From 933825c38999201d1041380d01195ce155137586 Mon Sep 17 00:00:00 2001
From: Mike <mgunsolus@lockportschools.net>
Date: Fri, 25 Aug 2023 11:28:19 -0400
Subject: [PATCH] I enjoyed this project and seeing some incedibly interesting
 data!

---
 .ipynb_checkpoints/argument-checkpoint.ipynb | 749 +++++++++++++++++++
 argument.ipynb                               | 465 +++++++++++-
 data/metro-grades.csv                        | 552 ++++++++++++++
 pyproject.toml                               |   3 +-
 4 files changed, 1729 insertions(+), 40 deletions(-)
 create mode 100644 .ipynb_checkpoints/argument-checkpoint.ipynb
 create mode 100644 data/metro-grades.csv

diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb
new file mode 100644
index 0000000..75ce1a3
--- /dev/null
+++ b/.ipynb_checkpoints/argument-checkpoint.ipynb
@@ -0,0 +1,749 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "worldwide-blood",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "understanding-numbers",
+   "metadata": {},
+   "source": [
+    "*✏️ Write 2-3 sentences describing your research.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "greater-circular",
+   "metadata": {},
+   "source": [
+    "## Overarching Question: [How has the foundational implementation of government redlining had an effect on generational wealth by race and socioeconomic standing over time?]✏"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "appreciated-testimony",
+   "metadata": {},
+   "source": [
+    "This is an important question as we look at our cities and, still today, see heavily segregated areas which must have a foundational reason.  Hopefully through this data set we see an irrefutable pattern that brings clarity to the overarching question."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "permanent-pollution",
+   "metadata": {},
+   "source": [
+    "# Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "technical-evans",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Include any import statements you will need\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "overhead-sigma",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
+    "\n",
+    "file_name = \"metro-grades.csv\"\n",
+    "dataset_path = \"data/\" + file_name\n",
+    "\n",
+    "df = pd.read_csv(dataset_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "heated-blade",
+   "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>metro_area</th>\n",
+       "      <th>holc_grade</th>\n",
+       "      <th>white_pop</th>\n",
+       "      <th>black_pop</th>\n",
+       "      <th>hisp_pop</th>\n",
+       "      <th>asian_pop</th>\n",
+       "      <th>other_pop</th>\n",
+       "      <th>total_pop</th>\n",
+       "      <th>pct_white</th>\n",
+       "      <th>pct_black</th>\n",
+       "      <th>...</th>\n",
+       "      <th>surr_area_white_pop</th>\n",
+       "      <th>surr_area_black_pop</th>\n",
+       "      <th>surr_area_hisp_pop</th>\n",
+       "      <th>surr_area_asian_pop</th>\n",
+       "      <th>surr_area_other_pop</th>\n",
+       "      <th>surr_area_pct_white</th>\n",
+       "      <th>surr_area_pct_black</th>\n",
+       "      <th>surr_area_pct_hisp</th>\n",
+       "      <th>surr_area_pct_asian</th>\n",
+       "      <th>surr_area_pct_other</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Akron, OH</td>\n",
+       "      <td>A</td>\n",
+       "      <td>24702</td>\n",
+       "      <td>8624</td>\n",
+       "      <td>956</td>\n",
+       "      <td>688</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>36963</td>\n",
+       "      <td>66.83</td>\n",
+       "      <td>23.33</td>\n",
+       "      <td>...</td>\n",
+       "      <td>304399</td>\n",
+       "      <td>70692</td>\n",
+       "      <td>11037</td>\n",
+       "      <td>17295</td>\n",
+       "      <td>23839</td>\n",
+       "      <td>71.24</td>\n",
+       "      <td>16.55</td>\n",
+       "      <td>2.58</td>\n",
+       "      <td>4.05</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Akron, OH</td>\n",
+       "      <td>B</td>\n",
+       "      <td>41531</td>\n",
+       "      <td>16499</td>\n",
+       "      <td>2208</td>\n",
+       "      <td>3367</td>\n",
+       "      <td>4211</td>\n",
+       "      <td>67816</td>\n",
+       "      <td>61.24</td>\n",
+       "      <td>24.33</td>\n",
+       "      <td>...</td>\n",
+       "      <td>304399</td>\n",
+       "      <td>70692</td>\n",
+       "      <td>11037</td>\n",
+       "      <td>17295</td>\n",
+       "      <td>23839</td>\n",
+       "      <td>71.24</td>\n",
+       "      <td>16.55</td>\n",
+       "      <td>2.58</td>\n",
+       "      <td>4.05</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Akron, OH</td>\n",
+       "      <td>C</td>\n",
+       "      <td>73105</td>\n",
+       "      <td>22847</td>\n",
+       "      <td>3149</td>\n",
+       "      <td>6291</td>\n",
+       "      <td>7302</td>\n",
+       "      <td>112694</td>\n",
+       "      <td>64.87</td>\n",
+       "      <td>20.27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>304399</td>\n",
+       "      <td>70692</td>\n",
+       "      <td>11037</td>\n",
+       "      <td>17295</td>\n",
+       "      <td>23839</td>\n",
+       "      <td>71.24</td>\n",
+       "      <td>16.55</td>\n",
+       "      <td>2.58</td>\n",
+       "      <td>4.05</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Akron, OH</td>\n",
+       "      <td>D</td>\n",
+       "      <td>6179</td>\n",
+       "      <td>6921</td>\n",
+       "      <td>567</td>\n",
+       "      <td>455</td>\n",
+       "      <td>1022</td>\n",
+       "      <td>15144</td>\n",
+       "      <td>40.80</td>\n",
+       "      <td>45.70</td>\n",
+       "      <td>...</td>\n",
+       "      <td>304399</td>\n",
+       "      <td>70692</td>\n",
+       "      <td>11037</td>\n",
+       "      <td>17295</td>\n",
+       "      <td>23839</td>\n",
+       "      <td>71.24</td>\n",
+       "      <td>16.55</td>\n",
+       "      <td>2.58</td>\n",
+       "      <td>4.05</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albany-Schenectady-Troy, NY</td>\n",
+       "      <td>A</td>\n",
+       "      <td>16989</td>\n",
+       "      <td>1818</td>\n",
+       "      <td>1317</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>1182</td>\n",
+       "      <td>23303</td>\n",
+       "      <td>72.91</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>...</td>\n",
+       "      <td>387016</td>\n",
+       "      <td>68371</td>\n",
+       "      <td>42699</td>\n",
+       "      <td>41112</td>\n",
+       "      <td>40596</td>\n",
+       "      <td>66.75</td>\n",
+       "      <td>11.79</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>7.09</td>\n",
+       "      <td>7.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    metro_area holc_grade  white_pop  black_pop  hisp_pop  \\\n",
+       "0                    Akron, OH          A      24702       8624       956   \n",
+       "1                    Akron, OH          B      41531      16499      2208   \n",
+       "2                    Akron, OH          C      73105      22847      3149   \n",
+       "3                    Akron, OH          D       6179       6921       567   \n",
+       "4  Albany-Schenectady-Troy, NY          A      16989       1818      1317   \n",
+       "\n",
+       "   asian_pop  other_pop  total_pop  pct_white  pct_black  ...  \\\n",
+       "0        688       1993      36963      66.83      23.33  ...   \n",
+       "1       3367       4211      67816      61.24      24.33  ...   \n",
+       "2       6291       7302     112694      64.87      20.27  ...   \n",
+       "3        455       1022      15144      40.80      45.70  ...   \n",
+       "4       1998       1182      23303      72.91       7.80  ...   \n",
+       "\n",
+       "   surr_area_white_pop  surr_area_black_pop  surr_area_hisp_pop  \\\n",
+       "0               304399                70692               11037   \n",
+       "1               304399                70692               11037   \n",
+       "2               304399                70692               11037   \n",
+       "3               304399                70692               11037   \n",
+       "4               387016                68371               42699   \n",
+       "\n",
+       "   surr_area_asian_pop  surr_area_other_pop  surr_area_pct_white  \\\n",
+       "0                17295                23839                71.24   \n",
+       "1                17295                23839                71.24   \n",
+       "2                17295                23839                71.24   \n",
+       "3                17295                23839                71.24   \n",
+       "4                41112                40596                66.75   \n",
+       "\n",
+       "   surr_area_pct_black  surr_area_pct_hisp  surr_area_pct_asian  \\\n",
+       "0                16.55                2.58                 4.05   \n",
+       "1                16.55                2.58                 4.05   \n",
+       "2                16.55                2.58                 4.05   \n",
+       "3                16.55                2.58                 4.05   \n",
+       "4                11.79                7.36                 7.09   \n",
+       "\n",
+       "   surr_area_pct_other  \n",
+       "0                 5.58  \n",
+       "1                 5.58  \n",
+       "2                 5.58  \n",
+       "3                 5.58  \n",
+       "4                 7.00  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1378c142-b0ef-4048-aad0-3674ec12c532",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"state\"] = df.metro_area.str.split(\", \").str.get(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "970ac473-ad2f-47df-b57d-f843992e2543",
+   "metadata": {},
+   "source": [
+    "# Messing Around"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "74a2f075-6acb-4d7c-b3b9-20d9bda274fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='holc_grade', ylabel='pct_white'>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot (data=df, y=\"pct_white\", x=\"holc_grade\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f71bb640-ea17-43c5-ba5e-0b785b7c1e63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='holc_grade', ylabel='pct_other'>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x200 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure, (ax0, ax1, ax2, ax3, ax4) = plt.subplots (1,5, figsize = (15, 2), sharey=True)\n",
+    "sns.barplot (data=df, y=\"pct_white\", x=\"holc_grade\", ax=ax0) \n",
+    "sns.barplot (data=df, y=\"pct_black\", x=\"holc_grade\", ax=ax1) \n",
+    "sns.barplot (data=df, y=\"pct_hisp\", x=\"holc_grade\", ax=ax2) \n",
+    "sns.barplot (data=df, y=\"pct_asian\", x=\"holc_grade\", ax=ax3) \n",
+    "sns.barplot (data=df, y=\"pct_other\", x=\"holc_grade\", ax=ax4) \n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "84231de8-ce69-4909-b69e-62a1744bad01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='holc_grade'>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "racial_perct = df.groupby (\"holc_grade\")[[\"pct_white\", \"pct_black\", \"pct_hisp\", \"pct_asian\", \"pct_other\"]].mean()\n",
+    "racial_perct.plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continental-franklin",
+   "metadata": {},
+   "source": [
+    "**Data Overview**\n",
+    "\n",
+    "The data above comes from a 2020 data set using the original redlining data maps from the 1930s.  Simply put, do these maps which were deemed illegal in 1967 still show that the damage over the years that they were used still exists today.  The data is relatively straight forward in that the drawing of these maps created zones, A being the most favorable, and D being the least favorable.  In the 1930s, the maps clearly show racial bias and those in the D  zones are heavily people of color who are unable to obtain mortgages, thus being subjected to having no way to gain equity in property, which most consider the best way to wealth over generations within a family.  Because of this practice, the question is really whether or not this has an effect on the current look of communities. Using racial population information from 2020 and combining this with the 1930s maps, we can see whether or not things have changed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a1614a90-f412-43a2-a246-eeb4df9ee9eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >], dtype=object)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "axes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infinite-instrument",
+   "metadata": {},
+   "source": [
+    "# Methods and Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "basic-canadian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import any helper files you need here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "recognized-positive",
+   "metadata": {},
+   "source": [
+    "## First Research Question: [What can we interpret from the data set regarding the percentage of whites and blacks living in various graded zones?]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-palmer",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "endless-variation",
+   "metadata": {},
+   "source": [
+    "*Explain how you will approach this research question below. Consider the following:* \n",
+    "  - *Which aspects of the dataset will you use?*\n",
+    "    We will graph the percentages of whites and blacks living in specific zones (A, B, C, D)\n",
+    "  - *How will you reorganize/store the data?*\n",
+    "    This data will be organized into two bar plots, side by side, to show the current state of whites and blacks in regard to where they live as it pertains to the zoning from the 1930s map\n",
+    "  - *What data science tools/functions will you use and why?* \n",
+    "  We will use seaborn barplot to run this data.  One important note is that we will make sure the y-axis in uniform to allow us to interpret the data appropriately.  This will allow us to avoid skewing the data.\n",
+    "✏"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d33c1cd3-a4a3-4f17-ad5f-3d27fdc29022",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "portuguese-japan",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "negative-highlight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#The thing to note as we look at the graphing done above is that there is a clear distinction between whites\n",
+    "#and blacks when it comes to today's residents various zones.  While things have certainly changed, the A zone \n",
+    "#is still predominately white, and you can see by the data that as the zones go from A-D, the white population in \n",
+    "#each decreases, while the black population over the same area increases.  This shows that while there are some improvements, \n",
+    "#we are still 90 years later, living in a very racially segregated society,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "victorian-burning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "collectible-puppy",
+   "metadata": {},
+   "source": [
+    "## Second Research Question: [What does this data show regarding generational wealth between all involved?]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demographic-future",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "incorporate-roller",
+   "metadata": {},
+   "source": [
+    "*Explain how you will approach this research question below. Consider the following:* \n",
+    "  - *Which aspects of the dataset will you use?*\n",
+    "     We will take a look again at the A-D zones and consider those in the most sought after zones as being the most economically viable.  Given that the data set is from 2020, and the maps were first created 90 years prior, it is fair to determine how well groups outside the white population are fairing in the A or B zones.\n",
+    "  - *How will you reorganize/store the data?*\n",
+    "  - Barplot with all groups/races side by side to see the distinctions\n",
+    "  - *What data science tools/functions will you use and why?*\n",
+    "        The mean of racial disparity between races both within separated bar plots, and one combined one to see patterns between zones\n",
+    "\n",
+    "✏️ *Write your answer below:*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "juvenile-creation",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "pursuant-surrey",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Immediately, one can tell that population percentage of whites goes down as the lettered zones get further in the alphabet\n",
+    "# whereas the opposite is true witb the black and hispanic zones.  The inference that can be made from this data is that because\n",
+    "# redlining created an inequitable system within the cities on the US, those in charge made it easier for those in certain areas to gain\n",
+    "# easy access to things like home ownership.  Given that home ownership in considered a great tool in obtaining generational wealth, it can\n",
+    "# also be seen that 90 years after the racially biased maps were implemented, that their effects are still here today.  Because this wealth\n",
+    "# is in fact generational, it clear that those in zones A and B have had an easier time growing this wealth than those in zones C and D -- the\n",
+    "# racial obviousness of said zones shows this as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "located-night",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infectious-symbol",
+   "metadata": {},
+   "source": [
+    "# Discussion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-camping",
+   "metadata": {
+    "code_folding": []
+   },
+   "source": [
+    "## Considerations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bearing-stadium",
+   "metadata": {},
+   "source": [
+    "*It's important to recognize the limitations of our research.\n",
+    "Consider the following:*\n",
+    "\n",
+    "- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
+    "- The results do show an accurate answer to this question. The major focus is the 90 year gap between the original map and the data that now incorporates it.\n",
+    "- *What were limitations of your datset?*\n",
+    "  It would be nice to have the same figures from the 1930 map to see a side-by-side comparison\n",
+    "- *Are there any known biases in the data?*\n",
+    "    Certainly my answer above adds some limitation to this and could create a greater bias of racial segregation given that we are guessing as to the original data and could be further of than we would like.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beneficial-invasion",
+   "metadata": {},
+   "source": [
+    "## Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "about-raise",
+   "metadata": {},
+   "source": [
+    "*Summarize what you discovered through the research. Consider the following:*\n",
+    "\n",
+    "- *What did you learn about your media consumption/digital habits?*\n",
+    "  I enjoy when data that is compiled so cleanly.  It is really interesting to be able to take such various pieces of the given data and plot it.  While I enjoyed putting this together, I can only imagine the complexities that are truly able to be used here.\n",
+    "- *Did the results make sense?*\n",
+    "  The results made sense, and while I needed some help putting them together, I enjoyed the different looks and avenues that could be assembled quickly and efficiently \n",
+    "- *What was most surprising?*\n",
+    "    I am not sure if the results of my data were very suprising but I was surpirsed by how panda will just take data if not given specifics and graph it how \"it\" sees fit.  \n",
+    "- *How will this project impact you going forward?*\n",
+    "  I have always liked FiveThirtyEight's analytical data for certain things and I enjoy that we are able to take that data and make more sense of it.  I would like to learn more about complexities within this data to see how we can go so much deeper with it.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ee86a83-ccb5-47c0-9b44-6f0be3ef4c68",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_json": true,
+   "text_representation": {
+    "extension": ".Rmd",
+    "format_name": "rmarkdown",
+    "format_version": "1.2",
+    "jupytext_version": "1.9.1"
+   }
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/argument.ipynb b/argument.ipynb
index 4ed27b4..75ce1a3 100644
--- a/argument.ipynb
+++ b/argument.ipynb
@@ -21,7 +21,7 @@
    "id": "greater-circular",
    "metadata": {},
    "source": [
-    "## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
+    "## Overarching Question: [How has the foundational implementation of government redlining had an effect on generational wealth by race and socioeconomic standing over time?]✏"
    ]
   },
   {
@@ -29,7 +29,7 @@
    "id": "appreciated-testimony",
    "metadata": {},
    "source": [
-    "*✏️ Write 2-3 sentences explaining why this question.*"
+    "This is an important question as we look at our cities and, still today, see heavily segregated areas which must have a foundational reason.  Hopefully through this data set we see an irrefutable pattern that brings clarity to the overarching question."
