\"Count\")\n", | |
| "\n", | |
| "table_2.sample(5)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ZUXVoGW6j8mI" | |
| }, | |
| "source": [ | |
| "f, ax = plt.subplots(figsize=(15, 8))\n", | |
| "sns.set_theme(style=\"whitegrid\")\n", | |
| "\n", | |
| "sns.barplot(x = 'Count', \n", | |
| " y='Year', \n", | |
| " hue='Type', \n", | |
| " data=table_2 , \n", | |
| " estimator=sum , \n", | |
| " ci = None, \n", | |
| " palette='tab20c',\n", | |
| " orient = 'h').set_title('*2021 contains data up to Oct 8th', loc = 'right')\n", | |
| "\n", | |
| "f.suptitle(f'Injuries from crashes on {open_street_name}')\n", | |
| "\n", | |
| "#add labels to each bar\n", | |
| "for p in ax.patches:\n", | |
| " ax.annotate(f'{p.get_width():.0f}', (p.get_x() + p.get_width() + 0.3 , p.get_y() + p.get_height() / 1.2),\n", | |
| " ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),\n", | |
| " textcoords='offset points')" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "NL-ATNeYXE82" | |
| }, | |
| "source": [ | |
| "#agg to see number of different groups INJURED\n", | |
| "p = t[t.Year >= 2018].groupby('Year').agg({\n", | |
| " 'NUMBER OF MOTORIST INJURED': sum,\n", | |
| " 'NUMBER OF CYCLIST INJURED': sum,\n", | |
| " 'NUMBER OF PEDESTRIANS INJURED': sum,\n", | |
| "})\n", | |
| "p " | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "YoiBw01GXHLz" | |
| }, | |
| "source": [ | |
| "t[t.Year >= 2018].groupby('Year').agg({\n", | |
| " 'NUMBER OF PEDESTRIANS KILLED': sum,\n", | |
| " 'NUMBER OF CYCLIST KILLED': sum,\n", | |
| " 'NUMBER OF MOTORIST KILLED': sum\n", | |
| "})" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "AAOUtBmbGLcd" | |
| }, | |
| "source": [ | |
| "## Area time series chart" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "6pqW1V6wGJqW" | |
| }, | |
| "source": [ | |
| "#query for near street points, and create columns for date and year\n", | |
| "nearstr_points = collision_crashes[collision_crashes.within(nearstr_poly.geometry[0])].copy()\n", | |
| "nearstr_points['DATE'] = pd.to_datetime(nearstr_points['CRASH DATE'], format= \"%m/%d/%Y\") \n", | |
| "nearstr_points['Year'] = nearstr_points['DATE'].dt.year\n", | |
| "\n", | |
| "onstr_points['DATE'] = pd.to_datetime(onstr_points['CRASH DATE'], format= \"%m/%d/%Y\") \n", | |
| "onstr_points['Year'] = onstr_points['DATE'].dt.year" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "AR_NnthGGbtU", | |
| "outputId": "d55a39c2-087b-453d-d21e-4ea114805be0" | |
| }, | |
| "source": [ | |
| "#filter for after 2018\n", | |
| "onstr_table = onstr_points[onstr_points['Year'] >= 2017]\n", | |
| "nearstr_table = nearstr_points[nearstr_points['Year'] >= 2017]\n", | |
| "\n", | |
| "#create tables\n", | |
| "onstr_monthly_count= pd.DataFrame(onstr_table.groupby(onstr_table[\"DATE\"].dt.to_period('M').dt.to_timestamp()).count()['COLLISION_ID']).reset_index().rename(columns = {'COLLISION_ID': 'On Street'})\n", | |
| "nearstr_monthly_count = pd.DataFrame(nearstr_table.groupby(nearstr_table[\"DATE\"].dt.to_period('M').dt.to_timestamp()).count()['COLLISION_ID']).reset_index().rename(columns = {'COLLISION_ID': 'On/Near Street'})\n", | |
