ebrpd_equity_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 858,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nTO DO \\n-Clean up Script\\n-Turn Script into function\\n-Further simplify/aggregate countywide stat excel outputs. \\n\\n\\n'"
      ]
     },
     "execution_count": 858,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "This script downloads census data exports county level data to an excel file and tract level data to shapefiles.\n",
    "Other notes: \n",
    "This script will be built out to include all relevant census variables. \n",
    "Environment is based off of Pin-Up environment. Jupytext and o\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "TO DO \n",
    "-Clean up Script\n",
    "-Turn Script into function\n",
    "-Further simplify/aggregate countywide stat excel outputs. \n",
    "- MHI column is exported as text instead of int. Fix data type\n",
    "- Calculate percentages of people who walk, bike, and people who walk, bike, or take transit. \n",
    "\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 859,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary libraries\n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "# import geojson\n",
    "import folium\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1091,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIPS CODES\n",
    "Alameda = \"001\"\n",
    "Contra_Costa = \"013\"\n",
    "Solano = \"095\"\n",
    "San_Francisco = \"075\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1092,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For each iteration change the county\n",
    "county = Alameda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1093,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set output filepaths\n",
    "Equity_Tracts = f'/Users/calvindechicago/PycharmProjects/AltaWork/AltaProjects/EBRPD/Equity_Data/equity_tracts_{county}.shp'\n",
    "\n",
    "Transportation_Tracts = '/Users/calvindechicago/PycharmProjects/AltaWork/AltaProjects/EBRPD/Equity_Data/transportation_tracts.shp'\n",
    "\n",
    "Age_Tracts = '/Users/calvindechicago/PycharmProjects/AltaWork/AltaProjects/EBRPD/Equity_Data/tract_age.shp'\n",
    "\n",
    "EXCEL_FP = f'/Users/calvindechicago/PycharmProjects/AltaWork/AltaProjects/EBRPD/Equity_Data/countylevel_demographics_{county}.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "metadata": {},
   "outputs": [],
   "source": [
    "#These are all of the main variables used to build a call url to the the census api website\n",
    "#Available APIs (--> 2018 ACS Detailed Tables Variables [ html | xml | json ])\n",
    "#https://www.census.gov/data/developers/data-sets.html\n",
    "\n",
    "HOST = \"https://api.census.gov/data\"\n",
    "year = \"2018\"\n",
    "#dataset = \"acs/acs5/subject\"\n",
    "dataset = \"acs/acs5\"\n",
    "api_key = \"f9e79198302081250c07d556f35d8a81cdae528a\"\n",
    "base_url = \"/\".join([HOST, year, dataset,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1095,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup request for Table B08006: Sex of Workers by Means of Transportation to Work\n",
    "# Setup request for Table B08014: SEX OF WORKERS BY VEHICLES AVAILABLE (Total and No vehicle columns only)\n",
    "# (COUNTY LEVEL)\n",
    "request_predicates = {}\n",
    "get_vars_transpo_mode = [\"NAME\",\"B08006_001E\",\"B08006_002E\",\"B08006_003E\", \"B08006_004E\",\n",
    "                         \"B08006_008E\",\"B08006_014E\",\"B08006_015E\",\"B08006_016E\", \n",
    "                         \"B08006_017E\", \"GEO_ID\", \"B08014_001E\", \"B08014_002E\" \n",
    "                        ]\n",
    "col_names_transpo_mode = [\"place_name\", \"total\",\"total_car_truck_van\",\n",
    "                          \"car_truck_van_drove_alone\",\"car_truck_van_carpooled\",\n",
    "                          \"public_transportation\", \"bike\", \"walk\", \"taxi_moto_other\",\n",
    "                          \"work_from_home\",\"geoid\", \"total_workers\", \"no_vehicle\",\n",
    "                          \"state_code\", \"county\"\n",
    "                         ]\n",
    "\n",
    "request_predicates[\"key\"] = api_key\n",
    "request_predicates[\"get\"] = \",\".join(get_vars_transpo_mode)\n",
    "\n",
    "\n",
    "#THESE PREDICATES GET AGGREGATE TOTALS FOR ENTIRE COUNTY (SACRAMENTO)\n",
    "#request_predicates[\"for\"] = \"county:067\" #Sacramento\n",
    "request_predicates[\"for\"] = f\"county:{county}\"  \n",
    "request_predicates[\"in\"] = \"state:06\"\n",
    "\n",
    "transpo_mode_county = requests.get(base_url, params=request_predicates)\n",
    "num_columns = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1096,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting up Means of Transportation to Workdata frame, getting rid of first header row\n",
    "df_transpo_mode_county = pd.DataFrame(columns=col_names_transpo_mode, data=transpo_mode_county.json()[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1097,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THESE PREDICATES GET DATA FOR EVERY TRACT in County\n",
    "# These revised predicates replace the 'for' and 'in' predicates above.\n",
    "#(TRACT LEVEL)\n",
    "\n",
    "request_predicates[\"for\"] = \"tract:*\"\n",
    "#request_predicates[\"in\"] = \"state:06+county:067\"\n",
    "request_predicates[\"in\"] = f\"state:06+county:{county}\"\n",
    "\n",
    "transpo_mode_tracts = requests.get(base_url, params=request_predicates)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1098,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting up Means of Transportation to Work at Tract Level data frame, getting rid of first header row\n",
    "#This includes appending 'tract' to columns list\n",
    "col_names_transpo_mode = [\"place_name\", \"total\",\"total_car_truck_van\",\n",
    "                          \"car_truck_van_drove_alone\",\"car_truck_van_carpooled\",\n",
    "                          \"public_transportation\", \"bike\", \"walk\", \"taxi_moto_other\",\n",
    "                          \"work_from_home\",\"geoid\", \"total_workers\", \"no_vehicle\",\"state_code\",\n",
    "                          \"county\", \"tract\" \n",
