dadams
6db5e0fff8
Update 8 scripts to use Path(__file__).parent.parent as PROJECT_ROOT so they resolve data/, output/, and internet_cables/ relative to the project root rather than the caller's working directory. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
323 lines
13 KiB
Python
323 lines
13 KiB
Python
#!/usr/bin/env python3
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"""Tract-level analysis of the 'concentrated costs / dispersed benefits' frame
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for US data-center siting.
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Cost-bearing universe = tracts that host at least one DC
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(public.data_center_census_tracts_2024)
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Comparison universe = ACS 2024 5-yr tracts in the selected states
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(census_tract_acs_2024_selected_states.csv)
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"""
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import csv
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import math
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import os
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import statistics
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from collections import Counter
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from pathlib import Path
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import psycopg2
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PROJECT_ROOT = Path(__file__).parent.parent
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CSV_PATH = PROJECT_ROOT / "data" / "census_tract_acs_2024_selected_states.csv"
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def connect():
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return psycopg2.connect(
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host=os.environ["PGWEB_HOST"],
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port=os.environ["PGWEB_PORT"],
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user=os.environ["PGWEB_USER"],
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password=os.environ["PGWEB_PASSWORD"],
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dbname="data_centers",
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)
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def gini(values):
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v = sorted(x for x in values if x is not None and x >= 0)
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n = len(v)
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if n == 0 or sum(v) == 0:
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return None
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cum = sum(i * x for i, x in enumerate(v, 1))
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return (2 * cum) / (n * sum(v)) - (n + 1) / n
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def hhi(shares):
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return sum(s * s for s in shares)
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def median(xs):
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xs = [x for x in xs if x is not None]
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return statistics.median(xs) if xs else None
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def mean(xs):
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xs = [x for x in xs if x is not None]
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return statistics.mean(xs) if xs else None
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def wmean(xs, ws):
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pairs = [(x, w) for x, w in zip(xs, ws) if x is not None and w is not None and w > 0]
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if not pairs:
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return None
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total = sum(w for _, w in pairs)
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return sum(x * w for x, w in pairs) / total
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def to_float(s):
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try:
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return float(s)
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except (TypeError, ValueError):
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return None
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def to_int(s):
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try:
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return int(float(s))
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except (TypeError, ValueError):
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return None
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def main():
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conn = connect()
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cur = conn.cursor()
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# DC-hosting tracts (the cost-bearing universe) ----------------------
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cur.execute(
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"""
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select
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geoid,
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statefp,
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data_center_count,
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population,
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households,
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broadband_subscription_pct,
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median_household_income,
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per_capita_income,
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poverty_rate,
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non_hispanic_white_pct,
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non_hispanic_black_pct,
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hispanic_latino_pct,
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non_hispanic_asian_pct,
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primary_industry,
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land_area_sqm,
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industry_information_workers,
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industry_total_workers
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from public.data_center_census_tracts_2024
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"""
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)
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dc_tracts = []
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for r in cur.fetchall():
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dc_tracts.append(
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{
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"geoid": r[0],
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"statefp": r[1],
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"dc_count": r[2] or 0,
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"pop": r[3],
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"hh": r[4],
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"broadband_pct": float(r[5]) if r[5] is not None else None,
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"mhi": r[6],
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"pci": r[7],
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"poverty": float(r[8]) if r[8] is not None else None,
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"white_pct": float(r[9]) if r[9] is not None else None,
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"black_pct": float(r[10]) if r[10] is not None else None,
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"hisp_pct": float(r[11]) if r[11] is not None else None,
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"asian_pct": float(r[12]) if r[12] is not None else None,
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"primary_industry": r[13],
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"land_sqm": r[14],
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"info_workers": r[15],
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"total_workers": r[16],
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}
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)
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# Distance from each DC tract to nearest cable (km) ----------------