| "\n", | |
| "merged_counts = pd.merge(onstr_monthly_count, nearstr_monthly_count, on = 'DATE')\n", | |
| "\n", | |
| "#subtract to get only near street counts \n", | |
| "merged_counts['Near Street'] = merged_counts['On/Near Street'] - merged_counts['On Street']\n", | |
| "\n", | |
| "merged_counts.sample(5)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "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>DATE</th>\n", | |
| " <th>On Street</th>\n", | |
| " <th>On/Near Street</th>\n", | |
| " <th>Near Street</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2017-02-01</td>\n", | |
| " <td>9</td>\n", | |
| " <td>12</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>21</th>\n", | |
| " <td>2018-10-01</td>\n", | |
| " <td>12</td>\n", | |
| " <td>16</td>\n", | |
| " <td>4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>36</th>\n", | |
| " <td>2020-01-01</td>\n", | |
| " <td>6</td>\n", | |
| " <td>8</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>18</th>\n", | |
| " <td>2018-07-01</td>\n", | |
| " <td>8</td>\n", | |
| " <td>12</td>\n", | |
| " <td>4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>49</th>\n", | |
| " <td>2021-02-01</td>\n", | |
| " <td>4</td>\n", | |
| " <td>6</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " DATE On Street On/Near Street Near Street\n", | |
| "1 2017-02-01 9 12 3\n", | |
| "21 2018-10-01 12 16 4\n", | |
| "36 2020-01-01 6 8 2\n", | |
| "18 2018-07-01 8 12 4\n", | |
| "49 2021-02-01 4 6 2" | |
| ] | |
| }, | |
| "execution_count": null, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "background_save": true | |
| }, | |
| "id": "NBEOr7RPGrSo", | |
| "outputId": "a62ef000-ab14-41b2-b1c6-84cda5f6b406" | |
| }, | |
| "source": [ | |
| "#create chart and labels\n", | |
| "f, ax = plt.subplots(figsize=(15, 8))\n", | |
| "sns.set_style(style=\"whitegrid\")\n", | |
| "\n", | |
| "#add stackplot\n", | |
| "color_map = [\"#3182bd\", \"#9ecae1\"]\n", | |
| "chart = plt.stackplot(merged_counts['DATE'], merged_counts['On Street'], merged_counts['Near Street'], \n", | |
| " step = 'pre', colors = color_map)\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "#y axis formating\n", | |
| "plt.locator_params(axis=\"y\", integer=True, tight=True)\n", | |
| "max = merged_counts['On/Near Street'].max() + 2\n", | |
| "ax.set_ylim([0, max])\n", | |
| "\n", | |
| "#title and legend\n", | |
| "plt.legend(loc=\"upper left\")\n", | |
| "ax.set( xlabel='Month', title = f'Crashes on {open_street_name}')\n", | |
| "\n", | |
| "p1 = Rectangle((0, 0), 1, 1, fc=color_map[0])\n", | |
| "p2 = Rectangle((0, 0), 1, 1, fc=color_map[1])\n", | |
| "plt.legend([p1, p2], ['On Open Street', 'Near Open Street'])\n", | |
| "\n", | |
| "#add background fill for when the openstreet started\n", | |
| "def floatHourToTime(fh):\n", | |
| " hours, hourSeconds = divmod(fh, 1)\n", | |
| "\n", | |
| " return datetime.fromordinal(datetime(1900, 1, 1).toordinal() + int(hours) - 2)\n", | |