    "                         ]\n",
    "df_transpo_mode_tracts = pd.DataFrame(columns=col_names_transpo_mode, data=transpo_mode_tracts.json()[1:])\n",
    "num_columns = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1099,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype_conversion = { \"total\": int,\n",
    "                    \"total_car_truck_van\": int,\n",
    "                    \"car_truck_van_drove_alone\": int,\n",
    "                    \"car_truck_van_carpooled\": int,\n",
    "                    \"public_transportation\": int,\n",
    "                    \"bike\": int,\n",
    "                    \"walk\": int, \n",
    "                    \"taxi_moto_other\": int, \n",
    "                    \"work_from_home\": int, \n",
    "                    \"total_workers\": int, \n",
    "                    \"no_vehicle\": int\n",
    "                }\n",
    "\n",
    "df_transpo_mode_tracts = df_transpo_mode_tracts.astype(dtype_conversion) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transpo_mode_tracts[\"pct_no_veh\"] = (df_transpo_mode_tracts[\"no_vehicle\"]/df_transpo_mode_tracts[\"total_workers\"]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The geoid field in the df_transpo_mode table does not match the Tigerlines geoid field. \n",
    "#This slices the the right 11 most digits, which match the geoid codes in the TigerLine file. \n",
    "#(... these are state ('06') for California, followed by county, followed by census tract)\n",
    "\n",
    "df_transpo_mode_tracts.insert(num_columns, \"geoid_join\",df_transpo_mode_tracts['geoid'].str.slice(-11), True) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1102,
   "metadata": {},
   "outputs": [],
   "source": [
    "## SETUP FOR TABLE B08001: SEX BY AGE\n",
    "#NAME dropped from request because only 50 variables per request are permitted.\n",
    "request_predicates = {}\n",
    "get_vars_age = [\"B01001_001E\", \"B01001_002E\",\"B01001_003E\", \"B01001_004E\",\"B01001_005E\", \n",
    "                \"B01001_006E\", \"B01001_007E\",\"B01001_008E\", \"B01001_009E\",\"B01001_010E\", \n",
    "                \"B01001_011E\", \"B01001_012E\",\"B01001_013E\", \"B01001_014E\",\"B01001_015E\", \n",
    "                \"B01001_016E\", \"B01001_017E\",\"B01001_018E\", \"B01001_019E\",\"B01001_020E\", \n",
    "                \"B01001_021E\", \"B01001_022E\",\"B01001_023E\", \"B01001_024E\",\"B01001_025E\", \n",
    "                \"B01001_026E\", \"B01001_027E\",\"B01001_028E\", \"B01001_029E\",\"B01001_030E\", \n",
    "                \"B01001_031E\", \"B01001_032E\",\"B01001_033E\", \"B01001_034E\",\"B01001_035E\", \n",
    "                \"B01001_036E\", \"B01001_037E\",\"B01001_038E\", \"B01001_039E\",\"B01001_040E\", \n",
    "                \"B01001_041E\", \"B01001_042E\",\"B01001_043E\", \"B01001_044E\",\"B01001_045E\", \n",
    "                \"B01001_046E\", \"B01001_047E\",\"B01001_048E\", \"B01001_049E\",\"GEO_ID\"\n",
    "                ]\n",
    "\n",
    "col_names_age = [\"total_pop\",\"total_male\",\"tl_m0_5\",\"tl_m5_9\", \"tl_m10_14\", \"tl_m15_17\", \n",
    "                 \"tl_m18_19\", \"tl_m20\", \"tl_m21\", \"tl_m22_24\", \"tl_m25_29\", \"tl_m30_34\",\n",
    "                 \"tl_m35_39\", \"tl_m40_44\", \"tl_m45_49\", \"tl_m50_54\", \"tl_m55_59\", \"tl_m60_61\",\n",
    "                 \"tl_m62_64\", \"tl_m65_66\", \"tl_m67_69\", \"tl_m70_74\", \"tl_m75_79\", \"tl_m80_84\",\n",
    "                 \"tl_m85_pl\",\"total_female\", \"tl_f0_5\",\"tl_f5_9\", \"tl_f10_14\", \"tl_f15_17\", \n",
    "                 \"tl_f18_19\", \"tl_f20\", \"tl_f21\", \"tl_f22_24\", \"tl_f25_29\", \"tl_f30_34\",\n",
    "                 \"tl_f35_39\", \"tl_f40_44\", \"tl_f45_49\", \"tl_f50_54\", \"tl_f55_59\", \"tl_f60_61\",\n",
    "                 \"tl_f62_64\", \"tl_f65_66\", \"tl_f67_69\", \"tl_f70_74\", \"tl_f75_79\", \"tl_f80_84\",\n",
    "                 \"tl_f85_pl\",\"geoid\",\"state\",\"county\"\n",
    "                ]\n",
    "\n",
    "request_predicates[\"key\"] = api_key\n",
    "request_predicates[\"get\"] = \",\".join(get_vars_age)\n",
    "\n",
    "#THESE PREDICATES GET AGGREGATE TOTALS FOR ENTIRE COUNTY (SACRAMENTO)\n",
    "request_predicates[\"for\"] = f\"county:{county}\"\n",
    "request_predicates[\"in\"] = \"state:06\"\n",
    "\n",
    "\n",
    "age_county = requests.get(base_url, params=request_predicates)\n",
    "num_columns = 49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting up AGE data frame, getting rid of first header row\n",
    "df_age_county = pd.DataFrame(columns=col_names_age, data=age_county.json()[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'request_predicates' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-164976dd25de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# THESE PREDICATES GET DATA FOR EVERY TRACT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mrequest_predicates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"for\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"tract:*\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mrequest_predicates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"in\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"state:06+county:{county}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# request_predicates[\"in\"] = \"state:06+county:067\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'request_predicates' is not defined"
     ]
    }
   ],
   "source": [
    "# THESE PREDICATES GET DATA FOR EVERY TRACT\n",
    "\n",
    "request_predicates[\"for\"] = \"tract:*\"\n",
    "request_predicates[\"in\"] = f\"state:06+county:{county}\"\n",
    "# request_predicates[\"in\"] = \"state:06+county:067\"\n",
    "\n",
    "age_tracts = requests.get(base_url, params=request_predicates)\n",
    "num_columns = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting up AGE TRACTS data frame, getting rid of first header row\n",
    "col_names_age = [\"total_pop\",\"total_male\",\"tl_m0_5\",\"tl_m5_9\", \"tl_m10_14\", \"tl_m15_17\", \n",
    "                 \"tl_m18_19\", \"tl_m20\", \"tl_m21\", \"tl_m22_24\", \"tl_m25_29\", \"tl_m30_34\",\n",
