diff --git a/notebooks/density-splits.ipynb b/notebooks/density-splits.ipynb
new file mode 100644
index 00000000..5c7f1a1a
--- /dev/null
+++ b/notebooks/density-splits.ipynb
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+{
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+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a00658f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os; os.chdir(\"..\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "cfa5b130",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "id": "b2d6c4cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "import tqdm.auto as tqdm\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from urbanstats.unrelated_projects.density_and_partisanship import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "id": "1cdc0009",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n = 3\n",
+ "bins, data = load_data(n=n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "id": "1ff3dbe5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pop = np.array(data.population)\n",
+ "dens = np.array(data.density)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "id": "520e2fab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dgt = np.array([\n",
+ " np.array(data[k])\n",
+ " for k in partisanship_columns\n",
+ "]).T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "id": "bdd777fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "states = sorted(set(data[\"state\"]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "id": "4ba30ba6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "usa_whole = compute_by_bin(bins, dgt, dens)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 194,
+ "id": "56fca443",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "state_overall = np.array(data.groupby(\"state\").sum()[partisanship_columns].loc[states])\n",
+ "state_margins = [margin(*x) for x in state_overall]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "id": "527318c9",
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+ " 0%| | 0/51 [00:00 "
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+ "plot_several_by_states(\n",
+ " states,\n",
+ " np.array([by_state[:, -1] - by_state[:, 0]]),\n",
+ " [\"Density polarization (most dense margin - least dense margin)\"],\n",
+ " aspect=0.7,\n",
+ ")"
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diff --git a/urbanstats/unrelated_projects/__init__.py b/urbanstats/unrelated_projects/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/urbanstats/unrelated_projects/density_and_partisanship.py b/urbanstats/unrelated_projects/density_and_partisanship.py
new file mode 100644
index 00000000..f4aa7a0f
--- /dev/null
+++ b/urbanstats/unrelated_projects/density_and_partisanship.py
@@ -0,0 +1,120 @@
+from typing import List
+import us
+import numpy as np
+
+from election_data import with_election_results
+from wquantiles import quantile
+
+partisanship_columns = [
+ ("2020 Presidential Election", "dem"),
+ ("2020 Presidential Election", "gop"),
+ ("2020 Presidential Election", "total"),
+]
+
+
+def geoid_to_state(x):
+ prefix = "7500000US"
+ assert x.startswith(prefix)
+ return x[len(prefix) : len(prefix) + 2]
+
+
+def margin(d, g, t):
+ return (d - g) / t
+
+
+def load_data(n):
+ data = with_election_results()
+ data = data[data[partisanship_columns[-1]] > 0].copy()
+ data["state"] = data.geoid.apply(geoid_to_state)
+ data = data.rename(columns={"ad_1": "density"})
+ data.density /= data.population
+ bins = compute_bins(data.total, data.density, n=n)
+ data["bin_idxs"] = bin_idx(bins, data.density)
+ return bins, data
+
+
+def compute_bins(pop, dens, n):
+ return [-np.inf] + [quantile(dens, pop, i / n) for i in range(1, n)] + [np.inf]
+
+
+def compute_by_bin(bins, dgt, dens):
+ return [
+ margin(*dgt[(low < dens) & (dens <= high)].sum(0))
+ for low, high in zip(bins, bins[1:])
+ ]
+
+
+def bin_idx(bins, dens):
+ out = np.zeros_like(dens, dtype=np.int16)
+ for i, (low, high) in enumerate(zip(bins, bins[1:])):
+ out[(low < dens) & (dens <= high)] = i + 1
+ return out
+
+
+size = 800
+
+
+def plot_by_state(states: List[str], values: np.ndarray, scale):
+ # choropleth
+ import plotly.graph_objects as go
+
+ # fig = px.choropleth(
+ # locations=[us.states.lookup(s).abbr for s in states],
+ # locationmode="USA-states",
+ # scope="usa",
+ # color=values,
+ # color_continuous_scale="RdBu",
+ # range_color=(-scale, scale),
+ # labels={"color": "Margin"},
+ # title=title,
+ # )
+
+ # return fig
+ chor = go.Choropleth(
+ z=values,
+ locationmode="USA-states",
+ locations=[us.states.lookup(s).abbr for s in states],
+ colorscale="RdBu",
+ zmin=-scale,
+ zmax=scale,
+ colorbar_title="Margin",
+ )
+ return chor
+
+
+def plot_several_by_states(states, all_values: List[np.ndarray], titles: List[str], aspect=0.4):
+ from plotly.subplots import make_subplots
+ import plotly.graph_objects as go
+
+ fig = make_subplots(
+ rows=len(all_values),
+ cols=1,
+ subplot_titles=titles,
+ shared_yaxes=True,
+ specs=[[{"type": "choropleth"}]] * len(all_values),
+ horizontal_spacing = 0.05,
+ vertical_spacing = 0.05,
+ )
+ for i, values in enumerate(all_values):
+ fig.add_trace(plot_by_state(states, values, 1), row=i + 1, col=1)
+ fig.update_geos(
+ projection_type="albers usa",
+ showlakes=True,
+ lakecolor="rgb(255, 255, 255)",
+ )
+ fig.update_layout(
+ autosize=False,
+ width=size,
+ height=size * len(all_values) * aspect,
+ )
+
+ return fig
+
+def plot_density_categories(states, by_state, suffix=""):
+ assert by_state.shape[1] == 3
+ fig = plot_several_by_states(
+ states,
+ list(by_state.T),
+ ["Among " + x + ": Margin" + suffix for x in ["Least Dense", "Middle Dense", "Most Dense"]]
+ )
+ return fig
\ No newline at end of file