Crime_Visualization_by_Police_District.ipynb

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  "metadata": {
    "colab": {
      "name": "Crime Visualization by Police District.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN+CAswquHIBZ58fv1nyDBq"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDelgBhloumO",
        "colab_type": "code",
        "outputId": "d0f0c853-adaa-415f-b052-6ac1d354abda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        }
      },
      "source": [
        "!pip install geopandas"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting geopandas\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5b/0c/e6c99e561b03482220f00443f610ccf4dce9b50f4b1093d735f93c6fc8c6/geopandas-0.6.2-py2.py3-none-any.whl (919kB)\n",
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            "\u001b[?25hRequirement already satisfied: shapely in /usr/local/lib/python3.6/dist-packages (from geopandas) (1.6.4.post2)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/f7/9899f8a9a2e38601472fe1079ce5088f58833221c8b8507d8b5eafd5404a/Fiona-1.8.13-cp36-cp36m-manylinux1_x86_64.whl (11.8MB)\n",
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            "Collecting munch\n",
            "  Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
            "Collecting cligj>=0.5\n",
            "  Downloading https://files.pythonhosted.org/packages/e4/be/30a58b4b0733850280d01f8bd132591b4668ed5c7046761098d665ac2174/cligj-0.5.0-py3-none-any.whl\n",
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            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.0->geopandas) (2018.9)\n",
            "Installing collected packages: click-plugins, munch, cligj, fiona, pyproj, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.5.0 fiona-1.8.13 geopandas-0.6.2 munch-2.5.0 pyproj-2.4.2.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "A6dVzpnnm7td",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import geopandas as gpd\n",
        "from shapely.geometry import Point"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QRAJxtBm_bf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv(\"train.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T8NiJRGfp8x3",
        "colab_type": "text"
      },
      "source": [
        "Remove unreasonable coordinates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObXI7a8Hu2Dw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "ec57f4b9-ceaa-439a-a19e-d59be43eba56"
      },
      "source": [
        "print(\"Longitude Min: {}\".format(data[\"X\"].min()))\n",
        "print(\"Longitude Max: {}\".format(data[\"X\"].max()))\n",
        "print(\"Latitude Min: {}\".format(data[\"Y\"].min()))\n",
        "print(\"Latitude Min: {}\".format(data[\"Y\"].max()))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Longitude Min: -122.51364206429\n",
            "Longitude Max: -120.5\n",
            "Latitude Min: 37.7078790224135\n",
            "Latitude Min: 90.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kD8rIBmXqHpr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data[data[\"Y\"] <= 40]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rb-hSn2epqzb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.sample(n=1000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7hVQoCSnizX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data[\"points\"] = data.apply(lambda row: Point(row[\"X\"], row[\"Y\"]), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eB-ZrkBrn6p8",
        "colab_type": "code",
        "outputId": "eea348bf-bc11-458c-c52a-36291989457b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dates</th>\n",
              "      <th>Category</th>\n",
              "      <th>Descript</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>PdDistrict</th>\n",
              "      <th>Resolution</th>\n",
              "      <th>Address</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>points</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>607802</th>\n",
              "      <td>2006-10-02 08:54:00</td>\n",
              "      <td>WARRANTS</td>\n",
              "      <td>ENROUTE TO OUTSIDE JURISDICTION</td>\n",
              "      <td>Monday</td>\n",
              "      <td>INGLESIDE</td>\n",
              "      <td>ARREST, BOOKED</td>\n",
              "      <td>100 Block of ALEMANY BL</td>\n",
              "      <td>-122.409215</td>\n",
              "      <td>37.736173</td>\n",
              "      <td>POINT (-122.409215255969 37.7361732498955)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147928</th>\n",
              "      <td>2013-05-30 08:30:00</td>\n",
              "      <td>LARCENY/THEFT</td>\n",
              "      <td>GRAND THEFT FROM LOCKED AUTO</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>NORTHERN</td>\n",
              "      <td>NONE</td>\n",
              "      <td>500 Block of TURK ST</td>\n",
              "      <td>-122.418193</td>\n",
              "      <td>37.782326</td>\n",
              "      <td>POINT (-122.418192861208 37.78232633689221)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141071</th>\n",
              "      <td>2013-06-29 18:00:00</td>\n",
              "      <td>LARCENY/THEFT</td>\n",
              "      <td>PETTY THEFT OF PROPERTY</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>SOUTHERN</td>\n",
              "      <td>NONE</td>\n",
              "      <td>700 Block of THE EMBARCADEROSOUTH ST</td>\n",
              "      <td>-122.400712</td>\n",
              "      <td>37.802385</td>\n",
              "      <td>POINT (-122.40071180486 37.80238450205921)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>857264</th>\n",
              "      <td>2003-04-14 16:00:00</td>\n",
              "      <td>MISSING PERSON</td>\n",
              "      <td>MISSING ADULT</td>\n",
              "      <td>Monday</td>\n",
              "      <td>RICHMOND</td>\n",
              "      <td>LOCATED</td>\n",
              "      <td>2500 Block of SUTTER ST</td>\n",
              "      <td>-122.442387</td>\n",
              "      <td>37.784984</td>\n",
              "      <td>POINT (-122.442386593523 37.7849835227905)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54120</th>\n",
              "      <td>2014-08-23 12:11:00</td>\n",
              "      <td>FRAUD</td>\n",
