Add FCC broadband build workflow and refresh enhanced cluster map
This commit is contained in:
@@ -31,6 +31,7 @@
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"source": [
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"import os\n",
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"import json\n",
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"import subprocess\n",
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"from html import escape\n",
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"from pathlib import Path\n",
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"\n",
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@@ -39,10 +40,12 @@
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"\n",
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"import pandas as pd\n",
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"import folium\n",
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"import psycopg2\n",
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"from folium import plugins\n",
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"\n",
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"print('pandas:', pd.__version__)\n",
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"print('folium:', folium.__version__)\n"
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"print('folium:', folium.__version__)\n",
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"print('psycopg2:', psycopg2.__version__)\n"
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]
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},
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{
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@@ -81,6 +84,17 @@
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"SHOW_HUC8_LAYER = True\n",
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"SHOW_STATE_ENERGY_LAYER = True\n",
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"\n",
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"# Existing DB-backed overlays.\n",
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"ENABLE_DB_LAYER_LOAD = True\n",
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"SHOW_INTERNET_CABLES_LAYER = True\n",
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"SHOW_OPPOSITION_CASES_LAYER = True\n",
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"SHOW_DROUGHT_AND_SMOKE_CONTEXT = True\n",
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"\n",
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"# New requested overlays.\n",
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"SHOW_CLIMATE_LAYER = True\n",
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"SHOW_BROADBAND_LAYER = True\n",
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"SHOW_ELECTION_LAYER = True\n",
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"\n",
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"OUTPUT_DIR.mkdir(exist_ok=True)\n",
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"print('points:', POINTS_CSV)\n",
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"print('clusters:', CLUSTERS_CSV)\n",
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@@ -159,9 +173,244 @@
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]
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},
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{
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"cell_type": "markdown",
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"cell_type": "code",
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"execution_count": null,
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"id": "6",
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"metadata": {},
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"outputs": [],
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"source": [
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"DB_NAME = 'data_centers'\n",
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"DB_REQUIRED_ENV = ['PGWEB_HOST', 'PGWEB_PORT', 'PGWEB_USER', 'PGWEB_PASSWORD']\n",
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"\n",
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"internet_cables_geojson = None\n",
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"opposition_cases = pd.DataFrame()\n",
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"drought_context = pd.DataFrame()\n",
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"smoke_context = pd.DataFrame()\n",
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"climate_context = pd.DataFrame()\n",
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"broadband_context = pd.DataFrame()\n",
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"election_context = pd.DataFrame()\n",
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"\n",
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"\n",
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"def load_zsh_secrets() -> None:\n",
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" secrets = Path.home() / '.zsh_secrets'\n",
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" if not secrets.exists():\n",
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" return\n",
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" result = subprocess.run(\n",
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" ['zsh', '-lc', 'source ~/.zsh_secrets >/dev/null 2>&1; env'],\n",
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" check=True,\n",
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" capture_output=True,\n",
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" text=True,\n",
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" )\n",
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" for line in result.stdout.splitlines():\n",
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" if '=' not in line:\n",
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" continue\n",
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" key, value = line.split('=', 1)\n",
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" if key and key not in os.environ:\n",
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" os.environ[key] = value\n",
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"\n",
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"\n",
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"def db_ready() -> bool:\n",
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" return all(os.getenv(k) for k in DB_REQUIRED_ENV)\n",
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"\n",
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"\n",
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"def get_conn():\n",
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" return psycopg2.connect(\n",
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" host=os.environ['PGWEB_HOST'],\n",
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" port=os.environ['PGWEB_PORT'],\n",
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" user=os.environ['PGWEB_USER'],\n",
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" password=os.environ['PGWEB_PASSWORD'],\n",
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" dbname=DB_NAME,\n",
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" )\n",
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"\n",
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"\n",
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"def load_optional_db_layers() -> None:\n",
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" global internet_cables_geojson, opposition_cases, drought_context, smoke_context\n",
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" global climate_context, broadband_context, election_context, points\n",
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"\n",
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" if not ENABLE_DB_LAYER_LOAD:\n",
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" print('DB layer load disabled')\n",
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" return\n",
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"\n",
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" load_zsh_secrets()\n",