    ]
   },
   {
@@ -42,26 +42,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "technical-evans",
    "metadata": {},
    "outputs": [],
    "source": [
     "#Include any import statements you will need\n",
     "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "overhead-sigma",
    "metadata": {},
    "outputs": [],
    "source": [
     "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
     "\n",
-    "file_name = \"YOUR_DATASET_FILE_NAME.csv\"\n",
+    "file_name = \"metro-grades.csv\"\n",
     "dataset_path = \"data/\" + file_name\n",
     "\n",
     "df = pd.read_csv(dataset_path)"
@@ -69,12 +70,353 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "heated-blade",
    "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>metro_area</th>\n",
+       "      <th>holc_grade</th>\n",
+       "      <th>white_pop</th>\n",
+       "      <th>black_pop</th>\n",
+       "      <th>hisp_pop</th>\n",
+       "      <th>asian_pop</th>\n",
+       "      <th>other_pop</th>\n",
+       "      <th>total_pop</th>\n",
+       "      <th>pct_white</th>\n",
+       "      <th>pct_black</th>\n",
+       "      <th>...</th>\n",
+       "      <th>surr_area_white_pop</th>\n",
+       "      <th>surr_area_black_pop</th>\n",
+       "      <th>surr_area_hisp_pop</th>\n",
+       "      <th>surr_area_asian_pop</th>\n",
+       "      <th>surr_area_other_pop</th>\n",
+       "      <th>surr_area_pct_white</th>\n",
+       "      <th>surr_area_pct_black</th>\n",
+       "      <th>surr_area_pct_hisp</th>\n",
+       "      <th>surr_area_pct_asian</th>\n",
+       "      <th>surr_area_pct_other</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Akron, OH</td>\n",
+       "      <td>A</td>\n",
+       "      <td>24702</td>\n",
+       "      <td>8624</td>\n",
+       "      <td>956</td>\n",
+       "      <td>688</td>\n",
+       "      <td>1993</td>\n",
+       "      <td>36963</td>\n",
+       "      <td>66.83</td>\n",
+       "      <td>23.33</td>\n",
+       "      <td>...</td>\n",
+       "      <td>304399</td>\n",
+       "      <td>70692</td>\n",
+       "      <td>11037</td>\n",
+       "      <td>17295</td>\n",
+       "      <td>23839</td>\n",
+       "      <td>71.24</td>\n",
+       "      <td>16.55</td>\n",
+       "      <td>2.58</td>\n",
+       "      <td>4.05</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Akron, OH</td>\n",
+       "      <td>B</td>\n",
+       "      <td>41531</td>\n",
+       "      <td>16499</td>\n",
+       "      <td>2208</td>\n",
+       "      <td>3367</td>\n",
+       "      <td>4211</td>\n",
+       "      <td>67816</td>\n",
+       "      <td>61.24</td>\n",
+       "      <td>24.33</td>\n",
+       "      <td>...</td>\n",
+       "      <td>304399</td>\n",
+       "      <td>70692</td>\n",
+       "      <td>11037</td>\n",
+       "      <td>17295</td>\n",
+       "      <td>23839</td>\n",
+       "      <td>71.24</td>\n",
+       "      <td>16.55</td>\n",
+       "      <td>2.58</td>\n",
+       "      <td>4.05</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Akron, OH</td>\n",
+       "      <td>C</td>\n",
+       "      <td>73105</td>\n",
+       "      <td>22847</td>\n",
+       "      <td>3149</td>\n",
+       "      <td>6291</td>\n",
+       "      <td>7302</td>\n",
+       "      <td>112694</td>\n",
+       "      <td>64.87</td>\n",
+       "      <td>20.27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>304399</td>\n",
+       "      <td>70692</td>\n",
+       "      <td>11037</td>\n",
+       "      <td>17295</td>\n",
+       "      <td>23839</td>\n",
+       "      <td>71.24</td>\n",
+       "      <td>16.55</td>\n",
+       "      <td>2.58</td>\n",
+       "      <td>4.05</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Akron, OH</td>\n",
+       "      <td>D</td>\n",
+       "      <td>6179</td>\n",
+       "      <td>6921</td>\n",
+       "      <td>567</td>\n",
+       "      <td>455</td>\n",
+       "      <td>1022</td>\n",
+       "      <td>15144</td>\n",
+       "      <td>40.80</td>\n",
+       "      <td>45.70</td>\n",
+       "      <td>...</td>\n",
+       "      <td>304399</td>\n",
+       "      <td>70692</td>\n",
+       "      <td>11037</td>\n",
+       "      <td>17295</td>\n",
+       "      <td>23839</td>\n",
+       "      <td>71.24</td>\n",
+       "      <td>16.55</td>\n",
+       "      <td>2.58</td>\n",
+       "      <td>4.05</td>\n",
+       "      <td>5.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Albany-Schenectady-Troy, NY</td>\n",
+       "      <td>A</td>\n",
+       "      <td>16989</td>\n",
+       "      <td>1818</td>\n",
+       "      <td>1317</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>1182</td>\n",
+       "      <td>23303</td>\n",
+       "      <td>72.91</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>...</td>\n",
+       "      <td>387016</td>\n",
+       "      <td>68371</td>\n",
+       "      <td>42699</td>\n",
+       "      <td>41112</td>\n",
+       "      <td>40596</td>\n",
+       "      <td>66.75</td>\n",
+       "      <td>11.79</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>7.09</td>\n",
+       "      <td>7.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    metro_area holc_grade  white_pop  black_pop  hisp_pop  \\\n",
+       "0                    Akron, OH          A      24702       8624       956   \n",
+       "1                    Akron, OH          B      41531      16499      2208   \n",
+       "2                    Akron, OH          C      73105      22847      3149   \n",
+       "3                    Akron, OH          D       6179       6921       567   \n",
+       "4  Albany-Schenectady-Troy, NY          A      16989       1818      1317   \n",
+       "\n",
+       "   asian_pop  other_pop  total_pop  pct_white  pct_black  ...  \\\n",
+       "0        688       1993      36963      66.83      23.33  ...   \n",
+       "1       3367       4211      67816      61.24      24.33  ...   \n",
+       "2       6291       7302     112694      64.87      20.27  ...   \n",
+       "3        455       1022      15144      40.80      45.70  ...   \n",
+       "4       1998       1182      23303      72.91       7.80  ...   \n",
+       "\n",
+       "   surr_area_white_pop  surr_area_black_pop  surr_area_hisp_pop  \\\n",
+       "0               304399                70692               11037   \n",
+       "1               304399                70692               11037   \n",
+       "2               304399                70692               11037   \n",
+       "3               304399                70692               11037   \n",
+       "4               387016                68371               42699   \n",
+       "\n",
+       "   surr_area_asian_pop  surr_area_other_pop  surr_area_pct_white  \\\n",
+       "0                17295                23839                71.24   \n",
+       "1                17295                23839                71.24   \n",
+       "2                17295                23839                71.24   \n",
+       "3                17295                23839                71.24   \n",
+       "4                41112                40596                66.75   \n",
+       "\n",
+       "   surr_area_pct_black  surr_area_pct_hisp  surr_area_pct_asian  \\\n",
+       "0                16.55                2.58                 4.05   \n",
+       "1                16.55                2.58                 4.05   \n",
+       "2                16.55                2.58                 4.05   \n",
+       "3                16.55                2.58                 4.05   \n",
+       "4                11.79                7.36                 7.09   \n",
+       "\n",
+       "   surr_area_pct_other  \n",
+       "0                 5.58  \n",
+       "1                 5.58  \n",
+       "2                 5.58  \n",
+       "3                 5.58  \n",
+       "4                 7.00  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1378c142-b0ef-4048-aad0-3674ec12c532",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "df.head()"
+    "df[\"state\"] = df.metro_area.str.split(\", \").str.get(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "970ac473-ad2f-47df-b57d-f843992e2543",
+   "metadata": {},
+   "source": [
+    "# Messing Around"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "74a2f075-6acb-4d7c-b3b9-20d9bda274fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='holc_grade', ylabel='pct_white'>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot (data=df, y=\"pct_white\", x=\"holc_grade\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f71bb640-ea17-43c5-ba5e-0b785b7c1e63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='holc_grade', ylabel='pct_other'>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x200 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure, (ax0, ax1, ax2, ax3, ax4) = plt.subplots (1,5, figsize = (15, 2), sharey=True)\n",
+    "sns.barplot (data=df, y=\"pct_white\", x=\"holc_grade\", ax=ax0) \n",
+    "sns.barplot (data=df, y=\"pct_black\", x=\"holc_grade\", ax=ax1) \n",
+    "sns.barplot (data=df, y=\"pct_hisp\", x=\"holc_grade\", ax=ax2) \n",
+    "sns.barplot (data=df, y=\"pct_asian\", x=\"holc_grade\", ax=ax3) \n",
+    "sns.barplot (data=df, y=\"pct_other\", x=\"holc_grade\", ax=ax4) \n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "84231de8-ce69-4909-b69e-62a1744bad01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='holc_grade'>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "racial_perct = df.groupby (\"holc_grade\")[[\"pct_white\", \"pct_black\", \"pct_hisp\", \"pct_asian\", \"pct_other\"]].mean()\n",
+    "racial_perct.plot.bar()"
    ]
   },
   {
@@ -84,7 +426,28 @@
    "source": [
     "**Data Overview**\n",
     "\n",
-    "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
+    "The data above comes from a 2020 data set using the original redlining data maps from the 1930s.  Simply put, do these maps which were deemed illegal in 1967 still show that the damage over the years that they were used still exists today.  The data is relatively straight forward in that the drawing of these maps created zones, A being the most favorable, and D being the least favorable.  In the 1930s, the maps clearly show racial bias and those in the D  zones are heavily people of color who are unable to obtain mortgages, thus being subjected to having no way to gain equity in property, which most consider the best way to wealth over generations within a family.  Because of this practice, the question is really whether or not this has an effect on the current look of communities. Using racial population information from 2020 and combining this with the 1930s maps, we can see whether or not things have changed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a1614a90-f412-43a2-a246-eeb4df9ee9eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<Axes: >, <Axes: >, <Axes: >, <Axes: >, <Axes: >], dtype=object)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "axes"
    ]
   },
   {
@@ -110,7 +473,7 @@
    "id": "recognized-positive",
    "metadata": {},
    "source": [
-    "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## First Research Question: [What can we interpret from the data set regarding the percentage of whites and blacks living in various graded zones?]\n"
    ]
   },
   {
@@ -127,14 +490,23 @@
    "metadata": {},
    "source": [
     "*Explain how you will approach this research question below. Consider the following:* \n",
-    "  - *Which aspects of the dataset will you use?* \n",
-    "  - *How will you reorganize/store the data?* \n",
+    "  - *Which aspects of the dataset will you use?*\n",
+    "    We will graph the percentages of whites and blacks living in specific zones (A, B, C, D)\n",
+    "  - *How will you reorganize/store the data?*\n",
+    "    This data will be organized into two bar plots, side by side, to show the current state of whites and blacks in regard to where they live as it pertains to the zoning from the 1930s map\n",
     "  - *What data science tools/functions will you use and why?* \n",
-    "  \n",
-    "✏️ *Write your answer below:*\n",
-    "\n"
+    "  We will use seaborn barplot to run this data.  One important note is that we will make sure the y-axis in uniform to allow us to interpret the data appropriately.  This will allow us to avoid skewing the data.\n",
+    "✏"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d33c1cd3-a4a3-4f17-ad5f-3d27fdc29022",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "portuguese-japan",
@@ -145,21 +517,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 41,
    "id": "negative-highlight",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
-    "### \n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "#The thing to note as we look at the graphing done above is that there is a clear distinction between whites\n",
+    "#and blacks when it comes to today's residents various zones.  While things have certainly changed, the A zone \n",
+    "#is still predominately white, and you can see by the data that as the zones go from A-D, the white population in \n",
+    "#each decreases, while the black population over the same area increases.  This shows that while there are some improvements, \n",
+    "#we are still 90 years later, living in a very racially segregated society,"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 42,
    "id": "victorian-burning",
    "metadata": {},
    "outputs": [],
@@ -172,7 +544,7 @@
    "id": "collectible-puppy",
    "metadata": {},
    "source": [
-    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## Second Research Question: [What does this data show regarding generational wealth between all involved?]\n"
    ]
   },
   {
@@ -189,9 +561,12 @@
    "metadata": {},
    "source": [
     "*Explain how you will approach this research question below. Consider the following:* \n",
-    "  - *Which aspects of the dataset will you use?* \n",
-    "  - *How will you reorganize/store the data?* \n",
-    "  - *What data science tools/functions will you use and why?* \n",
+    "  - *Which aspects of the dataset will you use?*\n",
+    "     We will take a look again at the A-D zones and consider those in the most sought after zones as being the most economically viable.  Given that the data set is from 2020, and the maps were first created 90 years prior, it is fair to determine how well groups outside the white population are fairing in the A or B zones.\n",
+    "  - *How will you reorganize/store the data?*\n",
+    "  - Barplot with all groups/races side by side to see the distinctions\n",
+    "  - *What data science tools/functions will you use and why?*\n",
+    "        The mean of racial disparity between races both within separated bar plots, and one combined one to see patterns between zones\n",
     "\n",
     "✏️ *Write your answer below:*\n"
    ]
@@ -206,21 +581,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 43,
    "id": "pursuant-surrey",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
-    "###\n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "# Immediately, one can tell that population percentage of whites goes down as the lettered zones get further in the alphabet\n",
+    "# whereas the opposite is true witb the black and hispanic zones.  The inference that can be made from this data is that because\n",
+    "# redlining created an inequitable system within the cities on the US, those in charge made it easier for those in certain areas to gain\n",
+    "# easy access to things like home ownership.  Given that home ownership in considered a great tool in obtaining generational wealth, it can\n",
+    "# also be seen that 90 years after the racially biased maps were implemented, that their effects are still here today.  Because this wealth\n",
+    "# is in fact generational, it clear that those in zones A and B have had an easier time growing this wealth than those in zones C and D -- the\n",
+    "# racial obviousness of said zones shows this as well."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 44,
    "id": "located-night",
    "metadata": {},
    "outputs": [],
@@ -255,10 +632,11 @@
     "Consider the following:*\n",
     "\n",
     "- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
+    "- The results do show an accurate answer to this question. The major focus is the 90 year gap between the original map and the data that now incorporates it.\n",