| " \n", | |
| "start_open_date = openstr['Open_Dat'].apply(floatHourToTime).min()\n", | |
| "# start_open_date = datetime(2021,8,1)Vanderbilt\n", | |
| "\n", | |
| "\n", | |
| "x_width = ax.get_xlim()[1] - ax.get_xlim()[0]\n", | |
| "rectangle = Rectangle((mdates.date2num(start_open_date), 0), x_width, max, facecolor=(0.5,0.5,0.5,0.2))\n", | |
| "ax.add_patch(rectangle)\n", | |
| "\n", | |
| "#https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Rectangle.html\n", | |
| "x_width2 = mdates.date2num(start_open_date) - mdates.date2num(datetime(2020,3,14))\n", | |
| "rectangle2 = Rectangle((mdates.date2num(datetime(2020,3,14)), 0), x_width2, \n", | |
| " max, facecolor=(0.9,0.2,0.2,0.2), linewidth = None)\n", | |
| "ax.add_patch(rectangle2)\n", | |
| "\n", | |
| "#add label for open date\n", | |
| "ax.annotate(f'Open Date \\n {start_open_date.strftime (\"%m-%d\")}', (mdates.date2num(start_open_date) + 10, max * .75),\n", | |
| " ha='left', va='center', fontsize=10, color='black', xytext=(0, 5),\n", | |
| " textcoords='offset points')\n", | |
| "#add label for covid shutdown\n", | |
| "ax.annotate('Shutdown starts', (mdates.date2num(datetime(2020,3,14)) + 10, max * .85),\n", | |
| " ha='left', va='center', fontsize=10, color='black', xytext=(0, 5),\n", | |
| " textcoords='offset points')\n", | |
| "#set order\n", | |
| "ax.set_zorder(1)\n", | |
| "chart[0].set_zorder(2)\n", | |
| "chart[1].set_zorder(2)\n", | |
| "\n", | |
| "#opacity\n", | |
| "chart[0].set_alpha(0.9)\n", | |
| "chart[1].set_alpha(0.9)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "No handles with labels found to put in legend.\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 1080x576 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "BH0Rs2_nZMN4" | |
| }, | |
| "source": [ | |
| "" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "IDBSWisUGH-s" | |
| }, | |
| "source": [ | |
| "## Crash Factors" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "IvI_xs5v036K" | |
| }, | |
| "source": [ | |
| "#get max value for year groupings\n", | |
| "x_max = 0\n", | |
| "for year in (2018,2019,2020,2021):\n", | |
| " precrash_year_select = t[t.Year == year]['PRE_CRASH']\n", | |
| " c = precrash_year_select.value_counts()\n", | |
| " if x_max < c[0]:\n", | |
| " x_max = c[0]\n", | |
| "\n", | |
| "round_value = 25\n", | |
| "x_max_round = round((x_max+round_value)/round_value)*round_value\n", | |
| "\n", | |
| "#create charts\n", | |
| "fig, ax = plt.subplots(1,4,figsize=(15, 6), sharey=True)\n", | |
| "sns.set_theme(style=\"whitegrid\")\n", | |
| "\n", | |
| "\n", | |
| "for i, l in enumerate([(2018, '#08519c'),(2019, '#3182bd'),(2020, '#6baed6'),(2021, '#bdd7e7')]):\n", | |
| " year, color = l\n", | |
| " precrash_year_select = t[t.Year == year]['PRE_CRASH']\n", | |
| " sns.countplot(\n", | |
| " y=precrash_year_select.index, \n", | |
| " data=precrash_year_select,\n", | |
| " color=color,\n", | |
| " ax = ax[i]\n", | |
| " ).set(xlim=[0,x_max_round])\n", | |
| " if year == 2021:\n", | |