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" if not db_ready():\n",
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" print('Skipping DB-backed layers: missing PGWEB_* environment variables')\n",
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" return\n",
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"\n",
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" with get_conn() as conn:\n",
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" if SHOW_INTERNET_CABLES_LAYER:\n",
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" cable_sql = \"\"\"\n",
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" select json_build_object(\n",
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" 'type','FeatureCollection',\n",
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" 'features', coalesce(json_agg(\n",
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" json_build_object(\n",
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" 'type','Feature',\n",
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" 'geometry', ST_AsGeoJSON(geom)::json,\n",
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" 'properties', json_build_object(\n",
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" 'feature_id', feature_id,\n",
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" 'name', name,\n",
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" 'owners', owners,\n",
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" 'rfs_year', rfs_year,\n",
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" 'decommission_year', decommission_year,\n",
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" 'length_km', length_km,\n",
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" 'cable_type', cable_type\n",
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" )\n",
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" )\n",
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" ), '[]'::json)\n",
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" ) as fc\n",
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" from public.internet_cables\n",
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" where geom is not null\n",
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" \"\"\"\n",
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" internet_cables_geojson = pd.read_sql(cable_sql, conn).iloc[0]['fc']\n",
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" n_cables = len(internet_cables_geojson.get('features', [])) if internet_cables_geojson else 0\n",
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" print(f'internet_cables features: {n_cables:,}')\n",
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"\n",
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" if SHOW_OPPOSITION_CASES_LAYER:\n",
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" opposition_sql = \"\"\"\n",
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" select\n",
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" id, location, state, lat, lon, investment_billion, status,\n",
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" developer, commons_type, governance_response, outcome, opposition_type, data_source\n",
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" from public.opposition_cases_geocoded\n",
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" where lat is not null and lon is not null\n",
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" \"\"\"\n",
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" opposition_cases = pd.read_sql(opposition_sql, conn)\n",
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" print(f'opposition_cases rows: {len(opposition_cases):,}')\n",
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"\n",
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" if SHOW_DROUGHT_AND_SMOKE_CONTEXT:\n",
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" drought_sql = \"\"\"\n",
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" select\n",
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" master_id, usdm_status, worst_dm_category, mean_dm_category,\n",
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" pct_weeks_in_d2_or_worse, pct_weeks_in_d3_or_worse,\n",
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" longest_d2_streak_weeks, longest_d3_streak_weeks\n",
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" from public.data_center_usdm_drought_exposure\n",
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" \"\"\"\n",
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" smoke_sql = \"\"\"\n",
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" select\n",
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" master_id, hms_status, smoke_period_start, smoke_period_end,\n",
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" days_observed, days_with_any_smoke, days_with_heavy_smoke,\n",
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" pct_days_with_any_smoke, pct_days_with_heavy_smoke,\n",
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" worst_density, mean_density_rank\n",
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" from public.data_center_hms_smoke_exposure\n",
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" \"\"\"\n",
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" drought_context = pd.read_sql(drought_sql, conn)\n",
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" smoke_context = pd.read_sql(smoke_sql, conn)\n",
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" print(f'drought_context rows: {len(drought_context):,}')\n",
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" print(f'smoke_context rows: {len(smoke_context):,}')\n",
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"\n",
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" if not drought_context.empty:\n",
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" cols = [c for c in drought_context.columns if c != 'master_id']\n",
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" points = points.merge(drought_context[['master_id'] + cols], on='master_id', how='left')\n",
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"\n",
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" if not smoke_context.empty:\n",
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" cols = [c for c in smoke_context.columns if c != 'master_id']\n",
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" points = points.merge(smoke_context[['master_id'] + cols], on='master_id', how='left')\n",
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"\n",
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" if SHOW_CLIMATE_LAYER:\n",
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" climate_sql = \"\"\"\n",
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" select\n",
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" master_id, mean_annual_temperature_c, mean_summer_temperature_c,\n",
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" max_wet_bulb_temperature_c, extreme_heat_days,\n",
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" annual_cooling_degree_days_c_mean, annual_precipitation_mm_mean\n",
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" from public.data_center_historical_climate\n",
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" \"\"\"\n",
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" climate_context = pd.read_sql(climate_sql, conn)\n",
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" print(f'climate_context rows: {len(climate_context):,}')\n",
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" if not climate_context.empty:\n",
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" cols = [c for c in climate_context.columns if c != 'master_id']\n",
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" points = points.merge(climate_context[['master_id'] + cols], on='master_id', how='left')\n",
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"\n",
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" if SHOW_BROADBAND_LAYER:\n",
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" broadband_sql = \"\"\"\n",
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" select\n",
