generate_webmap.py

"""
Generate a self-contained Leaflet.js web map of Redmond roads and neighborhoods.

Reads shapefiles from Inputs/, reprojects EPSG:2926 -> EPSG:4326,
fetches schools/restaurants/parks from OpenStreetMap via Overpass API,
loads parcel polygons with average slope data,
and writes a single HTML file with inline GeoJSON.
"""

import json
import geopandas as gpd
from shapely.geometry import Point
from pathlib import Path

# --- Paths ---
BASE_DIR = Path(__file__).resolve().parent.parent
ROADS_SHP = BASE_DIR / "No-License-Required-Zonal-Statistics-with-GeoPandas" / "Inputs" / "Roads" / "Redmond_Roads.shp"
NEIGHBORHOODS_SHP = BASE_DIR / "No-License-Required-Zonal-Statistics-with-GeoPandas" / "Inputs" / "Boundary" / "Redmond_Neighborhoods.shp"
PARCELS_GEOJSON = Path(__file__).resolve().parent / "parcels_with_slope.geojson"
OUTPUT_HTML = Path(__file__).resolve().parent / "Redmond_WebMap.html"

# --- Classification mapping ---
ROAD_CLASS_MAP = {
    0: "Unknown/Private",
    1: "Principal Arterial",
    2: "Minor Arterial",
    3: "Collector",
    4: "Local",
    5: "Private/Alley",
    520: "SR 520/Highway",
}

# --- Road colors by classification ---
ROAD_COLORS = {
    "Principal Arterial": "#e41a1c",
    "Minor Arterial": "#ff7f00",
    "Collector": "#4daf4a",
    "Local": "#377eb8",
    "Private/Alley": "#999999",
    "Unknown/Private": "#999999",
    "SR 520/Highway": "#984ea3",
}

# --- Road line weights by classification ---
ROAD_WEIGHTS = {
    "Principal Arterial": 4,
    "Minor Arterial": 3,
    "Collector": 2.5,
    "Local": 1.5,
    "Private/Alley": 1,
    "Unknown/Private": 1,
    "SR 520/Highway": 5,
}

# --- Neighborhood colors ---
NEIGHBORHOOD_COLORS = [
    "#1b9e77",  # teal
    "#d95f02",  # orange
    "#7570b3",  # purple
    "#e7298a",  # pink
    "#66a61e",  # green
    "#e6ab02",  # gold
]

# --- Point-of-interest icon config ---
POI_STYLES = {
    "schools": {"symbol": "S", "color": "#2980b9", "label": "School"},
    "restaurants": {"symbol": "R", "color": "#e74c3c", "label": "Restaurant"},
    "parks": {"symbol": "P", "color": "#27ae60", "label": "Park"},
}

# --- Parcel slope classification colors (green to red) ---
SLOPE_CLASS_COLORS = {
    "Gentle": "#2ecc71",
    "Moderate": "#f1c40f",
    "Steep": "#e67e22",
    "Very Steep": "#e74c3c",
}


def reduce_precision(geojson_dict, decimals=6):
    """Round all coordinates to the given number of decimal places."""
    def _round_coords(coords):
        if isinstance(coords[0], (list, tuple)):
            return [_round_coords(c) for c in coords]
        return [round(c, decimals) for c in coords]

    for feature in geojson_dict["features"]:
        geom = feature["geometry"]
        if geom and geom.get("coordinates"):
            geom["coordinates"] = _round_coords(geom["coordinates"])
    return geojson_dict


def load_roads():
    """Load roads shapefile, reproject, and trim columns."""
    print(f"Reading roads from {ROADS_SHP}")
    gdf = gpd.read_file(ROADS_SHP)
    print(f"  {len(gdf)} road segments, CRS={gdf.crs}")

    gdf = gdf.to_crs(epsg=4326)

    keep = ["StreetName", "d_Classifi", "MaxSpeedLi", "FromStreet", "ToStreet", "surface", "StreetWidt", "geometry"]
    gdf = gdf[keep].copy()