    "                 \"tl_m35_39\", \"tl_m40_44\", \"tl_m45_49\", \"tl_m50_54\", \"tl_m55_59\", \"tl_m60_61\",\n",
    "                 \"tl_m62_64\", \"tl_m65_66\", \"tl_m67_69\", \"tl_m70_74\", \"tl_m75_79\", \"tl_m80_84\",\n",
    "                 \"tl_m85_pl\",\"total_female\", \"tl_f0_5\",\"tl_f5_9\", \"tl_f10_14\", \"tl_f15_17\", \n",
    "                 \"tl_f18_19\", \"tl_f20\", \"tl_f21\", \"tl_f22_24\", \"tl_f25_29\", \"tl_f30_34\",\n",
    "                 \"tl_f35_39\", \"tl_f40_44\", \"tl_f45_49\", \"tl_f50_54\", \"tl_f55_59\", \"tl_f60_61\",\n",
    "                 \"tl_f62_64\", \"tl_f65_66\", \"tl_f67_69\", \"tl_f70_74\", \"tl_f75_79\", \"tl_f80_84\",\n",
    "                 \"tl_f85_pl\",\"geoid\",\"state\",\"county\", 'tract'\n",
    "                ]\n",
    "df_age_tracts = pd.DataFrame(columns=col_names_age, data=age_tracts.json()[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using dictionary to convert specific columns \n",
    "# convert_dict = {'A': int, \n",
    "#                 'C': float\n",
    "#                } \n",
    "  \n",
    "# df = df.astype(convert_dict) \n",
    "# print(df.dtypes) \n",
    "dtype_conversion = { \"total_pop\": int,\n",
    "                    \"total_male\": int,\n",
    "                    \"tl_m0_5\": int,\n",
    "                    \"tl_m5_9\": int,\n",
    "                    \"tl_m10_14\": int,\n",
    "                    \"tl_m15_17\": int,\n",
    "                    \"tl_m18_19\": int, \n",
    "                    \"tl_m20\": int, \n",
    "                    \"tl_m21\": int, \n",
    "                    \"tl_m22_24\": int, \n",
    "                    \"tl_m25_29\": int, \n",
    "                    \"tl_m30_34\": int,\n",
    "                    \"tl_m35_39\": int, \n",
    "                    \"tl_m40_44\": int, \n",
    "                    \"tl_m45_49\": int, \n",
    "                    \"tl_m50_54\": int, \n",
    "                    \"tl_m55_59\": int, \n",
    "                    \"tl_m60_61\": int,\n",
    "                    \"tl_m62_64\": int, \n",
    "                    \"tl_m65_66\": int, \n",
    "                    \"tl_m67_69\": int, \n",
    "                    \"tl_m70_74\": int, \n",
    "                    \"tl_m75_79\": int, \n",
    "                    \"tl_m80_84\": int,\n",
    "                    \"tl_m85_pl\": int,\n",
    "                    \"total_female\": int, \n",
    "                    \"tl_f0_5\": int,\n",
    "                    \"tl_f5_9\": int,\n",
    "                    \"tl_f10_14\": int,\n",
    "                    \"tl_f15_17\": int,\n",
    "                    \"tl_f18_19\": int,\n",
    "                    \"tl_f20\": int,\n",
    "                    \"tl_f21\": int,\n",
    "                    \"tl_f22_24\": int,\n",
    "                    \"tl_f25_29\": int,\n",
    "                    \"tl_f30_34\": int,\n",
    "                    \"tl_f35_39\": int,\n",
    "                    \"tl_f40_44\": int,\n",
    "                    \"tl_f45_49\": int,\n",
    "                    \"tl_f50_54\": int,\n",
    "                    \"tl_f55_59\": int,\n",
    "                    \"tl_f60_61\": int,\n",
    "                    \"tl_f62_64\": int,\n",
    "                    \"tl_f65_66\": int,\n",
    "                    \"tl_f67_69\": int,\n",
    "                    \"tl_f70_74\": int,\n",
    "                    \"tl_f75_79\": int,\n",
    "                    \"tl_f80_84\": int,\n",
    "                    \"tl_f85_pl\": int,\n",
    "                    \"county\": int,\n",
    "                    'tract': int\n",
    "                }\n",
    "df_age_tracts = df_age_tracts.astype(dtype_conversion) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1107,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_18_under = df_age_tracts[\"tl_m0_5\"] + df_age_tracts[\"tl_f0_5\"] + df_age_tracts[\"tl_m5_9\"] + df_age_tracts[\"tl_m10_14\"] + df_age_tracts[\"tl_m15_17\"] + df_age_tracts[\"tl_f5_9\"] + df_age_tracts[\"tl_f10_14\"] + df_age_tracts[\"tl_f15_17\"]\n",
    "\n",
    "age_65_over = df_age_tracts[\"tl_f65_66\"] + df_age_tracts[\"tl_f67_69\"] + df_age_tracts[\"tl_f70_74\"] + df_age_tracts[\"tl_f75_79\"] + df_age_tracts[\"tl_f80_84\"] + df_age_tracts[\"tl_f85_pl\"] + df_age_tracts[\"tl_m65_66\"] + df_age_tracts[\"tl_m67_69\"] + df_age_tracts[\"tl_m70_74\"] + df_age_tracts[\"tl_m75_79\"] + df_age_tracts[\"tl_m80_84\"] + df_age_tracts[\"tl_m85_pl\"]\n",
    "\n",
    "age_vulnerable = age_18_under + age_65_over\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_age_tracts[\"age_18_under\"] = age_18_under \n",
    "df_age_tracts[\"age_65_over\"] = age_65_over\n",
    "df_age_tracts[\"age_vulnerable\"] = age_vulnerable\n",
    "\n",
    "df_age_tracts[\"pct_age_18_under\"] = df_age_tracts[\"age_18_under\"]/df_age_tracts[\"total_pop\"]*100\n",
    "df_age_tracts[\"pct_age_65_over\"] = df_age_tracts[\"age_65_over\"]/df_age_tracts[\"total_pop\"]*100\n",
    "df_age_tracts[\"pct_age_vulnerable\"] = df_age_tracts[\"age_vulnerable\"]/df_age_tracts[\"total_pop\"]*100\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_age_tracts_simple = df_age_tracts[[\"total_pop\",\"age_18_under\",\"age_65_over\",\"age_vulnerable\", \"pct_age_18_under\",\"pct_age_65_over\",\"pct_age_vulnerable\",\"geoid\",\"state\",\"county\",\"tract\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1110,
   "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>total_pop</th>\n",
       "      <th>age_18_under</th>\n",
       "      <th>age_65_over</th>\n",
       "      <th>age_vulnerable</th>\n",
       "      <th>pct_age_18_under</th>\n",
       "      <th>pct_age_65_over</th>\n",
       "      <th>pct_age_vulnerable</th>\n",
       "      <th>geoid</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>tract</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6570</td>\n",
       "      <td>1265</td>\n",
       "      <td>1052</td>\n",
       "      <td>2317</td>\n",
       "      <td>19.254186</td>\n",
       "      <td>16.012177</td>\n",
       "      <td>35.266362</td>\n",
       "      <td>1400000US06001441501</td>\n",
       "      <td>06</td>\n",
       "      <td>1</td>\n",
       "      <td>441501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2079</td>\n",
