Complete task 1 in analysis1.ipynb
Some additional work in the process/wrangling phase may be necessary, which will be determined in task 3. Closes #14
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B53AB05285D710D6
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@ -27,9 +27,16 @@
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"- 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"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Analysis Pipeline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -41,12 +48,112 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Data Wrangling"
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"### Loading the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 76,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"## counties - Relating US counties to their long/lat position on the Earth\n",
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"counties = pd.read_csv(\"../data/raw/us-county-boundaries.csv\", sep=\";\")\n",
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"\n",
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"## pol - Election results from the 2012 American presidential election\n",
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"pol = pd.read_csv(\"../data/raw/countypres_2000-2020.csv\")\n",
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"\n",
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"## gb - the gaybourhoods dataset\n",
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"gb = pd.read_csv(\"../data/raw/gaybourhoods.csv\")\n",
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"\n",
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"# cords - mapping zip codes to long/lat coordinates\n",
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"cords = pd.read_csv(\"../data/raw/zip_lat_long.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Cleaning the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"counties = counties.rename({\n",
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" \"NAME\": \"name\",\n",
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" \"INTPTLAT\": \"lat\",\n",
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" \"INTPTLON\": \"long\",\n",
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"}, axis=\"columns\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# We only want 2012--the latest election before the gb data was collected\n",
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"pol = pol[pol[\"year\"] == 2012].reset_index()\n",
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"\n",
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"# Get rid of undesireable columns\n",
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"pol = pol.drop([\n",
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" \"year\", \"state\", \"county_fips\", \"office\",\n",
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" \"candidate\", \"version\", \"mode\", \"index\",\n",
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"], axis=\"columns\")\n",
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"\n",
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"# Change the column names to make them a little more friendly\n",
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"pol.rename({\n",
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" \"county_name\": \"county\",\n",
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" \"state_po\": \"state\",\n",
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" \"candidatevotes\": \"votes\",\n",
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" \"totalvotes\": \"total\"\n",
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"}, axis=\"columns\", inplace=True)\n",
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"\n",
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"# Make cells lowercase\n",
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"pol[\"county\"] = pol[\"county\"].apply(lambda x: x.capitalize())\n",
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"pol[\"party\"] = pol[\"party\"].apply(lambda x: x.capitalize())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's add long/lat columns to gb\n",
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"gb = gb.merge(cords, left_on=\"GEOID10\", right_on=\"ZIP\")\n",
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"\n",
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"# Get rid of unneeded columns\n",
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"gb = gb.drop([\n",
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" \"Mjoint_MF\", \"Mjoint_SS\", \"Mjoint_FF\", \"Mjoint_MM\",\n",
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" \"Cns_TotHH\", \"Cns_UPSS\", \"Cns_UPFF\", \"Cns_UPMM\",\n",
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" \"ParadeFlag\", \"FF_Tax\", \"FF_Cns\", \"MM_Tax\", \"MM_Cns\",\n",
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" \"SS_Index_Weight\", \"Parade_Weight\", \"Bars_Weight\",\n",
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" \"GEOID10\", \"ZIP\",\n",
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"], axis=\"columns\")\n",
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"\n",
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"# There's a lot of info baked into some of these columns. Especially the composite indexes.\n",
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"# We'll leave their names as is for easy reference even if they're a little ugly.\n",
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"gb = gb.rename({\n",
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" \"LAT\": \"lat\",\n",
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" \"LNG\": \"long\",\n",
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"}, axis=\"columns\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Process/Wrangle the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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@ -119,21 +226,12 @@
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"4 Cedar IA 41.772360 -91.132610"
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]
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},
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"execution_count": 76,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"## counties - Relating US counties to their long/lat position on the Earth\n",
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"counties = pd.read_csv(\"../data/raw/us-county-boundaries.csv\", sep=\";\")\n",
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"\n",
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"counties = counties.rename({\n",
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" \"NAME\": \"name\",\n",
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" \"INTPTLAT\": \"lat\",\n",
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" \"INTPTLON\": \"long\",\n",
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"}, axis=\"columns\")\n",
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"\n",
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"# Combine the county name with the state code\n",
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"def combine_name_state(row):\n",
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" row[\"name\"] = f\"{row['name']} {row['STUSAB']}\"\n",
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@ -150,7 +248,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 107,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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@ -241,37 +339,12 @@