     "- *What were limitations of your datset?*\n",
+    "  It would be nice to have the same figures from the 1930 map to see a side-by-side comparison\n",
     "- *Are there any known biases in the data?*\n",
-    "\n",
-    "✏️ *Write your answer below:*"
+    "    Certainly my answer above adds some limitation to this and could create a greater bias of racial segregation given that we are guessing as to the original data and could be further of than we would like.\n"
    ]
   },
   {
@@ -277,12 +655,23 @@
     "*Summarize what you discovered through the research. Consider the following:*\n",
     "\n",
     "- *What did you learn about your media consumption/digital habits?*\n",
+    "  I enjoy when data that is compiled so cleanly.  It is really interesting to be able to take such various pieces of the given data and plot it.  While I enjoyed putting this together, I can only imagine the complexities that are truly able to be used here.\n",
     "- *Did the results make sense?*\n",
+    "  The results made sense, and while I needed some help putting them together, I enjoyed the different looks and avenues that could be assembled quickly and efficiently \n",
     "- *What was most surprising?*\n",
+    "    I am not sure if the results of my data were very suprising but I was surpirsed by how panda will just take data if not given specifics and graph it how \"it\" sees fit.  \n",
     "- *How will this project impact you going forward?*\n",
-    "\n",
-    "✏️ *Write your answer below:*"
+    "  I have always liked FiveThirtyEight's analytical data for certain things and I enjoy that we are able to take that data and make more sense of it.  I would like to learn more about complexities within this data to see how we can go so much deeper with it.\n",
+    "\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ee86a83-ccb5-47c0-9b44-6f0be3ef4c68",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -310,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/data/metro-grades.csv b/data/metro-grades.csv
new file mode 100644
index 0000000..4483327
--- /dev/null
+++ b/data/metro-grades.csv
@@ -0,0 +1,552 @@
+metro_area,holc_grade,white_pop,black_pop,hisp_pop,asian_pop,other_pop,total_pop,pct_white,pct_black,pct_hisp,pct_asian,pct_other,lq_white,lq_black,lq_hisp,lq_asian,lq_other,surr_area_white_pop,surr_area_black_pop,surr_area_hisp_pop,surr_area_asian_pop,surr_area_other_pop,surr_area_pct_white,surr_area_pct_black,surr_area_pct_hisp,surr_area_pct_asian,surr_area_pct_other
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+"Akron, OH",B,"41531","16499","2208","3367","4211","67816",61.24,24.33,3.26,4.96,6.21,0.86,1.47,1.26,1.23,1.11,"304399","70692","11037","17295","23839",71.24,16.55,2.58,4.05,5.58
+"Akron, OH",C,"73105","22847","3149","6291","7302","112694",64.87,20.27,2.79,5.58,6.48,0.91,1.23,1.08,1.38,1.16,"304399","70692","11037","17295","23839",71.24,16.55,2.58,4.05,5.58
+"Akron, OH",D,"6179","6921","567","455","1022","15144",40.8,45.7,3.75,3,6.75,0.57,2.76,1.45,0.74,1.21,"304399","70692","11037","17295","23839",71.24,16.55,2.58,4.05,5.58
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+"Albany-Schenectady-Troy, NY",B,"26644","7094","4334","2509","4650","45230",58.91,15.68,9.58,5.55,10.28,0.88,1.33,1.3,0.78,1.47,"387016","68371","42699","41112","40596",66.75,11.79,7.36,7.09,7
+"Albany-Schenectady-Troy, NY",C,"56878","16795","10357","6355","11153","101538",56.02,16.54,10.2,6.26,10.98,0.84,1.4,1.39,0.88,1.57,"387016","68371","42699","41112","40596",66.75,11.79,7.36,7.09,7
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+"Allentown-Bethlehem-Easton, PA-NJ",A,"1076","71","367","21","82","1616",66.56,4.38,22.7,1.3,5.05,1.1,0.69,0.87,0.43,1.22,"69477","7364","30164","3524","4753",60.27,6.39,26.17,3.06,4.12
+"Allentown-Bethlehem-Easton, PA-NJ",B,"16774","1962","7935","731","1396","28798",58.25,6.81,27.55,2.54,4.85,0.97,1.07,1.05,0.83,1.18,"69477","7364","30164","3524","4753",60.27,6.39,26.17,3.06,4.12
+"Allentown-Bethlehem-Easton, PA-NJ",C,"4347","718","2967","184","346","8561",50.78,8.38,34.66,2.14,4.04,0.84,1.31,1.32,0.7,0.98,"69477","7364","30164","3524","4753",60.27,6.39,26.17,3.06,4.12
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+"Altoona, PA",B,"6612","166","117","55","318","7268",90.97,2.28,1.61,0.76,4.37,1.02,0.63,0.94,1.04,0.88,"50121","2059","970","412","2804",88.92,3.65,1.72,0.73,4.97
+"Altoona, PA",C,"23472","1174","490","118","1554","26808",87.56,4.38,1.83,0.44,5.8,0.98,1.2,1.06,0.6,1.17,"50121","2059","970","412","2804",88.92,3.65,1.72,0.73,4.97
+"Altoona, PA",D,"3920","267","71","24","268","4551",86.14,5.88,1.55,0.53,5.89,0.97,1.61,0.9,0.73,1.18,"50121","2059","970","412","2804",88.92,3.65,1.72,0.73,4.97
+"Amarillo, TX",A,"6107","297","1687","215","622","8927",68.41,3.32,18.89,2.4,6.97,1.62,0.38,0.46,0.62,1.57,"52724","10972","50830","4816","5556",42.21,8.78,40.7,3.86,4.45
+"Amarillo, TX",B,"7139","868","4223","150","732","13113",54.45,6.62,32.2,1.15,5.58,1.29,0.75,0.79,0.3,1.26,"52724","10972","50830","4816","5556",42.21,8.78,40.7,3.86,4.45
+"Amarillo, TX",C,"2140","775","5538","131","320","8904",24.03,8.7,62.2,1.48,3.59,0.57,0.99,1.53,0.38,0.81,"52724","10972","50830","4816","5556",42.21,8.78,40.7,3.86,4.45
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+"Asheville, NC",B,"6359","533","358","84","431","7765",81.9,6.86,4.62,1.08,5.54,1.1,0.71,0.52,0.85,0.96,"73949","9577","8774","1268","5723",74.48,9.65,8.84,1.28,5.76
+"Asheville, NC",C,"18708","2792","1780","251","1624","25156",74.37,11.1,7.07,1,6.46,1,1.15,0.8,0.78,1.12,"73949","9577","8774","1268","5723",74.48,9.65,8.84,1.28,5.76
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+"Bay City, MI",C,"9841","306","1160","60","759","12126",81.15,2.52,9.57,0.5,6.26,0.98,1.02,1.14,0.89,1.11,"39060","1159","3933","264","2643",83,2.46,8.36,0.56,5.62
+"Bay City, MI",D,"11449","562","1544","69","1088","14711",77.83,3.82,10.49,0.47,7.39,0.94,1.55,1.26,0.84,1.32,"39060","1159","3933","264","2643",83,2.46,8.36,0.56,5.62
+"Beaumont-Port Arthur, TX",A,"899","5122","1660","106","170","7957",11.29,64.37,20.86,1.34,2.14,0.28,2.16,0.9,0.38,0.71,"96080","70884","55440","8300","7206",40.39,29.79,23.3,3.49,3.03
+"Beaumont-Port Arthur, TX",B,"2546","11656","14156","1247","644","30248",8.42,38.53,46.8,4.12,2.13,0.21,1.29,2.01,1.18,0.7,"96080","70884","55440","8300","7206",40.39,29.79,23.3,3.49,3.03
+"Beaumont-Port Arthur, TX",C,"523","2992","1408","34","149","5107",10.24,58.59,27.56,0.67,2.93,0.25,1.97,1.18,0.19,0.97,"96080","70884","55440","8300","7206",40.39,29.79,23.3,3.49,3.03
+"Beaumont-Port Arthur, TX",D,"355","3473","856","18","159","4860",7.29,71.45,17.62,0.37,3.26,0.18,2.4,0.76,0.11,1.08,"96080","70884","55440","8300","7206",40.39,29.79,23.3,3.49,3.03
+"Binghamton, NY",A,"2255","183","170","186","176","2970",75.92,6.15,5.72,6.27,5.94,1.03,0.8,0.9,1,1.03,"106209","11020","9112","9003","8310",73.93,7.67,6.34,6.27,5.78
+"Binghamton, NY",B,"23858","3289","2270","1430","2205","33052",72.18,9.95,6.87,4.33,6.67,0.98,1.3,1.08,0.69,1.15,"106209","11020","9112","9003","8310",73.93,7.67,6.34,6.27,5.78
+"Binghamton, NY",C,"22834","3361","2433","1672","2292","32594",70.06,10.31,7.47,5.13,7.03,0.95,1.34,1.18,0.82,1.22,"106209","11020","9112","9003","8310",73.93,7.67,6.34,6.27,5.78
+"Binghamton, NY",D,"1512","473","261","104","205","2554",59.18,18.52,10.2,4.09,8.02,0.8,2.41,1.61,0.65,1.39,"106209","11020","9112","9003","8310",73.93,7.67,6.34,6.27,5.78
+"Birmingham-Hoover, AL",A,"5709","43","106","66","142","6065",94.12,0.7,1.74,1.09,2.35,2.16,0.02,0.35,0.53,0.77,"155725","166327","17741","7448","10877",43.48,46.44,4.95,2.08,3.04
+"Birmingham-Hoover, AL",B,"14948","6285","706","325","745","23009",64.96,27.32,3.07,1.41,3.24,1.49,0.59,0.62,0.68,1.07,"155725","166327","17741","7448","10877",43.48,46.44,4.95,2.08,3.04
+"Birmingham-Hoover, AL",C,"17363","28889","2665","1106","1667","51690",33.59,55.89,5.16,2.14,3.22,0.77,1.2,1.04,1.03,1.06,"155725","166327","17741","7448","10877",43.48,46.44,4.95,2.08,3.04
+"Birmingham-Hoover, AL",D,"8383","59889","4502","590","1931","75295",11.13,79.54,5.98,0.78,2.57,0.26,1.71,1.21,0.38,0.84,"155725","166327","17741","7448","10877",43.48,46.44,4.95,2.08,3.04
+"Boston-Cambridge-Newton, MA-NH",A,"39375","2251","2258","4979","2889","51753",76.08,4.35,4.36,9.62,5.58,1.2,0.56,0.33,0.99,0.88,"2716971","331571","562040","416219","271264",63.21,7.71,13.08,9.68,6.31
+"Boston-Cambridge-Newton, MA-NH",B,"170439","16978","16168","26299","17380","247264",68.93,6.87,6.54,10.64,7.03,1.09,0.89,0.5,1.1,1.11,"2716971","331571","562040","416219","271264",63.21,7.71,13.08,9.68,6.31
+"Boston-Cambridge-Newton, MA-NH",C,"452120","136685","141563","111654","74007","916030",49.36,14.92,15.45,12.19,8.08,0.78,1.93,1.18,1.26,1.28,"2716971","331571","562040","416219","271264",63.21,7.71,13.08,9.68,6.31
+"Boston-Cambridge-Newton, MA-NH",D,"179722","48748","92968","48110","28657","398204",45.13,12.24,23.35,12.08,7.2,0.71,1.59,1.79,1.25,1.14,"2716971","331571","562040","416219","271264",63.21,7.71,13.08,9.68,6.31
+"Bridgeport-Stamford-Norwalk, CT",A,"15079","1122","2539","2266","875","21881",68.91,5.13,11.6,10.35,4,1.29,0.51,0.46,1.42,1.01,"133943","25333","62695","18284","9915",53.54,10.13,25.06,7.31,3.96
+"Bridgeport-Stamford-Norwalk, CT",B,"21589","4396","10995","3950","1596","42526",50.77,10.34,25.85,9.29,3.75,0.95,1.02,1.03,1.27,0.95,"133943","25333","62695","18284","9915",53.54,10.13,25.06,7.31,3.96
+"Bridgeport-Stamford-Norwalk, CT",C,"12261","4626","13082","2597","1195","33760",36.32,13.7,38.75,7.69,3.54,0.68,1.35,1.55,1.05,0.89,"133943","25333","62695","18284","9915",53.54,10.13,25.06,7.31,3.96
+"Bridgeport-Stamford-Norwalk, CT",D,"2198","3478","6495","794","429","13393",16.41,25.97,48.49,5.93,3.21,0.31,2.56,1.94,0.81,0.81,"133943","25333","62695","18284","9915",53.54,10.13,25.06,7.31,3.96
+"Buffalo-Cheektowaga, NY",A,"15865","2173","969","640","908","20555",77.18,10.57,4.71,3.11,4.42,1.18,0.61,0.67,0.55,0.94,"530627","139564","57521","45662","38171",65.38,17.2,7.09,5.63,4.7
+"Buffalo-Cheektowaga, NY",B,"80832","37858","11286","7797","7587","145360",55.61,26.04,7.76,5.36,5.22,0.85,1.51,1.1,0.95,1.11,"530627","139564","57521","45662","38171",65.38,17.2,7.09,5.63,4.7
+"Buffalo-Cheektowaga, NY",C,"59609","53497","19055","12422","8350","152934",38.98,34.98,12.46,8.12,5.46,0.6,2.03,1.76,1.44,1.16,"530627","139564","57521","45662","38171",65.38,17.2,7.09,5.63,4.7
+"Buffalo-Cheektowaga, NY",D,"4726","5175","2977","206","840","13924",33.94,37.17,21.38,1.48,6.03,0.52,2.16,3.02,0.26,1.28,"530627","139564","57521","45662","38171",65.38,17.2,7.09,5.63,4.7
+"Canton-Massillon, OH",A,"2486","409","95","17","209","3217",77.29,12.72,2.96,0.53,6.5,1.13,0.68,0.7,1.02,0.79,"71884","19745","4466","541","8635",68.28,18.76,4.24,0.51,8.2
+"Canton-Massillon, OH",B,"17260","6172","1548","107","2856","27944",61.77,22.09,5.54,0.38,10.22,0.9,1.18,1.31,0.75,1.25,"71884","19745","4466","541","8635",68.28,18.76,4.24,0.51,8.2
+"Canton-Massillon, OH",C,"10272","5386","1045","27","1901","18632",55.13,28.91,5.61,0.14,10.2,0.81,1.54,1.32,0.28,1.24,"71884","19745","4466","541","8635",68.28,18.76,4.24,0.51,8.2
+"Canton-Massillon, OH",D,"1375","1429","187","3","332","3327",41.34,42.96,5.62,0.09,9.99,0.61,2.29,1.33,0.17,1.22,"71884","19745","4466","541","8635",68.28,18.76,4.24,0.51,8.2
+"Charleston, WV",A,"3254","187","71","83","235","3829",84.97,4.89,1.84,2.18,6.13,1.11,0.39,0.96,0.94,0.95,"47208","7649","1184","1423","3953",76.86,12.45,1.93,2.32,6.44
+"Charleston, WV",B,"5858","1614","204","164","633","8472",69.14,19.05,2.41,1.93,7.47,0.9,1.53,1.25,0.83,1.16,"47208","7649","1184","1423","3953",76.86,12.45,1.93,2.32,6.44
+"Charleston, WV",C,"7549","1931","262","134","815","10692",70.61,18.06,2.45,1.25,7.62,0.92,1.45,1.27,0.54,1.18,"47208","7649","1184","1423","3953",76.86,12.45,1.93,2.32,6.44
+"Charleston, WV",D,"2330","956","84","43","288","3701",62.96,25.82,2.26,1.17,7.78,0.82,2.07,1.17,0.51,1.21,"47208","7649","1184","1423","3953",76.86,12.45,1.93,2.32,6.44
+"Charlotte-Concord-Gastonia, NC-SC",A,"8651","387","388","188","299","9913",87.27,3.9,3.91,1.9,3.02,1.55,0.14,0.48,0.54,0.71,"75023","36710","10857","4665","5624",56.46,27.63,8.17,3.51,4.23
+"Charlotte-Concord-Gastonia, NC-SC",B,"8739","737","616","353","530","10974",79.63,6.71,5.61,3.22,4.83,1.41,0.24,0.69,0.92,1.14,"75023","36710","10857","4665","5624",56.46,27.63,8.17,3.51,4.23
+"Charlotte-Concord-Gastonia, NC-SC",C,"13978","5705","1498","1008","1108","23297",60,24.49,6.43,4.33,4.76,1.06,0.89,0.79,1.23,1.12,"75023","36710","10857","4665","5624",56.46,27.63,8.17,3.51,4.23
+"Charlotte-Concord-Gastonia, NC-SC",D,"10070","7462","1454","987","959","20932",48.11,35.65,6.94,4.72,4.58,0.85,1.29,0.85,1.34,1.08,"75023","36710","10857","4665","5624",56.46,27.63,8.17,3.51,4.23
+"Chattanooga, TN-GA",A,"4738","449","389","96","226","5898",80.33,7.61,6.6,1.62,3.83,1.38,0.29,0.69,1.18,0.84,"92469","41615","15196","2182","7263",58.26,26.22,9.57,1.37,4.58
+"Chattanooga, TN-GA",B,"12131","5352","1187","196","917","19784",61.32,27.05,6,0.99,4.64,1.05,1.03,0.63,0.72,1.01,"92469","41615","15196","2182","7263",58.26,26.22,9.57,1.37,4.58
+"Chattanooga, TN-GA",C,"11770","10500","6125","228","1289","29911",39.35,35.1,20.48,0.76,4.31,0.68,1.34,2.14,0.55,0.94,"92469","41615","15196","2182","7263",58.26,26.22,9.57,1.37,4.58
+"Chattanooga, TN-GA",D,"5340","5664","1409","212","578","13203",40.45,42.9,10.67,1.61,4.38,0.69,1.64,1.11,1.17,0.96,"92469","41615","15196","2182","7263",58.26,26.22,9.57,1.37,4.58
+"Chicago-Naperville-Elgin, IL-IN-WI",A,"46352","8447","7773","3201","2630","68403",67.76,12.35,11.36,4.68,3.84,1.39,0.74,0.48,0.64,1.13,"4471615","1531274","2187375","673021","310889",48.74,16.69,23.84,7.34,3.39
+"Chicago-Naperville-Elgin, IL-IN-WI",B,"202942","113905","90694","40663","17715","465918",43.56,24.45,19.47,8.73,3.8,0.89,1.46,0.82,1.19,1.12,"4471615","1531274","2187375","673021","310889",48.74,16.69,23.84,7.34,3.39
+"Chicago-Naperville-Elgin, IL-IN-WI",C,"579196","447000","705314","75961","58074","1865546",31.05,23.96,37.81,4.07,3.11,0.64,1.44,1.59,0.56,0.92,"4471615","1531274","2187375","673021","310889",48.74,16.69,23.84,7.34,3.39
+"Chicago-Naperville-Elgin, IL-IN-WI",D,"250471","297196","295129","63372","27927","934095",26.81,31.82,31.6,6.78,2.99,0.55,1.91,1.33,0.92,0.88,"4471615","1531274","2187375","673021","310889",48.74,16.69,23.84,7.34,3.39
+"Cincinnati, OH-KY-IN",A,"10921","231","460","153","479","12243",89.2,1.89,3.76,1.25,3.91,1.3,0.1,0.76,0.39,0.76,"917344","245761","66672","42435","68783",68.41,18.33,4.97,3.16,5.13
+"Cincinnati, OH-KY-IN",B,"26682","1389","1600","235","1979","31885",83.68,4.36,5.02,0.74,6.21,1.22,0.24,1.01,0.23,1.21,"917344","245761","66672","42435","68783",68.41,18.33,4.97,3.16,5.13
+"Cincinnati, OH-KY-IN",C,"35616","4566","4811","241","3364","48597",73.29,9.39,9.9,0.5,6.92,1.07,0.51,1.99,0.16,1.35,"917344","245761","66672","42435","68783",68.41,18.33,4.97,3.16,5.13
+"Cincinnati, OH-KY-IN",D,"5984","2281","1899","78","792","11034",54.23,20.67,17.21,0.7,7.18,0.79,1.13,3.46,0.22,1.4,"917344","245761","66672","42435","68783",68.41,18.33,4.97,3.16,5.13
+"Cleveland-Elyria, OH",A,"48311","13445","2968","2272","3335","70331",68.69,19.12,4.22,3.23,4.74,1.08,0.87,0.63,1.06,1.09,"1168228","402969","121587","55854","79749",63.89,22.04,6.65,3.05,4.36
+"Cleveland-Elyria, OH",B,"100214","54660","11665","3289","8045","177872",56.34,30.73,6.56,1.85,4.52,0.88,1.39,0.99,0.61,1.04,"1168228","402969","121587","55854","79749",63.89,22.04,6.65,3.05,4.36
+"Cleveland-Elyria, OH",C,"155360","136527","40191","7910","17076","357064",43.51,38.24,11.26,2.22,4.78,0.68,1.73,1.69,0.73,1.1,"1168228","402969","121587","55854","79749",63.89,22.04,6.65,3.05,4.36
+"Cleveland-Elyria, OH",D,"28391","73969","13274","3312","5349","124295",22.84,59.51,10.68,2.66,4.3,0.36,2.7,1.61,0.87,0.99,"1168228","402969","121587","55854","79749",63.89,22.04,6.65,3.05,4.36
+"Columbia, SC",A,"1861","39","69","34","61","2064",90.14,1.89,3.36,1.64,2.98,1.48,0.07,0.82,0.53,0.8,"44572","20779","3010","2273","2718",60.77,28.33,4.1,3.1,3.7
+"Columbia, SC",B,"4417","340","145","99","170","5172",85.4,6.57,2.81,1.92,3.29,1.41,0.23,0.69,0.62,0.89,"44572","20779","3010","2273","2718",60.77,28.33,4.1,3.1,3.7
+"Columbia, SC",C,"6956","1044","316","273","331","8920",77.99,11.71,3.54,3.06,3.71,1.28,0.41,0.86,0.99,1,"44572","20779","3010","2273","2718",60.77,28.33,4.1,3.1,3.7
