populate_foodbank_metadata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read GDP per capita figures from Wikipedia table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities=pd.read_csv(\"List_of_U.S._metropolitan_areas_by_GDP_per_capita_1.csv\", sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Rank</th>\n",
       "      <th>Metropolitan area</th>\n",
       "      <th>2017 GDP per capita \\r\\n(chained 2009 US-Dollars)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Midland, TX Metropolitan Statistical Area</td>\n",
       "      <td>174,749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>San Jose-Sunnyvale-Santa Clara, CA Metropolita...</td>\n",
       "      <td>128,308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>San Francisco–Oakland–Hayward, CA Metropolitan...</td>\n",
       "      <td>89,978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Bridgeport-Stamford-Norwalk, CT Metropolitan S...</td>\n",
       "      <td>87,082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Seattle-Tacoma-Bellevue, WA Metropolitan Stati...</td>\n",
       "      <td>80,833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                                  Metropolitan area  \\\n",
       "0   1.0          Midland, TX Metropolitan Statistical Area   \n",
       "1   2.0  San Jose-Sunnyvale-Santa Clara, CA Metropolita...   \n",
       "2   3.0  San Francisco–Oakland–Hayward, CA Metropolitan...   \n",
       "3   4.0  Bridgeport-Stamford-Norwalk, CT Metropolitan S...   \n",
       "4   5.0  Seattle-Tacoma-Bellevue, WA Metropolitan Stati...   \n",
       "\n",
       "  2017 GDP per capita \\r\\n(chained 2009 US-Dollars)  \n",
       "0                                           174,749  \n",
       "1                                           128,308  \n",
       "2                                            89,978  \n",
       "3                                            87,082  \n",
       "4                                            80,833  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read data from previous team's work, with some additional manual annotation of food stocks and annual program budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodbanks=pd.read_csv(\"foodbank_with_latlon.csv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Food Bank</th>\n",
       "      <th>ID</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>2021 Food Insecurity %</th>\n",
       "      <th>2021 Food Insecurity #</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>state</th>\n",
       "      <th>statecode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>lbs_distr</th>\n",
       "      <th>program_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Roadrunner Food Bank</td>\n",
       "      <td>1</td>\n",
       "      <td>2092454</td>\n",
       "      <td>0.155</td>\n",
       "      <td>323340</td>\n",
       "      <td>5840 Office Blvd NE</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>87109</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>NM</td>\n",
       "      <td>35.1506</td>\n",
       "      <td>-106.5690</td>\n",
       "      <td>58958429.0</td>\n",
       "      <td>140942492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High Plains Food Bank</td>\n",
       "      <td>2</td>\n",
       "      <td>475188</td>\n",
       "      <td>0.161</td>\n",
       "      <td>76360</td>\n",
       "      <td>815 S. Ross|PO Box 31803</td>\n",
       "      <td>Amarillo</td>\n",
       "      <td>79120</td>\n",
       "      <td>Texas</td>\n",
       "      <td>TX</td>\n",
       "      <td>35.1964</td>\n",
       "      <td>-101.8034</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Food Bank of Alaska, Inc.</td>\n",
       "      <td>3</td>\n",
       "      <td>737068</td>\n",
       "      <td>0.129</td>\n",
       "      <td>95420</td>\n",
       "      <td>2192 Viking Drive</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>99501</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>61.2116</td>\n",
       "      <td>-149.8761</td>\n",
       "      <td>11073479.0</td>\n",
       "      <td>24880966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atlanta Community Food Bank</td>\n",
       "      <td>4</td>\n",
       "      <td>6105046</td>\n",
       "      <td>0.117</td>\n",
       "      <td>715840</td>\n",
       "      <td>3400 N Desert Dr</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>30344</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>GA</td>\n",
       "      <td>33.6919</td>\n",
       "      <td>-84.4480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213237927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Golden Harvest Food Bank</td>\n",
       "      <td>5</td>\n",
       "      <td>831422</td>\n",
       "      <td>0.139</td>\n",
       "      <td>115340</td>\n",
       "      <td>3310 Commerce Drive</td>\n",
       "      <td>Augusta</td>\n",
       "      <td>30909</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>GA</td>\n",
       "      <td>33.4717</td>\n",
       "      <td>-82.0834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Food Bank  ID  Total Population  2021 Food Insecurity %  \\\n",
       "0         Roadrunner Food Bank   1           2092454                   0.155   \n",
       "1        High Plains Food Bank   2            475188                   0.161   \n",
       "2    Food Bank of Alaska, Inc.   3            737068                   0.129   \n",
       "3  Atlanta Community Food Bank   4           6105046                   0.117   \n",