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"4 Republican 66016 85338 0.773583 30.659218 -87.746067"
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]
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},
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"execution_count": 107,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"## pol - Election results from the 2012 American presidential election\n",
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"pol = pd.read_csv(\"../data/raw/countypres_2000-2020.csv\")\n",
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"\n",
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"# We only want 2012--the latest election before the gb data was collected\n",
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"\n",
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"pol = pol[pol[\"year\"] == 2012].reset_index()\n",
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"\n",
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"# Get rid of undesireable columns\n",
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"pol = pol.drop([\n",
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" \"year\", \"state\", \"county_fips\", \"office\",\n",
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" \"candidate\", \"version\", \"mode\", \"index\",\n",
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"], axis=\"columns\")\n",
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"\n",
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"# Change the column names to make them a little more friendly\n",
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"pol.rename({\n",
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" \"county_name\": \"county\",\n",
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" \"state_po\": \"state\",\n",
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" \"candidatevotes\": \"votes\",\n",
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" \"totalvotes\": \"total\"\n",
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"}, axis=\"columns\", inplace=True)\n",
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"\n",
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"# Make cells lowercase\n",
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"pol[\"county\"] = pol[\"county\"].apply(lambda x: x.capitalize())\n",
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"pol[\"party\"] = pol[\"party\"].apply(lambda x: x.capitalize())\n",
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"\n",
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"# Combine the county name with the state code\n",
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"def combine_name_state(row):\n",
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" row[\"county\"] = f\"{row['county']} {row['state']}\"\n",
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@ -453,29 +526,6 @@
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}
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],
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"source": [
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"## gb - the gaybourhoods dataset\n",
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"gb = pd.read_csv(\"../data/raw/gaybourhoods.csv\")\n",
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"cords = pd.read_csv(\"../data/raw/zip_lat_long.csv\")\n",
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"\n",
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"# Let's add long/lat columns to gb\n",
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"gb = gb.merge(cords, left_on=\"GEOID10\", right_on=\"ZIP\")\n",
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"\n",
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"# Get rid of unneeded columns\n",
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"gb = gb.drop([\n",
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" \"Mjoint_MF\", \"Mjoint_SS\", \"Mjoint_FF\", \"Mjoint_MM\",\n",
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" \"Cns_TotHH\", \"Cns_UPSS\", \"Cns_UPFF\", \"Cns_UPMM\",\n",
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" \"ParadeFlag\", \"FF_Tax\", \"FF_Cns\", \"MM_Tax\", \"MM_Cns\",\n",
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" \"SS_Index_Weight\", \"Parade_Weight\", \"Bars_Weight\",\n",
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" \"GEOID10\", \"ZIP\",\n",
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"], axis=\"columns\")\n",
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"\n",
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"# There's a lot of info baked into some of these columns. Especially the composite indexes.\n",
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"# We'll leave their names as is for easy reference even if they're a little ugly.\n",
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"gb = gb.rename({\n",
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" \"LAT\": \"lat\",\n",
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" \"LNG\": \"long\",\n",
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"}, axis=\"columns\")\n",
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"\n",
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"gb.to_csv(\"../data/processed/gaybourhoods-nat.csv\")\n",
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"gb.head()"
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]
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@ -742,9 +792,9 @@
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"\n",
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"**Is there a correlation between political alignment & living in neighbourhoods with large quantities of LGBT people?**\n",
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"1. Unify the political data with the gaybourhoods data set\n",
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" 1. Establish the best way to measure the distance from a given `gb` and a county (Euclidean distance? Some other measurement?)\n",
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" 2. Find the county that is closest to each observation by minimizing the function established in step (a)\n",
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" c. Merge the two tables. Each `gb` observation should then include a political breakdown of the nearest county during the 2012 presidential election\n",
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" 1. Establish the best way to measure the distance from a given `gb` and a county (Euclidean distance? Some other measurement?)\n",
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" 2. Find the county that is closest to each observation by minimizing the function established in step (a)\n",
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" 3. Merge the two tables. Each `gb` observation should then include a political breakdown of the nearest county during the 2012 presidential election\n",
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"2. Use this information to plot queerness by different metrics against political alignment and measure the correlation\n",
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"\n",
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"**Is there a correlation between geographical stratums & being LGBT?**\n",
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