+"Columbia, SC",D,"6235","3597","544","419","520","11316",55.1,31.79,4.81,3.7,4.6,0.91,1.12,1.17,1.2,1.24,"44572","20779","3010","2273","2718",60.77,28.33,4.1,3.1,3.7
+"Columbus, GA-AL",A,"270","50","16","7","23","366",73.79,13.64,4.3,1.87,6.4,2.02,0.27,0.6,2.19,1.23,"25956","35845","5056","609","3688",36.48,50.38,7.11,0.86,5.18
+"Columbus, GA-AL",B,"1656","687","160","26","160","2688",61.59,25.55,5.96,0.97,5.93,1.69,0.51,0.84,1.13,1.15,"25956","35845","5056","609","3688",36.48,50.38,7.11,0.86,5.18
+"Columbus, GA-AL",C,"2191","3493","435","42","416","6576",33.31,53.11,6.61,0.64,6.33,0.91,1.05,0.93,0.75,1.22,"25956","35845","5056","609","3688",36.48,50.38,7.11,0.86,5.18
+"Columbus, GA-AL",D,"2850","7320","942","55","564","11730",24.29,62.4,8.03,0.47,4.81,0.67,1.24,1.13,0.54,0.93,"25956","35845","5056","609","3688",36.48,50.38,7.11,0.86,5.18
+"Columbus, OH",A,"23756","2731","1205","477","1547","29715",79.95,9.19,4.05,1.6,5.21,1.43,0.35,0.56,0.33,0.93,"371107","175890","48054","31937","37074",55.88,26.49,7.24,4.81,5.58
+"Columbus, OH",B,"52383","18843","5168","1889","4955","83239",62.93,22.64,6.21,2.27,5.95,1.13,0.85,0.86,0.47,1.07,"371107","175890","48054","31937","37074",55.88,26.49,7.24,4.81,5.58
+"Columbus, OH",C,"68257","27401","7531","3917","6751","113858",59.95,24.07,6.61,3.44,5.93,1.07,0.91,0.91,0.72,1.06,"371107","175890","48054","31937","37074",55.88,26.49,7.24,4.81,5.58
+"Columbus, OH",D,"16377","12059","2366","890","2172","33864",48.36,35.61,6.99,2.63,6.41,0.87,1.34,0.97,0.55,1.15,"371107","175890","48054","31937","37074",55.88,26.49,7.24,4.81,5.58
+"Dallas-Fort Worth-Arlington, TX",A,"50939","2651","7161","2929","2831","66512",76.59,3.99,10.77,4.4,4.26,2.08,0.21,0.33,0.62,1.05,"1649321","853697","1469475","315093","181493",36.91,19.1,32.88,7.05,4.06
+"Dallas-Fort Worth-Arlington, TX",B,"43166","8975","51345","3148","3755","110388",39.1,8.13,46.51,2.85,3.4,1.06,0.43,1.41,0.4,0.84,"1649321","853697","1469475","315093","181493",36.91,19.1,32.88,7.05,4.06
+"Dallas-Fort Worth-Arlington, TX",C,"50951","38908","106411","5769","5889","207927",24.5,18.71,51.18,2.77,2.83,0.66,0.98,1.56,0.39,0.7,"1649321","853697","1469475","315093","181493",36.91,19.1,32.88,7.05,4.06
+"Dallas-Fort Worth-Arlington, TX",D,"22679","23220","66845","2851","2812","118407",19.15,19.61,56.45,2.41,2.37,0.52,1.03,1.72,0.34,0.58,"1649321","853697","1469475","315093","181493",36.91,19.1,32.88,7.05,4.06
+"Davenport-Moline-Rock Island, IA-IL",A,"1565","33","139","20","98","1856",84.34,1.79,7.51,1.1,5.26,1.26,0.13,0.67,0.46,0.85,"90227","17868","15019","3255","8348",66.97,13.26,11.15,2.42,6.2
+"Davenport-Moline-Rock Island, IA-IL",B,"14713","1784","1530","325","1035","19386",75.9,9.2,7.89,1.67,5.34,1.13,0.69,0.71,0.69,0.86,"90227","17868","15019","3255","8348",66.97,13.26,11.15,2.42,6.2
+"Davenport-Moline-Rock Island, IA-IL",C,"21021","3986","3474","507","2320","31309",67.14,12.73,11.1,1.62,7.41,1,0.96,1,0.67,1.2,"90227","17868","15019","3255","8348",66.97,13.26,11.15,2.42,6.2
+"Davenport-Moline-Rock Island, IA-IL",D,"3956","1295","763","63","480","6558",60.32,19.75,11.64,0.97,7.32,0.9,1.49,1.04,0.4,1.18,"90227","17868","15019","3255","8348",66.97,13.26,11.15,2.42,6.2
+"Dayton-Kettering, OH",A,"15825","3675","774","440","1182","21897",72.27,16.78,3.54,2.01,5.4,1.25,0.57,0.73,1.25,0.91,"154813","79341","12951","4282","15767",57.95,29.7,4.85,1.6,5.9
+"Dayton-Kettering, OH",B,"18875","15952","2153","681","2578","40238",46.91,39.64,5.35,1.69,6.41,0.81,1.33,1.1,1.06,1.09,"154813","79341","12951","4282","15767",57.95,29.7,4.85,1.6,5.9
+"Dayton-Kettering, OH",C,"31008","13124","3598","620","3286","51635",60.05,25.42,6.97,1.2,6.36,1.04,0.86,1.44,0.75,1.08,"154813","79341","12951","4282","15767",57.95,29.7,4.85,1.6,5.9
+"Dayton-Kettering, OH",D,"8883","10786","1072","179","1347","22269",39.89,48.44,4.81,0.81,6.05,0.69,1.63,0.99,0.5,1.03,"154813","79341","12951","4282","15767",57.95,29.7,4.85,1.6,5.9
+"Decatur, IL",A,"2114","309","87","17","151","2678",78.94,11.56,3.23,0.62,5.64,1.32,0.39,1.12,0.69,0.86,"30318","15115","1465","458","3335",59.81,29.82,2.89,0.9,6.58
+"Decatur, IL",B,"4144","3364","219","37","611","8375",49.48,40.17,2.62,0.45,7.29,0.83,1.35,0.91,0.49,1.11,"30318","15115","1465","458","3335",59.81,29.82,2.89,0.9,6.58
+"Decatur, IL",C,"6298","6624","491","70","1146","14630",43.05,45.28,3.36,0.48,7.83,0.72,1.52,1.16,0.53,1.19,"30318","15115","1465","458","3335",59.81,29.82,2.89,0.9,6.58
+"Decatur, IL",D,"2030","2250","153","22","434","4888",41.52,46.04,3.13,0.44,8.87,0.69,1.54,1.08,0.49,1.35,"30318","15115","1465","458","3335",59.81,29.82,2.89,0.9,6.58
+"Denver-Aurora-Lakewood, CO",A,"10605","270","962","294","730","12860",82.46,2.1,7.48,2.29,5.67,1.43,0.32,0.28,0.66,1.04,"460045","52036","212657","27752","43259",57.81,6.54,26.72,3.49,5.44
+"Denver-Aurora-Lakewood, CO",B,"29789","1400","3831","893","2130","38043",78.3,3.68,10.07,2.35,5.6,1.35,0.56,0.38,0.67,1.03,"460045","52036","212657","27752","43259",57.81,6.54,26.72,3.49,5.44
+"Denver-Aurora-Lakewood, CO",C,"114180","6878","24668","4492","9134","159351",71.65,4.32,15.48,2.82,5.73,1.24,0.66,0.58,0.81,1.05,"460045","52036","212657","27752","43259",57.81,6.54,26.72,3.49,5.44
+"Denver-Aurora-Lakewood, CO",D,"42365","6691","32194","2750","4547","88548",47.84,7.56,36.36,3.11,5.14,0.83,1.16,1.36,0.89,0.94,"460045","52036","212657","27752","43259",57.81,6.54,26.72,3.49,5.44
+"Des Moines-West Des Moines, IA",A,"24197","1940","1902","929","1588","30556",79.19,6.35,6.22,3.04,5.2,1.25,0.6,0.43,0.48,1.01,"160431","26698","36779","16060","13027",63.41,10.55,14.54,6.35,5.15
+"Des Moines-West Des Moines, IA",B,"17859","3533","4797","1786","1564","29540",60.46,11.96,16.24,6.05,5.29,0.95,1.13,1.12,0.95,1.03,"160431","26698","36779","16060","13027",63.41,10.55,14.54,6.35,5.15
+"Des Moines-West Des Moines, IA",C,"38390","8870","11152","5120","3443","66976",57.32,13.24,16.65,7.64,5.14,0.9,1.26,1.15,1.2,1,"160431","26698","36779","16060","13027",63.41,10.55,14.54,6.35,5.15
+"Des Moines-West Des Moines, IA",D,"18942","5498","7883","2929","1945","37197",50.92,14.78,21.19,7.87,5.23,0.8,1.4,1.46,1.24,1.02,"160431","26698","36779","16060","13027",63.41,10.55,14.54,6.35,5.15
+"Detroit-Warren-Dearborn, MI",A,"33745","37381","2040","900","2935","77002",43.82,48.55,2.65,1.17,3.81,0.77,1.79,0.49,0.2,0.81,"1954385","929170","184988","198693","162391",56.99,27.09,5.39,5.79,4.73
+"Detroit-Warren-Dearborn, MI",B,"67214","105556","6561","1444","7328","188103",35.73,56.12,3.49,0.77,3.9,0.63,2.07,0.65,0.13,0.82,"1954385","929170","184988","198693","162391",56.99,27.09,5.39,5.79,4.73
+"Detroit-Warren-Dearborn, MI",C,"278844","235388","45758","13811","30258","604058",46.16,38.97,7.58,2.29,5.01,0.81,1.44,1.4,0.39,1.06,"1954385","929170","184988","198693","162391",56.99,27.09,5.39,5.79,4.73
+"Detroit-Warren-Dearborn, MI",D,"88546","116045","37980","11152","13607","267328",33.12,43.41,14.21,4.17,5.09,0.58,1.6,2.63,0.72,1.07,"1954385","929170","184988","198693","162391",56.99,27.09,5.39,5.79,4.73
+"Dubuque, IA",A,"2980","165","98","15","155","3413",87.31,4.85,2.86,0.45,4.54,1.04,0.77,0.77,0.45,0.88,"47918","3587","2117","564","2934",83.89,6.28,3.71,0.99,5.14
+"Dubuque, IA",B,"15072","2179","1007","182","1303","19743",76.34,11.04,5.1,0.92,6.6,0.91,1.76,1.38,0.93,1.29,"47918","3587","2117","564","2934",83.89,6.28,3.71,0.99,5.14
+"Dubuque, IA",C,"5953","267","203","54","314","6792",87.65,3.93,2.99,0.8,4.63,1.04,0.63,0.81,0.81,0.9,"47918","3587","2117","564","2934",83.89,6.28,3.71,0.99,5.14
+"Dubuque, IA",D,"2880","375","173","20","217","3664",78.61,10.23,4.71,0.53,5.91,0.94,1.63,1.27,0.54,1.15,"47918","3587","2117","564","2934",83.89,6.28,3.71,0.99,5.14
+"Duluth, MN-WI",A,"7176","102","157","164","517","8117",88.41,1.26,1.93,2.02,6.37,1.03,0.46,0.89,1.5,0.81,"117643","3727","2974","1847","10723",85.92,2.72,2.17,1.35,7.83
+"Duluth, MN-WI",B,"14291","468","393","231","1383","16767",85.23,2.79,2.35,1.38,8.25,0.99,1.03,1.08,1.02,1.05,"117643","3727","2974","1847","10723",85.92,2.72,2.17,1.35,7.83
+"Duluth, MN-WI",C,"17676","1197","741","381","2234","22228",79.52,5.39,3.33,1.71,10.05,0.93,1.98,1.54,1.27,1.28,"117643","3727","2974","1847","10723",85.92,2.72,2.17,1.35,7.83
+"Duluth, MN-WI",D,"5730","191","168","78","631","6798",84.29,2.81,2.48,1.15,9.28,0.98,1.03,1.14,0.85,1.19,"117643","3727","2974","1847","10723",85.92,2.72,2.17,1.35,7.83
+"Durham-Chapel Hill, NC",A,"2448","187","341","88","200","3263",75.02,5.73,10.44,2.69,6.13,2.29,0.14,0.55,0.72,1.43,"24970","30644","14564","2852","3263",32.73,40.17,19.09,3.74,4.28
+"Durham-Chapel Hill, NC",B,"1354","362","223","44","137","2121",63.87,17.09,10.5,2.08,6.47,1.95,0.43,0.55,0.56,1.51,"24970","30644","14564","2852","3263",32.73,40.17,19.09,3.74,4.28
+"Durham-Chapel Hill, NC",C,"8233","6025","3967","861","937","20023",41.12,30.09,19.81,4.3,4.68,1.26,0.75,1.04,1.15,1.09,"24970","30644","14564","2852","3263",32.73,40.17,19.09,3.74,4.28
+"Durham-Chapel Hill, NC",D,"2335","6477","2546","305","522","12185",19.16,53.16,20.9,2.5,4.29,0.59,1.32,1.09,0.67,1,"24970","30644","14564","2852","3263",32.73,40.17,19.09,3.74,4.28
+"El Paso, TX",A,"1123","51","3910","47","93","5224",21.49,0.98,74.85,0.9,1.78,1.79,0.33,0.92,0.57,0.79,"13930","3463","94407","1826","2638",11.98,2.98,81.2,1.57,2.27
+"El Paso, TX",B,"1618","290","22147","90","253","24399",6.63,1.19,90.77,0.37,1.04,0.55,0.4,1.12,0.23,0.46,"13930","3463","94407","1826","2638",11.98,2.98,81.2,1.57,2.27
+"El Paso, TX",C,"936","271","11827","140","215","13389",6.99,2.03,88.33,1.05,1.61,0.58,0.68,1.09,0.67,0.71,"13930","3463","94407","1826","2638",11.98,2.98,81.2,1.57,2.27
+"El Paso, TX",D,"602","193","14977","38","140","15950",3.77,1.21,93.9,0.24,0.88,0.31,0.41,1.16,0.15,0.39,"13930","3463","94407","1826","2638",11.98,2.98,81.2,1.57,2.27
+"Elmira, NY",A,"277","31","21","2","20","351",78.83,8.77,6.09,0.7,5.6,1.02,0.92,1.34,0.8,0.74,"34907","4288","2046","396","3386",77.53,9.52,4.54,0.88,7.52
+"Elmira, NY",B,"7613","849","433","102","662","9659",78.82,8.79,4.48,1.06,6.86,1.02,0.92,0.99,1.2,0.91,"34907","4288","2046","396","3386",77.53,9.52,4.54,0.88,7.52
+"Elmira, NY",C,"15224","1702","854","150","1701","19631",77.55,8.67,4.35,0.76,8.67,1,0.91,0.96,0.87,1.15,"34907","4288","2046","396","3386",77.53,9.52,4.54,0.88,7.52
+"Elmira, NY",D,"3554","507","186","19","387","4653",76.38,10.9,4,0.4,8.31,0.99,1.14,0.88,0.46,1.11,"34907","4288","2046","396","3386",77.53,9.52,4.54,0.88,7.52
+"Erie, PA",A,"4305","102","96","87","156","4746",90.71,2.14,2.02,1.84,3.29,1.22,0.2,0.33,0.55,0.6,"125324","18053","10179","5656","9243",74.4,10.72,6.04,3.36,5.49
+"Erie, PA",B,"33288","3697","2536","882","2703","43106",77.22,8.58,5.88,2.05,6.27,1.04,0.8,0.97,0.61,1.14,"125324","18053","10179","5656","9243",74.4,10.72,6.04,3.36,5.49
+"Erie, PA",C,"27409","7863","3721","1370","2908","43271",63.34,18.17,8.6,3.17,6.72,0.85,1.7,1.42,0.94,1.22,"125324","18053","10179","5656","9243",74.4,10.72,6.04,3.36,5.49
+"Erie, PA",D,"5358","3113","1432","681","909","11492",46.62,27.09,12.46,5.92,7.91,0.63,2.53,2.06,1.76,1.44,"125324","18053","10179","5656","9243",74.4,10.72,6.04,3.36,5.49
+"Evansville, IN-KY",A,"4098","232","145","57","292","4824",84.95,4.82,3,1.18,6.06,1.16,0.33,0.72,1.7,0.86,"63444","12579","3581","595","6068",73.54,14.58,4.15,0.69,7.03
+"Evansville, IN-KY",B,"1764","1135","159","11","257","3325",53.04,34.13,4.78,0.33,7.72,0.72,2.34,1.15,0.47,1.1,"63444","12579","3581","595","6068",73.54,14.58,4.15,0.69,7.03
+"Evansville, IN-KY",C,"31500","5833","1939","238","3127","42639",73.88,13.68,4.55,0.56,7.33,1,0.94,1.1,0.81,1.04,"63444","12579","3581","595","6068",73.54,14.58,4.15,0.69,7.03
+"Evansville, IN-KY",D,"6788","2838","496","49","1066","11238",60.41,25.26,4.42,0.43,9.49,0.82,1.73,1.06,0.63,1.35,"63444","12579","3581","595","6068",73.54,14.58,4.15,0.69,7.03
+"Flint, MI",A,"593","140","36","21","64","854",69.43,16.34,4.25,2.48,7.51,1.21,0.53,0.94,2.46,1.22,"135599","73705","10653","2396","14619",57.22,31.1,4.5,1.01,6.17
+"Flint, MI",B,"3975","8756","618","105","935","14389",27.62,60.85,4.3,0.73,6.5,0.48,1.96,0.96,0.72,1.05,"135599","73705","10653","2396","14619",57.22,31.1,4.5,1.01,6.17
+"Flint, MI",C,"11045","14044","1614","138","2125","28966",38.13,48.48,5.57,0.48,7.34,0.67,1.56,1.24,0.47,1.19,"135599","73705","10653","2396","14619",57.22,31.1,4.5,1.01,6.17
+"Flint, MI",D,"18679","9305","1847","133","2527","32492",57.49,28.64,5.68,0.41,7.78,1,0.92,1.26,0.41,1.26,"135599","73705","10653","2396","14619",57.22,31.1,4.5,1.01,6.17
+"Fort Wayne, IN",A,"1681","64","119","41","125","2029",82.83,3.14,5.86,2.01,6.17,1.67,0.14,0.39,0.27,0.98,"67467","29812","20315","10066","8530",49.54,21.89,14.92,7.39,6.26
+"Fort Wayne, IN",B,"10029","1239","1770","443","1166","14647",68.47,8.46,12.08,3.02,7.96,1.38,0.39,0.81,0.41,1.27,"67467","29812","20315","10066","8530",49.54,21.89,14.92,7.39,6.26
+"Fort Wayne, IN",C,"23448","11063","9750","1364","3325","48950",47.9,22.6,19.92,2.79,6.79,0.97,1.03,1.34,0.38,1.08,"67467","29812","20315","10066","8530",49.54,21.89,14.92,7.39,6.26
+"Fort Wayne, IN",D,"742","1210","661","49","195","2856",25.96,42.35,23.13,1.72,6.84,0.52,1.93,1.55,0.23,1.09,"67467","29812","20315","10066","8530",49.54,21.89,14.92,7.39,6.26
+"Fresno, CA",A,"615","25","370","25","86","1121",54.83,2.26,33.02,2.19,7.7,4.11,0.3,0.5,0.21,2.2,"28916","16120","141886","22082","7576",13.35,7.44,65.51,10.2,3.5
+"Fresno, CA",B,"2846","284","6835","517","410","10892",26.13,2.61,62.76,4.74,3.77,1.96,0.35,0.96,0.47,1.08,"28916","16120","141886","22082","7576",13.35,7.44,65.51,10.2,3.5
+"Fresno, CA",C,"6214","3942","42059","4467","1980","58662",10.59,6.72,71.7,7.61,3.38,0.79,0.9,1.09,0.75,0.96,"28916","16120","141886","22082","7576",13.35,7.44,65.51,10.2,3.5
+"Fresno, CA",D,"883","1877","10951","1707","397","15814",5.58,11.87,69.25,10.79,2.51,0.42,1.59,1.06,1.06,0.72,"28916","16120","141886","22082","7576",13.35,7.44,65.51,10.2,3.5
+"Grand Rapids-Kentwood, MI",A,"6670","271","362","159","420","7882",84.63,3.43,4.59,2.02,5.33,1.35,0.26,0.29,0.65,1.01,"242564","50746","60780","12041","20352",62.76,13.13,15.73,3.12,5.27
+"Grand Rapids-Kentwood, MI",B,"23288","5436","5505","522","1942","36694",63.47,14.81,15,1.42,5.29,1.01,1.13,0.95,0.46,1.01,"242564","50746","60780","12041","20352",62.76,13.13,15.73,3.12,5.27
+"Grand Rapids-Kentwood, MI",C,"74531","18549","25847","3183","7190","129299",57.64,14.35,19.99,2.46,5.56,0.92,1.09,1.27,0.79,1.06,"242564","50746","60780","12041","20352",62.76,13.13,15.73,3.12,5.27
+"Grand Rapids-Kentwood, MI",D,"8448","5246","6196","190","1399","21480",39.33,24.43,28.85,0.88,6.51,0.63,1.86,1.83,0.28,1.24,"242564","50746","60780","12041","20352",62.76,13.13,15.73,3.12,5.27
+"Greensboro-High Point, NC",A,"4964","209","229","70","223","5695",87.16,3.67,4.02,1.23,3.91,2.17,0.09,0.4,0.3,0.85,"55145","56133","13906","5664","6306",40.21,40.93,10.14,4.13,4.6
+"Greensboro-High Point, NC",B,"4632","991","356","161","329","6468",71.6,15.31,5.51,2.48,5.09,1.78,0.37,0.54,0.6,1.11,"55145","56133","13906","5664","6306",40.21,40.93,10.14,4.13,4.6
+"Greensboro-High Point, NC",C,"7758","12864","2995","1166","1318","26101",29.72,49.28,11.48,4.47,5.05,0.74,1.2,1.13,1.08,1.1,"55145","56133","13906","5664","6306",40.21,40.93,10.14,4.13,4.6
+"Greensboro-High Point, NC",D,"882","10834","985","178","476","13355",6.6,81.12,7.38,1.33,3.57,0.16,1.98,0.73,0.32,0.78,"55145","56133","13906","5664","6306",40.21,40.93,10.14,4.13,4.6
+"Harrisburg-Carlisle, PA",A,"2191","728","393","66","227","3605",60.8,20.19,10.89,1.82,6.29,1.17,0.89,0.8,0.29,1.16,"96843","41916","25404","11512","10059",52.14,22.57,13.68,6.2,5.42
+"Harrisburg-Carlisle, PA",B,"13967","13841","7966","1489","2404","39667",35.21,34.89,20.08,3.75,6.06,0.68,1.55,1.47,0.61,1.12,"96843","41916","25404","11512","10059",52.14,22.57,13.68,6.2,5.42
+"Harrisburg-Carlisle, PA",C,"2458","4185","3303","201","638","10785",22.79,38.8,30.63,1.87,5.92,0.44,1.72,2.24,0.3,1.09,"96843","41916","25404","11512","10059",52.14,22.57,13.68,6.2,5.42
+"Harrisburg-Carlisle, PA",D,"8247","5777","3604","470","1315","19413",42.48,29.76,18.56,2.42,6.77,0.81,1.32,1.36,0.39,1.25,"96843","41916","25404","11512","10059",52.14,22.57,13.68,6.2,5.42
+"Hartford-East Hartford-Middletown, CT",A,"21765","2204","3010","2006","1487","30472",71.43,7.23,9.88,6.58,4.88,1.54,0.4,0.39,1.09,1.18,"232550","90555","127634","30178","20843",46.35,18.05,25.44,6.01,4.15