| " ax[i].set(ylabel=None, xlabel=None, title = '2021(Jan - Oct 8)')\n", | |
| " else:\n", | |
| " ax[i].set(ylabel=None, xlabel=None, title = year)\n", | |
| "\n", | |
| "fig.suptitle(f'Crash Factors on {open_street_name}', fontsize=20)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "TH3SNn13CalA" | |
| }, | |
| "source": [ | |
| "# Test some charts using Operational dataset" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Cq0ndGhn5k3P" | |
| }, | |
| "source": [ | |
| "ta_table = pd.read_csv(basePath / 'TA-CrossTab.csv')\n", | |
| "ta_table['Active vs Non-Operational'].value_counts()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "qM4tgr6nD5lH" | |
| }, | |
| "source": [ | |
| "\n", | |
| "#create unique key from Borough and On Street\n", | |
| "ta_table['Key'] = ta_table.apply(lambda r: r['Borough'] + '-' + r['On Street'], axis = 1)\n", | |
| "open_streets['Key'] = open_streets.apply(lambda r: r['Borough'] + '-' + r['On_Stree'], axis = 1)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "3a76mfSUDcml", | |
| "outputId": "ae6186d8-b522-44b3-a244-b39cfc10234f" | |
| }, | |
| "source": [ | |
| "# --- get monthly counts for all open streets\n", | |
| "def getMontlyCounts(row):\n", | |
| " key = row['Key']\n", | |
| " print(key)\n", | |
| " filter = open_streets['Key'] == key\n", | |
| "\n", | |
| " #buffer\n", | |
| " street = open_streets[filter].to_crs(\"EPSG:2263\").copy()\n", | |
| " street.geometry = street.buffer(60)\n", | |
| " filter_polygon = street.dissolve(by='On_Stree')\n", | |
| "\n", | |
| " #get collisions on the open street\n", | |
| " if len(filter_polygon.geometry):\n", | |
| " points = collision_crashes[collision_crashes.within(filter_polygon.geometry[0])].copy()\n", | |
| " points['DATE'] = pd.to_datetime(points['CRASH DATE'], format= \"%m/%d/%Y\") \n", | |
| " points['Year'] = points['DATE'].dt.year\n", | |
| " points['Month'] = points[\"DATE\"].dt.to_period('M').dt.to_timestamp()\n", | |
| "\n", | |
| " points_after2017 = points[points['Year'] >= 2017]\n", | |
| "\n", | |
| " counts = pd.DataFrame(points.groupby('Month').count()['COLLISION_ID']).reset_index().rename(columns = {'COLLISION_ID': 'Count'})\n", | |
| "\n", | |
| " return dict(counts)\n", | |
| " else:\n", | |
| " return None\n", | |
| "\n", | |
| "\n", | |
| "ta_table['Montly_Counts'] = ta_table.apply(getMontlyCounts, axis = 1)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Bronx-Alexander Avenue\n", | |
| "Bronx-Arthur Avenue\n", | |
| "Bronx-Boston Road\n", | |
| "Bronx-Bronx Park South\n", | |
| "Bronx-Cedar Avenue\n", | |
| "Bronx-Creston Avenue\n", | |
| "Bronx-Crotona Parkway\n", | |
| "Bronx-East 140th Street\n", | |
| "Bronx-East 144th Street\n", | |
| "Bronx-East 169th Street\n", | |
| "Bronx-Field Place\n", | |
| "Bronx-Findlay Avenue\n", | |
| "Bronx-Forest Avenue\n", | |
| "Bronx-Grant Avenue\n", | |
| "Bronx-Holland Avenue\n", | |
| "Bronx-Jackson Avenue\n", | |
| "Bronx-Jennings Street\n", | |
| "Bronx-McClellan St/Cromwell Ave\n", | |