    gdf["Classification"] = gdf["d_Classifi"].fillna(0).astype(int).map(ROAD_CLASS_MAP).fillna("Unknown/Private")
    gdf = gdf.drop(columns=["d_Classifi"])

    for col in ["StreetName", "FromStreet", "ToStreet"]:
        gdf[col] = gdf[col].fillna("")
    gdf["MaxSpeedLi"] = gdf["MaxSpeedLi"].fillna(0).astype(int)
    gdf["StreetWidt"] = gdf["StreetWidt"].fillna(0).astype(float).round(1)
    gdf["surface"] = gdf["surface"].fillna("")

    return gdf


def load_neighborhoods():
    """Load neighborhoods shapefile, reproject, compute area, and trim columns."""
    print(f"Reading neighborhoods from {NEIGHBORHOODS_SHP}")
    gdf = gpd.read_file(NEIGHBORHOODS_SHP)
    print(f"  {len(gdf)} neighborhoods, CRS={gdf.crs}")

    gdf["Area_Acres"] = (gdf["Shape_STAr"] / 43560.0).round(1)
    gdf = gdf.to_crs(epsg=4326)

    keep = ["NAME", "ABBEVIATIO", "Area_Acres", "geometry"]
    gdf = gdf[keep].copy()

    return gdf


def load_parcels():
    """Load parcels GeoJSON with slope data, reduce precision."""
    print(f"Reading parcels from {PARCELS_GEOJSON}")
    with open(PARCELS_GEOJSON, encoding="utf-8") as f:
        gj = json.load(f)
    gj = reduce_precision(gj, decimals=6)
    print(f"  {len(gj['features'])} parcels with slope data")
    return gj


# --- POI data from OpenStreetMap (via Overpass API) ---
RAW_SCHOOLS = [
    {"name": "44 School of Music", "lat": 47.675608, "lng": -122.126693},
    {"name": "Albert Einstein Elementary School", "lat": 47.702208, "lng": -122.098582},
    {"name": "Benjamin Rush Elementary School", "lat": 47.661567, "lng": -122.139267},
    {"name": "Brightmont Academy", "lat": 47.670142, "lng": -122.121738},
    {"name": "Clara Barton Elementary", "lat": 47.708279, "lng": -122.112139},
    {"name": "Dartmoor School", "lat": 47.672898, "lng": -122.102022},
    {"name": "Faith Lutheran School", "lat": 47.682511, "lng": -122.120134},
    {"name": "Horace Mann Elementary School", "lat": 47.691982, "lng": -122.113517},
    {"name": "Hwang's Taekwondo", "lat": 47.663863, "lng": -122.097444},
    {"name": "Little Folks Christian School", "lat": 47.685362, "lng": -122.117803},
    {"name": "Mighty Coders", "lat": 47.668729, "lng": -122.099559},
    {"name": "Norman Rockwell Elementary School", "lat": 47.699314, "lng": -122.124943},
    {"name": "Redmond Elementary School", "lat": 47.675978, "lng": -122.11713},
    {"name": "Redmond High School", "lat": 47.694745, "lng": -122.108086},
    {"name": "Redmond Middle School", "lat": 47.691089, "lng": -122.119818},
    {"name": "Sammamish Montessori School", "lat": 47.672252, "lng": -122.10147},
    {"name": "The Bear Creek School Valley Campus", "lat": 47.684964, "lng": -122.082666},
    {"name": "Willows Preparatory School", "lat": 47.710346, "lng": -122.131134},
]