+"Hartford-East Hartford-Middletown, CT",B,"20103","14231","15961","1387","2217","53900",37.3,26.4,29.61,2.57,4.11,0.8,1.46,1.16,0.43,0.99,"232550","90555","127634","30178","20843",46.35,18.05,25.44,6.01,4.15
+"Hartford-East Hartford-Middletown, CT",C,"22480","35157","58399","4120","5621","125778",17.87,27.95,46.43,3.28,4.47,0.39,1.55,1.83,0.54,1.08,"232550","90555","127634","30178","20843",46.35,18.05,25.44,6.01,4.15
+"Hartford-East Hartford-Middletown, CT",D,"1661","4324","8407","486","461","15338",10.83,28.19,54.81,3.17,3,0.23,1.56,2.15,0.53,0.72,"232550","90555","127634","30178","20843",46.35,18.05,25.44,6.01,4.15
+"Houston-The Woodlands-Sugar Land, TX",A,"16589","6910","6881","2778","1653","34811",47.66,19.85,19.77,7.98,4.75,1.61,1.05,0.47,1.37,1.48,"894064","572756","1275375","176052","97010",29.65,19,42.3,5.84,3.22
+"Houston-The Woodlands-Sugar Land, TX",B,"25188","5107","27135","2931","2451","62811",40.1,8.13,43.2,4.67,3.9,1.35,0.43,1.02,0.8,1.21,"894064","572756","1275375","176052","97010",29.65,19,42.3,5.84,3.22
+"Houston-The Woodlands-Sugar Land, TX",C,"16996","9825","62096","2045","2164","93125",18.25,10.55,66.68,2.2,2.32,0.62,0.56,1.58,0.38,0.72,"894064","572756","1275375","176052","97010",29.65,19,42.3,5.84,3.22
+"Houston-The Woodlands-Sugar Land, TX",D,"8108","14680","10239","1748","1324","36100",22.46,40.67,28.36,4.84,3.67,0.76,2.14,0.67,0.83,1.14,"894064","572756","1275375","176052","97010",29.65,19,42.3,5.84,3.22
+"Huntington-Ashland, WV-KY-OH",A,"7221","608","201","160","504","8694",83.06,6.99,2.31,1.84,5.8,0.98,1.09,1.25,1.48,1.05,"62135","4666","1351","904","4019",85.03,6.39,1.85,1.24,5.5
+"Huntington-Ashland, WV-KY-OH",B,"6286","719","186","142","442","7775",80.85,9.24,2.4,1.83,5.68,0.95,1.45,1.3,1.48,1.03,"62135","4666","1351","904","4019",85.03,6.39,1.85,1.24,5.5
+"Huntington-Ashland, WV-KY-OH",C,"20220","2068","463","284","1592","24627",82.11,8.4,1.88,1.15,6.46,0.97,1.32,1.02,0.93,1.18,"62135","4666","1351","904","4019",85.03,6.39,1.85,1.24,5.5
+"Huntington-Ashland, WV-KY-OH",D,"384","357","15","5","81","842",45.64,42.4,1.77,0.63,9.57,0.54,6.64,0.96,0.51,1.74,"62135","4666","1351","904","4019",85.03,6.39,1.85,1.24,5.5
+"Indianapolis-Carmel-Anderson, IN",A,"21440","1939","923","442","1097","25841",82.97,7.51,3.57,1.71,4.24,1.69,0.26,0.26,0.45,0.85,"440510","255828","123995","34160","44865",48.98,28.45,13.79,3.8,4.99
+"Indianapolis-Carmel-Anderson, IN",B,"31069","7302","4537","1567","2629","47103",65.96,15.5,9.63,3.33,5.58,1.35,0.54,0.7,0.88,1.12,"440510","255828","123995","34160","44865",48.98,28.45,13.79,3.8,4.99
+"Indianapolis-Carmel-Anderson, IN",C,"107449","83952","33853","5151","12410","242816",44.25,34.57,13.94,2.12,5.11,0.9,1.22,1.01,0.56,1.02,"440510","255828","123995","34160","44865",48.98,28.45,13.79,3.8,4.99
+"Indianapolis-Carmel-Anderson, IN",D,"54827","29069","17932","1998","5864","109690",49.98,26.5,16.35,1.82,5.35,1.02,0.93,1.19,0.48,1.07,"440510","255828","123995","34160","44865",48.98,28.45,13.79,3.8,4.99
+"Jackson, MI",A,"1589","131","137","25","129","2011",79.02,6.52,6.79,1.24,6.42,1.08,0.48,1.31,0.96,0.91,"49295","9140","3512","873","4742",72.96,13.53,5.2,1.29,7.02
+"Jackson, MI",B,"8422","2276","747","169","1045","12659",66.53,17.98,5.9,1.34,8.26,0.91,1.33,1.14,1.04,1.18,"49295","9140","3512","873","4742",72.96,13.53,5.2,1.29,7.02
+"Jackson, MI",C,"15605","3220","1297","156","1754","22033",70.83,14.62,5.89,0.71,7.96,0.97,1.08,1.13,0.55,1.13,"49295","9140","3512","873","4742",72.96,13.53,5.2,1.29,7.02
+"Jackson, MI",D,"2939","926","396","35","437","4732",62.11,19.56,8.36,0.74,9.23,0.85,1.45,1.61,0.57,1.31,"49295","9140","3512","873","4742",72.96,13.53,5.2,1.29,7.02
+"Jackson, MS",A,"3549","1641","154","104","185","5632",63.01,29.14,2.73,1.84,3.28,3.45,0.38,1.29,2.55,1.26,"8715","36422","1008","345","1248",18.26,76.3,2.11,0.72,2.61
+"Jackson, MS",B,"1432","5983","174","56","200","7845",18.26,76.27,2.21,0.71,2.55,1,1,1.05,0.99,0.98,"8715","36422","1008","345","1248",18.26,76.3,2.11,0.72,2.61
+"Jackson, MS",C,"432","3411","99","14","136","4092",10.55,83.36,2.43,0.35,3.32,0.58,1.09,1.15,0.49,1.27,"8715","36422","1008","345","1248",18.26,76.3,2.11,0.72,2.61
+"Jackson, MS",D,"647","6206","208","24","182","7267",8.9,85.4,2.87,0.33,2.51,0.49,1.12,1.36,0.45,0.96,"8715","36422","1008","345","1248",18.26,76.3,2.11,0.72,2.61
+"Jacksonville, FL",A,"6067","109","393","164","246","6979",86.93,1.55,5.64,2.35,3.52,2.35,0.03,0.64,1.01,0.73,"82335","105268","19507","5186","10698",36.92,47.21,8.75,2.33,4.8
+"Jacksonville, FL",B,"13253","3868","1312","380","1120","19931",66.49,19.41,6.58,1.91,5.62,1.8,0.41,0.75,0.82,1.17,"82335","105268","19507","5186","10698",36.92,47.21,8.75,2.33,4.8
+"Jacksonville, FL",C,"11377","9020","1802","463","1392","24054",47.3,37.5,7.49,1.93,5.79,1.28,0.79,0.86,0.83,1.21,"82335","105268","19507","5186","10698",36.92,47.21,8.75,2.33,4.8
+"Jacksonville, FL",D,"5169","28894","1784","248","1790","37885",13.64,76.27,4.71,0.65,4.73,0.37,1.62,0.54,0.28,0.98,"82335","105268","19507","5186","10698",36.92,47.21,8.75,2.33,4.8
+"Kalamazoo-Portage, MI",A,"1046","52","76","17","70","1261",82.95,4.12,6.05,1.32,5.55,1.38,0.19,0.7,0.61,0.73,"55406","19960","7943","2010","7018",60,21.62,8.6,2.18,7.6
+"Kalamazoo-Portage, MI",B,"5336","787","552","109","554","7338",72.72,10.72,7.52,1.48,7.56,1.21,0.5,0.87,0.68,0.99,"55406","19960","7943","2010","7018",60,21.62,8.6,2.18,7.6
+"Kalamazoo-Portage, MI",C,"14637","7823","3288","358","2380","28486",51.38,27.46,11.54,1.26,8.36,0.86,1.27,1.34,0.58,1.1,"55406","19960","7943","2010","7018",60,21.62,8.6,2.18,7.6
+"Kalamazoo-Portage, MI",D,"545","618","151","7","130","1451",37.54,42.58,10.43,0.51,8.94,0.63,1.97,1.21,0.23,1.18,"55406","19960","7943","2010","7018",60,21.62,8.6,2.18,7.6
+"Kansas City, MO-KS",A,"11945","232","537","215","539","13469",88.69,1.72,3.99,1.59,4.01,1.71,0.08,0.23,0.57,0.65,"363293","151921","122305","19426","42786",51.92,21.71,17.48,2.78,6.11
+"Kansas City, MO-KS",B,"22007","5066","2790","740","1768","32371",67.98,15.65,8.62,2.29,5.46,1.31,0.72,0.49,0.82,0.89,"363293","151921","122305","19426","42786",51.92,21.71,17.48,2.78,6.11
+"Kansas City, MO-KS",C,"49734","30796","26443","3353","6891","117217",42.43,26.27,22.56,2.86,5.88,0.82,1.21,1.29,1.03,0.96,"363293","151921","122305","19426","42786",51.92,21.71,17.48,2.78,6.11
+"Kansas City, MO-KS",D,"41926","42276","44246","4623","7939","141011",29.73,29.98,31.38,3.28,5.63,0.57,1.38,1.8,1.18,0.92,"363293","151921","122305","19426","42786",51.92,21.71,17.48,2.78,6.11
+"Knoxville, TN",A,"4028","144","179","127","196","4673",86.19,3.08,3.82,2.71,4.19,1.28,0.18,0.49,1.48,0.71,"112094","28172","12965","3047","9750",67.52,16.97,7.81,1.84,5.87
+"Knoxville, TN",B,"7328","1342","713","305","495","10182",71.96,13.18,7,3,4.86,1.07,0.78,0.9,1.63,0.83,"112094","28172","12965","3047","9750",67.52,16.97,7.81,1.84,5.87
+"Knoxville, TN",C,"21298","6880","2579","694","1879","33330",63.9,20.64,7.74,2.08,5.64,0.95,1.22,0.99,1.13,0.96,"112094","28172","12965","3047","9750",67.52,16.97,7.81,1.84,5.87
+"Knoxville, TN",D,"11269","6170","3083","327","1481","22331",50.46,27.63,13.81,1.47,6.63,0.75,1.63,1.77,0.8,1.13,"112094","28172","12965","3047","9750",67.52,16.97,7.81,1.84,5.87
+"Lancaster, PA",A,"5357","406","1030","304","342","7439",72.01,5.46,13.85,4.09,4.59,1.6,0.49,0.39,0.99,1.08,"34959","8743","27527","3226","3315",44.95,11.24,35.4,4.15,4.26
+"Lancaster, PA",B,"10807","3147","9640","904","1158","25657",42.12,12.27,37.57,3.52,4.51,0.94,1.09,1.06,0.85,1.06,"34959","8743","27527","3226","3315",44.95,11.24,35.4,4.15,4.26
+"Lancaster, PA",C,"158","19","128","30","16","351",45,5.34,36.57,8.57,4.52,1,0.47,1.03,2.07,1.06,"34959","8743","27527","3226","3315",44.95,11.24,35.4,4.15,4.26
+"Lancaster, PA",D,"183","351","1072","35","48","1689",10.81,20.78,63.51,2.06,2.83,0.24,1.85,1.79,0.5,0.66,"34959","8743","27527","3226","3315",44.95,11.24,35.4,4.15,4.26
+"Lansing-East Lansing, MI",A,"4044","535","328","239","306","5452",74.18,9.81,6.02,4.38,5.61,1.27,0.55,0.56,0.73,0.82,"120427","36705","21964","12272","14133",58.6,17.86,10.69,5.97,6.88
+"Lansing-East Lansing, MI",B,"18205","3734","2533","1356","1828","27655",65.83,13.5,9.16,4.9,6.61,1.12,0.76,0.86,0.82,0.96,"120427","36705","21964","12272","14133",58.6,17.86,10.69,5.97,6.88
+"Lansing-East Lansing, MI",C,"36812","14721","10209","2361","5664","69767",52.76,21.1,14.63,3.38,8.12,0.9,1.18,1.37,0.57,1.18,"120427","36705","21964","12272","14133",58.6,17.86,10.69,5.97,6.88
+"Lansing-East Lansing, MI",D,"1647","763","594","92","286","3381",48.72,22.57,17.55,2.71,8.44,0.83,1.26,1.64,0.45,1.23,"120427","36705","21964","12272","14133",58.6,17.86,10.69,5.97,6.88
+"Lexington-Fayette, KY",A,"3749","43","115","63","139","4108",91.25,1.04,2.79,1.54,3.39,1.36,0.07,0.29,0.46,0.69,"51446","11761","7342","2576","3787",66.89,15.29,9.55,3.35,4.92
+"Lexington-Fayette, KY",B,"6312","399","412","273","349","7746",81.49,5.16,5.32,3.52,4.51,1.22,0.34,0.56,1.05,0.92,"51446","11761","7342","2576","3787",66.89,15.29,9.55,3.35,4.92
+"Lexington-Fayette, KY",C,"11709","2855","1397","535","965","17460",67.06,16.35,8,3.06,5.52,1,1.07,0.84,0.91,1.12,"51446","11761","7342","2576","3787",66.89,15.29,9.55,3.35,4.92
+"Lexington-Fayette, KY",D,"4157","3279","879","116","546","8977",46.31,36.53,9.79,1.3,6.08,0.69,2.39,1.03,0.39,1.23,"51446","11761","7342","2576","3787",66.89,15.29,9.55,3.35,4.92
+"Lima, OH",A,"1272","168","63","13","130","1646",77.25,10.23,3.85,0.79,7.89,1.19,0.45,0.97,0.92,1.04,"32359","11403","1977","427","3794",64.77,22.82,3.96,0.85,7.59
+"Lima, OH",B,"5000","1925","358","27","802","8112",61.63,23.73,4.41,0.33,9.89,0.95,1.04,1.12,0.39,1.3,"32359","11403","1977","427","3794",64.77,22.82,3.96,0.85,7.59
+"Lima, OH",C,"8466","3582","631","82","1244","14004",60.45,25.57,4.51,0.58,8.88,0.93,1.12,1.14,0.68,1.17,"32359","11403","1977","427","3794",64.77,22.82,3.96,0.85,7.59
+"Lima, OH",D,"2046","1117","162","8","321","3653",56,30.57,4.44,0.22,8.78,0.86,1.34,1.12,0.25,1.16,"32359","11403","1977","427","3794",64.77,22.82,3.96,0.85,7.59
+"Lincoln, NE",A,"21748","1368","2227","871","1723","27937",77.85,4.9,7.97,3.12,6.17,1.04,0.97,0.84,0.64,1.04,"167288","11353","21276","10856","13276",74.67,5.07,9.5,4.85,5.93
+"Lincoln, NE",B,"24405","2258","4199","1602","2519","34983",69.76,6.46,12,4.58,7.2,0.93,1.27,1.26,0.95,1.21,"167288","11353","21276","10856","13276",74.67,5.07,9.5,4.85,5.93
+"Lincoln, NE",C,"30964","3176","6178","2977","3209","46504",66.58,6.83,13.29,6.4,6.9,0.89,1.35,1.4,1.32,1.16,"167288","11353","21276","10856","13276",74.67,5.07,9.5,4.85,5.93
+"Lincoln, NE",D,"3815","219","408","189","229","4861",78.49,4.5,8.39,3.9,4.72,1.05,0.89,0.88,0.8,0.8,"167288","11353","21276","10856","13276",74.67,5.07,9.5,4.85,5.93
+"Little Rock-North Little Rock-Conway, AR",A,"7758","1683","358","131","573","10502",73.87,16.02,3.4,1.25,5.46,1.72,0.36,0.49,0.89,1.14,"35044","35864","5652","1148","3901",42.94,43.95,6.93,1.41,4.78
+"Little Rock-North Little Rock-Conway, AR",B,"4541","3905","454","98","520","9518",47.71,41.03,4.77,1.03,5.47,1.11,0.93,0.69,0.73,1.14,"35044","35864","5652","1148","3901",42.94,43.95,6.93,1.41,4.78
+"Little Rock-North Little Rock-Conway, AR",C,"2452","7501","518","76","523","11071",22.15,67.76,4.68,0.69,4.73,0.52,1.54,0.68,0.49,0.99,"35044","35864","5652","1148","3901",42.94,43.95,6.93,1.41,4.78
+"Little Rock-North Little Rock-Conway, AR",D,"739","6785","301","40","342","8208",9,82.67,3.67,0.49,4.17,0.21,1.88,0.53,0.35,0.87,"35044","35864","5652","1148","3901",42.94,43.95,6.93,1.41,4.78
+"Los Angeles-Long Beach-Anaheim, CA",A,"105689","12769","24966","31932","11471","186826",56.57,6.83,13.36,17.09,6.14,2.24,1.06,0.28,1.02,1.53,"2865758","732335","5415084","1901342","455077",25.21,6.44,47.63,16.72,4
+"Los Angeles-Long Beach-Anaheim, CA",B,"247872","57465","228153","81961","32785","648236",38.24,8.86,35.2,12.64,5.06,1.52,1.38,0.74,0.76,1.26,"2865758","732335","5415084","1901342","455077",25.21,6.44,47.63,16.72,4
+"Los Angeles-Long Beach-Anaheim, CA",C,"533108","205869","1233028","355088","91402","2418494",22.04,8.51,50.98,14.68,3.78,0.87,1.32,1.07,0.88,0.94,"2865758","732335","5415084","1901342","455077",25.21,6.44,47.63,16.72,4
+"Los Angeles-Long Beach-Anaheim, CA",D,"172344","88680","741481","119721","38578","1160805",14.85,7.64,63.88,10.31,3.32,0.59,1.19,1.34,0.62,0.83,"2865758","732335","5415084","1901342","455077",25.21,6.44,47.63,16.72,4
+"Louisville/Jefferson County, KY-IN",A,"17428","414","546","301","783","19473",89.5,2.13,2.81,1.55,4.02,1.64,0.07,0.39,0.61,0.76,"190102","106960","24986","8791","18560",54.41,30.61,7.15,2.52,5.31
+"Louisville/Jefferson County, KY-IN",B,"21106","9441","1262","632","1662","34102",61.89,27.68,3.7,1.85,4.87,1.14,0.9,0.52,0.74,0.92,"190102","106960","24986","8791","18560",54.41,30.61,7.15,2.52,5.31
+"Louisville/Jefferson County, KY-IN",C,"42462","31762","5173","1945","4932","86274",49.22,36.82,6,2.25,5.72,0.9,1.2,0.84,0.9,1.08,"190102","106960","24986","8791","18560",54.41,30.61,7.15,2.52,5.31
+"Louisville/Jefferson County, KY-IN",D,"19738","21738","1805","835","2582","46698",42.27,46.55,3.87,1.79,5.53,0.78,1.52,0.54,0.71,1.04,"190102","106960","24986","8791","18560",54.41,30.61,7.15,2.52,5.31
+"Lynchburg, VA",A,"1666","250","86","19","141","2161",77.09,11.57,3.97,0.86,6.51,1.43,0.34,0.9,0.71,1.05,"25277","16225","2066","565","2905",53.74,34.49,4.39,1.2,6.17
+"Lynchburg, VA",B,"2073","527","144","29","258","3031",68.4,17.38,4.76,0.94,8.52,1.27,0.5,1.08,0.78,1.38,"25277","16225","2066","565","2905",53.74,34.49,4.39,1.2,6.17
+"Lynchburg, VA",C,"3170","5021","363","79","631","9263",34.22,54.2,3.92,0.85,6.82,0.64,1.57,0.89,0.71,1.1,"25277","16225","2066","565","2905",53.74,34.49,4.39,1.2,6.17
+"Lynchburg, VA",D,"411","1667","87","11","191","2367",17.35,70.43,3.68,0.44,8.08,0.32,2.04,0.84,0.37,1.31,"25277","16225","2066","565","2905",53.74,34.49,4.39,1.2,6.17
+"Macon-Bibb County, GA",A,"312","47","6","6","26","398",78.46,11.77,1.57,1.58,6.62,3.42,0.17,0.37,1.97,1.94,"14914","44634","2799","522","2216",22.92,68.58,4.3,0.8,3.4
+"Macon-Bibb County, GA",B,"1601","1043","89","30","111","2873",55.71,36.29,3.09,1.05,3.85,2.43,0.53,0.72,1.31,1.13,"14914","44634","2799","522","2216",22.92,68.58,4.3,0.8,3.4
+"Macon-Bibb County, GA",C,"1712","3856","202","31","255","6055",28.27,63.67,3.34,0.51,4.21,1.23,0.93,0.78,0.63,1.24,"14914","44634","2799","522","2216",22.92,68.58,4.3,0.8,3.4
+"Macon-Bibb County, GA",D,"2815","11500","708","124","570","15717",17.91,73.17,4.5,0.79,3.63,0.78,1.07,1.05,0.98,1.07,"14914","44634","2799","522","2216",22.92,68.58,4.3,0.8,3.4
+"Madison, WI",A,"4782","79","230","207","330","5628",84.98,1.4,4.09,3.67,5.86,1.23,0.19,0.43,0.43,1.07,"132989","13987","18456","16335","10524",69.16,7.27,9.6,8.5,5.47
+"Madison, WI",B,"9767","196","527","523","677","11690",83.55,1.68,4.51,4.47,5.79,1.21,0.23,0.47,0.53,1.06,"132989","13987","18456","16335","10524",69.16,7.27,9.6,8.5,5.47
+"Madison, WI",C,"39031","1976","3338","5468","2946","52759",73.98,3.75,6.33,10.36,5.58,1.07,0.51,0.66,1.22,1.02,"132989","13987","18456","16335","10524",69.16,7.27,9.6,8.5,5.47
+"Madison, WI",D,"9801","993","1358","1030","765","13947",70.27,7.12,9.74,7.39,5.48,1.02,0.98,1.01,0.87,1,"132989","13987","18456","16335","10524",69.16,7.27,9.6,8.5,5.47
+"Manchester-Nashua, NH",A,"2367","95","179","112","143","2895",81.77,3.27,6.17,3.85,4.93,1.09,0.67,0.54,0.98,1.01,"89550","5865","13646","4716","5843",74.86,4.9,11.41,3.94,4.88
+"Manchester-Nashua, NH",B,"4915","112","362","94","284","5766",85.23,1.94,6.27,1.63,4.92,1.14,0.4,0.55,0.41,1.01,"89550","5865","13646","4716","5843",74.86,4.9,11.41,3.94,4.88
+"Manchester-Nashua, NH",C,"31977","2440","5977","1663","2283","44340",72.12,5.5,13.48,3.75,5.15,0.96,1.12,1.18,0.95,1.05,"89550","5865","13646","4716","5843",74.86,4.9,11.41,3.94,4.88
+"Manchester-Nashua, NH",D,"10158","1475","3554","772","976","16935",59.98,8.71,20.99,4.56,5.77,0.8,1.78,1.84,1.16,1.18,"89550","5865","13646","4716","5843",74.86,4.9,11.41,3.94,4.88
+"Memphis, TN-MS-AR",A,"8959","1235","441","271","467","11373",78.78,10.86,3.88,2.38,4.11,2.82,0.19,0.45,1.23,1.22,"59648","124683","18258","4124","7185",27.89,58.29,8.54,1.93,3.36
+"Memphis, TN-MS-AR",B,"21832","13925","2309","1157","1859","41082",53.14,33.9,5.62,2.82,4.52,1.91,0.58,0.66,1.46,1.35,"59648","124683","18258","4124","7185",27.89,58.29,8.54,1.93,3.36
+"Memphis, TN-MS-AR",C,"7395","31463","3961","707","1449","44975",16.44,69.96,8.81,1.57,3.22,0.59,1.2,1.03,0.82,0.96,"59648","124683","18258","4124","7185",27.89,58.29,8.54,1.93,3.36