              "      <td>FALSE ID TO PEACE OFFICER</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>TENDERLOIN</td>\n",
              "      <td>NONE</td>\n",
              "      <td>400 Block of ELLIS ST</td>\n",
              "      <td>-122.413609</td>\n",
              "      <td>37.784697</td>\n",
              "      <td>POINT (-122.413609328985 37.784696907904)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Dates  ...                                       points\n",
              "607802  2006-10-02 08:54:00  ...   POINT (-122.409215255969 37.7361732498955)\n",
              "147928  2013-05-30 08:30:00  ...  POINT (-122.418192861208 37.78232633689221)\n",
              "141071  2013-06-29 18:00:00  ...   POINT (-122.40071180486 37.80238450205921)\n",
              "857264  2003-04-14 16:00:00  ...   POINT (-122.442386593523 37.7849835227905)\n",
              "54120   2014-08-23 12:11:00  ...    POINT (-122.413609328985 37.784696907904)\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "P5fhsogIn-GM",
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      },
      "source": [
        "geo_data = gpd.GeoDataFrame(data, geometry=\"points\")\n",
        "geo_data.crs = {\"init\": \"epsg:4326\"}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmM8p1SToNdn",
        "colab_type": "code",
        "outputId": "d2c4ee33-449b-4683-d8b9-102e15e55881",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "geo_data.plot()\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k683CmyPK1ziYzjFKkMJ8oJMEOcpvun9gqKYJvVrr1vmknUTt6syHTvqdWafC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\nusNIX6YsswAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aP7k9BLtrsbq",
        "colab_type": "text"
      },
      "source": [
        "Police Department Districts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDz_tPoEqaQs",
        "colab_type": "code",
        "outputId": "f98c1dd9-c952-40a6-f50e-b4cf1b8f78a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "data[\"PdDistrict\"].unique()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['INGLESIDE', 'NORTHERN', 'SOUTHERN', 'RICHMOND', 'TENDERLOIN',\n",
              "       'MISSION', 'TARAVAL', 'CENTRAL', 'BAYVIEW', 'PARK'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0_poJPIrG6L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sfpd = gpd.read_file(\"current_police_districts.geojson\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCZUHRqarUDL",
        "colab_type": "code",
        "outputId": "7d2a3893-2620-473c-97ad-590e583b619a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "sfpd.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>shape_area</th>\n",
              "      <th>shape_leng</th>\n",
              "      <th>company</th>\n",
              "      <th>shape_le_1</th>\n",
              "      <th>district</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>91344142.1925</td>\n",
              "      <td>87550.2751419</td>\n",
              "      <td>B</td>\n",
              "      <td>100231.353916</td>\n",
              "      <td>SOUTHERN</td>\n",
              "      <td>MULTIPOLYGON (((-122.39186 37.79425, -122.3917...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>201384622.317</td>\n",
              "      <td>163013.798332</td>\n",
              "      <td>C</td>\n",
              "      <td>144143.480351</td>\n",
              "      <td>BAYVIEW</td>\n",
              "      <td>MULTIPOLYGON (((-122.38098 37.76480, -122.3810...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>80623839.7922</td>\n",
              "      <td>40152.783389</td>\n",
              "      <td>D</td>\n",
              "      <td>40518.8342346</td>\n",
              "      <td>MISSION</td>\n",
              "      <td>MULTIPOLYGON (((-122.40954 37.76932, -122.4086...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>82781685.5603</td>\n",
              "      <td>56493.858208</td>\n",
              "      <td>E</td>\n",
              "      <td>50608.3103205</td>\n",
              "      <td>NORTHERN</td>\n",
              "      <td>MULTIPOLYGON (((-122.43379 37.80793, -122.4337...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11072154.5623</td>\n",
              "      <td>12424.2689691</td>\n",
              "      <td>J</td>\n",
              "      <td>18796.7841847</td>\n",
              "      <td>TENDERLOIN</td>\n",
              "      <td>MULTIPOLYGON (((-122.40217 37.78626, -122.4171...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      shape_area  ...                                           geometry\n",
              "0  91344142.1925  ...  MULTIPOLYGON (((-122.39186 37.79425, -122.3917...\n",
              "1  201384622.317  ...  MULTIPOLYGON (((-122.38098 37.76480, -122.3810...\n",
              "2  80623839.7922  ...  MULTIPOLYGON (((-122.40954 37.76932, -122.4086...\n",
              "3  82781685.5603  ...  MULTIPOLYGON (((-122.43379 37.80793, -122.4337...\n",
              "4  11072154.5623  ...  MULTIPOLYGON (((-122.40217 37.78626, -122.4171...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvaK5H-Wrc7l",
        "colab_type": "code",
        "outputId": "214d9ee5-5100-475b-fed4-6eb82896a67b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "sfpd.plot()\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1biDq+iIp5eVR9jUQnmwZ/t4IlEdJ9wzwTOxVnR4rynM40u7k73+9la9ct3TE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\nwLTzXpl3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgtvEgT4slnL",
        "colab_type": "code",
        "outputId": "861c3ebf-9d18-474b-c66f-7f23ff96b3b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "fig, ax = plt.subplots(1)\n",
        "sfpd_map = sfpd.plot(ax=ax, color=\"black\")\n",
        "geo_data.plot(ax=sfpd_map, color=\"red\")\n",
        "ax.set_title(\"Crime in San Francisco\")\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w448/5vPPPyeRSL5z2b9/f1tiNCZTDLdlfr17d0cbE0wnSn3q4gbz7p0cNJdn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\nIAg+JDAKgiD4kMAoCILg4/+TDdwd4VRaUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDLwE08LnoLe",
        "colab_type": "text"
      },
      "source": [
        "References:\n",
        "\n",
        "\n",
        "*   https://data.sfgov.org/Public-Safety/Current-Police-Districts/wkhw-cjsf\n",
        "\n"
      ]
    }
  ]
}