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" master_id, census_broadband_subscription_pct,\n",
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" fcc_bdc_status, fcc_bdc_as_of_date,\n",
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" fcc_provider_count, fcc_fiber_provider_count, fcc_cable_provider_count,\n",
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" fcc_fixed_wireless_provider_count,\n",
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" fcc_max_advertised_download_mbps, fcc_max_advertised_upload_mbps,\n",
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" fcc_100_20_provider_count\n",
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" from public.data_center_broadband_connection\n",
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" \"\"\"\n",
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" broadband_context = pd.read_sql(broadband_sql, conn)\n",
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" print(f'broadband_context rows: {len(broadband_context):,}')\n",
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" if not broadband_context.empty:\n",
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" cols = [c for c in broadband_context.columns if c != 'master_id']\n",
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" points = points.merge(broadband_context[['master_id'] + cols], on='master_id', how='left')\n",
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"\n",
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" if SHOW_ELECTION_LAYER:\n",
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" election_sql = \"\"\"\n",
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" with best_match as (\n",
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" select distinct on (m.master_id)\n",
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" m.master_id,\n",
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" m.state_code as election_state_code,\n",
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" m.join_method as election_join_method,\n",
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" m.match_distance_m as election_match_distance_m,\n",
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" f.feature_id, f.layer_id, f.properties,\n",
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" ST_Y(ST_PointOnSurface(f.geom)) as election_latitude,\n",
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" ST_X(ST_PointOnSurface(f.geom)) as election_longitude\n",
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" from public.data_center_rdh_precinct_vote_matches m\n",
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" join public.rdh_precinct_vote_features f\n",
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" on f.feature_id = m.feature_id and f.layer_id = m.layer_id\n",
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" where f.geom is not null\n",
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" order by m.master_id,\n",
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" case m.join_method when 'point_in_precinct' then 0 else 1 end,\n",
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" m.match_distance_m asc nulls last\n",
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" )\n",
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" select\n",
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" master_id, election_state_code, election_join_method, election_match_distance_m,\n",
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" feature_id, layer_id, election_latitude, election_longitude,\n",
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" coalesce((properties->>'LOCALITY'), '') as election_locality,\n",
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" coalesce((properties->>'PRECINCT'), '') as election_precinct,\n",
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" nullif(properties->>'G20PREDBID','')::double precision as election_biden_votes,\n",
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" nullif(properties->>'G20PRERTRU','')::double precision as election_trump_votes,\n",
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" case\n",
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" when (coalesce(nullif(properties->>'G20PREDBID','')::double precision,0)\n",
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" + coalesce(nullif(properties->>'G20PRERTRU','')::double precision,0)) > 0\n",
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" then 100.0 * coalesce(nullif(properties->>'G20PREDBID','')::double precision,0)\n",
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" / (coalesce(nullif(properties->>'G20PREDBID','')::double precision,0)\n",
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" + coalesce(nullif(properties->>'G20PRERTRU','')::double precision,0))\n",
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" end as election_biden_share_pct,\n",
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" case\n",
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" when (coalesce(nullif(properties->>'G20PREDBID','')::double precision,0)\n",
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" + coalesce(nullif(properties->>'G20PRERTRU','')::double precision,0)) > 0\n",
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" then 100.0 * coalesce(nullif(properties->>'G20PRERTRU','')::double precision,0)\n",
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" / (coalesce(nullif(properties->>'G20PREDBID','')::double precision,0)\n",
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" + coalesce(nullif(properties->>'G20PRERTRU','')::double precision,0))\n",
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" end as election_trump_share_pct\n",
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" from best_match\n",
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" \"\"\"\n",
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" election_context = pd.read_sql(election_sql, conn)\n",
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" if not election_context.empty:\n",
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" election_context['election_trump_margin_pct'] = (\n",
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" election_context['election_trump_share_pct'] - election_context['election_biden_share_pct']\n",
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" )\n",
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" print(f'election_context rows: {len(election_context):,}')\n",
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" if not election_context.empty:\n",
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" cols = [c for c in election_context.columns if c != 'master_id']\n",
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" points = points.merge(election_context[['master_id'] + cols], on='master_id', how='left')\n",
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"\n",
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"\n",
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"load_optional_db_layers()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7",
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"metadata": {},
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"source": [
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"## Optional DB-backed Layer Context\n",
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"\n",
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"This section pulls additional overlays directly from PostGIS:\n",
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"- `public.internet_cables` (line layer)\n",
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"- `public.opposition_cases_geocoded` (point layer)\n",
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"- `public.data_center_usdm_drought_exposure` (point popup enrichment)\n",
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"- `public.data_center_hms_smoke_exposure` (point popup enrichment)\n",
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"\n",
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"If DB credentials are unavailable, map generation still works with CSV/GeoJSON sources."