RAW_RESTAURANTS = [
    {"name": "#K-Street KBBQ", "lat": 47.670806, "lng": -122.121252, "cuisine": "korean", "rating": 4.5},
    {"name": "A Ma Chicken Rice", "lat": 47.670144, "lng": -122.116831, "cuisine": "chicken;hainanese;cambodian", "rating": 4.5},
    {"name": "Agave Cocina & Cantina", "lat": 47.671961, "lng": -122.11175, "cuisine": "mexican", "rating": 4.3},
    {"name": "Asiana Bistro", "lat": 47.66729, "lng": -122.102567, "cuisine": "asian", "rating": 4.0},
    {"name": "BJ's", "lat": 47.66876, "lng": -122.11986, "cuisine": "american", "rating": 3.9},
    {"name": "Bandidos Mexican Grill", "lat": 47.675998, "lng": -122.122529, "cuisine": "mexican", "rating": 4.8},
    {"name": "Bangkok Basil", "lat": 47.669348, "lng": -122.130531, "cuisine": "thai", "rating": 4.3},
    {"name": "Biryani Bowl", "lat": 47.674635, "lng": -122.12224, "cuisine": "indian", "rating": 3.6},
    {"name": "Boling Point", "lat": 47.680171, "lng": -122.125094, "cuisine": "chinese;hotpot", "rating": 4.1},
    {"name": "Bombay Bistro", "lat": 47.674744, "lng": -122.129712, "cuisine": "indian", "rating": 4.4},
    {"name": "Bommarillu Biryani", "lat": 47.673021, "lng": -122.112738, "cuisine": "indian;pasta;pizza", "rating": 4.0},
    {"name": "Bon Korean Cuisine", "lat": 47.670217, "lng": -122.116089, "cuisine": "", "rating": 4.3},
    {"name": "Caadxi Mezcaleria", "lat": 47.67685, "lng": -122.12731, "cuisine": "mexican", "rating": 4.3},
    {"name": "Chatpata By Kanishka", "lat": 47.672386, "lng": -122.116979, "cuisine": "indian", "rating": 4.4},
    {"name": "Dong Ting Chun", "lat": 47.670373, "lng": -122.119692, "cuisine": "hunan", "rating": 3.8},
    {"name": "Dough Zone", "lat": 47.670932, "lng": -122.113949, "cuisine": "chinese", "rating": 4.0},
    {"name": "Due Cucina", "lat": 47.673178, "lng": -122.119601, "cuisine": "italian", "rating": 4.6},
    {"name": "Family Pancake House", "lat": 47.668503, "lng": -122.104491, "cuisine": "", "rating": 4.3},
    {"name": "Fuji Steakhouse", "lat": 47.669819, "lng": -122.120351, "cuisine": "japanese;steak_house;sushi", "rating": 4.1},
    {"name": "Garlic Crush", "lat": 47.674048, "lng": -122.126114, "cuisine": "mediterranean", "rating": 4.1},
    {"name": "Hello Poke", "lat": 47.673597, "lng": -122.124359, "cuisine": "hawaiian", "rating": 4.4},
    {"name": "Just Poke", "lat": 47.669831, "lng": -122.121734, "cuisine": "poke", "rating": 4.1},
    {"name": "Kanishka", "lat": 47.672493, "lng": -122.11782, "cuisine": "indian", "rating": 4.1},
    {"name": "Kizuki Ramen & Izakaya", "lat": 47.670003, "lng": -122.119427, "cuisine": "ramen", "rating": 4.5},
    {"name": "Kobuta and Ookami", "lat": 47.672997, "lng": -122.1125, "cuisine": "japanese;izakaya", "rating": 4.3},
    {"name": "La Quemada Tienda Carnicera", "lat": 47.674748, "lng": -122.123302, "cuisine": "mexican", "rating": 4.4},
    {"name": "Los Chilangos", "lat": 47.680171, "lng": -122.125217, "cuisine": "mexican", "rating": 4.5},
    {"name": "Matts' Rotisserie & Oyster Lounge", "lat": 47.669721, "lng": -122.120035, "cuisine": "regional", "rating": 4.3},
    {"name": "Mendocino Farms", "lat": 47.672634, "lng": -122.120301, "cuisine": "", "rating": 4.4},
    {"name": "Momiji", "lat": 47.672344, "lng": -122.119141, "cuisine": "", "rating": 4.5},
    {"name": "Neville's Restaurant", "lat": 47.675996, "lng": -122.126572, "cuisine": "", "rating": 4.4},
    {"name": "Niko Teriyaki", "lat": 47.675058, "lng": -122.12785, "cuisine": "asian", "rating": 4.1},
