notebooks/queries/EconomicCharacteristics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example queries for Economic Characteristics on COVID-19 Knowledge Graph\n",
    "[Work in progress]\n",
    "\n",
    "This notebook demonstrates how to run Cypher queries to get [Economic Characteristics from the American Community Survey 2018 5-year data](https://data.census.gov/cedsci/table?tid=ACSDP5Y2018.DP03) at multiple levels of geographic granularity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from py2neo import Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = None  # display all rows\n",
    "pd.options.display.max_columns = None  # display all columsns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Connect to COVID-19-Net Knowledge Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = Graph(\"bolt://132.249.238.185:7687\", user=\"reader\", password=\"demo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Economic Characteristics\n",
    "The following variables are available for queries. Variable names ending with Pct represent values in percent, whereas all other variables represent counts. For details see [Subject Definitions](https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2018_ACSSubjectDefinitions.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Commuting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "             'DP03_0018E': 'workers16YearsAndOver',\n",
    "             'DP03_0019E': 'droveAloneToWorkInCarTruckOrVan',\n",
    "             'DP03_0019PE': 'droveAloneToWorkInCarTruckOrVanPct',\n",
    "             'DP03_0020E': 'carpooledToWorkInCarTruckOrVan',\n",
    "             'DP03_0020PE': 'carpooledToWorkInCarTruckOrVanPct',\n",
    "             'DP03_0021E': 'publicTransportToWork',\n",
    "             'DP03_0021PE': 'publicTransportToWorkPct',\n",
    "             'DP03_0022E': 'walkedToWork',\n",
    "             'DP03_0022PE': 'walkedToWorkPct',\n",
    "             'DP03_0023E': 'otherMeansOfCommutingToWork',\n",
    "             'DP03_0023PE': 'otherMeansOfCommutingToWorkPct',\n",
    "             'DP03_0024E': 'workedAtHome',\n",
    "             'DP03_0024PE': 'workedAtHomePct',\n",
    "             'DP03_0025E': 'meanTravelTimeToWorkMinutes',"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Employment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "             'DP03_0001E': 'population16YearsAndOver',\n",
    "             'DP03_0002E': 'population16YearsAndOverInLaborForce',\n",
    "             'DP03_0002PE': 'population16YearsAndOverInLaborForcePct',\n",
    "             'DP03_0003E': 'population16YearsAndOverInCivilianLaborForce',\n",
    "             'DP03_0003PE': 'population16YearsAndOverInCivilianLaborForcePct',\n",
    "             'DP03_0006E': 'population16YearsAndOverInArmedForces',\n",
    "             'DP03_0006PE': 'population16YearsAndOverInArmedForcesPct',\n",
    "             'DP03_0007E': 'population16YearsAndOverNotInLaborForce',\n",
    "             'DP03_0007PE': 'population16YearsAndOverNotInLaborForcePct',"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HealthInsurance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "             'DP03_0095E': 'civilianNoninstitutionalizedPopulation',\n",
    "             'DP03_0096E': 'withHealthInsuranceCoverage',\n",
    "             'DP03_0096PE': 'withHealthInsuranceCoveragePct',\n",
    "             'DP03_0097E': 'withPrivateHealthInsurance',\n",
    "             'DP03_0097PE': 'withPrivateHealthInsurancePct',\n",
    "             'DP03_0098E': 'withPublicCoverage',\n",
    "             'DP03_0098PE': 'withPublicCoveragePct',\n",
    "             'DP03_0099E': 'noHealthInsuranceCoverage',\n",
    "             'DP03_0099PE': 'noHealthInsuranceCoveragePct',"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "             'DP03_0051E': 'totalHouseholds',\n",
    "             'DP03_0052E': 'householdIncomeLessThan10000USD',\n",
    "             'DP03_0052PE': 'householdIncomeLessThan10000USDPct',\n",
    "             'DP03_0053E': 'householdIncome10000To14999USD',\n",
    "             'DP03_0053PE': 'householdIncome10000To14999USDPct',\n",
    "             'DP03_0054E': 'householdIncome15000To24999USD',\n",
    "             'DP03_0054PE': 'householdIncome15000To24999USDPct',\n",
    "             'DP03_0055E': 'householdIncome25000To34999USD',\n",
    "             'DP03_0055PE': 'householdIncome25000To34999USDPct',\n",
    "             'DP03_0056E': 'householdIncome35000To49999USD',\n",
    "             'DP03_0056PE': 'householdIncome35000To49999USDPct',\n",
    "             'DP03_0057E': 'householdIncome50000To74999USD',\n",
    "             'DP03_0057PE': 'householdIncome50000To74999USDPct',\n",
    "             'DP03_0058E': 'householdIncome75000To99999USD',\n",
    "             'DP03_0058PE': 'householdIncome75000To99999USDPct',\n",
    "             'DP03_0059E': 'householdIncome100000To149999USD',\n",
    "             'DP03_0059PE': 'householdIncome100000To149999USDPct',\n",
    "             'DP03_0060E': 'householdIncome150000To199999USD',\n",