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]
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},
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{
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"cell_type": "markdown",
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"id": "8",
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"metadata": {},
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"source": [
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"## Map Helpers"
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]
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@@ -169,7 +418,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7",
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"id": "9",
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -180,6 +429,8 @@
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"NOISE_COLOR = '#9ca3af'\n",
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"CENTROID_COLOR = '#111827'\n",
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"STATE_ENERGY_COLOR = '#f59e0b'\n",
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"INTERNET_CABLE_COLOR = '#7c3aed'\n",
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"OPPOSITION_CASE_COLOR = '#b91c1c'\n",
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"\n",
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"cluster_info = clusters.set_index('cluster_id').to_dict('index')\n",
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"\n",
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@@ -217,6 +468,50 @@
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" return f'Cluster ID {cluster_id}', f'{point_count:,}', f'Rank {rank} of {n_clusters} by size'\n",
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"\n",
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"\n",
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"def climate_color(mean_summer_c):\n",
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" if pd.isna(mean_summer_c):\n",
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" return '#94a3b8'\n",
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" if mean_summer_c >= 32:\n",
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" return '#7f1d1d'\n",
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" if mean_summer_c >= 29:\n",
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" return '#b91c1c'\n",
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" if mean_summer_c >= 26:\n",
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" return '#ea580c'\n",
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" if mean_summer_c >= 23:\n",
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" return '#f59e0b'\n",
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" return '#0284c7'\n",
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"\n",
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"\n",
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"def broadband_color(provider_count):\n",
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" if pd.isna(provider_count):\n",
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" return '#94a3b8'\n",
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" p = float(provider_count)\n",
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" if p >= 20:\n",
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" return '#166534'\n",
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" if p >= 10:\n",
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" return '#16a34a'\n",
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" if p >= 5:\n",
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" return '#65a30d'\n",
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" if p >= 2:\n",
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" return '#ca8a04'\n",
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" return '#b45309'\n",
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"\n",
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"\n",
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"def election_color(margin_pct):\n",
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" if pd.isna(margin_pct):\n",
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" return '#94a3b8'\n",
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" m = float(margin_pct)\n",
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" if m >= 20:\n",
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" return '#7f1d1d'\n",
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" if m >= 5:\n",