+"Memphis, TN-MS-AR",D,"6411","40967","1894","684","1541","51496",12.45,79.55,3.68,1.33,2.99,0.45,1.36,0.43,0.69,0.89,"59648","124683","18258","4124","7185",27.89,58.29,8.54,1.93,3.36
+"Miami-Fort Lauderdale-Pompano Beach, FL",A,"15441","1002","19378","699","1765","38286",40.33,2.62,50.62,1.83,4.61,2.81,0.15,0.78,1.53,1.85,"160777","193809","723188","13318","27810",14.37,17.32,64.63,1.19,2.49
+"Miami-Fort Lauderdale-Pompano Beach, FL",B,"21523","5111","44671","1305","2735","75345",28.57,6.78,59.29,1.73,3.63,1.99,0.39,0.92,1.45,1.46,"160777","193809","723188","13318","27810",14.37,17.32,64.63,1.19,2.49
+"Miami-Fort Lauderdale-Pompano Beach, FL",C,"25997","14078","120894","2416","4752","168137",15.46,8.37,71.9,1.44,2.83,1.08,0.48,1.11,1.21,1.14,"160777","193809","723188","13318","27810",14.37,17.32,64.63,1.19,2.49
+"Miami-Fort Lauderdale-Pompano Beach, FL",D,"5731","37805","75763","546","2004","121849",4.7,31.03,62.18,0.45,1.64,0.33,1.79,0.96,0.38,0.66,"160777","193809","723188","13318","27810",14.37,17.32,64.63,1.19,2.49
+"Milwaukee-Waukesha, WI",A,"18562","4552","1236","702","1248","26301",70.58,17.31,4.7,2.67,4.74,1.36,0.75,0.31,0.52,1.04,"542455","242028","156702","53517","47372",52.06,23.23,15.04,5.14,4.55
+"Milwaukee-Waukesha, WI",B,"56900","24711","9262","3411","4836","99120",57.41,24.93,9.34,3.44,4.88,1.1,1.07,0.62,0.67,1.07,"542455","242028","156702","53517","47372",52.06,23.23,15.04,5.14,4.55
+"Milwaukee-Waukesha, WI",C,"99810","91603","50236","9649","12327","263626",37.86,34.75,19.06,3.66,4.68,0.73,1.5,1.27,0.71,1.03,"542455","242028","156702","53517","47372",52.06,23.23,15.04,5.14,4.55
+"Milwaukee-Waukesha, WI",D,"55179","35108","47596","5823","7121","150828",36.58,23.28,31.56,3.86,4.72,0.7,1,2.1,0.75,1.04,"542455","242028","156702","53517","47372",52.06,23.23,15.04,5.14,4.55
+"Minneapolis-St. Paul-Bloomington, MN-WI",A,"54422","2380","2850","2653","3780","66086",82.35,3.6,4.31,4.01,5.72,1.39,0.24,0.44,0.39,0.92,"750034","187333","124556","129699","78693",59.04,14.75,9.81,10.21,6.19
+"Minneapolis-St. Paul-Bloomington, MN-WI",B,"142550","21767","21138","11676","14591","211722",67.33,10.28,9.98,5.51,6.89,1.14,0.7,1.02,0.54,1.11,"750034","187333","124556","129699","78693",59.04,14.75,9.81,10.21,6.19
+"Minneapolis-St. Paul-Bloomington, MN-WI",C,"92973","53600","29830","30944","15626","222973",41.7,24.04,13.38,13.88,7.01,0.71,1.63,1.36,1.36,1.13,"750034","187333","124556","129699","78693",59.04,14.75,9.81,10.21,6.19
+"Minneapolis-St. Paul-Bloomington, MN-WI",D,"39558","30475","9175","11681","6144","97032",40.77,31.41,9.46,12.04,6.33,0.69,2.13,0.96,1.18,1.02,"750034","187333","124556","129699","78693",59.04,14.75,9.81,10.21,6.19
+"Mobile, AL",A,"220","54","8","1","10","294",74.86,18.49,2.77,0.36,3.52,2.42,0.29,1.32,0.75,1.06,"12761","25995","865","196","1367",30.99,63.12,2.1,0.48,3.32
+"Mobile, AL",B,"1673","231","61","24","90","2079",80.5,11.12,2.92,1.14,4.32,2.6,0.18,1.39,2.4,1.3,"12761","25995","865","196","1367",30.99,63.12,2.1,0.48,3.32
+"Mobile, AL",C,"5725","4362","297","54","432","10870",52.67,40.13,2.73,0.5,3.98,1.7,0.64,1.3,1.04,1.2,"12761","25995","865","196","1367",30.99,63.12,2.1,0.48,3.32
+"Mobile, AL",D,"1468","10140","133","21","353","12115",12.12,83.7,1.1,0.18,2.91,0.39,1.33,0.52,0.37,0.88,"12761","25995","865","196","1367",30.99,63.12,2.1,0.48,3.32
+"Montgomery, AL",A,"2581","593","101","31","156","3462",74.56,17.13,2.9,0.89,4.51,2.14,0.31,0.57,0.86,1.28,"26496","42303","3860","785","2676",34.81,55.57,5.07,1.03,3.52
+"Montgomery, AL",B,"1648","1501","79","32","92","3351",49.18,44.79,2.35,0.95,2.74,1.41,0.81,0.46,0.92,0.78,"26496","42303","3860","785","2676",34.81,55.57,5.07,1.03,3.52
+"Montgomery, AL",C,"4823","7477","1481","93","561","14433",33.41,51.8,10.26,0.64,3.88,0.96,0.93,2.02,0.62,1.1,"26496","42303","3860","785","2676",34.81,55.57,5.07,1.03,3.52
+"Montgomery, AL",D,"3145","10738","876","161","431","15350",20.49,69.96,5.71,1.05,2.81,0.59,1.26,1.13,1.02,0.8,"26496","42303","3860","785","2676",34.81,55.57,5.07,1.03,3.52
+"Muskegon, MI",A,"706","12","33","4","39","794",88.9,1.47,4.19,0.56,4.89,1.54,0.05,0.57,0.95,0.74,"40830","19405","5210","413","4671",57.89,27.51,7.39,0.59,6.62
+"Muskegon, MI",B,"3614","2285","817","44","475","7234",49.95,31.59,11.29,0.6,6.57,0.86,1.15,1.53,1.03,0.99,"40830","19405","5210","413","4671",57.89,27.51,7.39,0.59,6.62
+"Muskegon, MI",C,"4665","5043","1171","36","979","11894",39.22,42.4,9.84,0.3,8.23,0.68,1.54,1.33,0.52,1.24,"40830","19405","5210","413","4671",57.89,27.51,7.39,0.59,6.62
+"Muskegon, MI",D,"7755","8234","1525","70","1340","18926",40.98,43.51,8.06,0.37,7.08,0.71,1.58,1.09,0.63,1.07,"40830","19405","5210","413","4671",57.89,27.51,7.39,0.59,6.62
+"Nashville-Davidson--Murfreesboro--Franklin, TN",A,"27202","639","897","909","992","30639",88.78,2.09,2.93,2.97,3.24,1.56,0.08,0.28,0.93,0.78,"182676","81216","33753","10241","13290",56.88,25.29,10.51,3.19,4.14
+"Nashville-Davidson--Murfreesboro--Franklin, TN",B,"29020","4289","2098","2656","1843","39906",72.72,10.75,5.26,6.66,4.62,1.28,0.42,0.5,2.09,1.12,"182676","81216","33753","10241","13290",56.88,25.29,10.51,3.19,4.14
+"Nashville-Davidson--Murfreesboro--Franklin, TN",C,"19861","5473","1919","785","1333","29371",67.62,18.63,6.53,2.67,4.54,1.19,0.74,0.62,0.84,1.1,"182676","81216","33753","10241","13290",56.88,25.29,10.51,3.19,4.14
+"Nashville-Davidson--Murfreesboro--Franklin, TN",D,"45131","29886","9290","2429","4133","90869",49.67,32.89,10.22,2.67,4.55,0.87,1.3,0.97,0.84,1.1,"182676","81216","33753","10241","13290",56.88,25.29,10.51,3.19,4.14
+"New Castle, PA",A,"1274","96","59","11","108","1549",82.26,6.23,3.84,0.69,6.99,1.04,0.61,1.21,1.88,0.93,"24235","3146","975","113","2311",78.74,10.22,3.17,0.37,7.51
+"New Castle, PA",B,"6151","814","169","32","638","7805",78.82,10.43,2.17,0.41,8.17,1,1.02,0.69,1.12,1.09,"24235","3146","975","113","2311",78.74,10.22,3.17,0.37,7.51
+"New Castle, PA",C,"6084","1072","323","20","752","8252",73.73,12.99,3.92,0.24,9.12,0.94,1.27,1.24,0.65,1.21,"24235","3146","975","113","2311",78.74,10.22,3.17,0.37,7.51
+"New Castle, PA",D,"1035","415","77","2","174","1702",60.8,24.36,4.53,0.09,10.21,0.77,2.38,1.43,0.25,1.36,"24235","3146","975","113","2311",78.74,10.22,3.17,0.37,7.51
+"New Haven-Milford, CT",A,"4461","713","747","547","361","6828",65.33,10.44,10.94,8.01,5.29,1.09,0.84,0.57,1.92,1.23,"542154","112197","174360","37726","39057",59.87,12.39,19.26,4.17,4.31
+"New Haven-Milford, CT",B,"18965","6682","7764","1428","1891","36731",51.63,18.19,21.14,3.89,5.15,0.86,1.47,1.1,0.93,1.19,"542154","112197","174360","37726","39057",59.87,12.39,19.26,4.17,4.31
+"New Haven-Milford, CT",C,"47616","40240","51420","5311","7520","152107",31.3,26.46,33.81,3.49,4.94,0.52,2.14,1.76,0.84,1.15,"542154","112197","174360","37726","39057",59.87,12.39,19.26,4.17,4.31
+"New Haven-Milford, CT",D,"8993","12524","17377","1449","1874","42217",21.3,29.67,41.16,3.43,4.44,0.36,2.39,2.14,0.82,1.03,"542154","112197","174360","37726","39057",59.87,12.39,19.26,4.17,4.31
+"New Orleans-Metairie, LA",A,"6292","714","577","177","276","8036",78.3,8.88,7.19,2.2,3.43,1.81,0.23,0.59,0.91,0.81,"198792","175278","55631","11069","19404",43.2,38.09,12.09,2.41,4.22
+"New Orleans-Metairie, LA",B,"15299","2735","2084","553","983","21654",70.65,12.63,9.63,2.55,4.54,1.64,0.33,0.8,1.06,1.08,"198792","175278","55631","11069","19404",43.2,38.09,12.09,2.41,4.22
+"New Orleans-Metairie, LA",C,"48025","28576","8976","1414","4242","91233",52.64,31.32,9.84,1.55,4.65,1.22,0.82,0.81,0.64,1.1,"198792","175278","55631","11069","19404",43.2,38.09,12.09,2.41,4.22
+"New Orleans-Metairie, LA",D,"29081","55441","7192","1199","4235","97149",29.93,57.07,7.4,1.23,4.36,0.69,1.5,0.61,0.51,1.03,"198792","175278","55631","11069","19404",43.2,38.09,12.09,2.41,4.22
+"New York-Newark-Jersey City, NY-NJ-PA",A,"205702","31768","36632","24816","14949","313867",65.54,10.12,11.67,7.91,4.76,1.85,0.56,0.42,0.56,1.03,"5169444","2626933","4074221","2077111","675943",35.35,17.96,27.86,14.2,4.62
+"New York-Newark-Jersey City, NY-NJ-PA",B,"752223","346677","503237","214336","86779","1903251",39.52,18.21,26.44,11.26,4.56,1.12,1.01,0.95,0.79,0.99,"5169444","2626933","4074221","2077111","675943",35.35,17.96,27.86,14.2,4.62
+"New York-Newark-Jersey City, NY-NJ-PA",C,"1164087","894704","1492338","767862","239048","4558038",25.54,19.63,32.74,16.85,5.24,0.72,1.09,1.18,1.19,1.13,"5169444","2626933","4074221","2077111","675943",35.35,17.96,27.86,14.2,4.62
+"New York-Newark-Jersey City, NY-NJ-PA",D,"914030","781692","1029074","339307","153671","3217775",28.41,24.29,31.98,10.54,4.78,0.8,1.35,1.15,0.74,1.03,"5169444","2626933","4074221","2077111","675943",35.35,17.96,27.86,14.2,4.62
+"Ogden-Clearfield, UT",A,"4566","67","1016","60","322","6031",75.71,1.11,16.84,0.99,5.35,1.26,0.54,0.54,0.79,0.98,"50541","1743","26098","1055","4579",60.16,2.07,31.06,1.26,5.45
+"Ogden-Clearfield, UT",B,"8139","342","5263","150","881","14775",55.09,2.32,35.62,1.01,5.96,0.92,1.12,1.15,0.81,1.09,"50541","1743","26098","1055","4579",60.16,2.07,31.06,1.26,5.45
+"Ogden-Clearfield, UT",C,"9927","373","8464","277","1096","20136",49.3,1.85,42.04,1.37,5.44,0.82,0.89,1.35,1.09,1,"50541","1743","26098","1055","4579",60.16,2.07,31.06,1.26,5.45
+"Ogden-Clearfield, UT",D,"9492","380","4788","240","756","15655",60.63,2.43,30.58,1.53,4.83,1.01,1.17,0.98,1.22,0.89,"50541","1743","26098","1055","4579",60.16,2.07,31.06,1.26,5.45
+"Oklahoma City, OK",A,"11667","1930","2148","775","1909","18430",63.3,10.47,11.66,4.21,10.36,1.73,0.68,0.33,1.53,1.08,"94436","39686","92072","7108","24755",36.59,15.38,35.68,2.75,9.59
+"Oklahoma City, OK",B,"7264","1236","7517","850","1875","18741",38.76,6.6,40.11,4.54,10,1.06,0.43,1.12,1.65,1.04,"94436","39686","92072","7108","24755",36.59,15.38,35.68,2.75,9.59
+"Oklahoma City, OK",C,"12323","8075","19552","1214","3904","45068",27.34,17.92,43.38,2.69,8.66,0.75,1.17,1.22,0.98,0.9,"94436","39686","92072","7108","24755",36.59,15.38,35.68,2.75,9.59
+"Oklahoma City, OK",D,"5052","3328","12078","239","1988","22685",22.27,14.67,53.24,1.05,8.77,0.61,0.95,1.49,0.38,0.91,"94436","39686","92072","7108","24755",36.59,15.38,35.68,2.75,9.59
+"Omaha-Council Bluffs, NE-IA",A,"38591","4137","4707","2953","2937","53325",72.37,7.76,8.83,5.54,5.51,1.22,0.61,0.49,1.32,0.97,"274171","59096","84107","19376","26178",59.23,12.77,18.17,4.19,5.65
+"Omaha-Council Bluffs, NE-IA",B,"73234","22628","32872","3931","8499","141164",51.88,16.03,23.29,2.78,6.02,0.88,1.26,1.28,0.67,1.06,"274171","59096","84107","19376","26178",59.23,12.77,18.17,4.19,5.65
+"Omaha-Council Bluffs, NE-IA",C,"21495","7297","19256","1412","2454","51914",41.41,14.06,37.09,2.72,4.73,0.7,1.1,2.04,0.65,0.84,"274171","59096","84107","19376","26178",59.23,12.77,18.17,4.19,5.65
+"Omaha-Council Bluffs, NE-IA",D,"14561","3625","7350","428","1540","27503",52.94,13.18,26.72,1.56,5.6,0.89,1.03,1.47,0.37,0.99,"274171","59096","84107","19376","26178",59.23,12.77,18.17,4.19,5.65
+"Peoria, IL",A,"1891","304","119","41","124","2478",76.28,12.28,4.81,1.64,4.99,1.29,0.47,0.7,0.88,0.81,"69807","30777","8184","2193","7249",59.05,26.04,6.92,1.86,6.13
+"Peoria, IL",B,"4522","1435","468","137","433","6995",64.65,20.52,6.69,1.95,6.19,1.09,0.79,0.97,1.05,1.01,"69807","30777","8184","2193","7249",59.05,26.04,6.92,1.86,6.13
+"Peoria, IL",C,"13646","10521","3411","454","2171","30203",45.18,34.83,11.29,1.5,7.19,0.77,1.34,1.63,0.81,1.17,"69807","30777","8184","2193","7249",59.05,26.04,6.92,1.86,6.13
+"Peoria, IL",D,"2791","6749","1198","30","687","11454",24.36,58.92,10.45,0.26,6,0.41,2.26,1.51,0.14,0.98,"69807","30777","8184","2193","7249",59.05,26.04,6.92,1.86,6.13
+"Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",A,"39353","36088","9329","7290","4408","96470",40.79,37.41,9.67,7.56,4.57,0.81,1.43,0.81,1.01,1.06,"1716542","898482","411152","255332","147490",50.06,26.2,11.99,7.45,4.3
+"Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",B,"192442","211220","96146","43182","26410","569401",33.8,37.1,16.89,7.58,4.64,0.68,1.42,1.41,1.02,1.08,"1716542","898482","411152","255332","147490",50.06,26.2,11.99,7.45,4.3
+"Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",C,"104259","179683","79519","16407","15719","395587",26.36,45.42,20.1,4.15,3.97,0.53,1.73,1.68,0.56,0.92,"1716542","898482","411152","255332","147490",50.06,26.2,11.99,7.45,4.3
+"Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",D,"210709","202592","83584","44126","23221","564232",37.34,35.91,14.81,7.82,4.12,0.75,1.37,1.24,1.05,0.96,"1716542","898482","411152","255332","147490",50.06,26.2,11.99,7.45,4.3
+"Phoenix-Mesa-Chandler, AZ",A,"3000","193","856","151","259","4459",67.28,4.32,19.19,3.39,5.82,1.9,0.53,0.4,1.35,0.91,"55623","12810","74974","3961","10109",35.32,8.13,47.61,2.52,6.42
+"Phoenix-Mesa-Chandler, AZ",B,"10946","1167","12625","637","1750","27126",40.35,4.3,46.54,2.35,6.45,1.14,0.53,0.98,0.93,1.01,"55623","12810","74974","3961","10109",35.32,8.13,47.61,2.52,6.42
+"Phoenix-Mesa-Chandler, AZ",C,"10231","1663","10034","883","1841","24652",41.5,6.75,40.7,3.58,7.47,1.17,0.83,0.85,1.42,1.16,"55623","12810","74974","3961","10109",35.32,8.13,47.61,2.52,6.42
+"Phoenix-Mesa-Chandler, AZ",D,"2518","2553","9668","368","819","15926",15.81,16.03,60.71,2.31,5.14,0.45,1.97,1.28,0.92,0.8,"55623","12810","74974","3961","10109",35.32,8.13,47.61,2.52,6.42
+"Pittsburgh, PA",A,"25473","777","1236","3125","1496","32107",79.34,2.42,3.85,9.73,4.66,1.06,0.19,1.37,2.04,1.01,"903740","155986","33877","57618","55896",74.87,12.92,2.81,4.77,4.63
+"Pittsburgh, PA",B,"121234","23918","5992","9270","9167","169582",71.49,14.1,3.53,5.47,5.41,0.95,1.09,1.26,1.15,1.17,"903740","155986","33877","57618","55896",74.87,12.92,2.81,4.77,4.63
+"Pittsburgh, PA",C,"110738","42730","6521","6072","10195","176256",62.83,24.24,3.7,3.45,5.78,0.84,1.88,1.32,0.72,1.25,"903740","155986","33877","57618","55896",74.87,12.92,2.81,4.77,4.63
+"Pittsburgh, PA",D,"52948","34640","3352","3127","5592","99658",53.13,34.76,3.36,3.14,5.61,0.71,2.69,1.2,0.66,1.21,"903740","155986","33877","57618","55896",74.87,12.92,2.81,4.77,4.63
+"Portland-Vancouver-Hillsboro, OR-WA",A,"14353","232","973","811","1320","17689",81.14,1.31,5.5,4.59,7.46,1.2,0.29,0.49,0.59,0.87,"537516","35372","88663","61858","68272",67.9,4.47,11.2,7.81,8.62
+"Portland-Vancouver-Hillsboro, OR-WA",B,"63944","2422","5525","3552","6957","82400",77.6,2.94,6.71,4.31,8.44,1.14,0.66,0.6,0.55,0.98,"537516","35372","88663","61858","68272",67.9,4.47,11.2,7.81,8.62
+"Portland-Vancouver-Hillsboro, OR-WA",C,"125734","9895","20188","14595","17162","187575",67.03,5.28,10.76,7.78,9.15,0.99,1.18,0.96,1,1.06,"537516","35372","88663","61858","68272",67.9,4.47,11.2,7.81,8.62
+"Portland-Vancouver-Hillsboro, OR-WA",D,"38716","3192","4796","3101","4857","54662",70.83,5.84,8.77,5.67,8.89,1.04,1.31,0.78,0.73,1.03,"537516","35372","88663","61858","68272",67.9,4.47,11.2,7.81,8.62
+"Portsmouth, OH",A,"188","13","9","3","14","228",82.69,5.92,3.94,1.24,6.21,0.93,1.63,1.99,2.22,1.32,"26422","1080","588","166","1397",89.11,3.64,1.98,0.56,4.71
+"Portsmouth, OH",B,"2264","153","49","36","149","2651",85.39,5.77,1.85,1.36,5.63,0.96,1.58,0.93,2.43,1.2,"26422","1080","588","166","1397",89.11,3.64,1.98,0.56,4.71
+"Portsmouth, OH",C,"7373","382","270","34","479","8539",86.34,4.48,3.16,0.4,5.61,0.97,1.23,1.6,0.72,1.19,"26422","1080","588","166","1397",89.11,3.64,1.98,0.56,4.71
+"Portsmouth, OH",D,"2831","260","80","13","221","3406",83.12,7.64,2.36,0.37,6.5,0.93,2.1,1.19,0.67,1.38,"26422","1080","588","166","1397",89.11,3.64,1.98,0.56,4.71
+"Poughkeepsie-Newburgh-Middletown, NY",A,"1900","570","446","185","218","3319",57.24,17.18,13.44,5.56,6.57,1.33,0.62,0.68,1.66,1.08,"21681","13925","9905","1686","3054",43.14,27.71,19.71,3.36,6.08
+"Poughkeepsie-Newburgh-Middletown, NY",B,"4454","2691","2149","178","604","10076",44.2,26.7,21.33,1.77,5.99,1.02,0.96,1.08,0.53,0.99,"21681","13925","9905","1686","3054",43.14,27.71,19.71,3.36,6.08
+"Poughkeepsie-Newburgh-Middletown, NY",C,"4443","5659","3375","295","848","14620",30.39,38.71,23.09,2.02,5.8,0.7,1.4,1.17,0.6,0.95,"21681","13925","9905","1686","3054",43.14,27.71,19.71,3.36,6.08
+"Poughkeepsie-Newburgh-Middletown, NY",D,"1615","1842","1129","88","318","4991",32.35,36.9,22.62,1.76,6.37,0.75,1.33,1.15,0.52,1.05,"21681","13925","9905","1686","3054",43.14,27.71,19.71,3.36,6.08
+"Providence-Warwick, RI-MA",A,"19746","2089","4731","1524","3093","31182",63.32,6.7,15.17,4.89,9.92,1.08,0.95,0.66,1.1,1.44,"418787","50514","165024","31822","49147",58.55,7.06,23.07,4.45,6.87