    {"name": "Noburu Ramen & Sushi", "lat": 47.681368, "lng": -122.1229, "cuisine": "ramen;sushi", "rating": 4.4},
    {"name": "Ooba Tooba", "lat": 47.676796, "lng": -122.130089, "cuisine": "mexican", "rating": 4.5},
    {"name": "Oto Sushi", "lat": 47.67598, "lng": -122.125935, "cuisine": "sushi", "rating": 4.4},
    {"name": "Pagliacci Pizza", "lat": 47.673793, "lng": -122.122452, "cuisine": "pizza", "rating": 4.3},
    {"name": "Pomegranate Bistro", "lat": 47.666453, "lng": -122.099524, "cuisine": "american;italian", "rating": 4.3},
    {"name": "Prime Steakhouse", "lat": 47.673384, "lng": -122.122142, "cuisine": "steak_house", "rating": 4.3},
    {"name": "Racha Noodle & Thai", "lat": 47.682012, "lng": -122.123474, "cuisine": "thai", "rating": 4.1},
    {"name": "Red Robin", "lat": 47.670224, "lng": -122.114338, "cuisine": "burger", "rating": 3.9},
    {"name": "Ristorante Tropea", "lat": 47.677999, "lng": -122.122344, "cuisine": "italian", "rating": 4.5},
    {"name": "Rocky's Empanadas", "lat": 47.6623, "lng": -122.09503, "cuisine": "argentinian;empanada", "rating": 4.7},
    {"name": "Sages Restaurant", "lat": 47.676793, "lng": -122.127708, "cuisine": "italian", "rating": 4.4},
    {"name": "Sahara", "lat": 47.66729, "lng": -122.103158, "cuisine": "mediterranean", "rating": 4.6},
    {"name": "Sip Thai Zooom", "lat": 47.673038, "lng": -122.119055, "cuisine": "thai", "rating": 4.3},
    {"name": "Spark Pizza", "lat": 47.675705, "lng": -122.121283, "cuisine": "pizza", "rating": 4.6},
    {"name": "Stone Korean Restaurant", "lat": 47.667433, "lng": -122.104771, "cuisine": "korean", "rating": 4.0},
    {"name": "Taqueria Gallo", "lat": 47.672857, "lng": -122.117216, "cuisine": "mexican", "rating": 3.9},
    {"name": "Taste of Hyderabad", "lat": 47.66449, "lng": -122.099227, "cuisine": "indian;hyderabad;buffet", "rating": 3.9},
    {"name": "Tavolata", "lat": 47.674567, "lng": -122.125909, "cuisine": "italian", "rating": 4.0},
    {"name": "Thai Ginger", "lat": 47.670639, "lng": -122.120653, "cuisine": "thai", "rating": 3.7},
    {"name": "The British Pantry Ltd.", "lat": 47.675981, "lng": -122.126264, "cuisine": "british", "rating": 4.6},
    {"name": "The Matador", "lat": 47.673189, "lng": -122.123026, "cuisine": "mexican", "rating": 4.3},
    {"name": "The Original Pancake House", "lat": 47.670583, "lng": -122.119972, "cuisine": "breakfast;pancake", "rating": 4.0},
    {"name": "The Third Place", "lat": 47.673575, "lng": -122.12291, "cuisine": "korean", "rating": 4.2},
    {"name": "Tipsy Cow Burger Bar", "lat": 47.67311, "lng": -122.122332, "cuisine": "burger", "rating": 4.5},
    {"name": "Willows Deli", "lat": 47.686227, "lng": -122.142272, "cuisine": "american;sandwich", "rating": 4.5},
    {"name": "Woomadang", "lat": 47.68198, "lng": -122.125186, "cuisine": "korean", "rating": 4.4},
    {"name": "Yummy Pho", "lat": 47.681641, "lng": -122.123162, "cuisine": "vietnamese", "rating": 4.3},
    {"name": "Yummy Teriyaki", "lat": 47.672611, "lng": -122.109968, "cuisine": "asian", "rating": 4.4},
    {"name": "Zaucer Pizza", "lat": 47.683521, "lng": -122.144175, "cuisine": "pizza", "rating": 4.7},
    {"name": "Zeeks Pizza - Redmond", "lat": 47.6746, "lng": -122.127006, "cuisine": "pizza", "rating": 4.0},
    {"name": "Zio Sal Ristorante Italiano", "lat": 47.671461, "lng": -122.119096, "cuisine": "italian", "rating": 4.1},
    {"name": "jaShn", "lat": 47.669696, "lng": -122.119964, "cuisine": "indian", "rating": 4.5},
]

