diff --git a/analysis/analysis1.ipynb b/analysis/analysis1.ipynb
index f0deb8d..18ed229 100644
--- a/analysis/analysis1.ipynb
+++ b/analysis/analysis1.ipynb
@@ -27,130 +27,77 @@
"- Obviously, visualizing this among many aspects of the other research questions would involve projecting the data onto a map of the United States, so visualizing this research question would motivate many of the visualizations for other components of this project"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analysis Pipeline"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
- "import seaborn as sns"
+ "import seaborn as sns\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.patches as mpatches\n",
+ "from matplotlib.colors import LinearSegmentedColormap\n",
+ "\n",
+ "sns.set_theme(style=\"white\")\n",
+ "sns.set(font_scale=1.2)\n",
+ "sns.set_palette(\"copper_r\")\n",
+ "plt.rcParams[\"axes.labelsize\"] = 12\n",
+ "\n",
+ "# Absolutely diabolical method of doing relative imports with a package who shares its name with\n",
+ "# something in the stdlib in Jupyter Lab because it seems impossible otherwise\n",
+ "__import__(\"sys\").path.append(\"./code\")\n",
+ "from project_functions1 import *\n",
+ "\n",
+ "CITIES = [\n",
+ " { \"name\": \"Atlanta\", \"pos\": (-84.791944, -84.001944, 33.478333, 34.0375) },\n",
+ " { \"name\": \"Austin\", \"pos\": (-98.071667, -97.371111, 30.033889, 30.569722) },\n",
+ " { \"name\": \"Boston\", \"pos\": (-71.284444, -70.880278, 42.206389, 42.484444) },\n",
+ " { \"name\": \"Chicago\", \"pos\": (-88.044167, -87.508333, 41.624444, 42.026389) },\n",
+ " { \"name\": \"Denver\", \"pos\": (-105.104444, -104.625556, 39.578611, 39.920278) },\n",
+ " { \"name\": \"Houston\", \"pos\": (-95.858333, -95.013056, 29.472778, 30.134167) },\n",
+ " { \"name\": \"Los Angeles\", \"pos\": (-118.615556, -117.618333, 33.701111, 34.411667) },\n",
+ " { \"name\": \"Miami\", \"pos\": (-80.2375, -80.145833, 25.734722, 25.812222) },\n",
+ " { \"name\": \"New York\", \"pos\": (-74.459722, -73.393333, 40.355556, 41.102222) },\n",
+ " { \"name\": \"New Orleans\", \"pos\": (-90.220833, -89.915833, 29.813056, 30.045556) },\n",
+ " { \"name\": \"Philadelphia\", \"pos\": (-75.344167, -74.94, 39.846667, 40.146389) },\n",
+ " { \"name\": \"Portland\", \"pos\": (-122.795278, -122.493333, 45.465556, 45.654444) },\n",
+ " { \"name\": \"San Francisco\", \"pos\": (-122.521667, -122.352778, 37.690278, 37.812222) },\n",
+ " { \"name\": \"Seattle\", \"pos\": (-122.473056, -122.151667, 47.493333, 47.718611) },\n",
+ " { \"name\": \"Washington DC\", \"pos\": (-77.1125, -76.919722, 38.833333, 38.963889) },\n",
+ "]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Data Wrangling"
+ "## Data Analysis Pipeline"
]
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " name | \n",
- " lat | \n",
- " long | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 0 | \n",
- " Hancock OH | \n",
- " 41.000471 | \n",
- " -83.666033 | \n",
- "
\n",
- " \n",
- " 1 | \n",
- " Stafford VA | \n",
- " 38.413261 | \n",
- " -77.451334 | \n",
- "
\n",
- " \n",
- " 2 | \n",
- " Webster NE | \n",
- " 40.180646 | \n",
- " -98.498590 | \n",
- "
\n",
- " \n",
- " 3 | \n",
- " Dimmit TX | \n",
- " 28.423587 | \n",
- " -99.765871 | \n",
- "
\n",
- " \n",
- " 4 | \n",
- " Cedar IA | \n",
- " 41.772360 | \n",
- " -91.132610 | \n",
- "
\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " name lat long\n",
- "0 Hancock OH 41.000471 -83.666033\n",
- "1 Stafford VA 38.413261 -77.451334\n",