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" return '#dc2626'\n",
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" if m <= -20:\n",
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" return '#1e3a8a'\n",
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" if m <= -5:\n",
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" return '#2563eb'\n",
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" return '#6b7280'\n",
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"\n",
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"\n",
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"def point_popup(row):\n",
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" cluster_label, cluster_size, cluster_rank = cluster_label_and_size(row.cluster_id)\n",
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" nearest = row.nearest_neighbor_km\n",
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@@ -263,6 +558,76 @@
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" {seds_note}\n",
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" '''\n",
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"\n",
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" drought_lines = ''\n",
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" if hasattr(row, 'usdm_status') and pd.notna(row.usdm_status):\n",
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" drought_lines = f'''\n",
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" <hr style=\"margin: 6px 0;\">\n",
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" <strong>Drought context (USDM)</strong><br>\n",
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" Status: {clean_value(row.usdm_status)}<br>\n",
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" Worst DM category: {fmt_number(row.worst_dm_category)}<br>\n",
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" Mean DM category: {fmt_number(row.mean_dm_category, 2)}<br>\n",
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" % weeks D2+: {fmt_number(row.pct_weeks_in_d2_or_worse, 1, suffix='%')}<br>\n",
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" % weeks D3+: {fmt_number(row.pct_weeks_in_d3_or_worse, 1, suffix='%')}<br>\n",
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" Longest D2 streak: {fmt_number(row.longest_d2_streak_weeks)} weeks<br>\n",
|
||||
" Longest D3 streak: {fmt_number(row.longest_d3_streak_weeks)} weeks<br>\n",
|
||||
" '''\n",
|
||||
"\n",
|
||||
" smoke_lines = ''\n",
|
||||
" if hasattr(row, 'hms_status') and pd.notna(row.hms_status):\n",
|
||||
" smoke_lines = f'''\n",
|
||||
" <hr style=\"margin: 6px 0;\">\n",
|
||||
" <strong>Wildfire smoke context (HMS)</strong><br>\n",
|
||||
" Status: {clean_value(row.hms_status)}<br>\n",
|
||||
" Observed days: {fmt_number(row.days_observed)}<br>\n",
|
||||
" Any-smoke days: {fmt_number(row.days_with_any_smoke)} ({fmt_number(row.pct_days_with_any_smoke, 1, suffix='%')})<br>\n",
|
||||
" Heavy-smoke days: {fmt_number(row.days_with_heavy_smoke)} ({fmt_number(row.pct_days_with_heavy_smoke, 1, suffix='%')})<br>\n",
|
||||
" Worst density class: {clean_value(row.worst_density)}<br>\n",
|
||||
" Mean density rank: {fmt_number(row.mean_density_rank, 2)}<br>\n",
|
||||
" '''\n",
|
||||
"\n",
|
||||
" climate_lines = ''\n",
|
||||
" if hasattr(row, 'mean_summer_temperature_c') and pd.notna(row.mean_summer_temperature_c):\n",
|
||||
" climate_lines = f'''\n",
|
||||
" <hr style=\"margin: 6px 0;\">\n",
|
||||
" <strong>Climate context</strong><br>\n",
|
||||
" Mean annual temp: {fmt_number(row.mean_annual_temperature_c, 1, suffix=' C')}<br>\n",
|
||||
" Mean summer temp: {fmt_number(row.mean_summer_temperature_c, 1, suffix=' C')}<br>\n",
|
||||
" Max wet-bulb temp: {fmt_number(row.max_wet_bulb_temperature_c, 1, suffix=' C')}<br>\n",
|
||||
" Extreme heat days: {fmt_number(row.extreme_heat_days)}<br>\n",
|
||||
" Annual CDD mean: {fmt_number(row.annual_cooling_degree_days_c_mean, 0)}<br>\n",
|
||||
" Annual precip mean: {fmt_number(row.annual_precipitation_mm_mean, 0, suffix=' mm')}<br>\n",
|
||||
" '''\n",
|
||||
"\n",
|
||||
" broadband_lines = ''\n",
|
||||
" if hasattr(row, 'fcc_bdc_status') and pd.notna(row.fcc_bdc_status):\n",
|
||||
" broadband_lines = f'''\n",
|
||||
" <hr style=\"margin: 6px 0;\">\n",
|
||||
" <strong>Broadband context</strong><br>\n",
|
||||
" FCC BDC status: {clean_value(row.fcc_bdc_status)}<br>\n",
|
||||
" FCC as-of date: {clean_value(row.fcc_bdc_as_of_date)}<br>\n",
|
||||
" Census broadband subscription: {fmt_number(row.census_broadband_subscription_pct, 1, suffix='%')}<br>\n",