+"Providence-Warwick, RI-MA",B,"62780","14997","51194","6393","15038","150402",41.74,9.97,34.04,4.25,10,0.71,1.41,1.48,0.96,1.46,"418787","50514","165024","31822","49147",58.55,7.06,23.07,4.45,6.87
+"Providence-Warwick, RI-MA",C,"58997","19016","72242","8549","11756","170561",34.59,11.15,42.36,5.01,6.89,0.59,1.58,1.84,1.13,1,"418787","50514","165024","31822","49147",58.55,7.06,23.07,4.45,6.87
+"Providence-Warwick, RI-MA",D,"7036","1311","5555","591","1444","15937",44.15,8.23,34.86,3.71,9.06,0.75,1.17,1.51,0.83,1.32,"418787","50514","165024","31822","49147",58.55,7.06,23.07,4.45,6.87
+"Pueblo, CO",A,"2688","74","1586","31","260","4639",57.95,1.59,34.19,0.66,5.61,1.4,0.71,0.67,1.21,1.17,"33826","1825","41532","446","3911",41.48,2.24,50.93,0.55,4.8
+"Pueblo, CO",B,"8199","369","10066","113","1108","19855",41.29,1.86,50.7,0.57,5.58,1,0.83,1,1.04,1.16,"33826","1825","41532","446","3911",41.48,2.24,50.93,0.55,4.8
+"Pueblo, CO",C,"3210","253","5486","45","535","9528",33.69,2.65,57.58,0.47,5.61,0.81,1.19,1.13,0.86,1.17,"33826","1825","41532","446","3911",41.48,2.24,50.93,0.55,4.8
+"Pueblo, CO",D,"2293","174","3668","31","382","6548",35.02,2.65,56.02,0.47,5.84,0.84,1.19,1.1,0.86,1.22,"33826","1825","41532","446","3911",41.48,2.24,50.93,0.55,4.8
+"Racine, WI",A,"495","15","64","7","30","611",81.04,2.41,10.51,1.17,4.87,1.58,0.12,0.48,1.15,0.85,"49521","19349","20911","976","5512",51.44,20.1,21.72,1.01,5.73
+"Racine, WI",B,"8176","1261","2220","68","682","12407",65.9,10.17,17.89,0.55,5.5,1.28,0.51,0.82,0.54,0.96,"49521","19349","20911","976","5512",51.44,20.1,21.72,1.01,5.73
+"Racine, WI",C,"11416","6466","7246","126","1644","26898",42.44,24.04,26.94,0.47,6.11,0.83,1.2,1.24,0.46,1.07,"49521","19349","20911","976","5512",51.44,20.1,21.72,1.01,5.73
+"Racine, WI",D,"6290","4225","5052","97","974","16638",37.8,25.39,30.37,0.58,5.85,0.73,1.26,1.4,0.58,1.02,"49521","19349","20911","976","5512",51.44,20.1,21.72,1.01,5.73
+"Richmond, VA",A,"7543","525","201","104","265","8638",87.32,6.08,2.33,1.21,3.06,2.11,0.14,0.26,0.42,0.63,"126888","128479","27279","8711","14842",41.44,41.96,8.91,2.85,4.85
+"Richmond, VA",B,"17423","3059","928","444","1114","22969",75.85,13.32,4.04,1.93,4.85,1.83,0.32,0.45,0.68,1,"126888","128479","27279","8711","14842",41.44,41.96,8.91,2.85,4.85
+"Richmond, VA",C,"19374","13623","1886","846","2017","37745",51.33,36.09,5,2.24,5.34,1.24,0.86,0.56,0.79,1.1,"126888","128479","27279","8711","14842",41.44,41.96,8.91,2.85,4.85
+"Richmond, VA",D,"9280","23337","1698","813","1869","36996",25.08,63.08,4.59,2.2,5.05,0.61,1.5,0.52,0.77,1.04,"126888","128479","27279","8711","14842",41.44,41.96,8.91,2.85,4.85
+"Roanoke, VA",A,"997","10","17","15","36","1074",92.84,0.89,1.54,1.4,3.34,1.38,0.05,0.23,0.47,0.62,"126614","33584","12344","5556","10172",67.25,17.84,6.56,2.95,5.4
+"Roanoke, VA",B,"4806","575","520","136","359","6395",75.15,8.99,8.13,2.12,5.61,1.12,0.5,1.24,0.72,1.04,"126614","33584","12344","5556","10172",67.25,17.84,6.56,2.95,5.4
+"Roanoke, VA",C,"15314","6126","1840","434","1643","25357",60.39,24.16,7.26,1.71,6.48,0.9,1.35,1.11,0.58,1.2,"126614","33584","12344","5556","10172",67.25,17.84,6.56,2.95,5.4
+"Roanoke, VA",D,"10196","5012","1639","242","1288","18377",55.48,27.28,8.92,1.32,7.01,0.83,1.53,1.36,0.45,1.3,"126614","33584","12344","5556","10172",67.25,17.84,6.56,2.95,5.4
+"Rochester, MN",A,"1253","18","80","148","64","1563",80.18,1.13,5.12,9.46,4.1,1.18,0.11,0.61,1.28,0.67,"15925","2321","1963","1724","1437",68.14,9.93,8.4,7.38,6.15
+"Rochester, MN",B,"2819","322","362","378","278","4158",67.79,7.75,8.71,9.08,6.67,0.99,0.78,1.04,1.23,1.09,"15925","2321","1963","1724","1437",68.14,9.93,8.4,7.38,6.15
+"Rochester, MN",C,"4247","518","569","448","411","6192",68.58,8.36,9.19,7.23,6.64,1.01,0.84,1.09,0.98,1.08,"15925","2321","1963","1724","1437",68.14,9.93,8.4,7.38,6.15
+"Rochester, MN",D,"680","94","82","143","57","1056",64.39,8.91,7.8,13.51,5.39,0.94,0.9,0.93,1.83,0.88,"15925","2321","1963","1724","1437",68.14,9.93,8.4,7.38,6.15
+"Rochester, NY",A,"10306","476","557","439","654","12433",82.89,3.83,4.48,3.53,5.26,1.49,0.17,0.34,0.86,1.09,"248955","99333","59483","18333","21660",55.6,22.18,13.28,4.09,4.84
+"Rochester, NY",B,"24215","14682","4947","1811","2507","48161",50.28,30.48,10.27,3.76,5.21,0.9,1.37,0.77,0.92,1.08,"248955","99333","59483","18333","21660",55.6,22.18,13.28,4.09,4.84
+"Rochester, NY",C,"64342","48070","30238","4395","8099","155143",41.47,30.98,19.49,2.83,5.22,0.75,1.4,1.47,0.69,1.08,"248955","99333","59483","18333","21660",55.6,22.18,13.28,4.09,4.84
+"Rochester, NY",D,"6230","16269","8421","842","1663","33424",18.64,48.67,25.19,2.52,4.98,0.34,2.19,1.9,0.61,1.03,"248955","99333","59483","18333","21660",55.6,22.18,13.28,4.09,4.84
+"Rockford, IL",A,"1861","105","220","36","129","2351",79.14,4.49,9.35,1.55,5.47,1.47,0.23,0.51,0.57,0.98,"93312","33989","32009","4683","9722",53.72,19.57,18.43,2.7,5.6
+"Rockford, IL",B,"9656","2355","2285","284","885","15466",62.43,15.22,14.78,1.84,5.73,1.16,0.78,0.8,0.68,1.02,"93312","33989","32009","4683","9722",53.72,19.57,18.43,2.7,5.6
+"Rockford, IL",C,"13794","6813","6120","564","1858","29150",47.32,23.37,21,1.94,6.37,0.88,1.19,1.14,0.72,1.14,"93312","33989","32009","4683","9722",53.72,19.57,18.43,2.7,5.6
+"Rockford, IL",D,"12012","10910","9816","850","2064","35653",33.69,30.6,27.53,2.39,5.79,0.63,1.56,1.49,0.88,1.03,"93312","33989","32009","4683","9722",53.72,19.57,18.43,2.7,5.6
+"Sacramento-Roseville-Folsom, CA",A,"3521","54","606","386","418","4986",70.62,1.08,12.16,7.75,8.39,1.68,0.12,0.41,0.77,0.97,"103578","23051","73467","24625","21401",42.08,9.37,29.85,10.01,8.7
+"Sacramento-Roseville-Folsom, CA",B,"10313","513","2510","858","1344","15537",66.38,3.3,16.16,5.52,8.65,1.58,0.35,0.54,0.55,0.99,"103578","23051","73467","24625","21401",42.08,9.37,29.85,10.01,8.7
+"Sacramento-Roseville-Folsom, CA",C,"30587","5424","18434","5032","5366","64843",47.17,8.36,28.43,7.76,8.28,1.12,0.89,0.95,0.78,0.95,"103578","23051","73467","24625","21401",42.08,9.37,29.85,10.01,8.7
+"Sacramento-Roseville-Folsom, CA",D,"5845","885","2922","1543","1179","12374",47.23,7.16,23.61,12.47,9.53,1.12,0.76,0.79,1.25,1.1,"103578","23051","73467","24625","21401",42.08,9.37,29.85,10.01,8.7
+"Saginaw, MI",A,"942","304","185","5","85","1521",61.9,20,12.19,0.35,5.56,1.34,0.59,0.9,0.35,1.03,"39102","28876","11469","848","4571",46.07,34.03,13.51,1,5.39
+"Saginaw, MI",B,"4488","1206","895","41","355","6985",64.25,17.26,12.82,0.59,5.08,1.39,0.51,0.95,0.59,0.94,"39102","28876","11469","848","4571",46.07,34.03,13.51,1,5.39
+"Saginaw, MI",C,"12404","17652","5942","150","2242","38390",32.31,45.98,15.48,0.39,5.84,0.7,1.35,1.15,0.39,1.08,"39102","28876","11469","848","4571",46.07,34.03,13.51,1,5.39
+"Saginaw, MI",D,"1616","1582","821","12","307","4338",37.26,36.47,18.93,0.27,7.08,0.81,1.07,1.4,0.27,1.31,"39102","28876","11469","848","4571",46.07,34.03,13.51,1,5.39
+"Salt Lake City, UT",A,"10892","72","579","496","630","12669",85.97,0.57,4.57,3.92,4.98,1.41,0.18,0.21,0.7,0.64,"156318","8185","56880","14340","19843",61.17,3.2,22.26,5.61,7.76
+"Salt Lake City, UT",B,"40632","750","5960","1955","3530","52827",76.91,1.42,11.28,3.7,6.68,1.26,0.44,0.51,0.66,0.86,"156318","8185","56880","14340","19843",61.17,3.2,22.26,5.61,7.76
+"Salt Lake City, UT",C,"22032","936","8788","2034","2836","36625",60.16,2.55,23.99,5.55,7.74,0.98,0.8,1.08,0.99,1,"156318","8185","56880","14340","19843",61.17,3.2,22.26,5.61,7.76
+"Salt Lake City, UT",D,"19786","2425","19269","2777","4972","49228",40.19,4.93,39.14,5.64,10.1,0.66,1.54,1.76,1.01,1.3,"156318","8185","56880","14340","19843",61.17,3.2,22.26,5.61,7.76
+"San Antonio-New Braunfels, TX",A,"5588","377","12361","209","437","18972",29.45,1.99,65.15,1.1,2.3,2.05,0.4,0.83,1.29,1.28,"71814","24698","390337","4276","9015",14.36,4.94,78.05,0.85,1.8
+"San Antonio-New Braunfels, TX",B,"15862","1195","46005","548","1430","65040",24.39,1.84,70.73,0.84,2.2,1.7,0.37,0.91,0.99,1.22,"71814","24698","390337","4276","9015",14.36,4.94,78.05,0.85,1.8
+"San Antonio-New Braunfels, TX",C,"8610","2009","92951","540","1252","105362",8.17,1.91,88.22,0.51,1.19,0.57,0.39,1.13,0.6,0.66,"71814","24698","390337","4276","9015",14.36,4.94,78.05,0.85,1.8
+"San Antonio-New Braunfels, TX",D,"7057","5540","69962","446","1212","84218",8.38,6.58,83.07,0.53,1.44,0.58,1.33,1.06,0.62,0.8,"71814","24698","390337","4276","9015",14.36,4.94,78.05,0.85,1.8
+"San Diego-Chula Vista-Carlsbad, CA",A,"14807","334","2676","939","1397","20152",73.48,1.66,13.28,4.66,6.93,1.8,0.24,0.4,0.36,1.08,"503181","83252","407558","157305","79059",40.9,6.77,33.13,12.79,6.43
+"San Diego-Chula Vista-Carlsbad, CA",B,"48240","1952","12656","4150","5125","72122",66.89,2.71,17.55,5.75,7.11,1.64,0.4,0.53,0.45,1.11,"503181","83252","407558","157305","79059",40.9,6.77,33.13,12.79,6.43
+"San Diego-Chula Vista-Carlsbad, CA",C,"77313","12765","60394","16176","11062","177711",43.51,7.18,33.98,9.1,6.22,1.06,1.06,1.03,0.71,0.97,"503181","83252","407558","157305","79059",40.9,6.77,33.13,12.79,6.43
+"San Diego-Chula Vista-Carlsbad, CA",D,"22635","18273","92320","18688","6796","158711",14.26,11.51,58.17,11.77,4.28,0.35,1.7,1.76,0.92,0.67,"503181","83252","407558","157305","79059",40.9,6.77,33.13,12.79,6.43
+"San Francisco-Oakland-Berkeley, CA",A,"34681","1588","4081","9715","4808","54873",63.2,2.89,7.44,17.7,8.76,1.85,0.32,0.36,0.61,1.26,"681698","180454","409441","581656","137973",34.24,9.06,20.56,29.21,6.93
+"San Francisco-Oakland-Berkeley, CA",B,"107751","16766","28223","78623","19076","250439",43.02,6.69,11.27,31.39,7.62,1.26,0.74,0.55,1.07,1.1,"681698","180454","409441","581656","137973",34.24,9.06,20.56,29.21,6.93
+"San Francisco-Oakland-Berkeley, CA",C,"173369","44514","89892","109533","31959","449266",38.59,9.91,20.01,24.38,7.11,1.13,1.09,0.97,0.83,1.03,"681698","180454","409441","581656","137973",34.24,9.06,20.56,29.21,6.93
+"San Francisco-Oakland-Berkeley, CA",D,"109640","48363","93222","85612","24286","361123",30.36,13.39,25.81,23.71,6.73,0.89,1.48,1.26,0.81,0.97,"681698","180454","409441","581656","137973",34.24,9.06,20.56,29.21,6.93
+"San Jose-Sunnyvale-Santa Clara, CA",A,"1525","72","733","493","237","3060",49.85,2.36,23.95,16.1,7.74,1.72,0.68,0.64,0.65,1.49,"90969","10860","118075","78430","16336",28.91,3.45,37.52,24.92,5.19
+"San Jose-Sunnyvale-Santa Clara, CA",B,"10493","498","4646","2655","1381","19672",53.34,2.53,23.62,13.5,7.02,1.85,0.73,0.63,0.54,1.35,"90969","10860","118075","78430","16336",28.91,3.45,37.52,24.92,5.19
+"San Jose-Sunnyvale-Santa Clara, CA",C,"16361","1649","20398","7654","2818","48880",33.47,3.37,41.73,15.66,5.77,1.16,0.98,1.11,0.63,1.11,"90969","10860","118075","78430","16336",28.91,3.45,37.52,24.92,5.19
+"San Jose-Sunnyvale-Santa Clara, CA",D,"9346","1812","24840","7765","2070","45833",20.39,3.95,54.2,16.94,4.52,0.71,1.15,1.44,0.68,0.87,"90969","10860","118075","78430","16336",28.91,3.45,37.52,24.92,5.19
+"Savannah, GA",A,"1648","1436","175","48","171","3477",47.39,41.29,5.02,1.39,4.91,1.35,0.78,1.04,0.45,1.33,"21125","31938","2912","1866","2224",35.17,53.17,4.85,3.11,3.7
+"Savannah, GA",B,"4835","6800","497","178","524","12834",37.67,52.99,3.88,1.38,4.08,1.07,1,0.8,0.45,1.1,"21125","31938","2912","1866","2224",35.17,53.17,4.85,3.11,3.7
+"Savannah, GA",C,"3410","5453","462","386","422","10133",33.65,53.82,4.56,3.81,4.17,0.96,1.01,0.94,1.22,1.13,"21125","31938","2912","1866","2224",35.17,53.17,4.85,3.11,3.7
+"Savannah, GA",D,"2119","2798","363","383","182","5845",36.26,47.86,6.21,6.56,3.11,1.03,0.9,1.28,2.11,0.84,"21125","31938","2912","1866","2224",35.17,53.17,4.85,3.11,3.7
+"Scranton--Wilkes-Barre, PA",A,"27713","4962","9614","743","2009","45041",61.53,11.02,21.35,1.65,4.46,0.84,1.34,1.64,1.08,1.1,"87597","9877","15580","1836","4844",73.16,8.25,13.01,1.53,4.05
+"Scranton--Wilkes-Barre, PA",B,"9338","834","1070","82","598","11923",78.32,7,8.98,0.69,5.02,1.07,0.85,0.69,0.45,1.24,"87597","9877","15580","1836","4844",73.16,8.25,13.01,1.53,4.05
+"Scranton--Wilkes-Barre, PA",D,"11185","475","765","140","415","12980",86.17,3.66,5.89,1.08,3.2,1.18,0.44,0.45,0.7,0.79,"87597","9877","15580","1836","4844",73.16,8.25,13.01,1.53,4.05
+"Seattle-Tacoma-Bellevue, WA",A,"17317","386","1050","1306","1801","21859",79.22,1.76,4.8,5.97,8.24,1.43,0.25,0.42,0.37,0.84,"1416366","181637","290897","418258","251649",55.35,7.1,11.37,16.35,9.83
+"Seattle-Tacoma-Bellevue, WA",B,"160312","6296","16231","25415","20483","228736",70.09,2.75,7.1,11.11,8.95,1.27,0.39,0.62,0.68,0.91,"1416366","181637","290897","418258","251649",55.35,7.1,11.37,16.35,9.83
+"Seattle-Tacoma-Bellevue, WA",C,"129746","24703","22566","36646","24340","238000",54.52,10.38,9.48,15.4,10.23,0.98,1.46,0.83,0.94,1.04,"1416366","181637","290897","418258","251649",55.35,7.1,11.37,16.35,9.83
+"Seattle-Tacoma-Bellevue, WA",D,"53406","13212","12930","19644","10966","110159",48.48,11.99,11.74,17.83,9.96,0.88,1.69,1.03,1.09,1.01,"1416366","181637","290897","418258","251649",55.35,7.1,11.37,16.35,9.83
+"Shreveport-Bossier City, LA",A,"8730","1382","585","130","563","11389",76.65,12.13,5.13,1.14,4.94,2.44,0.21,1.07,1.03,1.21,"32727","60890","5012","1155","4252",31.46,58.53,4.82,1.11,4.09
+"Shreveport-Bossier City, LA",B,"787","3630","187","19","196","4819",16.33,75.33,3.89,0.38,4.07,0.52,1.29,0.81,0.35,0.99,"32727","60890","5012","1155","4252",31.46,58.53,4.82,1.11,4.09
+"Shreveport-Bossier City, LA",C,"2241","10263","625","88","704","13922",16.1,73.72,4.49,0.64,5.06,0.51,1.26,0.93,0.57,1.24,"32727","60890","5012","1155","4252",31.46,58.53,4.82,1.11,4.09
+"Shreveport-Bossier City, LA",D,"968","8202","352","32","340","9894",9.78,82.89,3.56,0.33,3.44,0.31,1.42,0.74,0.29,0.84,"32727","60890","5012","1155","4252",31.46,58.53,4.82,1.11,4.09
+"Sioux City, IA-NE-SD",A,"5116","635","1583","361","701","8397",60.93,7.57,18.86,4.3,8.35,1.01,1.28,0.79,1.38,1.22,"67833","6665","26665","3490","7665",60.39,5.93,23.74,3.11,6.82
+"Sioux City, IA-NE-SD",B,"9430","1360","4985","645","1578","17998",52.4,7.56,27.7,3.58,8.77,0.87,1.27,1.17,1.15,1.28,"67833","6665","26665","3490","7665",60.39,5.93,23.74,3.11,6.82
+"Sioux City, IA-NE-SD",C,"17378","1341","7154","877","2180","28929",60.07,4.63,24.73,3.03,7.54,0.99,0.78,1.04,0.98,1.1,"67833","6665","26665","3490","7665",60.39,5.93,23.74,3.11,6.82
+"Sioux City, IA-NE-SD",D,"18970","1548","3942","766","1573","26799",70.79,5.78,14.71,2.86,5.87,1.17,0.97,0.62,0.92,0.86,"67833","6665","26665","3490","7665",60.39,5.93,23.74,3.11,6.82
+"South Bend-Mishawaka, IN-MI",A,"5944","504","507","89","335","7378",80.55,6.83,6.88,1.2,4.54,1.25,0.43,0.61,0.47,0.75,"135151","33520","23603","5332","12736",64.25,15.94,11.22,2.53,6.05
+"South Bend-Mishawaka, IN-MI",B,"7757","2144","1158","97","812","11969",64.81,17.91,9.68,0.81,6.78,1.01,1.12,0.86,0.32,1.12,"135151","33520","23603","5332","12736",64.25,15.94,11.22,2.53,6.05
+"South Bend-Mishawaka, IN-MI",C,"27288","12112","9812","585","3909","53706",50.81,22.55,18.27,1.09,7.28,0.79,1.42,1.63,0.43,1.2,"135151","33520","23603","5332","12736",64.25,15.94,11.22,2.53,6.05
+"South Bend-Mishawaka, IN-MI",D,"4557","3504","1992","81","804","10937",41.66,32.03,18.21,0.74,7.35,0.65,2.01,1.62,0.29,1.21,"135151","33520","23603","5332","12736",64.25,15.94,11.22,2.53,6.05
+"Spokane-Spokane Valley, WA",A,"7091","96","407","135","634","8363",84.8,1.15,4.86,1.61,7.58,1.1,0.44,0.68,0.59,0.72,"182782","6171","16915","6466","25102",76.98,2.6,7.12,2.72,10.57
+"Spokane-Spokane Valley, WA",B,"20842","448","1480","435","2262","25467",81.84,1.76,5.81,1.71,8.88,1.06,0.68,0.82,0.63,0.84,"182782","6171","16915","6466","25102",76.98,2.6,7.12,2.72,10.57
+"Spokane-Spokane Valley, WA",C,"69523","2689","6960","2480","10794","92446",75.2,2.91,7.53,2.68,11.68,0.98,1.12,1.06,0.99,1.1,"182782","6171","16915","6466","25102",76.98,2.6,7.12,2.72,10.57
+"Spokane-Spokane Valley, WA",D,"14712","863","1710","728","2708","20722",71,4.17,8.25,3.52,13.07,0.92,1.6,1.16,1.29,1.24,"182782","6171","16915","6466","25102",76.98,2.6,7.12,2.72,10.57
+"Springfield, IL",A,"4429","245","187","75","242","5178",85.54,4.72,3.61,1.46,4.68,1.35,0.19,1.06,0.8,0.71,"53922","21122","2883","1542","5639",63.36,24.82,3.39,1.81,6.63
+"Springfield, IL",B,"7669","1824","386","110","740","10730",71.48,17,3.6,1.03,6.9,1.13,0.69,1.06,0.57,1.04,"53922","21122","2883","1542","5639",63.36,24.82,3.39,1.81,6.63
+"Springfield, IL",C,"4912","2143","308","161","659","8182",60.03,26.19,3.76,1.96,8.05,0.95,1.06,1.11,1.08,1.22,"53922","21122","2883","1542","5639",63.36,24.82,3.39,1.81,6.63