- "2 Webster NE 40.180646 -98.498590\n",
- "3 Dimmit TX 28.423587 -99.765871\n",
- "4 Cedar IA 41.772360 -91.132610"
- ]
- },
- "execution_count": 76,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "## counties - Relating US counties to their long/lat position on the Earth\n",
- "counties = pd.read_csv(\"../data/raw/us-county-boundaries.csv\", sep=\";\")\n",
- "\n",
- "counties = counties.rename({\n",
- " \"NAME\": \"name\",\n",
- " \"INTPTLAT\": \"lat\",\n",
- " \"INTPTLON\": \"long\",\n",
- "}, axis=\"columns\")\n",
- "\n",
- "# Combine the county name with the state code\n",
- "def combine_name_state(row):\n",
- " row[\"name\"] = f\"{row['name']} {row['STUSAB']}\"\n",
- " return row\n",
- "\n",
- "counties = counties.apply(combine_name_state, axis=\"columns\")\n",
- "\n",
- "# We don't need this column anymore\n",
- "counties = counties.drop([\"STUSAB\"], axis=\"columns\")\n",
- "\n",
- "counties.to_csv(\"../data/processed/us-county-boundaries.csv\")\n",
- "counties.head()"
+ "# Now in one, new-and-improved, non-descript method imported from another file\n",
+ "gb, pol, counties, cords = load_and_process()"
]
},
{
"cell_type": "code",
- "execution_count": 107,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -174,310 +121,314 @@
" \n",
" \n",
" | \n",
- " party | \n",
- " votes | \n",
- " total | \n",
- " percent | \n",
- " lat | \n",
- " long | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 0 | \n",
- " Democrat | \n",
- " 6363 | \n",
- " 23932 | \n",
- " 0.265878 | \n",
- " 32.532237 | \n",
- " -86.646439 | \n",
- "
\n",
- " \n",
- " 1 | \n",
- " Republican | \n",
- " 17379 | \n",
- " 23932 | \n",
- " 0.726183 | \n",
- " 32.532237 | \n",
- " -86.646439 | \n",
- "
\n",
- " \n",
- " 2 | \n",
- " Other | \n",
- " 190 | \n",
- " 23932 | \n",
- " 0.007939 | \n",
- " 32.532237 | \n",
- " -86.646439 | \n",
- "
\n",
- " \n",
- " 3 | \n",
- " Democrat | \n",
- " 18424 | \n",
- " 85338 | \n",
- " 0.215894 | \n",
- " 30.659218 | \n",
- " -87.746067 | \n",
- "
\n",
- " \n",
- " 4 | \n",
- " Republican | \n",
- " 66016 | \n",
- " 85338 | \n",
- " 0.773583 | \n",
- " 30.659218 | \n",
- " -87.746067 | \n",
- "
\n",
- " \n",
- "\n",
- ""
- ],
- "text/plain": [
- " party votes total percent lat long\n",
- "0 Democrat 6363 23932 0.265878 32.532237 -86.646439\n",
- "1 Republican 17379 23932 0.726183 32.532237 -86.646439\n",
- "2 Other 190 23932 0.007939 32.532237 -86.646439\n",
- "3 Democrat 18424 85338 0.215894 30.659218 -87.746067\n",
- "4 Republican 66016 85338 0.773583 30.659218 -87.746067"
- ]
- },
- "execution_count": 107,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "## pol - Election results from the 2012 American presidential election\n",
- "pol = pd.read_csv(\"../data/raw/countypres_2000-2020.csv\")\n",
- "\n",
- "# We only want 2012--the latest election before the gb data was collected\n",
- "\n",
- "pol = pol[pol[\"year\"] == 2012].reset_index()\n",
- "\n",
- "# Get rid of undesireable columns\n",
- "pol = pol.drop([\n",
- " \"year\", \"state\", \"county_fips\", \"office\",\n",
- " \"candidate\", \"version\", \"mode\", \"index\",\n",
- "], axis=\"columns\")\n",
- "\n",
- "# Change the column names to make them a little more friendly\n",
- "pol.rename({\n",
- " \"county_name\": \"county\",\n",
- " \"state_po\": \"state\",\n",
- " \"candidatevotes\": \"votes\",\n",
- " \"totalvotes\": \"total\"\n",
- "}, axis=\"columns\", inplace=True)\n",
- "\n",
- "# Make cells lowercase\n",