    "             'DP03_0060PE': 'householdIncome150000To199999USDPct',\n",
    "             'DP03_0061E': 'householdIncomeMoreThan200000USD',\n",
    "             'DP03_0061PE': 'householdIncomeMoreThan200000USDPct',\n",
    "             'DP03_0062E': 'medianHouseholdIncomeUSD',\n",
    "             'DP03_0063E': 'meanHouseholdIncomeUSD',"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Occupation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "             'DP03_0026E': 'civilianEmployedPopulation16YearsAndOver',\n",
    "             'DP03_0027E': 'managementBusinessScienceAndArtsOccupations',\n",
    "             'DP03_0027PE': 'managementBusinessScienceAndArtsOccupationsPct',\n",
    "             'DP03_0028E': 'serviceOccupations',\n",
    "             'DP03_0028PE': 'serviceOccupationsPct',\n",
    "             'DP03_0029E': 'salesAndOfficeOccupations',\n",
    "             'DP03_0029PE': 'salesAndOfficeOccupationsPct',\n",
    "             'DP03_0030E': 'naturalResourcesConstructionAndMaintenanceOccupations',\n",
    "             'DP03_0030PE': 'naturalResourcesConstructionAndMaintenanceOccupationsPct',\n",
    "             'DP03_0031E': 'productionTransportationAndMaterialMovingOccupations',\n",
    "             'DP03_0031PE': 'productionTransportationAndMaterialMovingOccupationsPct'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geographic granularity\n",
    "Data are available at 3 levels of granularity:\n",
    "* US County\n",
    "* US Zip Code\n",
    "* US Census Tract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Data By US County"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the state and county fips codes are available, economic characteristics can be retrieved directly.\n",
    "\n",
    "Note, fips codes are represented as strings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: Commuting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_fips = '06'\n",
    "county_fips = '073'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c.stateFips</th>\n",
       "      <th>c.countyFips</th>\n",
       "      <th>c.droveAloneToWorkInCarTruckOrVanPct</th>\n",
       "      <th>c.publicTransportToWorkPct</th>\n",
       "      <th>c.walkedToWorkPct</th>\n",
       "      <th>c.otherMeansOfCommutingToWorkPct</th>\n",
       "      <th>c.workedAtHomePct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06</td>\n",
       "      <td>073</td>\n",
       "      <td>76.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  c.stateFips c.countyFips  c.droveAloneToWorkInCarTruckOrVanPct  \\\n",
       "0          06          073                                  76.3   \n",
       "\n",
       "   c.publicTransportToWorkPct  c.walkedToWorkPct  \\\n",
       "0                         2.9                2.9   \n",
       "\n",
       "   c.otherMeansOfCommutingToWorkPct  c.workedAtHomePct  \n",
       "0                               2.3                7.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (c:Commuting{countyFips:$county_fips, stateFips:$state_fips})\n",
    "RETURN c.stateFips, c.countyFips, c.droveAloneToWorkInCarTruckOrVanPct, c.publicTransportToWorkPct, \n",
    "       c.walkedToWorkPct, c.otherMeansOfCommutingToWorkPct, c.workedAtHomePct\n",
    "\"\"\"\n",
    "df = graph.run(query, county_fips=county_fips, state_fips=state_fips).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: Get Employment characteristics by traversing the KG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "county = 'Los Angeles County'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a.name</th>\n",
       "      <th>e.population16YearsAndOverInLaborForcePct</th>\n",
       "      <th>e.population16YearsAndOverInCivilianLaborForcePct</th>\n",
       "      <th>e.population16YearsAndOverInArmedForcesPct</th>\n",
       "      <th>e.population16YearsAndOverNotInLaborForcePct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Los Angeles County</td>\n",
       "      <td>64.5</td>\n",
       "      <td>64.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a.name  e.population16YearsAndOverInLaborForcePct  \\\n",
       "0  Los Angeles County                                       64.5   \n",
       "\n",
       "   e.population16YearsAndOverInCivilianLaborForcePct  \\\n",
       "0                                               64.4   \n",
       "\n",
       "   e.population16YearsAndOverInArmedForcesPct  \\\n",
       "0                                         0.0   \n",
       "\n",
       "   e.population16YearsAndOverNotInLaborForcePct  \n",
       "0                                            35  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (a:Admin2{name:$admin2})-[:HAS_ECONOMICS]-(:Economics)-[:HAS_EMPLOYMENT]-(e:Employment)\n",
    "RETURN a.name, e.population16YearsAndOverInLaborForcePct, e.population16YearsAndOverInCivilianLaborForcePct,\n",
    "       e.population16YearsAndOverInArmedForcesPct, e.population16YearsAndOverNotInLaborForcePct\n",
    "\"\"\"\n",