       "      <td>453</td>\n",
       "      <td>391</td>\n",
       "      <td>844</td>\n",
       "      <td>21.789322</td>\n",
       "      <td>18.807119</td>\n",
       "      <td>40.596441</td>\n",
       "      <td>1400000US06001404700</td>\n",
       "      <td>06</td>\n",
       "      <td>1</td>\n",
       "      <td>404700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7748</td>\n",
       "      <td>1484</td>\n",
       "      <td>795</td>\n",
       "      <td>2279</td>\n",
       "      <td>19.153330</td>\n",
       "      <td>10.260712</td>\n",
       "      <td>29.414042</td>\n",
       "      <td>1400000US06001442500</td>\n",
       "      <td>06</td>\n",
       "      <td>1</td>\n",
       "      <td>442500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5301</td>\n",
       "      <td>1347</td>\n",
       "      <td>472</td>\n",
       "      <td>1819</td>\n",
       "      <td>25.410300</td>\n",
       "      <td>8.903980</td>\n",
       "      <td>34.314280</td>\n",
       "      <td>1400000US06001450300</td>\n",
       "      <td>06</td>\n",
       "      <td>1</td>\n",
       "      <td>450300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5971</td>\n",
       "      <td>1393</td>\n",
       "      <td>795</td>\n",
       "      <td>2188</td>\n",
       "      <td>23.329426</td>\n",
       "      <td>13.314353</td>\n",
       "      <td>36.643778</td>\n",
       "      <td>1400000US06001450607</td>\n",
       "      <td>06</td>\n",
       "      <td>1</td>\n",
       "      <td>450607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_pop  age_18_under  age_65_over  age_vulnerable  pct_age_18_under  \\\n",
       "0       6570          1265         1052            2317         19.254186   \n",
       "1       2079           453          391             844         21.789322   \n",
       "2       7748          1484          795            2279         19.153330   \n",
       "3       5301          1347          472            1819         25.410300   \n",
       "4       5971          1393          795            2188         23.329426   \n",
       "\n",
       "   pct_age_65_over  pct_age_vulnerable                 geoid state  county  \\\n",
       "0        16.012177           35.266362  1400000US06001441501    06       1   \n",
       "1        18.807119           40.596441  1400000US06001404700    06       1   \n",
       "2        10.260712           29.414042  1400000US06001442500    06       1   \n",
       "3         8.903980           34.314280  1400000US06001450300    06       1   \n",
       "4        13.314353           36.643778  1400000US06001450607    06       1   \n",
       "\n",
       "    tract  \n",
       "0  441501  \n",
       "1  404700  \n",
       "2  442500  \n",
       "3  450300  \n",
       "4  450607  "
      ]
     },
     "execution_count": 1110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_tracts_simple.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add geoid_join column \n",
    "df_age_tracts_simple.insert(8, \"geoid_join\",df_transpo_mode_tracts['geoid'].str.slice(-11), True) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#B19013 Median Household Income - TRACT\n",
    "\n",
    "# THESE PREDICATES GET DATA FOR EVERY TRACT\n",
    "#NAME dropped from request because only 50 variables per request are permitted.\n",
    "request_predicates = {}\n",
    "get_vars_mhi = [\"NAME\",\"B19013_001E\", \"GEO_ID\"\n",
    "                ]\n",
    "\n",
    "col_names_mhi = [\"place_name\", \"mhi\",\n",
    "                 \"geoid\",\"state\",\"county\", \"tract\"\n",
    "                ]\n",
    "\n",
    "request_predicates[\"key\"] = api_key\n",
    "request_predicates[\"get\"] = \",\".join(get_vars_mhi)\n",
    "\n",
    "\n",
    "\n",
    "request_predicates[\"for\"] = \"tract:*\"\n",
    "request_predicates[\"in\"] = f\"state:06+county:{county}\"\n",
    "# request_predicates[\"in\"] = \"state:06+county:067\"\n",
    "\n",
    "mhi_tracts = requests.get(base_url, params=request_predicates)\n",
    "num_columns = 50\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#B19013 Median Household Income - COUNTY\n",
    "\n",
    "request_predicates = {}\n",
    "get_vars_mhi_county = [\"NAME\",\"B19013_001E\", \"GEO_ID\"\n",
    "                ]\n",
    "\n",
    "col_names_mhi_county = [\"place_name\", \"mhi\",\n",
    "                 \"geoid\",\"state\",\"county\"\n",
    "                ]\n",
    "\n",
    "request_predicates[\"key\"] = api_key\n",
    "request_predicates[\"get\"] = \",\".join(get_vars_mhi_county)\n",
    "\n",
    "\n",
    "#THESE PREDICATES GET AGGREGATE TOTALS FOR ENTIRE COUNTY (SACRAMENTO)\n",
    "#request_predicates[\"for\"] = \"county:067\" #Sacramento\n",
    "request_predicates[\"for\"] = f\"county:{county}\"  \n",
    "request_predicates[\"in\"] = \"state:06\"\n",
    "\n",
    "mhi_county = requests.get(base_url, params=request_predicates)\n",
    "num_columns = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n"
     ]
    }
   ],
   "source": [
    "print(mhi_tracts_county)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mhi_county = pd.DataFrame(columns=col_names_mhi_county, data=mhi_county.json()[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1116,
   "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>place_name</th>\n",
       "      <th>mhi</th>\n",
       "      <th>geoid</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alameda County, California</td>\n",
       "      <td>92574</td>\n",
       "      <td>0500000US06001</td>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   place_name    mhi           geoid state county\n",
       "0  Alameda County, California  92574  0500000US06001    06    001"
      ]
     },
     "execution_count": 1116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mhi_county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting up AGE data frame, getting rid of first header row\n",
    "df_mhi = pd.DataFrame(columns=col_names_mhi, data=mhi_tracts.json()[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mhi.insert(6, \"geoid_join\",df_mhi['geoid'].str.slice(-11), True) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1119,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_age_simplified = df_age_tracts_simple[['total_pop', 'age_18_under', 'age_65_over', 'age_vulnerable',\n",