|
||||
" Provider count: {fmt_number(row.fcc_provider_count)}<br>\n",
|
||||
" Fiber providers: {fmt_number(row.fcc_fiber_provider_count)}<br>\n",
|
||||
" Cable providers: {fmt_number(row.fcc_cable_provider_count)}<br>\n",
|
||||
" Fixed wireless providers: {fmt_number(row.fcc_fixed_wireless_provider_count)}<br>\n",
|
||||
" Max advertised down/up: {fmt_number(row.fcc_max_advertised_download_mbps, 0, suffix=' /')} {fmt_number(row.fcc_max_advertised_upload_mbps, 0, suffix=' Mbps')}<br>\n",
|
||||
" Providers >=100/20: {fmt_number(row.fcc_100_20_provider_count)}<br>\n",
|
||||
" '''\n",
|
||||
"\n",
|
||||
" election_lines = ''\n",
|
||||
" if hasattr(row, 'election_biden_share_pct') and pd.notna(row.election_biden_share_pct):\n",
|
||||
" election_lines = f'''\n",
|
||||
" <hr style=\"margin: 6px 0;\">\n",
|
||||
" <strong>Election context (2020 precinct)</strong><br>\n",
|
||||
" State: {clean_value(row.election_state_code)}<br>\n",
|
||||
" Locality: {clean_value(row.election_locality)}<br>\n",
|
||||
" Precinct: {clean_value(row.election_precinct)}<br>\n",
|
||||
" Biden share: {fmt_number(row.election_biden_share_pct, 1, suffix='%')}<br>\n",
|
||||
" Trump share: {fmt_number(row.election_trump_share_pct, 1, suffix='%')}<br>\n",
|
||||
" Trump margin: {fmt_number(row.election_trump_margin_pct, 1, suffix=' pp')}<br>\n",
|
||||
" Join method: {clean_value(row.election_join_method)}<br>\n",
|
||||
" '''\n",
|
||||
"\n",
|
||||
" return folium.Popup(f'''\n",
|
||||
" <div style=\"font-family: system-ui, sans-serif; min-width: 310px; max-width: 420px;\">\n",
|
||||
" <strong>{title}</strong><br>\n",
|
||||
@@ -278,6 +643,11 @@
|
||||
" {huc8_lines}\n",
|
||||
" {ruca_lines}\n",
|
||||
" {energy_lines}\n",
|
||||
" {drought_lines}\n",
|
||||
" {smoke_lines}\n",
|
||||
" {climate_lines}\n",
|
||||
" {broadband_lines}\n",
|
||||
" {election_lines}\n",
|
||||
" </div>\n",
|
||||
" ''', max_width=460)\n",
|
||||
"\n",
|
||||
@@ -347,12 +717,101 @@
|
||||
" IM3 avg siting score: {fmt_number(row.im3_avg_weighted_siting_score, 3)}<br>\n",
|
||||
" {seds_note}\n",
|
||||
" </div>\n",
|
||||
" ''', max_width=380)\n"
|
||||
" ''', max_width=380)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def cable_style(_feature):\n",
|
||||
" return {'color': INTERNET_CABLE_COLOR, 'weight': 1.6, 'opacity': 0.45}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def cable_popup(feature):\n",
|
||||
" p = feature.get('properties', {})\n",
|
||||
" return folium.Popup(f'''\n",
|
||||
" <div style=\"font-family: system-ui, sans-serif; min-width: 280px;\">\n",
|
||||
" <strong>{clean_value(p.get('name') or 'Internet cable')}</strong><br>\n",
|
||||
" Owners: {clean_value(p.get('owners'))}<br>\n",
|
||||
" Type: {clean_value(p.get('cable_type'))}<br>\n",
|
||||
" RFS year: {fmt_number(p.get('rfs_year'))}<br>\n",
|
||||
" Decommission year: {fmt_number(p.get('decommission_year'))}<br>\n",
|
||||
" Length: {fmt_number(p.get('length_km'), 0, suffix=' km')}<br>\n",
|
||||
" Feature ID: {clean_value(p.get('feature_id'))}\n",
|
||||
" </div>\n",
|
||||
" ''', max_width=380)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def opposition_popup(row):\n",
|
||||
" return folium.Popup(f'''\n",
|
||||
" <div style=\"font-family: system-ui, sans-serif; min-width: 280px;\">\n",
|
||||
" <strong>Opposition case {fmt_number(row.id)}</strong><br>\n",
|
||||
" Location: {clean_value(row.location)}<br>\n",
|
||||
" State: {clean_value(row.state)}<br>\n",
|
||||
" <hr style=\"margin: 6px 0;\">\n",
|
||||
" Status: {clean_value(row.status)}<br>\n",
|
||||
" Developer: {clean_value(row.developer)}<br>\n",
|
||||
" Investment: {fmt_number(row.investment_billion, 2, prefix='$', suffix='B')}<br>\n",
|
||||
" Opposition type: {clean_value(row.opposition_type)}<br>\n",
|
||||
" Commons type: {clean_value(row.commons_type)}<br>\n",
|
||||
" Governance response: {clean_value(row.governance_response)}<br>\n",
|
||||
" Outcome: {clean_value(row.outcome)}<br>\n",
|
||||
" Source: {clean_value(row.data_source)}\n",
|
||||
" </div>\n",
|
||||
" ''', max_width=400)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "10",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def add_overlay_legend(map_obj: folium.Map) -> None:\n",
|
||||
" legend_html = \"\"\"\n",
|
||||
" <div style=\"\n",
|
||||
" position: fixed;\n",
|
||||
" bottom: 30px;\n",
|
||||
" left: 30px;\n",
|
||||
" z-index: 9999;\n",
|
||||
" background: rgba(255, 255, 255, 0.96);\n",
|
||||
" border: 1px solid #d1d5db;\n",
|
||||