| "Bronx-Nelson Avenue\n", | |
| "Bronx-Oak Tree Place\n", | |
| "Bronx-Randall Avenue\n", | |
| "Bronx-Reservoir Oval East and West\n", | |
| "Bronx-Rockwood Street\n", | |
| "Bronx-Stickball Boulevard\n", | |
| "Bronx-Tibbett Avenue\n", | |
| "Bronx-Trinity Avenue\n", | |
| "Bronx-West 236th Street\n", | |
| "Bronx-West 238th Street\n", | |
| "Bronx-Yates Avenue\n", | |
| "Bronx-Willis Avenue\n", | |
| "Brooklyn-1st Place\n", | |
| "Brooklyn-21st Street\n", | |
| "Brooklyn-2nd Street\n", | |
| "Brooklyn-Third Avenue\n", | |
| "Brooklyn-38th Street\n", | |
| "Brooklyn-4th Place\n", | |
| "Brooklyn-4th Street\n", | |
| "Brooklyn-5th Avenue\n", | |
| "Brooklyn-5th Avenue\n", | |
| "Brooklyn-5th Avenue\n", | |
| "Brooklyn-5th Avenue\n", | |
| "Brooklyn-5th Avenue\n", | |
| "Brooklyn-5th Avenue\n", | |
| "Brooklyn-5th Avenue\n", | |
| "Brooklyn-61st Street\n", | |
| "Brooklyn-6th Avenue\n", | |
| "Brooklyn-6th Avenue\n", | |
| "Brooklyn-Arlington Place\n", | |
| "Brooklyn-Berry Street\n", | |
| "Brooklyn-Blake Avenue\n", | |
| "Brooklyn-Blake Avenue\n", | |
| "Brooklyn-Bragg Street\n", | |
| "Brooklyn-Cadman Plaza East\n", | |
| "Brooklyn-Calder Place\n", | |
| "Brooklyn-Carroll Street\n", | |
| "Brooklyn-Chester Avenue\n", | |
| "Brooklyn-Columbia Place\n", | |
| "Brooklyn-Congress Street\n", | |
| "Brooklyn-Decatur Street\n", | |
| "Brooklyn-Driggs Avenue\n", | |
| "Brooklyn-Dumont Avenue\n", | |
| "Brooklyn-East 16th Street\n", | |
| "Brooklyn-East 18th Street\n", | |
| "Brooklyn-East 32nd Street\n", | |
| "Brooklyn-Ellery Street\n", | |
| "Brooklyn-Graham Avenue\n", | |
| "Brooklyn-Grand Street\n", | |
| "Brooklyn-Grattan Street\n", | |
| "Brooklyn-Hall Street\n", | |
| "Brooklyn-Henry Street\n", | |
| "Brooklyn-Hoyt Street\n", | |
| "Brooklyn-Humboldt Street\n", | |
| "Brooklyn-Jefferson Avenue\n", | |
| "Brooklyn-Joralemon Street\n", | |
| "Brooklyn-Lawrence Street\n", | |
| "Brooklyn-Leonard Street\n", | |
| "Brooklyn-Lincoln Place\n", | |
| "Brooklyn-Livonia Avenue\n", | |
| "Brooklyn-Macon Street\n", | |
| "Brooklyn-Main Street\n", | |
| "Brooklyn-Montague Street\n", | |
| "Brooklyn-Moore Street\n", | |
| "Brooklyn-North 3rd Street\n", | |
| "Brooklyn-Nassau Avenue\n", | |
| "Brooklyn-Newkirk Avenue\n", | |
| "Brooklyn-Noll Street\n", | |
| "Brooklyn-North Elliott Place\n", | |
| "Brooklyn-Pacific Street\n", | |
| "Brooklyn-Park Place\n", | |
| "Brooklyn-Prospect Place\n", | |
| "Brooklyn-Randolph Street\n", | |
| "Brooklyn-Rapelye Street\n", | |
| "Brooklyn-Reed Street\n", | |
| "Brooklyn-Rockwell Place\n", | |
| "Brooklyn-Russell Street\n", | |
| "Brooklyn-Schenectady Avenue\n", | |
| "Brooklyn-Sharon Street\n", | |
| "Brooklyn-Somers Street\n", | |
| "Brooklyn-South 9th Street\n", | |
| "Brooklyn-South Portland Avenue\n", | |
| "Brooklyn-St. Marks Place\n", | |
| "Brooklyn-Stanley Avenue\n", | |
| "Brooklyn-Stanley Avenue\n", | |
| "Brooklyn-State Street\n", | |
| "Brooklyn-State Street\n", | |
| "Brooklyn-Suydam Street\n", | |
| "Brooklyn-Tompkins Avenue\n", | |
| "Brooklyn-Troutman Street\n", | |