    "       'pct_age_18_under', 'pct_age_65_over', 'pct_age_vulnerable',\n",
    "       'geoid_join']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1120,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#df_transpo_mode_tracts\n",
    "df_mhi_simplified = df_mhi[['mhi','geoid_join']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate all Equity geodataframes\n",
    "# In [4]: frames = [df1, df2, df3]\n",
    "# In [5]: result = pd.concat(frames)\n",
    "\n",
    "transpo_mhi = pd.merge(df_transpo_mode_tracts, df_mhi_simplified, on=\"geoid_join\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1122,
   "metadata": {},
   "outputs": [],
   "source": [
    "transpo_mhi_age = pd.merge(transpo_mhi,df_age_simplified,  on= \"geoid_join\" )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join the census data to Tigerline Cartographic Boundary census tract geometries. \n",
    "# Census Tract Tigerline California Census Tracts location (ftp url included in notes below)\n",
    "\n",
    "census_tracts_shp = '/Users/calvindechicago/PycharmProjects/AltaWork/automated_census_mapping/cb_2018_06_tract_500k/cb_2018_06_tract_500k.shp'\n",
    "# Verify that filepath is correct\n",
    "os.path.isfile(census_tracts_shp)\n",
    "\n",
    "#This reads the census tracts shapefile into a geodataframe\n",
    "gdf = gpd.read_file(census_tracts_shp)\n",
    "\n",
    "#Make tigerline boundary columns lowercase\n",
    "gdf.columns = map(str.lower, gdf.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1124,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_equity_geom = gdf.merge(transpo_mhi_age,left_on='geoid',right_on='geoid_join')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(final_equity_geom))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_equity_geom.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1127,
   "metadata": {},
   "outputs": [],
   "source": [
    "calenviroscreen = '/Users/calvindechicago/PycharmProjects/AltaWork/automated_census_mapping/CESJune2018Update_SHP/CES3June2018Update.shp'\n",
    "\n",
    "#This reads the CalEnviroScreen tracts shapefile into a geodataframe\n",
    "ces_gdf = gpd.read_file(calenviroscreen)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4269>\n",
       "Name: NAD83\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: North America - NAD83\n",
       "- bounds: (167.65, 14.92, -47.74, 86.46)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 1128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_equity_geom.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tract', 'pop2010', 'California', 'ZIP', 'City', 'Longitude',\n",
       "       'Latitude', 'CIscore', 'CIscoreP', 'ozone', 'ozoneP', 'pm', 'pmP',\n",
       "       'diesel', 'dieselP', 'drink', 'drinkP', 'pest', 'pestP', 'RSEIhaz',\n",
       "       'RSEIhazP', 'traffic', 'trafficP', 'cleanups', 'cleanupsP', 'gwthreats',\n",
       "       'gwthreatsP', 'haz', 'hazP', 'iwb', 'iwbP', 'swis', 'swisP',\n",
       "       'Pollution', 'PollutionS', 'PollutionP', 'asthma', 'asthmaP', 'lbw',\n",
       "       'lbwP', 'cvd', 'cvdP', 'edu', 'eduP', 'ling', 'lingP', 'pov', 'povP',\n",
       "       'unemp', 'unempP', 'housingB', 'housingBP', 'PopChar', 'PopCharSco',\n",
       "       'PopCharP', 'Children_u', 'Pop_11_64_', 'Elderly_ov', 'Hispanic_p',\n",
       "       'White_pct', 'African_Am', 'Native_Ame', 'Asian_Amer', 'Other_pct',\n",
       "       'OBJECTID_1', 'CIdecile', 'CIvigintil', 'Shape_Leng', 'Shape_Area',\n",
       "       'CES2018_Rn', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ces_gdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert weird CES tract id floats to ints. \n",
    "ces_gdf.tract = ces_gdf.tract.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1131,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now convert new and cleaner integers to strings\n",
    "ces_gdf.tract = ces_gdf.tract.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Append 0 to beginning of each tract id\n",
    "ces_gdf.tract = '0' + ces_gdf.tract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       06083002103\n",
       "1       06083002402\n",
       "2       06083002102\n",
       "3       06083002010\n",
       "4       06083002009\n",
       "           ...     \n",
       "8030    06037430302\n",
       "8031    06037430723\n",
       "8032    06037431100\n",
       "8033    06037533603\n",
       "8034    06037534101\n",
       "Name: tract, Length: 8035, dtype: object"
      ]
     },
     "execution_count": 1133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ces_gdf.tract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1134,
   "metadata": {},
   "outputs": [],
   "source": [
    "ces_gdf_simplified = ces_gdf[['tract','CIscore', 'CIscoreP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f909e0cd9d0>"
      ]
     },
     "execution_count": 1135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "final_equity_geom.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1136,
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_equity_geom = gdf.merge(transpo_mhi_age,left_on='geoid',right_on='geoid_join')\n",
    "\n",
    "\n",
    "final_equity_output = final_equity_geom.merge(ces_gdf_simplified, left_on='geoid_join', right_on='tract')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(final_equity_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>statefp</th>\n",
       "      <th>countyfp</th>\n",
       "      <th>tractce</th>\n",
       "      <th>affgeoid</th>\n",
       "      <th>geoid_x</th>\n",
       "      <th>name</th>\n",
       "      <th>lsad</th>\n",
       "      <th>aland</th>\n",
       "      <th>awater</th>\n",
       "      <th>geometry</th>\n",
       "      <th>...</th>\n",
       "      <th>total_pop</th>\n",
       "      <th>age_18_under</th>\n",
       "      <th>age_65_over</th>\n",
       "      <th>age_vulnerable</th>\n",
       "      <th>pct_age_18_under</th>\n",
       "      <th>pct_age_65_over</th>\n",
       "      <th>pct_age_vulnerable</th>\n",