" border-radius: 8px;\n",
|
||||
" box-shadow: 0 2px 8px rgba(0,0,0,0.15);\n",
|
||||
" padding: 10px 12px;\n",
|
||||
" font-family: system-ui, -apple-system, Segoe UI, Roboto, sans-serif;\n",
|
||||
" font-size: 12px;\n",
|
||||
" line-height: 1.35;\n",
|
||||
" min-width: 260px;\n",
|
||||
" \">\n",
|
||||
" <div style=\"font-weight: 700; margin-bottom: 6px;\">Overlay Legend</div>\n",
|
||||
"\n",
|
||||
" <div style=\"font-weight: 600; margin-top: 4px;\">Climate (mean summer temperature)</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#0284c7;margin-right:6px;\"></span>< 23 C</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#f59e0b;margin-right:6px;\"></span>23-25.9 C</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#ea580c;margin-right:6px;\"></span>26-28.9 C</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#b91c1c;margin-right:6px;\"></span>29-31.9 C</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#7f1d1d;margin-right:6px;\"></span>>= 32 C</div>\n",
|
||||
"\n",
|
||||
" <div style=\"font-weight: 600; margin-top: 6px;\">Broadband (FCC provider count)</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#b45309;margin-right:6px;\"></span>0-1</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#ca8a04;margin-right:6px;\"></span>2-4</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#65a30d;margin-right:6px;\"></span>5-9</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#16a34a;margin-right:6px;\"></span>10-19</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#166534;margin-right:6px;\"></span>>= 20</div>\n",
|
||||
"\n",
|
||||
" <div style=\"font-weight: 600; margin-top: 6px;\">Election (Trump margin, pp)</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#1e3a8a;margin-right:6px;\"></span><= -20</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#2563eb;margin-right:6px;\"></span>-19.9 to -5</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#6b7280;margin-right:6px;\"></span>-4.9 to 4.9</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#dc2626;margin-right:6px;\"></span>5 to 19.9</div>\n",
|
||||
" <div><span style=\"display:inline-block;width:10px;height:10px;background:#7f1d1d;margin-right:6px;\"></span>>= 20</div>\n",
|
||||
" </div>\n",
|
||||
" \"\"\"\n",
|
||||
" map_obj.get_root().html.add_child(folium.Element(legend_html))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "8",
|
||||
"id": "11",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Build The Map"
|
||||
@@ -361,7 +820,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9",
|
||||
"id": "12",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -373,6 +832,11 @@
|
||||
"\n",
|
||||
" huc8_layer = folium.FeatureGroup(name='HUC8 watersheds with data centers', show=False)\n",
|
||||
" state_energy_layer = folium.FeatureGroup(name='State energy demand context (IM3 / SEDS)', show=False)\n",
|
||||
" cables_layer = folium.FeatureGroup(name='Internet cable network', show=False)\n",
|
||||
" opposition_layer = folium.FeatureGroup(name='Opposition cases', show=False)\n",
|
||||
" climate_layer = folium.FeatureGroup(name='Climate stress context', show=False)\n",
|
||||
" broadband_layer = folium.FeatureGroup(name='Broadband capacity context', show=False)\n",
|
||||
" election_layer = folium.FeatureGroup(name='Election context (2020 precinct match)', show=False)\n",
|
||||
" clustered_layer = folium.FeatureGroup(name='Data centers: clustered', show=True)\n",
|
||||
" noise_layer = folium.FeatureGroup(name='Data centers: noise / isolated', show=True)\n",
|
||||
" centroid_layer = folium.FeatureGroup(name='Cluster centroids and p90 radius', show=True)\n",
|
||||
@@ -408,6 +872,90 @@
|
||||
" tooltip=f'{row.state_code}: IM3 {fmt_number(power, suffix=\" MW\")}',\n",
|
||||
" ).add_to(state_energy_layer)\n",
|
||||
"\n",
|
||||
" if SHOW_INTERNET_CABLES_LAYER and internet_cables_geojson is not None:\n",
|
||||
" folium.GeoJson(\n",
|
||||
" internet_cables_geojson,\n",
|
||||
" name='Internet cable network',\n",
|
||||
" style_function=cable_style,\n",
|
||||
" highlight_function=lambda _f: {'weight': 3.0, 'opacity': 0.85},\n",
|
||||
" popup=cable_popup,\n",
|
||||
" tooltip=folium.GeoJsonTooltip(\n",
|
||||
" fields=['name', 'cable_type', 'rfs_year'],\n",
|
||||
" aliases=['Cable', 'Type', 'RFS year'],\n",
|
||||
" localize=True,\n",
|
||||
" sticky=False,\n",
|
||||
" ),\n",
|
||||
" ).add_to(cables_layer)\n",
|
||||
"\n",
|
||||
" if SHOW_OPPOSITION_CASES_LAYER and not opposition_cases.empty:\n",
|
||||
" for row in opposition_cases.itertuples(index=False):\n",
|
||||