| "Brooklyn-Underhill Avenue\n", | |
| "Brooklyn-Van Siclen Avenue\n", | |
| "Brooklyn-Vanderbilt Avenue\n", | |
| "Brooklyn-Washington Street\n", | |
| "Brooklyn-West 9th Street\n", | |
| "Brooklyn-Williams Avenue\n", | |
| "Brooklyn-Willoughby Avenue\n", | |
| "Brooklyn-Willow Place\n", | |
| "Brooklyn-Willoughby Street\n", | |
| "Brooklyn-Willow Street\n", | |
| "Brooklyn-Wolcott Street\n", | |
| "Brooklyn-Wyckoff Street\n", | |
| "Manhattan-9th Avenue\n", | |
| "Manhattan-Academy Street\n", | |
| "Manhattan-Amsterdam Avenue\n", | |
| "Manhattan-Ann Street\n", | |
| "Manhattan-Avenue B\n", | |
| "Manhattan-Bleecker Street\n", | |
| "Manhattan-Bond Street\n", | |
| "Manhattan-Broadway\n", | |
| "Manhattan-Broadway\n", | |
| "Manhattan-Broadway\n", | |
| "Manhattan-Broadway\n", | |
| "Manhattan-Broadway\n", | |
| "Manhattan-Broadway\n", | |
| "Manhattan-Broome Street\n", | |
| "Manhattan-Broome Street\n", | |
| "Manhattan-Canal Street\n", | |
| "Manhattan-Christopher Street\n", | |
| "Manhattan-Columbus Avenue\n", | |
| "Manhattan-Cornelia Street\n", | |
| "Manhattan-Dominick Street\n", | |
| "Manhattan-Doyers Street\n", | |
| "Manhattan-Duane Street\n", | |
| "Manhattan-Dyckman Street\n", | |
| "Manhattan-East 101st Street\n", | |
| "Manhattan-East 104th Street\n", | |
| "Manhattan-East 115th Street\n", | |
| "Manhattan-East 16th Street\n", | |
| "Manhattan-East 18th Street\n", | |
| "Manhattan-East 19th Street\n", | |
| "Manhattan-East 20th Street\n", | |
| "Manhattan-East 28th Street\n", | |
| "Manhattan-East 32nd Street\n", | |
| "Manhattan-East 4th Street\n", | |
| "Manhattan-East 6th Street\n", | |
| "Manhattan-East 7th Street\n", | |
| "Manhattan-East 90th Street\n", | |
| "Manhattan-East End Avenue\n", | |
| "Manhattan-Edgecombe Avenue\n", | |
| "Manhattan-Elizabeth Street\n", | |
| "Manhattan-Frederick Douglass Boulevard\n", | |
| "Manhattan-Front Street\n", | |
| "Manhattan-Gansevoort Street\n", | |
| "Manhattan-Greenwich Street\n", | |
| "Manhattan-Hester Street\n", | |
| "Manhattan-Hudson Blvd East\n", | |
| "Manhattan-Hudson Blvd West\n", | |
| "Manhattan-Irving Place\n", | |
| "Manhattan-Jane Street\n", | |
| "Manhattan-Jefferson Street\n", | |
| "Manhattan-Jones Street\n", | |
| "Manhattan-King Street\n", | |
| "Manhattan-Lafayette Street\n", | |
| "Manhattan-Laurel Hill Terrace\n", | |
| "Manhattan-Little 6th Avenue\n", | |
| "Manhattan-Little West 12th Street\n", | |
| "Manhattan-Ludlow Street\n", | |
| "Manhattan-MacDougal Street\n", | |
| "Manhattan-Montgomery Street\n", | |
| "Manhattan-Morningside Avenue\n", | |
| "Manhattan-Morton Street\n", | |
| "Manhattan-Mulberry Street\n", | |
| "Manhattan-Orchard Street\n", | |
| "Manhattan-Pearl Street\n", | |
| "Manhattan-Pell Street\n", | |
| "Manhattan-Pine Street\n", | |
| "Manhattan-Pleasant Avenue\n", | |
| "Manhattan-Rivington Street\n", | |
| "Manhattan-Rivington Street\n", | |
| "Manhattan-Rivington Street\n", | |
| "Manhattan-Rivington Street\n", | |
| "Manhattan-Rivington Street\n", | |