       "      <th>tract_y</th>\n",
       "      <th>CIscore</th>\n",
       "      <th>CIscoreP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06</td>\n",
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       "      <td>4251.01</td>\n",
       "      <td>CT</td>\n",
       "      <td>590870</td>\n",
       "      <td>2045459</td>\n",
       "      <td>POLYGON ((-122.31419 37.84231, -122.29923 37.8...</td>\n",
       "      <td>...</td>\n",
       "      <td>1588</td>\n",
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       "      <td>548</td>\n",
       "      <td>3.463476</td>\n",
       "      <td>31.045340</td>\n",
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       "      <td>30.63</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
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       "      <td>1400000US06001428600</td>\n",
       "      <td>06001428600</td>\n",
       "      <td>4286</td>\n",
       "      <td>CT</td>\n",
       "      <td>898967</td>\n",
       "      <td>1080420</td>\n",
       "      <td>POLYGON ((-122.27993 37.76818, -122.27849 37.7...</td>\n",
       "      <td>...</td>\n",
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       "      <td>824</td>\n",
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       "      <td>15.125324</td>\n",
       "      <td>23.739556</td>\n",
       "      <td>38.864880</td>\n",
       "      <td>06001428600</td>\n",
       "      <td>14.47</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>432600</td>\n",
       "      <td>1400000US06001432600</td>\n",
       "      <td>06001432600</td>\n",
       "      <td>4326</td>\n",
       "      <td>CT</td>\n",
       "      <td>1673450</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.16751 37.72632, -122.16108 37.7...</td>\n",
       "      <td>...</td>\n",
       "      <td>7314</td>\n",
       "      <td>1296</td>\n",
       "      <td>939</td>\n",
       "      <td>2235</td>\n",
       "      <td>17.719442</td>\n",
       "      <td>12.838392</td>\n",
       "      <td>30.557834</td>\n",
       "      <td>06001432600</td>\n",
       "      <td>36.40</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>433200</td>\n",
       "      <td>1400000US06001433200</td>\n",
       "      <td>06001433200</td>\n",
       "      <td>4332</td>\n",
       "      <td>CT</td>\n",
       "      <td>3174901</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.16667 37.71042, -122.15559 37.7...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06</td>\n",
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       "      <td>1400000US06001433900</td>\n",
       "      <td>06001433900</td>\n",
       "      <td>4339</td>\n",
       "      <td>CT</td>\n",
       "      <td>816585</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.12091 37.69998, -122.11723 37.7...</td>\n",
       "      <td>...</td>\n",
       "      <td>7440</td>\n",
       "      <td>2026</td>\n",
       "      <td>496</td>\n",
       "      <td>2522</td>\n",
       "      <td>27.231183</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>33.897849</td>\n",
       "      <td>06001433900</td>\n",
       "      <td>32.01</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
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       "      <td>1400000US06001427300</td>\n",
       "      <td>06001427300</td>\n",
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       "      <td>CT</td>\n",
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       "      <td>POLYGON ((-122.27671 37.77719, -122.27663 37.7...</td>\n",
       "      <td>...</td>\n",
       "      <td>5084</td>\n",
       "      <td>1057</td>\n",
       "      <td>728</td>\n",
       "      <td>1785</td>\n",
       "      <td>20.790716</td>\n",
       "      <td>14.319434</td>\n",
       "      <td>35.110149</td>\n",
       "      <td>06001427300</td>\n",
       "      <td>33.76</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>428400</td>\n",
       "      <td>1400000US06001428400</td>\n",
       "      <td>06001428400</td>\n",
       "      <td>4284</td>\n",
       "      <td>CT</td>\n",
       "      <td>810521</td>\n",
       "      <td>1168052</td>\n",
       "      <td>POLYGON ((-122.25493 37.75988, -122.25462 37.7...</td>\n",
       "      <td>...</td>\n",
       "      <td>4653</td>\n",
       "      <td>852</td>\n",
       "      <td>701</td>\n",
       "      <td>1553</td>\n",
       "      <td>18.310767</td>\n",
       "      <td>15.065549</td>\n",
       "      <td>33.376316</td>\n",
       "      <td>06001428400</td>\n",
       "      <td>25.13</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>430600</td>\n",
       "      <td>1400000US06001430600</td>\n",
       "      <td>06001430600</td>\n",
       "      <td>4306</td>\n",
       "      <td>CT</td>\n",
       "      <td>2261416</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.10628 37.70976, -122.10524 37.7...</td>\n",
       "      <td>...</td>\n",
       "      <td>6624</td>\n",
       "      <td>1264</td>\n",
       "      <td>1506</td>\n",
       "      <td>2770</td>\n",
       "      <td>19.082126</td>\n",
       "      <td>22.735507</td>\n",
       "      <td>41.817633</td>\n",
       "      <td>06001430600</td>\n",
       "      <td>14.94</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>451501</td>\n",
       "      <td>1400000US06001451501</td>\n",
       "      <td>06001451501</td>\n",
       "      <td>4515.01</td>\n",
       "      <td>CT</td>\n",
       "      <td>5051294</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-121.76526 37.67997, -121.75512 37.6...</td>\n",
       "      <td>...</td>\n",
       "      <td>4751</td>\n",
       "      <td>1137</td>\n",
       "      <td>818</td>\n",
       "      <td>1955</td>\n",
       "      <td>23.931804</td>\n",
       "      <td>17.217428</td>\n",
       "      <td>41.149232</td>\n",
       "      <td>06001451501</td>\n",
       "      <td>26.20</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>435102</td>\n",
       "      <td>1400000US06001435102</td>\n",
       "      <td>06001435102</td>\n",
       "      <td>4351.02</td>\n",
       "      <td>CT</td>\n",