" marker_radius = 5 if pd.isna(row.investment_billion) else max(5, min(14, 4 + float(row.investment_billion) ** 0.5 * 2.2))\n",
|
||||
" folium.CircleMarker(\n",
|
||||
" location=[row.lat, row.lon],\n",
|
||||
" radius=marker_radius,\n",
|
||||
" color='#7f1d1d',\n",
|
||||
" fill=True,\n",
|
||||
" fill_color=OPPOSITION_CASE_COLOR,\n",
|
||||
" fill_opacity=0.75,\n",
|
||||
" weight=1.2,\n",
|
||||
" popup=opposition_popup(row),\n",
|
||||
" tooltip=f\"Opposition case: {row.state} ({clean_value(row.status)})\",\n",
|
||||
" ).add_to(opposition_layer)\n",
|
||||
"\n",
|
||||
" if SHOW_CLIMATE_LAYER:\n",
|
||||
" climate_rows = points_df.dropna(subset=['mean_summer_temperature_c']) if 'mean_summer_temperature_c' in points_df.columns else pd.DataFrame()\n",
|
||||
" for row in climate_rows.itertuples(index=False):\n",
|
||||
" color = climate_color(row.mean_summer_temperature_c)\n",
|
||||
" radius = max(4, min(12, 3 + (float(row.extreme_heat_days) if pd.notna(row.extreme_heat_days) else 0.0) ** 0.5 / 2.0))\n",
|
||||
" folium.CircleMarker(\n",
|
||||
" location=[row.latitude, row.longitude],\n",
|
||||
" radius=radius,\n",
|
||||
" color=color,\n",
|
||||
" fill=True,\n",
|
||||
" fill_color=color,\n",
|
||||
" fill_opacity=0.35,\n",
|
||||
" weight=1,\n",
|
||||
" tooltip=f\"Climate: summer {fmt_number(row.mean_summer_temperature_c, 1, suffix=' C')}; heat days {fmt_number(row.extreme_heat_days)}\",\n",
|
||||
" ).add_to(climate_layer)\n",
|
||||
"\n",
|
||||
" if SHOW_BROADBAND_LAYER:\n",
|
||||
" bb_rows = points_df.dropna(subset=['fcc_provider_count']) if 'fcc_provider_count' in points_df.columns else pd.DataFrame()\n",
|
||||
" for row in bb_rows.itertuples(index=False):\n",
|
||||
" color = broadband_color(row.fcc_provider_count)\n",
|
||||
" speed = float(row.fcc_max_advertised_download_mbps) if pd.notna(row.fcc_max_advertised_download_mbps) else 0.0\n",
|
||||
" radius = max(4, min(12, 4 + speed ** 0.5 / 10.0))\n",
|
||||
" folium.CircleMarker(\n",
|
||||
" location=[row.latitude, row.longitude],\n",
|
||||
" radius=radius,\n",
|
||||
" color=color,\n",
|
||||
" fill=True,\n",
|
||||
" fill_color=color,\n",
|
||||
" fill_opacity=0.3,\n",
|
||||
" weight=1,\n",
|
||||
" tooltip=f\"Broadband: providers {fmt_number(row.fcc_provider_count)}; max down {fmt_number(row.fcc_max_advertised_download_mbps, 0, suffix=' Mbps')}\",\n",
|
||||
" ).add_to(broadband_layer)\n",
|
||||
"\n",
|
||||
" if SHOW_ELECTION_LAYER and not election_context.empty:\n",
|
||||
" for row in election_context.dropna(subset=['election_latitude', 'election_longitude']).itertuples(index=False):\n",
|
||||
" margin = getattr(row, 'election_trump_margin_pct')\n",
|
||||
" color = election_color(margin)\n",
|
||||
" radius = max(4, min(11, 4 + abs(float(margin)) / 8.0)) if pd.notna(margin) else 5\n",
|
||||
" tip = (\n",
|
||||
" f\"Election precinct: {row.election_state_code} {clean_value(row.election_locality)}; \"\n",
|
||||
" f\"Biden {fmt_number(row.election_biden_share_pct, 1, suffix='%')} / \"\n",
|
||||
" f\"Trump {fmt_number(row.election_trump_share_pct, 1, suffix='%')}\"\n",
|
||||
" )\n",
|
||||
" folium.CircleMarker(\n",
|
||||
" location=[row.election_latitude, row.election_longitude],\n",
|
||||
" radius=radius,\n",
|
||||
" color=color,\n",
|
||||
" fill=True,\n",
|
||||
" fill_color=color,\n",
|
||||
" fill_opacity=0.4,\n",
|
||||
" weight=1,\n",
|
||||
" tooltip=tip,\n",
|
||||
" ).add_to(election_layer)\n",
|
||||
"\n",
|
||||
" bounds = []\n",
|
||||
" for row in points_df.itertuples(index=False):\n",
|
||||
" cluster_label, cluster_size, _ = cluster_label_and_size(row.cluster_id)\n",
|
||||
@@ -453,6 +1001,11 @@
|
||||
"\n",
|
||||
" huc8_layer.add_to(m)\n",
|
||||
" state_energy_layer.add_to(m)\n",
|
||||
" cables_layer.add_to(m)\n",
|
||||
" opposition_layer.add_to(m)\n",
|
||||
" climate_layer.add_to(m)\n",
|
||||
" broadband_layer.add_to(m)\n",
|
||||
" election_layer.add_to(m)\n",
|
||||
" clustered_layer.add_to(m)\n",
|
||||
" noise_layer.add_to(m)\n",
|
||||
" centroid_layer.add_to(m)\n",
|
||||
@@ -468,7 +1021,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "10",
|
||||
"id": "13",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Export HTML"
|
||||
@@ -477,7 +1030,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "11",
|
||||
"id": "14",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -487,7 +1040,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "12",
|
||||
"id": "15",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Feature Staging Area\n",
|
||||
|
||||
Reference in New Issue
Block a user