| "Manhattan-Rutherford Place\n", | |
| "Manhattan-Spring Street\n", | |
| "Manhattan-Spring Street\n", | |
| "Manhattan-St Marks Place\n", | |
| "Manhattan-St Marks Place\n", | |
| "Manhattan-Stanton Street\n", | |
| "Manhattan-Staple Street\n", | |
| "Manhattan-Thompson Street\n", | |
| "Manhattan-University Place\n", | |
| "Manhattan-Washington Place\n", | |
| "Manhattan-Waverly Place\n", | |
| "Manhattan-West 103rd Street\n", | |
| "Manhattan-West 114th Street\n", | |
| "Manhattan-West 117th Street\n", | |
| "Manhattan-West 120th Street\n", | |
| "Manhattan-West 129th Street\n", | |
| "Manhattan-West 138th Street\n", | |
| "Manhattan-West 13th Street\n", | |
| "Manhattan-West 143rd Street\n", | |
| "Manhattan-West 150th Street\n", | |
| "Manhattan-West 159th Street\n", | |
| "Manhattan-West 161st Street\n", | |
| "Manhattan-West 164th Street\n", | |
| "Manhattan-West 21st Street\n", | |
| "Manhattan-West 22nd Street\n", | |
| "Manhattan-West 22nd Street\n", | |
| "Manhattan-West 25th Street\n", | |
| "Manhattan-West 46th Street\n", | |
| "Manhattan-West 46th Street\n", | |
| "Manhattan-West 92nd Street\n", | |
| "Queens-107th Avenue\n", | |
| "Queens-108th Avenue\n", | |
| "Queens-109th Avenue\n", | |
| "Queens-120th Street\n", | |
| "Queens-155th Street\n", | |
| "Queens-159th Street\n", | |
| "Queens-165th Street\n", | |
| "Queens-169th Street\n", | |
| "Queens-31st Avenue\n", | |
| "Queens-34th Avenue\n", | |
| "Queens-35th Avenue\n", | |
| "Queens-37th Street\n", | |
| "Queens-39th Avenue\n", | |
| "Queens-41st Avenue\n", | |
| "Queens-60th Street\n", | |
| "Queens-60th Street\n", | |
| "Queens-66th Road\n", | |
| "Queens-68th Road\n", | |
| "Queens-77th Street\n", | |
| "Queens-99th Street\n", | |
| "Queens-Barron Street\n", | |
| "Queens-Barton Avenue\n", | |
| "Queens-Beach 12th Street\n", | |
| "Queens-Bell Boulevard\n", | |
| "Queens-Court Square West\n", | |
| "Queens-Dieterle Crescent\n", | |
| "Queens-Ditmars Boulevard\n", | |
| "Queens-East Main Drive\n", | |
| "Queens-Freedom Drive\n", | |
| "Queens-122nd Avenue/Lakeview Ln/Lakeview Blvd\n", | |
| "Queens-Meadow Lake Drive\n", | |
| "Queens-Newtown Avenue\n", | |
| "Queens-Onderdonk Avenue\n", | |
| "Queens-Peck Avenue\n", | |
| "Queens-Reads Lane\n", | |
| "Queens-Rockaway Freeway\n", | |
| "Queens-Roosevelt Avenue\n", | |
| "Queens-Shore Boulevard\n", | |
| "Queens-Skillman Avenue\n", | |
| "Queens-West Main Drive\n", | |
| "Queens-Woodside Avenue\n", | |
| "Staten Island-9th Street\n", | |
| "Staten Island-Bank Street\n", | |
| "Staten Island-Canal Street\n", | |
| "Staten Island-Henderson Avenue\n", | |
| "Staten Island-Hill Street\n", | |
| "Staten Island-Minthorne Street\n", | |
| "Staten Island-New Street\n", | |
| "Staten Island-Silver Lake Park Road\n", | |
| "Staten Island-Van Duzer Street\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "ksqQvmI0SRm3" | |
| }, | |
| "source": [ | |
| "#generate line chart " | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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