       "      <td>4225670</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.06510 37.65165, -122.06114 37.6...</td>\n",
       "      <td>...</td>\n",
       "      <td>6245</td>\n",
       "      <td>848</td>\n",
       "      <td>814</td>\n",
       "      <td>1662</td>\n",
       "      <td>13.578863</td>\n",
       "      <td>13.034428</td>\n",
       "      <td>26.613291</td>\n",
       "      <td>06001435102</td>\n",
       "      <td>16.90</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    statefp countyfp tractce              affgeoid      geoid_x     name lsad  \\\n",
       "0        06      001  425101  1400000US06001425101  06001425101  4251.01   CT   \n",
       "1        06      001  428600  1400000US06001428600  06001428600     4286   CT   \n",
       "2        06      001  432600  1400000US06001432600  06001432600     4326   CT   \n",
       "3        06      001  433200  1400000US06001433200  06001433200     4332   CT   \n",
       "4        06      001  433900  1400000US06001433900  06001433900     4339   CT   \n",
       "..      ...      ...     ...                   ...          ...      ...  ...   \n",
       "355      06      001  427300  1400000US06001427300  06001427300     4273   CT   \n",
       "356      06      001  428400  1400000US06001428400  06001428400     4284   CT   \n",
       "357      06      001  430600  1400000US06001430600  06001430600     4306   CT   \n",
       "358      06      001  451501  1400000US06001451501  06001451501  4515.01   CT   \n",
       "359      06      001  435102  1400000US06001435102  06001435102  4351.02   CT   \n",
       "\n",
       "       aland   awater                                           geometry  ...  \\\n",
       "0     590870  2045459  POLYGON ((-122.31419 37.84231, -122.29923 37.8...  ...   \n",
       "1     898967  1080420  POLYGON ((-122.27993 37.76818, -122.27849 37.7...  ...   \n",
       "2    1673450        0  POLYGON ((-122.16751 37.72632, -122.16108 37.7...  ...   \n",
       "3    3174901        0  POLYGON ((-122.16667 37.71042, -122.15559 37.7...  ...   \n",
       "4     816585        0  POLYGON ((-122.12091 37.69998, -122.11723 37.7...  ...   \n",
       "..       ...      ...                                                ...  ...   \n",
       "355  1950568   861367  POLYGON ((-122.27671 37.77719, -122.27663 37.7...  ...   \n",
       "356   810521  1168052  POLYGON ((-122.25493 37.75988, -122.25462 37.7...  ...   \n",
       "357  2261416        0  POLYGON ((-122.10628 37.70976, -122.10524 37.7...  ...   \n",
       "358  5051294        0  POLYGON ((-121.76526 37.67997, -121.75512 37.6...  ...   \n",
       "359  4225670        0  POLYGON ((-122.06510 37.65165, -122.06114 37.6...  ...   \n",
       "\n",
       "    total_pop  age_18_under  age_65_over  age_vulnerable  pct_age_18_under  \\\n",
       "0        1588            55          493             548          3.463476   \n",
       "1        3471           525          824            1349         15.125324   \n",
       "2        7314          1296          939            2235         17.719442   \n",
       "3        7625          1944          951            2895         25.495082   \n",
       "4        7440          2026          496            2522         27.231183   \n",
       "..        ...           ...          ...             ...               ...   \n",
       "355      5084          1057          728            1785         20.790716   \n",
       "356      4653           852          701            1553         18.310767   \n",
       "357      6624          1264         1506            2770         19.082126   \n",
       "358      4751          1137          818            1955         23.931804   \n",
       "359      6245           848          814            1662         13.578863   \n",
       "\n",
       "     pct_age_65_over  pct_age_vulnerable      tract_y  CIscore  CIscoreP  \n",
       "0          31.045340           34.508816  06001425101    30.63        61  \n",
       "1          23.739556           38.864880  06001428600    14.47        24  \n",
       "2          12.838392           30.557834  06001432600    36.40        71  \n",
       "3          12.472131           37.967213  06001433200    44.90        83  \n",
       "4           6.666667           33.897849  06001433900    32.01        63  \n",
       "..               ...                 ...          ...      ...       ...  \n",
       "355        14.319434           35.110149  06001427300    33.76        66  \n",
       "356        15.065549           33.376316  06001428400    25.13        50  \n",
       "357        22.735507           41.817633  06001430600    14.94        25  \n",
       "358        17.217428           41.149232  06001451501    26.20        52  \n",
       "359        13.034428           26.613291  06001435102    16.90        30  \n",
       "\n",
       "[360 rows x 39 columns]"
      ]
     },
     "execution_count": 1138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_equity_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['statefp', 'countyfp', 'tractce', 'affgeoid', 'geoid', 'name', 'lsad',\n",
       "       'aland', 'awater', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1140,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_equity_output.to_file(Equity_Tracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f907e6050d0>"
      ]
     },
     "execution_count": 1141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "final_equity_geom.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1142,
   "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>statefp</th>\n",
       "      <th>countyfp</th>\n",
       "      <th>tractce</th>\n",
       "      <th>affgeoid</th>\n",
       "      <th>geoid_x</th>\n",
       "      <th>name</th>\n",
       "      <th>lsad</th>\n",
       "      <th>aland</th>\n",
       "      <th>awater</th>\n",
       "      <th>geometry</th>\n",
       "      <th>...</th>\n",
       "      <th>total_pop</th>\n",
       "      <th>age_18_under</th>\n",
       "      <th>age_65_over</th>\n",
       "      <th>age_vulnerable</th>\n",
       "      <th>pct_age_18_under</th>\n",
       "      <th>pct_age_65_over</th>\n",
       "      <th>pct_age_vulnerable</th>\n",