- "pol[\"county\"] = pol[\"county\"].apply(lambda x: x.capitalize())\n",
- "pol[\"party\"] = pol[\"party\"].apply(lambda x: x.capitalize())\n",
- "\n",
- "# Combine the county name with the state code\n",
- "def combine_name_state(row):\n",
- " row[\"county\"] = f\"{row['county']} {row['state']}\"\n",
- " return row\n",
- "\n",
- "pol = pol.apply(combine_name_state, axis=\"columns\")\n",
- "\n",
- "# Add a percent column which will be useful when graphing\n",
- "pol[\"percent\"] = pol[\"votes\"] / pol[\"total\"]\n",
- "\n",
- "# Attach long/lat data to each row\n",
- "pol = pol.merge(counties, left_on=\"county\", right_on=\"name\")\n",
- "\n",
- "# Now we can get rid of the state columns\n",
- "pol = pol.drop([\"state\", \"name\", \"county\"], axis=\"columns\")\n",
- "\n",
- "pol.to_csv(\"../data/processed/election-2012.csv\", index=False)\n",
- "pol.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " Cns_RateMM | \n",
- " CountBars | \n",
- " FF_Index | \n",
- " MM_Index | \n",
" SS_Index | \n",
" TOTINDEX | \n",
" lat | \n",
" long | \n",
+ " kinsey | \n",
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+ " neighbourhood_kinsey | \n",
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2328 rows × 7 columns
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"
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],
"text/plain": [
- " Tax_Mjoint TaxRate_SS TaxRate_FF TaxRate_MM Cns_RateSS Cns_RateFF \\\n",
- "0 2120 203.301887 28.773585 174.528302 77.125329 6.931719 \n",
- "1 5080 205.511811 33.464567 172.047244 88.478367 15.617404 \n",
- "2 5790 107.772021 16.753022 91.018998 46.771050 5.745582 \n",
- "3 3510 80.056980 21.082621 58.974359 31.619291 9.315448 \n",
- "4 2660 91.353383 12.781955 78.571429 21.763042 3.142678 \n",
+ " SS_Index TOTINDEX lat long kinsey percent_democrat \\\n",
+ "0 55.012669 67.077054 34.093828 -118.381697 6 0.456450 \n",
+ "1 58.412517 61.866815 37.758057 -122.435410 6 0.742633 \n",
+ "2 30.731192 37.908747 40.742039 -74.000620 6 0.775215 \n",
+ "3 21.995808 37.530067 40.734012 -74.006746 6 0.794248 \n",
+ "4 21.284224 35.843573 37.773134 -122.411167 5 0.742633 \n",
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+ "2323 0.000000 0.000000 45.528666 -122.678981 0 0.753689 \n",
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+ "2325 0.000000 0.000000 47.449678 -122.307657 0 0.543670 \n",
+ "2326 0.000000 0.000000 47.604569 -122.335359 0 0.545340 \n",
+ "2327 0.000000 0.000000 47.649339 -122.310294 0 0.545340 \n",
"\n",
- " Cns_RateMM CountBars FF_Index MM_Index SS_Index TOTINDEX \\\n",
- "0 70.193610 15 6.724415 48.288254 55.012669 67.077054 \n",
- "1 72.860963 17 9.834048 48.578469 58.412517 61.866815 \n",
- "2 41.025469 5 4.370779 26.360413 30.731192 37.908747 \n",
- "3 22.303843 10 6.055939 15.939869 21.995808 37.530067 \n",
- "4 18.620365 9 3.004058 18.280165 21.284224 35.843573 \n",
+ " neighbourhood_kinsey \n",
+ "0 1.132075 \n",
+ "1 2.533333 \n",
+ "2 1.091667 \n",
+ "3 1.101695 \n",
+ "4 2.533333 \n",
+ "... ... \n",
+ "2323 1.708333 \n",
+ "2324 2.148148 \n",
+ "2325 1.357143 \n",
+ "2326 2.148148 \n",
+ "2327 1.714286 \n",
"\n",
- " lat long \n",
- "0 34.093828 -118.381697 \n",
- "1 37.758057 -122.435410 \n",
- "2 40.742039 -74.000620 \n",
- "3 40.734012 -74.006746 \n",
- "4 37.773134 -122.411167 "
+ "[2328 rows x 7 columns]"
]
},
- "execution_count": 87,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "## gb - the gaybourhoods dataset\n",
- "gb = pd.read_csv(\"../data/raw/gaybourhoods.csv\")\n",