    "df = graph.run(query, admin2=county).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Data by US Postal Code\n",
    "\n",
    "Note, postal codes are represented as strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_code = '92130'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>h.postalCode</th>\n",
       "      <th>h.withHealthInsuranceCoveragePct</th>\n",
       "      <th>h.withPrivateHealthInsurancePct</th>\n",
       "      <th>h.withPublicCoveragePct</th>\n",
       "      <th>h.noHealthInsuranceCoveragePct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92130</td>\n",
       "      <td>97.6</td>\n",
       "      <td>88.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  h.postalCode  h.withHealthInsuranceCoveragePct  \\\n",
       "0        92130                              97.6   \n",
       "\n",
       "   h.withPrivateHealthInsurancePct  h.withPublicCoveragePct  \\\n",
       "0                             88.7                     14.6   \n",
       "\n",
       "   h.noHealthInsuranceCoveragePct  \n",
       "0                             2.4  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (h:HealthInsurance{postalCode: $zip_code})\n",
    "RETURN h.postalCode, h.withHealthInsuranceCoveragePct, h.withPrivateHealthInsurancePct,\n",
    "       h.withPublicCoveragePct, h.noHealthInsuranceCoveragePct\n",
    "\"\"\"\n",
    "df = graph.run(query, zip_code=zip_code).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: List income data for Zip codes with a place name\n",
    "\n",
    "Note, Zip code areas may cross city boundaries. Place names are the preferred names used by the US Postal Service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Zip code</th>\n",
       "      <th>Median Household Income</th>\n",
       "      <th>Mean Household Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91910</td>\n",
       "      <td>61922</td>\n",
       "      <td>78413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91911</td>\n",
       "      <td>58598</td>\n",
       "      <td>71107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91913</td>\n",
       "      <td>96617</td>\n",
       "      <td>117117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91914</td>\n",
       "      <td>133730</td>\n",
       "      <td>148439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91915</td>\n",
       "      <td>112083</td>\n",
       "      <td>122929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Zip code  Median Household Income  Mean Household Income\n",
       "0    91910                    61922                  78413\n",
       "1    91911                    58598                  71107\n",
       "2    91913                    96617                 117117\n",
       "3    91914                   133730                 148439\n",
       "4    91915                   112083                 122929"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place_name = 'Chula Vista'\n",
    "\n",
    "query = \"\"\"\n",
    "MATCH (p:PostalCode{placeName:$place_name})-[:HAS_ECONOMICS]-(:Economics)-[:HAS_INCOME]-(i:Income)\n",
    "RETURN p.name AS `Zip code`, \n",
    "       i.medianHouseholdIncomeUSD AS `Median Household Income`, \n",
    "       i.meanHouseholdIncomeUSD AS `Mean Household Income`\n",
    "\"\"\"\n",
    "df = graph.run(query, place_name=place_name).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x='Zip code', \n",
    "            y=[\"Median Household Income\", \"Mean Household Income\"], \n",
    "            title='Income [USD]', rot=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Data by US Census Tract\n",
    "\n",
    "Note, tracts are represented at strings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: Occupations for a tract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "tract = '06073008324'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>o.tract</th>\n",
       "      <th>o.managementBusinessScienceAndArtsOccupationsPct</th>\n",
       "      <th>o.serviceOccupationsPct</th>\n",
       "      <th>o.salesAndOfficeOccupationsPct</th>\n",
       "      <th>o.naturalResourcesConstructionAndMaintenanceOccupationsPct</th>\n",
       "      <th>o.productionTransportationAndMaterialMovingOccupationsPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06073008324</td>\n",
       "      <td>65.5</td>\n",
       "      <td>10.7</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       o.tract  o.managementBusinessScienceAndArtsOccupationsPct  \\\n",
       "0  06073008324                                              65.5   \n",
       "\n",
       "   o.serviceOccupationsPct  o.salesAndOfficeOccupationsPct  \\\n",
       "0                     10.7                            17.8   \n",
       "\n",
       "   o.naturalResourcesConstructionAndMaintenanceOccupationsPct  \\\n",
       "0                                                4.2            \n",
       "\n",
       "   o.productionTransportationAndMaterialMovingOccupationsPct  \n",
       "0                                                1.9          "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (o:Occupation{tract: $tract})\n",
