water-pollution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "waiting-candle",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupational-bloom",
   "metadata": {},
   "source": [
    "##### This is the Jupyter Notebook file for my project exploring land use and water pollution. The original datasets, as well as all but one of the output files (manufacturing.json), are not included in the repository at this time because of their size. A link to the original can be found in the readme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "portuguese-consent",
   "metadata": {},
   "outputs": [],
   "source": [
    "water = gpd.read_file('../data/water-pollution.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aggressive-transcription",
   "metadata": {},
   "outputs": [],
   "source": [
    "land_use = gpd.read_file('../data/land-use.geojson')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "based-mechanism",
   "metadata": {},
   "source": [
    "##### The datasets are loaded. Jupyter Notebooks is used to produce JSONs with the required data. First, extraneous columns are removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "opposite-enzyme",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>N-Nitrosodiphenylamine</td>\n",
       "      <td>Sediment</td>\n",
       "      <td>COMMENCEMENT BAY (INNER)</td>\n",
       "      <td>Marine</td>\n",
       "      <td>5056.169772</td>\n",
       "      <td>1.208041e+06</td>\n",
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       "      <td>12086</td>\n",
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       "      <td>High Molecular Weight Polycyclic Aromatic Hydr...</td>\n",
       "      <td>Sediment</td>\n",
       "      <td>POSSESSION SOUND (NORTH)</td>\n",
       "      <td>Marine</td>\n",
       "      <td>1773.571951</td>\n",
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       "      <td>Hexachlorobenzene</td>\n",
       "      <td>Tissue</td>\n",
       "      <td>SNAKE RIVER (SACAJAWEA LAKE)</td>\n",
       "      <td>Freshwater</td>\n",
       "      <td>138331.620486</td>\n",
       "      <td>1.067388e+08</td>\n",
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       "      <td>Tissue</td>\n",
       "      <td>WASHINGTON LAKE</td>\n",
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       "      <td>11482.153854</td>\n",
       "      <td>7.368716e+06</td>\n",
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       "      <td>Bacteria</td>\n",
       "      <td>Water</td>\n",
       "      <td>SWAN CREEK</td>\n",
       "      <td>Freshwater</td>\n",
       "      <td>2156.133871</td>\n",
       "      <td>9.781690e+04</td>\n",
       "      <td>MULTIPOLYGON (((1169938.983 698869.937, 116991...</td>\n",
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      "text/plain": [
       "       OBJECTID  ListingNumber CategoryCode  \\\n",
       "6890       6891         512626            1   \n",
       "12085     12086         606060            1   \n",
       "428         429          52577            1   \n",
       "5829       5830          43484            1   \n",
       "17463     17464           7514            1   \n",
       "\n",
       "                                           ParameterName MediumName  \\\n",
       "6890                              N-Nitrosodiphenylamine   Sediment   \n",
       "12085  High Molecular Weight Polycyclic Aromatic Hydr...   Sediment   \n",
       "428                                    Hexachlorobenzene     Tissue   \n",
       "5829                                   Hexachlorobenzene     Tissue   \n",
       "17463                                           Bacteria      Water   \n",
       "\n",
       "               ListingWaterbodyName EnvironmentTypeCode   Shape_Length  \\\n",
       "6890       COMMENCEMENT BAY (INNER)              Marine    5056.169772   \n",
       "12085      POSSESSION SOUND (NORTH)              Marine    1773.571951   \n",
       "428    SNAKE RIVER (SACAJAWEA LAKE)          Freshwater  138331.620486   \n",
       "5829                WASHINGTON LAKE          Freshwater   11482.153854   \n",
       "17463                    SWAN CREEK          Freshwater    2156.133871   \n",
       "\n",
       "         Shape_Area                                           geometry  \n",
       "6890   1.208041e+06  MULTIPOLYGON (((1170903.885 710487.104, 117089...  \n",
       "12085  1.218117e+05  MULTIPOLYGON (((1211260.523 962339.402, 121126...  \n",
       "428    1.067388e+08  MULTIPOLYGON (((2133670.544 454594.402, 213388...  \n",
       "5829   7.368716e+06  MULTIPOLYGON (((1204568.000 882537.917, 120464...  \n",
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      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water = water.drop(['AssessmentUnitNumber','AssessmentUnitTypeCode','NHDReachCode','NHDFromMeasurePercent','NHDToMeasurePercent','GridCellNumber','UnmappableCode'], axis=1)\n",
    "water.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "alpha-footage",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open ('water_data.json', 'w') as f:\n",
    "    f.write(water.to_json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "married-night",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        OBJECTID  LANDUSE_CD  Shape_Length     Shape_Area  \\\n",
       "50296      50167          11    665.873538   25538.809016   \n",
       "581035    580906          11   1781.364482  149885.217907   \n",
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       "\n",
       "                                                 geometry  \n",
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "land_use = land_use.drop(['COUNTY_NM','Perimeter','New_Area','New_Acres','NewArea','NewAcres','layer','path'], axis=1)\n",
    "land_use.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "casual-outside",
   "metadata": {},
   "source": [
    "##### The land use file is split up into eight different datasets depending on type. This will reduce file size and simplify the final code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "legal-screening",
   "metadata": {},
   "outputs": [],
   "source": [
    "residential = land_use.loc[(land_use['LANDUSE_CD'] > 10) & (land_use['LANDUSE_CD'] < 20)]\n",
    "with open('residential.json', 'w') as f:\n",
    "    f.write(residential.to_json())\n",