       "      <th>tract_y</th>\n",
       "      <th>CIscore</th>\n",
       "      <th>CIscoreP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>06</td>\n",
       "      <td>001</td>\n",
       "      <td>450752</td>\n",
       "      <td>1400000US06001450752</td>\n",
       "      <td>06001450752</td>\n",
       "      <td>4507.52</td>\n",
       "      <td>CT</td>\n",
       "      <td>17421881</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-121.88157 37.73733, -121.87354 37.7...</td>\n",
       "      <td>...</td>\n",
       "      <td>6642</td>\n",
       "      <td>2051</td>\n",
       "      <td>490</td>\n",
       "      <td>2541</td>\n",
       "      <td>30.879253</td>\n",
       "      <td>7.377296</td>\n",
       "      <td>38.256549</td>\n",
       "      <td>06001450752</td>\n",
       "      <td>17.21</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    statefp countyfp tractce              affgeoid      geoid_x     name lsad  \\\n",
       "322      06      001  450752  1400000US06001450752  06001450752  4507.52   CT   \n",
       "\n",
       "        aland  awater                                           geometry  ...  \\\n",
       "322  17421881       0  POLYGON ((-121.88157 37.73733, -121.87354 37.7...  ...   \n",
       "\n",
       "    total_pop  age_18_under  age_65_over  age_vulnerable  pct_age_18_under  \\\n",
       "322      6642          2051          490            2541         30.879253   \n",
       "\n",
       "     pct_age_65_over  pct_age_vulnerable      tract_y  CIscore  CIscoreP  \n",
       "322         7.377296           38.256549  06001450752    17.21        31  \n",
       "\n",
       "[1 rows x 39 columns]"
      ]
     },
     "execution_count": 1142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test that previous join error did not occur. \n",
    "final_equity_output.loc[final_equity_output['geoid_join']=='06001450752']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      06001425101\n",
       "1      06001428600\n",
       "2      06001432600\n",
       "3      06001433200\n",
       "4      06001433900\n",
       "          ...     \n",
       "355    06001427300\n",
       "356    06001428400\n",
       "357    06001430600\n",
       "358    06001451501\n",
       "359    06001435102\n",
       "Name: geoid_join, Length: 360, dtype: object"
      ]
     },
     "execution_count": 1143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_equity_output['geoid_join']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Export county level dataframes to excel\n",
    "\n",
    "with pd.ExcelWriter(EXCEL_FP) as writer:\n",
    "    df_age_county.to_excel(writer, sheet_name='county_age')\n",
    "    df_transpo_mode_county.to_excel(writer, sheet_name='county_transpo_mode')\n",
    "    df_mhi_county.to_excel(writer, sheet_name='county_mhi')\n",
    "\n",
    "# OTHER EXAMPLES FOUND WHILE TROUBLESHOOTING\n",
    "# with pd.ExcelWriter(\"test.xlsx\", engine='openpyxl', mode='a') as writer:\n",
    "#     df.to_excel(writer)\n",
    "# with pd.ExcelWriter(Excel_FP) as writer:\n",
    "#     bike_crashes_by_year.to_excel(writer, sheet_name='b_crashes_by_year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SOURCES\n",
    "#https://www.w3schools.com/tags/ref_urlencode.ASP\n",
    "#https://www.census.gov/content/dam/Census/data/developers/api-user-guide/api-guide.pdf\n",
    "#https://api.census.gov/data/2018/acs/acs5/variables.html\n",
    "#https://api.census.gov/data/2018/acs/acs5/subject/variables.html\n",
    "#https://www.youtube.com/watch?v=Wi0_Mb0e4JM\n",
    "#https://atcoordinates.info/2019/09/24/examples-of-using-the-census-bureaus-api-with-python/\n",
    "#--downloading tigerline from zip -- \n",
    "#http://andrewgaidus.com/Dot_Density_County_Maps/\n",
    "#--Python for reading zip tigerline shpfile --\n",
    "#http://andrewgaidus.com/Reading_Zipped_Shapefiles/\n",
    "#--Aaron's ATP Data Mining Project would also be useful--\n",
    "#https://github.com/AltaPlanning/GIS-notebooks/tree/master/2020-000%20ATP%20Data%20Mining\n",
    "#https://automating-gis-processes.github.io/site/notebooks/L5/interactive-map-folium.html\n",
    "\n",
    "#-- geographies and summary levels --\n",
    "#https://censusreporter.org/topics/geography/\n",
    "#geo_ids=140|04000US06  --> this should be a all tracts in California\n",
    "\n",
    "#There is no great way to use the api to return census tract geometries: the geography api functions \n",
    "#only seem to allow calling a specific geoid. One option would be to loop through geoids and call census reporter\n",
    "#to request geography for each geoid, but that would involve a lot of calls. \n",
    "#https://api.censusreporter.org/1.0/data/show/latest?table_ids=B01001&geo_ids=140|04000US06\n",
    "#error\"You requested 8057 geoids. The maximum is 3500. Please contact us for bulk data.\"\n",
    "\n",
    "\n",
    "\n",
    "# MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2018 INFLATION-ADJUSTED DOLLARS) BY MEANS OF TRANSPORTATION TO WORK\n",
    "# Survey/Program: American Community Survey\n",
    "# Universe: Workers 16 years and over with earnings\n",
    "# Year: 2018\n",
    "# Estimates: 1-Year\n",
    "# Table ID: B08121\n",
    "\n",
    "# ### OTHER NOTES\n",
    "#I was a bit confused about obtaining the Census Tiger boundaries. The Tigerweb REST service seemed geared towards\n",
    "#delivering Web Map Service (WMS) map images. We want the spatial data! Hopefully the stack exchange post linked below\n",
    "#clears some of the confusion up.\n",
    "#https://gis.stackexchange.com/questions/269650/how-to-bring-the-tiger-census-reporter-api-to-geopandas\n",
    "#--->I'm not sure if these geometries still exist on the census api. \n",
    "#zipfiles can be downloaded at the ftp site below. I am using blog and aaron's atp data mining python as examples. \n",
    "#For now I am just going to manually unzip census geometry, but a link to a tutorial is included below to automate\n",
    "#download, unzipping, and processing the geometry. \n",
    "\n",
    "#ftp://ftp2.census.gov/geo/tiger/TIGER2018/TRACT/tl_2018_06_tract.zip\n",
    "\n"
   ]
  }
 ],
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