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cur.execute(
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"""
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with cables as (select ST_Union(geom)::geography g from public.internet_cables)
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select t.geoid,
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ST_Distance(ST_Centroid(t.geom)::geography, c.g) / 1000.0
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from public.data_center_census_tracts_2024 t, cables c
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"""
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)
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dist_by_geoid = {r[0]: float(r[1]) for r in cur.fetchall()}
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for t in dc_tracts:
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t["dist_km"] = dist_by_geoid.get(t["geoid"])
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cur.close()
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conn.close()
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# Comparison universe from the wider ACS CSV ------------------------
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universe = []
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with open(CSV_PATH, newline="", encoding="utf-8") as f:
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for row in csv.DictReader(f):
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universe.append(
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{
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"geoid": row["geoid"],
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"statefp": row["statefp"],
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"pop": to_int(row["population"]),
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"broadband_pct": to_float(row["broadband_subscription_pct"]),
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"mhi": to_int(row["median_household_income"]),
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"pci": to_int(row["per_capita_income"]),
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"poverty": to_float(row["poverty_rate"]),
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"white_pct": to_float(row["non_hispanic_white_pct"]),
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"black_pct": to_float(row["non_hispanic_black_pct"]),
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"hisp_pct": to_float(row["hispanic_latino_pct"]),
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"asian_pct": to_float(row["non_hispanic_asian_pct"]),
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}
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)
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dc_geoids = {t["geoid"] for t in dc_tracts}
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non_dc = [u for u in universe if u["geoid"] not in dc_geoids]
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# Restrict comparison to states actually represented in the DC sample
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dc_states = {t["statefp"] for t in dc_tracts}
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universe_in_dc_states = [u for u in universe if u["statefp"] in dc_states]
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non_dc_in_dc_states = [u for u in non_dc if u["statefp"] in dc_states]
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# ============== report ==============
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print("=" * 72)
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print("TRACT-LEVEL CONCENTRATED COSTS / DISPERSED BENEFITS ANALYSIS")
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print("=" * 72)
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total_dc = sum(t["dc_count"] for t in dc_tracts)
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print(f"\nDC-hosting tracts: {len(dc_tracts):,}")
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print(f"Data centers in those tracts: {total_dc:,}")
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print(f"ACS universe (selected states): {len(universe):,} tracts")
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print(f"States represented in DC sample: {len(dc_states)}")
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print(f"Universe restricted to DC states: {len(universe_in_dc_states):,} tracts")
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# --- Cost concentration at the tract level ---
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print("\n" + "-" * 72)
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print("1. COST CONCENTRATION (DCs across tracts)")
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print("-" * 72)
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counts = [t["dc_count"] for t in dc_tracts]
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shares = [c / total_dc for c in counts]
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g_dc = gini(counts)
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h_dc = hhi(shares)
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print(f"Gini of DC counts across DC-hosting tracts: {g_dc:.3f}")
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print(f"HHI of DC shares across DC-hosting tracts: {h_dc:.4f}")
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# Top 1% / 5% of tracts share
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top1 = max(1, len(counts) // 100)
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top5 = max(1, len(counts) // 20)
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s = sorted(counts, reverse=True)
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print(f"Top 1% of DC-hosting tracts ({top1:>3} tracts) hold "
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f"{sum(s[:top1])/total_dc*100:5.1f}% of all DCs")
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print(f"Top 5% of DC-hosting tracts ({top5:>3} tracts) hold "
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f"{sum(s[:top5])/total_dc*100:5.1f}% of all DCs")
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print(f"Top 20% of DC-hosting tracts ({len(counts)//5:>3} tracts) hold "
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f"{sum(s[:len(counts)//5])/total_dc*100:5.1f}% of all DCs")
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# How small a fraction of population lives in a DC tract?
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pop_dc = sum(t["pop"] or 0 for t in dc_tracts)
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pop_universe = sum(u["pop"] or 0 for u in universe_in_dc_states)
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print(f"\nPopulation living in a DC-hosting tract: {pop_dc:>11,}")
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print(f"Total population (DC-states ACS universe): {pop_universe:>11,}")
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if pop_universe:
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print(f" -> {pop_dc/pop_universe*100:.2f}% of people in DC-hosting states "
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f"live in a DC-hosting tract")
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# Per-capita DC density
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if pop_dc:
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print(f" -> 1 DC per {pop_dc/total_dc:,.0f} residents in DC-hosting tracts")
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if pop_universe and total_dc:
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print(f" vs. 1 DC per {pop_universe/total_dc:,.0f} residents "
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f"averaged across DC-state population")
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# --- Profile of cost-bearing communities ---
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print("\n" + "-" * 72)
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print("2. WHO BEARS THE COSTS? (ACS profile of DC tracts vs. peer tracts)")
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print("-" * 72)
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fields = [
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("Median household income ($)", "mhi", "{:>10,.0f}"),
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("Per-capita income ($)", "pci", "{:>10,.0f}"),
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("Broadband subscription (%)", "broadband_pct", "{:>10,.1f}"),
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("Poverty rate (%)", "poverty", "{:>10,.1f}"),