    "residential = gpd.read_file('residential.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "norwegian-groove",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "            id  LANDUSE_CD  OBJECTID     Shape_Area  Shape_Length  \\\n",
       "69846   145784          11    145655  726991.332948   5552.196067   \n",
       "327036  655145          13    655016  346897.585944   3241.058102   \n",
       "98093   206272          14    206143   17652.651748    531.996078   \n",
       "114794  238770          11    238641   87102.579494   1365.313209   \n",
       "293982  583065          11    582936  173877.522363   1650.328080   \n",
       "\n",
       "                                                 geometry  \n",
       "69846   MULTIPOLYGON (((1177234.750 990771.083, 117742...  \n",
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       "293982  MULTIPOLYGON (((1007907.583 538614.167, 100790...  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residential.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "revolutionary-omega",
   "metadata": {},
   "outputs": [],
   "source": [
    "manufacturing = land_use.loc[(land_use['LANDUSE_CD'] > 20) & (land_use['LANDUSE_CD'] < 40)]\n",
    "with open('manufacturing.json', 'w') as f:\n",
    "    f.write(manufacturing.to_json())\n",
    "manufacturing = gpd.read_file('manufacturing.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "southeast-saint",
   "metadata": {},
   "outputs": [],
   "source": [
    "transportation = land_use.loc[(land_use['LANDUSE_CD'] > 40) & (land_use['LANDUSE_CD'] < 50)]\n",
    "with open('transportation.json', 'w') as f:\n",
    "    f.write(transportation.to_json())\n",
    "transportation = gpd.read_file('transportation.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "generous-choice",
   "metadata": {},
   "outputs": [],
   "source": [
    "trade = land_use.loc[(land_use['LANDUSE_CD'] > 49) & (land_use['LANDUSE_CD'] < 60)]\n",
    "with open('trade.json', 'w') as f:\n",
    "    f.write(trade.to_json())\n",
    "trade = gpd.read_file('trade.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "architectural-arlington",
   "metadata": {},
   "outputs": [],
   "source": [
    "service = land_use.loc[(land_use['LANDUSE_CD'] > 60) & (land_use['LANDUSE_CD'] < 70)]\n",
    "with open('service.json', 'w') as f:\n",
    "    f.write(service.to_json())\n",
    "service = gpd.read_file('service.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "headed-kennedy",
   "metadata": {},
   "outputs": [],
   "source": [
    "recreational = land_use.loc[(land_use['LANDUSE_CD'] > 70) & (land_use['LANDUSE_CD'] < 80)]\n",
    "with open('recreational.json', 'w') as f:\n",
    "    f.write(recreational.to_json())\n",
    "recreational = gpd.read_file('recreational.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "future-castle",
   "metadata": {},
   "outputs": [],
   "source": [
    "production = land_use.loc[(land_use['LANDUSE_CD'] > 80) & (land_use['LANDUSE_CD'] < 90)]\n",
    "with open('production.json', 'w') as f:\n",
    "    f.write(production.to_json())\n",
    "production = gpd.read_file('production.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "concerned-hygiene",
   "metadata": {},
   "outputs": [],
   "source": [
    "undeveloped = land_use.loc[land_use['LANDUSE_CD'] > 90]\n",
    "with open('undeveloped.json', 'w') as f:\n",
    "    f.write(undeveloped.to_json())\n",
    "undeveloped = gpd.read_file('undeveloped.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lucky-highland",
   "metadata": {},
   "source": [
    "##### In order to focus the project, the water pollution file is searched and sorted for the most common kinds of pollution. This automates the process of finding the most substantive data points to investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "natural-carter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ParameterName\n",
       "Temperature                         3629\n",
       "Bacteria                            3112\n",
       "Dissolved Oxygen                    2185\n",
       "pH                                  1387\n",
       "Mercury                              700\n",
       "Ammonia-N                            461\n",
       "Copper                               439\n",
       "Polychlorinated Biphenyls (PCBs)     427\n",
       "Zinc                                 416\n",
       "Arsenic                              399\n",
       "Name: ListingNumber, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pollution = water.set_index([\"ParameterName\", \"OBJECTID\"]).count(level=\"ParameterName\")\n",
    "pollution = pollution['ListingNumber']\n",
    "pollution = pollution.sort_values(ascending=False)\n",
    "pollution.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "executive-browser",
   "metadata": {},
   "outputs": [],
   "source": [
    "ammonia = water.loc[water['ParameterName'] == 'Ammonia-N']\n",
    "with open('ammonia.json', 'w') as f:\n",
    "    f.write(ammonia.to_json())\n",
    "ammonia = gpd.read_file('ammonia.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "changed-despite",
   "metadata": {},
   "outputs": [],
   "source": [
    "copper = water.loc[water['ParameterName'] == 'Copper']\n",
    "with open('copper.json', 'w') as f:\n",
    "    f.write(copper.to_json())\n",
    "copper = gpd.read_file('copper.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cooperative-grain",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcb = water.loc[water['ParameterName'] == 'Polychlorinated Biphenyls (PCBs)']\n",
    "with open('pcb.json', 'w') as f:\n",
    "    f.write(pcb.to_json())\n",
    "pcb = gpd.read_file('pcb.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "seasonal-ceramic",
   "metadata": {},
   "outputs": [],
   "source": [
    "zinc = water.loc[water['ParameterName'] == 'Zinc']\n",
    "with open('zinc.json', 'w') as f:\n",
    "    f.write(zinc.to_json())\n",
    "zinc = gpd.read_file('zinc.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "numerical-science",
   "metadata": {},
   "outputs": [],
   "source": [
    "arsenic = water.loc[water['ParameterName'] == 'Arsenic']\n",
    "with open('arsenic.json', 'w') as f:\n",
    "    f.write(arsenic.to_json())\n",
    "arsenic = gpd.read_file('arsenic.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lined-workshop",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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