RAW_PARKS = [
    {"name": "Albert Anderson Memorial Park", "lat": 47.672971, "lng": -122.115588},
    {"name": "Avignon Estate Community Park", "lat": 47.687755, "lng": -122.130997},
    {"name": "Bear Creek Park", "lat": 47.67253, "lng": -122.108537},
    {"name": "Downtown Park", "lat": 47.67423, "lng": -122.124773},
    {"name": "Dudley Carter Park", "lat": 47.671191, "lng": -122.127895},
    {"name": "Farrel-McWhirter Park", "lat": 47.695301, "lng": -122.081306},
    {"name": "Flagpole Plaza Park", "lat": 47.674189, "lng": -122.121767},
    {"name": "Grass Lawn Park", "lat": 47.668092, "lng": -122.145963},
    {"name": "Greystone Community Park", "lat": 47.705722, "lng": -122.126098},
    {"name": "Hedgewood East Community Park", "lat": 47.709446, "lng": -122.118373},
    {"name": "Heron Rookery Park", "lat": 47.672159, "lng": -122.125956},
    {"name": "Jonathan Hartman Park", "lat": 47.69103, "lng": -122.108139},
    {"name": "Juel Community Park", "lat": 47.700899, "lng": -122.087058},
    {"name": "Kensington Community Park", "lat": 47.704268, "lng": -122.127659},
    {"name": "Luke McRedmond Landing", "lat": 47.673147, "lng": -122.131104},
    {"name": "Martin Park", "lat": 47.676918, "lng": -122.080938},
    {"name": "Meadow Park", "lat": 47.696388, "lng": -122.125709},
    {"name": "Nike Neighborhood Park", "lat": 47.682932, "lng": -122.10767},
    {"name": "Northstar Neighborhood Park", "lat": 47.7049, "lng": -122.117119},
    {"name": "O'Leary Park", "lat": 47.674148, "lng": -122.123087},
    {"name": "Parkridge at the Woodlands Park", "lat": 47.704352, "lng": -122.112201},
    {"name": "Perrigo Heights Park", "lat": 47.688429, "lng": -122.106786},
    {"name": "Perrigo Park", "lat": 47.682923, "lng": -122.084376},
    {"name": "Prescott Community Park", "lat": 47.708295, "lng": -122.118167},
    {"name": "Redmond Bike Park", "lat": 47.688829, "lng": -122.111392},
    {"name": "Redmond Central Connector Park", "lat": 47.673293, "lng": -122.126537},
    {"name": "Redmond West Wetlands", "lat": 47.660042, "lng": -122.137608},
    {"name": "Rotary Park", "lat": 47.67293, "lng": -122.132875},
    {"name": "Sammamish Valley Park", "lat": 47.706024, "lng": -122.151118},
    {"name": "Scotts Pond Park", "lat": 47.675782, "lng": -122.152395},
    {"name": "Sixty Acres North", "lat": 47.705869, "lng": -122.138295},
    {"name": "Sixty Acres South", "lat": 47.702832, "lng": -122.139469},
    {"name": "Smith Woods", "lat": 47.710141, "lng": -122.103884},
    {"name": "Southeast Redmond Park", "lat": 47.666285, "lng": -122.085831},
    {"name": "Spiritbrook Park", "lat": 47.66469, "lng": -122.138963},
    {"name": "Sportsman Park", "lat": 47.663739, "lng": -122.080751},
    {"name": "Sunset Gardens Park", "lat": 47.686096, "lng": -122.09583},
    {"name": "The Edge Skate Park", "lat": 47.677094, "lng": -122.124933},
    {"name": "The Stroll", "lat": 47.677399, "lng": -122.131867},
    {"name": "Trailside Community Park", "lat": 47.701026, "lng": -122.110588},
    {"name": "Welcome Park", "lat": 47.676661, "lng": -122.152355},
    {"name": "Westside Park", "lat": 47.659259, "lng": -122.130082},
    {"name": "Willows Creek Neighborhood Park", "lat": 47.681818, "lng": -122.152021},
    {"name": "Woodbridge Private Park", "lat": 47.663826, "lng": -122.085856},
    {"name": "Woodbridge Swing Park", "lat": 47.664813, "lng": -122.086083},
    {"name": "Woodlands Community Park", "lat": 47.70724, "lng": -122.111916},
    {"name": "Woodlands Park", "lat": 47.7063, "lng": -122.109656},
    {"name": "Woodlands West Park & Playground", "lat": 47.705435, "lng": -122.113284},
]