       "4     Golden Harvest Food Bank   5            831422                   0.139   \n",
       "\n",
       "   2021 Food Insecurity #                   address         city  zip_code  \\\n",
       "0                  323340       5840 Office Blvd NE  Albuquerque     87109   \n",
       "1                   76360  815 S. Ross|PO Box 31803     Amarillo     79120   \n",
       "2                   95420         2192 Viking Drive    Anchorage     99501   \n",
       "3                  715840          3400 N Desert Dr      Atlanta     30344   \n",
       "4                  115340       3310 Commerce Drive      Augusta     30909   \n",
       "\n",
       "        state statecode  latitude  longitude   lbs_distr  program_budget  \n",
       "0  New Mexico        NM   35.1506  -106.5690  58958429.0     140942492.0  \n",
       "1       Texas        TX   35.1964  -101.8034         NaN             NaN  \n",
       "2      Alaska        AK   61.2116  -149.8761  11073479.0      24880966.0  \n",
       "3     Georgia        GA   33.6919   -84.4480         NaN     213237927.0  \n",
       "4     Georgia        GA   33.4717   -82.0834         NaN             NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodbanks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Annotate with GDP per capita of metro area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "metros=list(cities[\"Metropolitan area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp=list(cities[\"2017 GDP per capita \\r\\n(chained 2009 US-Dollars)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_match=list(foodbanks[\"city\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge GDPs based on partial city match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdps=[]\n",
    "for c in city_match:\n",
    "    out=np.nan\n",
    "    for m in range(0, len(metros)):\n",
    "        if c in metros[m]:\n",
    "            out=int(gdp[m].replace(\",\", \"\"))\n",
    "    gdps.append(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodbanks[\"metro gdp per capita\"]=gdps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foodbanks[\"metro gdp per capita\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing GDP per capita with average value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "foodbanks[\"metro gdp per capita\"].fillna(int(foodbanks[\"metro gdp per capita\"].mean()), inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression to predict pounds of food per year from food insecure population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=foodbanks.dropna(subset=[\"lbs_distr\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear correlation between pounds of food distributed and food insecure population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lbs_distr', ylabel='2021 Food Insecurity #'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot.scatter(x=\"lbs_distr\", y=\"2021 Food Insecurity #\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Less correlation between GDP per capita and pounds, so use insecurity pop to regress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lbs_distr', ylabel='metro gdp per capita'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot.scatter(x=\"lbs_distr\", y=\"metro gdp per capita\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=temp[\"lbs_distr\"]\n",
    "y=temp[\"2021 Food Insecurity #\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intercept, r, p, std_err=stats.linregress(y, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_pounds(row):\n",
    "    if not pd.isna(row[\"lbs_distr\"]):\n",
    "        return row[\"lbs_distr\"]\n",
    "    else:\n",
    "        return slope * row[\"2021 Food Insecurity #\"] + intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodbanks[\"estimated_pounds_per_year\"]=foodbanks.apply(lambda row: fill_pounds(row), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression to predict program budget from food insecure population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp=foodbanks.dropna(subset=[\"program_budget\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=temp[\"program_budget\"]\n",
    "y=temp[\"2021 Food Insecurity #\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear correlation between program budget and food insecure population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='program_budget', ylabel='2021 Food Insecurity #'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot.scatter(x=\"program_budget\", y=\"2021 Food Insecurity #\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Less correlation between GDP per capita and pounds, so use insecurity pop to regress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='program_budget', ylabel='metro gdp per capita'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot.scatter(x=\"program_budget\", y=\"metro gdp per capita\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intercept, r, p, std_err=stats.linregress(y, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_budget(row):\n",
    "    if not pd.isna(row[\"program_budget\"]):\n",
    "        return row[\"program_budget\"]\n",
    "    else:\n",
    "        return slope * row[\"2021 Food Insecurity #\"] + intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodbanks[\"estimated_budget\"]=foodbanks.apply(lambda row: fill_budget(row), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "foodbanks.to_csv(\"input.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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