+"Springfield, IL",D,"8428","7654","743","145","1336","18306",46.04,41.81,4.06,0.79,7.3,0.73,1.68,1.2,0.44,1.1,"53922","21122","2883","1542","5639",63.36,24.82,3.39,1.81,6.63
+"Springfield, MA",A,"1204","33","143","28","47","1454",82.76,2.24,9.85,1.94,3.21,1.49,0.52,0.28,1.15,0.87,"66933","5205","41940","2026","4441",55.53,4.32,34.79,1.68,3.68
+"Springfield, MA",B,"9584","659","6735","193","708","17879",53.6,3.69,37.67,1.08,3.96,0.97,0.85,1.08,0.64,1.07,"66933","5205","41940","2026","4441",55.53,4.32,34.79,1.68,3.68
+"Springfield, MA",C,"23040","1377","11893","546","1553","38409",59.98,3.59,30.96,1.42,4.04,1.08,0.83,0.89,0.85,1.1,"66933","5205","41940","2026","4441",55.53,4.32,34.79,1.68,3.68
+"Springfield, MA",D,"5031","489","6580","168","371","12638",39.81,3.87,52.07,1.33,2.93,0.72,0.9,1.5,0.79,0.8,"66933","5205","41940","2026","4441",55.53,4.32,34.79,1.68,3.68
+"Springfield, MO",A,"945","28","41","16","73","1103",85.68,2.5,3.75,1.48,6.59,1.1,0.42,0.63,0.91,0.74,"66103","5044","5050","1377","7545",77.66,5.93,5.93,1.62,8.86
+"Springfield, MO",B,"4312","98","219","128","361","5118",84.26,1.91,4.28,2.49,7.05,1.09,0.32,0.72,1.54,0.8,"66103","5044","5050","1377","7545",77.66,5.93,5.93,1.62,8.86
+"Springfield, MO",C,"12441","1207","951","479","1332","16410",75.81,7.36,5.79,2.92,8.12,0.98,1.24,0.98,1.81,0.92,"66103","5044","5050","1377","7545",77.66,5.93,5.93,1.62,8.86
+"Springfield, MO",D,"19179","1756","1601","229","2539","25304",75.8,6.94,6.33,0.9,10.04,0.98,1.17,1.07,0.56,1.13,"66103","5044","5050","1377","7545",77.66,5.93,5.93,1.62,8.86
+"Springfield, OH",A,"2967","457","146","21","248","3839",77.28,11.91,3.82,0.54,6.46,1.14,0.64,0.77,0.81,0.82,"38585","10482","2829","375","4450",68.03,18.48,4.99,0.66,7.84
+"Springfield, OH",B,"6538","1663","544","74","901","9720",67.26,17.11,5.6,0.76,9.27,0.99,0.93,1.12,1.16,1.18,"38585","10482","2829","375","4450",68.03,18.48,4.99,0.66,7.84
+"Springfield, OH",C,"11544","3485","962","61","1411","17462",66.11,19.96,5.51,0.35,8.08,0.97,1.08,1.1,0.53,1.03,"38585","10482","2829","375","4450",68.03,18.48,4.99,0.66,7.84
+"Springfield, OH",D,"2135","1452","262","6","444","4300",49.66,33.77,6.1,0.15,10.33,0.73,1.83,1.22,0.23,1.32,"38585","10482","2829","375","4450",68.03,18.48,4.99,0.66,7.84
+"St. Joseph, MO-KS",A,"319","18","37","12","24","410",77.79,4.33,8.98,2.94,5.97,1,0.71,1.19,2.68,0.84,"49467","3885","4793","693","4513",78.08,6.13,7.57,1.09,7.12
+"St. Joseph, MO-KS",B,"2561","99","217","56","222","3155",81.2,3.13,6.87,1.76,7.05,1.04,0.51,0.91,1.61,0.99,"49467","3885","4793","693","4513",78.08,6.13,7.57,1.09,7.12
+"St. Joseph, MO-KS",C,"3940","202","340","54","428","4963",79.38,4.06,6.85,1.08,8.62,1.02,0.66,0.91,0.99,1.21,"49467","3885","4793","693","4513",78.08,6.13,7.57,1.09,7.12
+"St. Joseph, MO-KS",D,"16151","1487","2342","118","1923","22022",73.34,6.75,10.64,0.54,8.73,0.94,1.1,1.41,0.49,1.23,"49467","3885","4793","693","4513",78.08,6.13,7.57,1.09,7.12
+"St. Louis, MO-IL",A,"49210","12504","2524","4225","3411","71874",68.47,17.4,3.51,5.88,4.75,1.38,0.46,0.74,1.81,0.96,"461043","350098","44529","30335","46177",49.46,37.56,4.78,3.25,4.95
+"St. Louis, MO-IL",B,"92515","74109","9092","8445","9960","194121",47.66,38.18,4.68,4.35,5.13,0.96,1.02,0.98,1.34,1.04,"461043","350098","44529","30335","46177",49.46,37.56,4.78,3.25,4.95
+"St. Louis, MO-IL",C,"52876","68455","6392","3166","7247","138137",38.28,49.56,4.63,2.29,5.25,0.77,1.32,0.97,0.7,1.06,"461043","350098","44529","30335","46177",49.46,37.56,4.78,3.25,4.95
+"St. Louis, MO-IL",D,"24435","41450","5363","1391","3859","76499",31.94,54.18,7.01,1.82,5.04,0.65,1.44,1.47,0.56,1.02,"461043","350098","44529","30335","46177",49.46,37.56,4.78,3.25,4.95
+"Stockton, CA",A,"1872","152","1320","227","299","3870",48.36,3.92,34.12,5.86,7.74,3.32,0.38,0.58,0.53,1.62,"24328","17194","99118","18601","8007",14.55,10.28,59.26,11.12,4.79
+"Stockton, CA",B,"4757","1179","8404","931","1164","16434",28.94,7.17,51.14,5.66,7.08,1.99,0.7,0.86,0.51,1.48,"24328","17194","99118","18601","8007",14.55,10.28,59.26,11.12,4.79
+"Stockton, CA",C,"2874","1831","12263","647","888","18504",15.53,9.9,66.27,3.5,4.8,1.07,0.96,1.12,0.31,1,"24328","17194","99118","18601","8007",14.55,10.28,59.26,11.12,4.79
+"Stockton, CA",D,"1673","1390","19853","2127","844","25887",6.46,5.37,76.69,8.22,3.26,0.44,0.52,1.29,0.74,0.68,"24328","17194","99118","18601","8007",14.55,10.28,59.26,11.12,4.79
+"Syracuse, NY",A,"8104","1786","825","356","699","11771",68.85,15.17,7.01,3.02,5.94,1.15,0.77,0.88,0.52,0.89,"149706","48886","19782","14443","16678",60,19.59,7.93,5.79,6.68
+"Syracuse, NY",B,"32335","10682","4552","3458","3649","54676",59.14,19.54,8.33,6.32,6.67,0.99,1,1.05,1.09,1,"149706","48886","19782","14443","16678",60,19.59,7.93,5.79,6.68
+"Syracuse, NY",C,"31144","19700","6178","4366","5210","66596",46.77,29.58,9.28,6.56,7.82,0.78,1.51,1.17,1.13,1.17,"149706","48886","19782","14443","16678",60,19.59,7.93,5.79,6.68
+"Syracuse, NY",D,"5435","5310","2741","1051","1336","15873",34.24,33.45,17.27,6.62,8.41,0.57,1.71,2.18,1.14,1.26,"149706","48886","19782","14443","16678",60,19.59,7.93,5.79,6.68
+"Tampa-St. Petersburg-Clearwater, FL",A,"8698","157","1079","297","430","10661",81.59,1.48,10.12,2.78,4.04,1.52,0.1,0.44,0.69,0.83,"694493","187621","298272","52216","62888",53.61,14.48,23.02,4.03,4.85
+"Tampa-St. Petersburg-Clearwater, FL",B,"27945","2559","6359","1024","2038","39924",69.99,6.41,15.93,2.56,5.11,1.31,0.44,0.69,0.64,1.05,"694493","187621","298272","52216","62888",53.61,14.48,23.02,4.03,4.85
+"Tampa-St. Petersburg-Clearwater, FL",C,"42280","19204","8881","1570","4007","75941",55.67,25.29,11.69,2.07,5.28,1.04,1.75,0.51,0.51,1.09,"694493","187621","298272","52216","62888",53.61,14.48,23.02,4.03,4.85
+"Tampa-St. Petersburg-Clearwater, FL",D,"32163","35900","20712","2471","4348","95594",33.65,37.55,21.67,2.58,4.55,0.63,2.59,0.94,0.64,0.94,"694493","187621","298272","52216","62888",53.61,14.48,23.02,4.03,4.85
+"Terre Haute, IN",A,"1679","87","53","17","113","1949",86.14,4.47,2.72,0.85,5.82,1.08,0.5,0.81,0.61,0.9,"43392","4896","1829","767","3533",79.74,9,3.36,1.41,6.49
+"Terre Haute, IN",B,"2033","177","58","4","178","2450",82.97,7.21,2.38,0.18,7.26,1.04,0.8,0.71,0.13,1.12,"43392","4896","1829","767","3533",79.74,9,3.36,1.41,6.49
+"Terre Haute, IN",C,"14583","1975","763","181","1555","19057",76.52,10.36,4.01,0.95,8.16,0.96,1.15,1.19,0.67,1.26,"43392","4896","1829","767","3533",79.74,9,3.36,1.41,6.49
+"Terre Haute, IN",D,"8614","1297","441","100","823","11275",76.4,11.5,3.91,0.89,7.3,0.96,1.28,1.16,0.63,1.12,"43392","4896","1829","767","3533",79.74,9,3.36,1.41,6.49
+"Toledo, OH",A,"15003","3220","1175","722","1065","21186",70.82,15.2,5.54,3.41,5.03,1.11,0.72,0.69,2.6,0.87,"238126","78874","30095","4904","21703",63.72,21.11,8.05,1.31,5.81
+"Toledo, OH",B,"29029","16868","4294","465","3413","54069",53.69,31.2,7.94,0.86,6.31,0.84,1.48,0.99,0.66,1.09,"238126","78874","30095","4904","21703",63.72,21.11,8.05,1.31,5.81
+"Toledo, OH",C,"56046","26023","12430","640","7009","102149",54.87,25.48,12.17,0.63,6.86,0.86,1.21,1.51,0.48,1.18,"238126","78874","30095","4904","21703",63.72,21.11,8.05,1.31,5.81
+"Toledo, OH",D,"2957","4981","1085","27","575","9625",30.72,51.75,11.27,0.28,5.98,0.48,2.45,1.4,0.22,1.03,"238126","78874","30095","4904","21703",63.72,21.11,8.05,1.31,5.81
+"Topeka, KS",A,"3833","168","423","33","343","4800",79.85,3.51,8.81,0.68,7.15,1.37,0.31,0.42,0.83,0.86,"44616","8737","16047","627","6384",58.39,11.43,21,0.82,8.36
+"Topeka, KS",B,"8067","839","2491","74","1076","12547",64.3,6.69,19.85,0.59,8.58,1.1,0.58,0.95,0.72,1.03,"44616","8737","16047","627","6384",58.39,11.43,21,0.82,8.36
+"Topeka, KS",C,"6915","1645","2601","99","1073","12333",56.07,13.34,21.09,0.8,8.7,0.96,1.17,1,0.97,1.04,"44616","8737","16047","627","6384",58.39,11.43,21,0.82,8.36
+"Topeka, KS",D,"7541","2608","5338","87","1488","17062",44.2,15.29,31.29,0.51,8.72,0.76,1.34,1.49,0.62,1.04,"44616","8737","16047","627","6384",58.39,11.43,21,0.82,8.36
+"Trenton-Princeton, NJ",A,"1499","5964","1094","102","388","9047",16.57,65.93,12.09,1.12,4.29,0.41,2.58,0.46,0.26,1.22,"105858","66932","68357","11525","9170",40.43,25.56,26.11,4.4,3.5
+"Trenton-Princeton, NJ",B,"5765","11335","8102","515","986","26702",21.59,42.45,30.34,1.93,3.69,0.53,1.66,1.16,0.44,1.05,"105858","66932","68357","11525","9170",40.43,25.56,26.11,4.4,3.5
+"Trenton-Princeton, NJ",C,"15438","20036","33975","1465","2134","73048",21.13,27.43,46.51,2.01,2.92,0.52,1.07,1.78,0.46,0.83,"105858","66932","68357","11525","9170",40.43,25.56,26.11,4.4,3.5
+"Trenton-Princeton, NJ",D,"963","7647","5393","75","446","14524",6.63,52.65,37.13,0.52,3.07,0.16,2.06,1.42,0.12,0.88,"105858","66932","68357","11525","9170",40.43,25.56,26.11,4.4,3.5
+"Tulsa, OK",A,"12907","897","1031","200","2303","17337",74.45,5.17,5.94,1.15,13.28,1.45,0.34,0.38,0.55,0.86,"107491","31810","33015","4383","32470",51.39,15.21,15.78,2.1,15.52
+"Tulsa, OK",B,"11066","2223","1339","390","2746","17764",62.29,12.51,7.54,2.2,15.46,1.21,0.82,0.48,1.05,1,"107491","31810","33015","4383","32470",51.39,15.21,15.78,2.1,15.52
+"Tulsa, OK",C,"8884","2374","6341","268","3207","21074",42.15,11.27,30.09,1.27,15.22,0.82,0.74,1.91,0.61,0.98,"107491","31810","33015","4383","32470",51.39,15.21,15.78,2.1,15.52
+"Tulsa, OK",D,"6893","5020","5381","231","3776","21300",32.36,23.57,25.26,1.09,17.73,0.63,1.55,1.6,0.52,1.14,"107491","31810","33015","4383","32470",51.39,15.21,15.78,2.1,15.52
+"Utica-Rome, NY",A,"3496","102","175","118","138","4029",86.77,2.54,4.35,2.92,3.43,1.34,0.23,0.42,0.32,0.74,"64346","11130","10332","9132","4598",64.64,11.18,10.38,9.17,4.62
+"Utica-Rome, NY",B,"5390","418","546","473","318","7146",75.43,5.86,7.65,6.62,4.45,1.17,0.52,0.74,0.72,0.96,"64346","11130","10332","9132","4598",64.64,11.18,10.38,9.17,4.62
+"Utica-Rome, NY",C,"21616","6117","5102","5377","2013","40225",53.74,15.21,12.68,13.37,5,0.83,1.36,1.22,1.46,1.08,"64346","11130","10332","9132","4598",64.64,11.18,10.38,9.17,4.62
+"Utica-Rome, NY",D,"10159","3298","2941","2009","1041","19447",52.24,16.96,15.12,10.33,5.35,0.81,1.52,1.46,1.13,1.16,"64346","11130","10332","9132","4598",64.64,11.18,10.38,9.17,4.62
+"Virginia Beach-Norfolk-Newport News, VA-NC",A,"5002","1189","405","161","458","7215",69.33,16.48,5.61,2.23,6.34,1.73,0.39,0.72,0.72,0.99,"299021","316582","57673","23166","47931",40.17,42.53,7.75,3.11,6.44
+"Virginia Beach-Norfolk-Newport News, VA-NC",B,"13801","2595","1457","605","1458","19917",69.29,13.03,7.31,3.04,7.32,1.72,0.31,0.94,0.98,1.14,"299021","316582","57673","23166","47931",40.17,42.53,7.75,3.11,6.44
+"Virginia Beach-Norfolk-Newport News, VA-NC",C,"35394","41538","6807","2375","6451","92564",38.24,44.87,7.35,2.57,6.97,0.95,1.06,0.95,0.82,1.08,"299021","316582","57673","23166","47931",40.17,42.53,7.75,3.11,6.44
+"Virginia Beach-Norfolk-Newport News, VA-NC",D,"12213","47265","4093","1331","3820","68722",17.77,68.78,5.96,1.94,5.56,0.44,1.62,0.77,0.62,0.86,"299021","316582","57673","23166","47931",40.17,42.53,7.75,3.11,6.44
+"Waco, TX",A,"1355","543","1286","10","104","3299",41.08,16.46,38.99,0.31,3.16,1.33,0.71,0.98,0.13,0.82,"26467","20043","34059","1983","3314",30.82,23.34,39.67,2.31,3.86
+"Waco, TX",B,"5887","4300","10611","612","769","22179",26.54,19.39,47.84,2.76,3.47,0.86,0.83,1.21,1.19,0.9,"26467","20043","34059","1983","3314",30.82,23.34,39.67,2.31,3.86
+"Waco, TX",C,"4263","2568","6355","396","457","14039",30.36,18.29,45.27,2.82,3.26,0.99,0.78,1.14,1.22,0.84,"26467","20043","34059","1983","3314",30.82,23.34,39.67,2.31,3.86
+"Waco, TX",D,"2994","3296","2995","578","518","10381",28.84,31.75,28.85,5.57,4.99,0.94,1.36,0.73,2.41,1.29,"26467","20043","34059","1983","3314",30.82,23.34,39.67,2.31,3.86
+"Waterloo-Cedar Falls, IA",A,"4060","462","325","129","312","5287",76.79,8.74,6.15,2.43,5.89,1.25,0.42,0.77,0.74,0.91,"33764","11293","4355","1803","3557",61.64,20.62,7.95,3.29,6.49
+"Waterloo-Cedar Falls, IA",B,"7274","2167","1173","782","930","12326",59.01,17.58,9.52,6.34,7.55,0.96,0.85,1.2,1.93,1.16,"33764","11293","4355","1803","3557",61.64,20.62,7.95,3.29,6.49
+"Waterloo-Cedar Falls, IA",C,"6231","2741","1206","250","811","11240",55.44,24.39,10.73,2.23,7.22,0.9,1.18,1.35,0.68,1.11,"33764","11293","4355","1803","3557",61.64,20.62,7.95,3.29,6.49
+"Waterloo-Cedar Falls, IA",D,"2880","2975","633","163","543","7195",40.03,41.35,8.8,2.27,7.55,0.65,2.01,1.11,0.69,1.16,"33764","11293","4355","1803","3557",61.64,20.62,7.95,3.29,6.49
+"Wheeling, WV-OH",A,"1671","67","33","35","77","1884",88.72,3.57,1.73,1.87,4.11,1.01,0.8,1.13,2.27,0.72,"39393","2011","689","372","2573",87.46,4.47,1.53,0.83,5.71
+"Wheeling, WV-OH",B,"3970","83","70","56","191","4370",90.84,1.9,1.6,1.29,4.37,1.04,0.43,1.04,1.56,0.77,"39393","2011","689","372","2573",87.46,4.47,1.53,0.83,5.71
+"Wheeling, WV-OH",C,"6480","402","158","70","443","7554",85.79,5.33,2.09,0.93,5.86,0.98,1.19,1.37,1.12,1.03,"39393","2011","689","372","2573",87.46,4.47,1.53,0.83,5.71
+"Wheeling, WV-OH",D,"4374","585","102","53","523","5636",77.6,10.37,1.81,0.94,9.27,0.89,2.32,1.18,1.14,1.62,"39393","2011","689","372","2573",87.46,4.47,1.53,0.83,5.71
+"Wichita, KS",A,"7876","1453","1785","192","965","12271",64.18,11.84,14.55,1.56,7.87,1.36,0.73,0.57,0.44,1.05,"84502","29033","45804","6327","13378",47.2,16.22,25.58,3.53,7.47
+"Wichita, KS",B,"5390","438","959","81","520","7388",72.95,5.93,12.98,1.09,7.04,1.55,0.37,0.51,0.31,0.94,"84502","29033","45804","6327","13378",47.2,16.22,25.58,3.53,7.47
+"Wichita, KS",C,"5644","1147","3640","295","928","11655",48.43,9.84,31.23,2.53,7.96,1.03,0.61,1.22,0.72,1.07,"84502","29033","45804","6327","13378",47.2,16.22,25.58,3.53,7.47
+"Wichita, KS",D,"19617","7801","16744","981","3671","48815",40.19,15.98,34.3,2.01,7.52,0.85,0.99,1.34,0.57,1.01,"84502","29033","45804","6327","13378",47.2,16.22,25.58,3.53,7.47
+"Winston-Salem, NC",A,"4192","137","166","71","171","4738",88.48,2.89,3.51,1.5,3.62,2.49,0.07,0.18,1.06,0.89,"38537","42604","21141","1539","4420",35.6,39.36,19.53,1.42,4.08
+"Winston-Salem, NC",B,"4479","2309","742","85","360","7976",56.16,28.95,9.31,1.07,4.52,1.58,0.74,0.48,0.75,1.11,"38537","42604","21141","1539","4420",35.6,39.36,19.53,1.42,4.08
+"Winston-Salem, NC",C,"7418","10612","7289","308","1238","26865",27.61,39.5,27.13,1.15,4.61,0.78,1,1.39,0.81,1.13,"38537","42604","21141","1539","4420",35.6,39.36,19.53,1.42,4.08
+"Winston-Salem, NC",D,"807","6774","1759","43","404","9787",8.24,69.22,17.97,0.44,4.12,0.23,1.76,0.92,0.31,1.01,"38537","42604","21141","1539","4420",35.6,39.36,19.53,1.42,4.08
+"York-Hanover, PA",A,"2705","170","230","108","132","3344",80.89,5.08,6.87,3.22,3.95,1.1,0.62,0.61,1.51,0.86,"252133","27842","38586","7290","15634",73.83,8.15,11.3,2.13,4.58
+"York-Hanover, PA",B,"2608","596","850","105","226","4386",59.48,13.59,19.39,2.4,5.15,0.81,1.67,1.72,1.12,1.12,"252133","27842","38586","7290","15634",73.83,8.15,11.3,2.13,4.58
+"York-Hanover, PA",C,"11544","6136","10562","262","1972","30476",37.88,20.13,34.66,0.86,6.47,0.51,2.47,3.07,0.4,1.41,"252133","27842","38586","7290","15634",73.83,8.15,11.3,2.13,4.58
+"York-Hanover, PA",D,"3449","2788","4794","96","634","11761",29.32,23.71,40.76,0.82,5.39,0.4,2.91,3.61,0.38,1.18,"252133","27842","38586","7290","15634",73.83,8.15,11.3,2.13,4.58
+"Youngstown-Warren-Boardman, OH-PA",A,"4566","1962","426","68","361","7383",61.84,26.57,5.77,0.92,4.89,0.83,1.81,1.22,1.17,0.99,"279003","54773","17687","2931","18410",74.84,14.69,4.74,0.79,4.94
+"Youngstown-Warren-Boardman, OH-PA",B,"16173","6839","1475","175","1611","26273",61.56,26.03,5.61,0.67,6.13,0.82,1.77,1.18,0.85,1.24,"279003","54773","17687","2931","18410",74.84,14.69,4.74,0.79,4.94
+"Youngstown-Warren-Boardman, OH-PA",C,"32498","14924","4263","252","3631","55569",58.48,26.86,7.67,0.45,6.53,0.78,1.83,1.62,0.58,1.32,"279003","54773","17687","2931","18410",74.84,14.69,4.74,0.79,4.94
+"Youngstown-Warren-Boardman, OH-PA",D,"9730","8977","4169","65","1221","24162",40.27,37.15,17.25,0.27,5.05,0.54,2.53,3.64,0.34,1.02,"279003","54773","17687","2931","18410",74.84,14.69,4.74,0.79,4.94
diff --git a/pyproject.toml b/pyproject.toml
index 6f5e780..ef41c95 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,10 +4,9 @@ version = "0.1.0"
 description = ""
 authors = ["Chris Proctor <chris@chrisproctor.net>"]
 readme = "README.md"
-packages = [{include = "project_argument"}]
 
 [tool.poetry.dependencies]
-python = "^3.11"
+python = "^3.10"
 jupyter = "^1.0.0"
 seaborn = "^0.12.2"
 pandas = "^2.0.3"