    "RETURN o.tract,\n",
    "       o.managementBusinessScienceAndArtsOccupationsPct,\n",
    "       o.serviceOccupationsPct,\n",
    "       o.salesAndOfficeOccupationsPct,\n",
    "       o.naturalResourcesConstructionAndMaintenanceOccupationsPct,\n",
    "       o.productionTransportationAndMaterialMovingOccupationsPct\n",
    "\"\"\"\n",
    "df = graph.run(query, tract=tract).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: List Occupations for all tracts in a county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>State</th>\n",
       "      <th>County</th>\n",
       "      <th>Tract</th>\n",
       "      <th>o.managementBusinessScienceAndArtsOccupationsPct</th>\n",
       "      <th>o.serviceOccupationsPct</th>\n",
       "      <th>o.salesAndOfficeOccupationsPct</th>\n",
       "      <th>o.naturalResourcesConstructionAndMaintenanceOccupationsPct</th>\n",
       "      <th>o.productionTransportationAndMaterialMovingOccupationsPct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>Orange County</td>\n",
       "      <td>06059087505</td>\n",
       "      <td>18.8</td>\n",
       "      <td>18.2</td>\n",
       "      <td>33.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>California</td>\n",
       "      <td>Orange County</td>\n",
       "      <td>06059032061</td>\n",
       "      <td>57.4</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>California</td>\n",
       "      <td>Orange County</td>\n",
       "      <td>06059099903</td>\n",
       "      <td>29.4</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>Orange County</td>\n",
       "      <td>06059990100</td>\n",
       "      <td>-666666666.0</td>\n",
       "      <td>-666666666.0</td>\n",
       "      <td>-666666666.0</td>\n",
       "      <td>-666666666.0</td>\n",
       "      <td>-666666666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>Orange County</td>\n",
       "      <td>06059099902</td>\n",
       "      <td>37.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>9.2</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State         County        Tract  \\\n",
       "0  California  Orange County  06059087505   \n",
       "1  California  Orange County  06059032061   \n",
       "2  California  Orange County  06059099903   \n",
       "3  California  Orange County  06059990100   \n",
       "4  California  Orange County  06059099902   \n",
       "\n",
       "   o.managementBusinessScienceAndArtsOccupationsPct  o.serviceOccupationsPct  \\\n",
       "0                                              18.8                     18.2   \n",
       "1                                              57.4                     10.6   \n",
       "2                                              29.4                     21.7   \n",
       "3                                      -666666666.0             -666666666.0   \n",
       "4                                              37.8                     18.1   \n",
       "\n",
       "   o.salesAndOfficeOccupationsPct  \\\n",
       "0                            33.1   \n",
       "1                            27.7   \n",
       "2                            21.2   \n",
       "3                    -666666666.0   \n",
       "4                            22.5   \n",
       "\n",
       "   o.naturalResourcesConstructionAndMaintenanceOccupationsPct  \\\n",
       "0                                               14.0            \n",
       "1                                                3.0            \n",
       "2                                               11.5            \n",
       "3                                       -666666666.0            \n",
       "4                                                9.2            \n",
       "\n",
       "   o.productionTransportationAndMaterialMovingOccupationsPct  \n",
       "0                                               15.9          \n",
       "1                                                1.4          \n",
       "2                                               16.1          \n",
       "3                                       -666666666.0          \n",
       "4                                               12.4          "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = 'California'\n",
    "county = 'Orange County'\n",
    "\n",
    "query = \"\"\"\n",
    "MATCH (a1:Admin1{name: $admin1})-[:IN]-(a2:Admin2{name: $admin2})-[:IN]-(t:Tract)-[:HAS_ECONOMICS]-(:Economics)-[:HAS_OCCUPATION]-(o:Occupation)\n",
    "RETURN a1.name AS State, a2.name AS County, t.name AS Tract,\n",
    "       o.managementBusinessScienceAndArtsOccupationsPct,\n",
    "       o.serviceOccupationsPct,\n",
    "       o.salesAndOfficeOccupationsPct,\n",
    "       o.naturalResourcesConstructionAndMaintenanceOccupationsPct,\n",
    "       o.productionTransportationAndMaterialMovingOccupationsPct\n",
    "\"\"\"\n",
    "df = graph.run(query, admin1=state, admin2=county).to_data_frame()\n",
    "df.head()"
   ]
  }
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