- "cords = pd.read_csv(\"../data/raw/zip_lat_long.csv\")\n",
- "\n",
- "# Let's add long/lat columns to gb\n",
- "gb = gb.merge(cords, left_on=\"GEOID10\", right_on=\"ZIP\")\n",
- "\n",
- "# Get rid of unneeded columns\n",
- "gb = gb.drop([\n",
- " \"Mjoint_MF\", \"Mjoint_SS\", \"Mjoint_FF\", \"Mjoint_MM\",\n",
- " \"Cns_TotHH\", \"Cns_UPSS\", \"Cns_UPFF\", \"Cns_UPMM\",\n",
- " \"ParadeFlag\", \"FF_Tax\", \"FF_Cns\", \"MM_Tax\", \"MM_Cns\",\n",
- " \"SS_Index_Weight\", \"Parade_Weight\", \"Bars_Weight\",\n",
- " \"GEOID10\", \"ZIP\",\n",
- "], axis=\"columns\")\n",
- "\n",
- "# There's a lot of info baked into some of these columns. Especially the composite indexes.\n",
- "# We'll leave their names as is for easy reference even if they're a little ugly.\n",
- "gb = gb.rename({\n",
- " \"LAT\": \"lat\",\n",
- " \"LNG\": \"long\",\n",
- "}, axis=\"columns\")\n",
- "\n",
"gb.to_csv(\"../data/processed/gaybourhoods-nat.csv\")\n",
- "gb.head()"
+ "\n",
+ "SS_INDEX_MAX = gb.SS_Index.max()\n",
+ "\n",
+ "gb"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " county | \n",
+ " lat | \n",
+ " long | \n",
+ " percent | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Autauga AL | \n",
+ " 32.532237 | \n",
+ " -86.646439 | \n",
+ " 0.265878 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Baldwin AL | \n",
+ " 30.659218 | \n",
+ " -87.746067 | \n",
+ " 0.215894 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Barbour AL | \n",
+ " 31.870253 | \n",
+ " -85.405103 | \n",
+ " 0.513685 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Bibb AL | \n",
+ " 33.015893 | \n",
+ " -87.127148 | \n",
+ " 0.261520 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Blount AL | \n",
+ " 33.977357 | \n",
+ " -86.566440 | \n",
+ " 0.123719 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 2879 | \n",
+ " Sweetwater WY | \n",
+ " 41.660328 | \n",
+ " -108.875677 | \n",
+ " 0.282569 | \n",
+ "
\n",
+ " \n",
+ " 2880 | \n",
+ " Teton WY | \n",
+ " 44.048662 | \n",
+ " -110.426087 | \n",
+ " 0.541957 | \n",
+ "
\n",
+ " \n",
+ " 2881 | \n",
+ " Uinta WY | \n",
+ " 41.284726 | \n",
+ " -110.558947 | \n",
+ " 0.190655 | \n",
+ "
\n",
+ " \n",
+ " 2882 | \n",
+ " Washakie WY | \n",
+ " 43.878830 | \n",
+ " -107.669052 | \n",
+ " 0.201318 | \n",
+ "
\n",
+ " \n",
+ " 2883 | \n",
+ " Weston WY | \n",
+ " 43.846213 | \n",
+ " -104.570020 | \n",
+ " 0.125633 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
2884 rows × 4 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " county lat long percent\n",
+ "0 Autauga AL 32.532237 -86.646439 0.265878\n",
+ "1 Baldwin AL 30.659218 -87.746067 0.215894\n",
+ "2 Barbour AL 31.870253 -85.405103 0.513685\n",
+ "3 Bibb AL 33.015893 -87.127148 0.261520\n",
+ "4 Blount AL 33.977357 -86.566440 0.123719\n",
+ "... ... ... ... ...\n",
+ "2879 Sweetwater WY 41.660328 -108.875677 0.282569\n",
+ "2880 Teton WY 44.048662 -110.426087 0.541957\n",
+ "2881 Uinta WY 41.284726 -110.558947 0.190655\n",
+ "2882 Washakie WY 43.878830 -107.669052 0.201318\n",
+ "2883 Weston WY 43.846213 -104.570020 0.125633\n",
+ "\n",
+ "[2884 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pol.to_csv(\"../data/processed/election-2012.csv\")\n",
+ "pol"
]
},
{
@@ -493,12 +444,12 @@
},
{
"cell_type": "code",
- "execution_count": 109,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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\n",
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