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("Non-Hispanic White (%)", "white_pct", "{:>10,.1f}"),
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("Non-Hispanic Black (%)", "black_pct", "{:>10,.1f}"),
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("Hispanic/Latino (%)", "hisp_pct", "{:>10,.1f}"),
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("Non-Hispanic Asian (%)", "asian_pct", "{:>10,.1f}"),
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]
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label_w = max(len(lbl) for lbl, *_ in fields)
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print(f"{'Field':<{label_w}} {'DC tracts':>12} {'Non-DC peers':>14} "
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f"{'Δ (DC − peer)':>15}")
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for label, key, fmt in fields:
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dc_med = median([t[key] for t in dc_tracts])
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peer_med = median([u[key] for u in non_dc_in_dc_states])
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if dc_med is None or peer_med is None:
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continue
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delta = dc_med - peer_med
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cell_dc = fmt.format(dc_med)
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cell_pe = fmt.format(peer_med)
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cell_dl = fmt.format(delta)
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print(f"{label:<{label_w}} {cell_dc} {cell_pe} {cell_dl}")
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print("\nPopulation-weighted means (DC tracts):")
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pops = [t["pop"] for t in dc_tracts]
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for label, key, _ in fields:
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wm = wmean([t[key] for t in dc_tracts], pops)
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if wm is not None:
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print(f" {label:<{label_w}} {wm:>12,.1f}")
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print("\nPrimary-industry mix of DC-hosting tracts (count of tracts):")
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for industry, n in Counter(t["primary_industry"] for t in dc_tracts).most_common(10):
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print(f" {n:>4} {industry}")
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# --- Cable vs. inland subgroups ---
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print("\n" + "-" * 72)
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print("3. CABLE-ADJACENT vs. INLAND DC TRACTS")
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print("-" * 72)
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near = [t for t in dc_tracts if t.get("dist_km") is not None and t["dist_km"] <= 100]
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far = [t for t in dc_tracts if t.get("dist_km") is not None and t["dist_km"] > 100]
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print(f"≤100 km from a submarine cable: {len(near):>3} tracts, "
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f"{sum(t['dc_count'] for t in near):>4} DCs")
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print(f">100 km from a submarine cable: {len(far):>3} tracts, "
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f"{sum(t['dc_count'] for t in far):>4} DCs")
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if near and far:
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print(f"{' Median MHI':<28} near={median([t['mhi'] for t in near]):>10,.0f} "
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f"far={median([t['mhi'] for t in far]):>10,.0f}")
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print(f"{' Median broadband %':<28} near={median([t['broadband_pct'] for t in near]):>10,.1f} "
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f"far={median([t['broadband_pct'] for t in far]):>10,.1f}")
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print(f"{' Median DC count':<28} near={median([t['dc_count'] for t in near]):>10,.0f} "
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f"far={median([t['dc_count'] for t in far]):>10,.0f}")
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# --- Benefit-side proxy ---
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print("\n" + "-" * 72)
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print("4. BENEFIT DISPERSION (broadband subscribers across all tracts)")
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print("-" * 72)
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# Total broadband subscribers approx = households * broadband_pct
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subs = []
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for u in universe_in_dc_states:
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hh = None # households not in CSV; use population/2.5 fallback
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if u["pop"] and u["broadband_pct"] is not None:
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est_hh = u["pop"] / 2.5
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subs.append(est_hh * u["broadband_pct"] / 100.0)
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total_subs = sum(subs)
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sg = gini(subs)
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sh = hhi([s / total_subs for s in subs]) if total_subs else None
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print(f"Estimated total broadband subscribers (DC states): {total_subs:>14,.0f}")
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print(f"Gini of subscribers across {len(subs):,} tracts: {sg:.3f}")
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print(f"HHI of subscribers across tracts: {sh:.5f}")
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# Compare to DC HHI
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print(f"\nSide-by-side concentration (lower = more dispersed):")
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print(f" HHI of DCs across DC-hosting tracts: {h_dc:.4f}")
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print(f" HHI of broadband subs across DC-state tracts: {sh:.5f} "
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f"({h_dc/sh:.0f}x more concentrated for DCs)")
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print("\n" + "=" * 72)
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print("BOTTOM LINE")
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print("=" * 72)
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n_above_500 = sum(1 for t in dc_tracts if t["dc_count"] >= 5)
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print(f"""
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- DCs are extremely concentrated at the tract level: top 1% of host tracts
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hold {sum(s[:top1])/total_dc*100:.0f}% of all DCs; top 5% hold {sum(s[:top5])/total_dc*100:.0f}%.
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- Only {pop_dc/pop_universe*100:.2f}% of residents of the DC-hosting states actually
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live in a DC-hosting tract — costs (land use, power draw, water, traffic,
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noise) fall on a tiny minority of communities.
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- DC-hosting tracts skew {'wealthier' if median([t['mhi'] for t in dc_tracts]) > median([u['mhi'] for u in non_dc_in_dc_states]) else 'poorer'} and {'higher' if median([t['broadband_pct'] for t in dc_tracts]) > median([u['broadband_pct'] for u in non_dc_in_dc_states]) else 'lower'} broadband than peer
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tracts. See deltas above for the demographic profile.
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- Broadband subscribers (proxy for who consumes cloud services) are far more
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evenly distributed than DCs — HHI roughly {h_dc/sh:.0f}× lower.
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That asymmetry IS the classic concentrated-cost / dispersed-benefit shape.
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""")
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if __name__ == "__main__":
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main()
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