def filter_pois_inside(pois, nbr_union):
    """Keep only POIs that fall inside the neighborhood boundaries."""
    return [p for p in pois if nbr_union.contains(Point(p["lng"], p["lat"]))]


def to_geojson_dict(gdf):
    """Convert GeoDataFrame to a GeoJSON dict with reduced precision."""
    gj = json.loads(gdf.to_json())
    return reduce_precision(gj)


def build_html(roads_geojson, neighborhoods_geojson, parcels_geojson, schools, restaurants, parks):
    """Generate the self-contained HTML string."""
    nbr_color_map = {}
    for i, feat in enumerate(neighborhoods_geojson["features"]):
        name = feat["properties"]["NAME"]
        nbr_color_map[name] = NEIGHBORHOOD_COLORS[i % len(NEIGHBORHOOD_COLORS)]

    roads_json = json.dumps(roads_geojson)
    neighborhoods_json = json.dumps(neighborhoods_geojson)
    parcels_json = json.dumps(parcels_geojson)
    schools_json = json.dumps(schools)
    restaurants_json = json.dumps(restaurants)
    parks_json = json.dumps(parks)
    road_colors_json = json.dumps(ROAD_COLORS)
    road_weights_json = json.dumps(ROAD_WEIGHTS)
    nbr_color_map_json = json.dumps(nbr_color_map)
    poi_styles_json = json.dumps(POI_STYLES)
    slope_colors_json = json.dumps(SLOPE_CLASS_COLORS)

    html = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Redmond Roads &amp; Neighborhoods</title>
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
<script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
<style>
  body {{ margin: 0; padding: 0; }}
  #map {{ width: 100vw; height: 100vh; }}
  .legend {{
    background: white;
    padding: 10px 14px;
    border-radius: 6px;
    box-shadow: 0 1px 5px rgba(0,0,0,0.3);
    font: 13px/1.6 Arial, sans-serif;
    max-height: 60vh;
    overflow-y: auto;
  }}
  .legend h4 {{ margin: 0 0 6px 0; font-size: 14px; }}
  .legend-item {{ display: flex; align-items: center; margin-bottom: 3px; }}
  .legend-color {{
    width: 20px; height: 12px;
    margin-right: 8px; flex-shrink: 0;
    border: 1px solid #ccc;
  }}
  .legend-line {{
    width: 20px; height: 0;
    margin-right: 8px; flex-shrink: 0;
  }}
  .legend-icon {{
    width: 20px; height: 20px;
    margin-right: 8px; flex-shrink: 0;
    border-radius: 50%;
    display: flex; align-items: center; justify-content: center;
    font-size: 11px; font-weight: bold; color: #fff;
    border: 2px solid #fff;
    box-shadow: 0 1px 3px rgba(0,0,0,0.3);
  }}
  .legend hr {{ margin: 6px 0; border: none; border-top: 1px solid #ddd; }}
</style>
</head>
<body>
<div id="map"></div>
<script>
(function() {{
  // --- Data ---
  var roadsData = {roads_json};
  var neighborhoodsData = {neighborhoods_json};
  var parcelsData = {parcels_json};
  var schoolsData = {schools_json};
  var restaurantsData = {restaurants_json};
  var parksData = {parks_json};
  var roadColors = {road_colors_json};
  var roadWeights = {road_weights_json};
  var nbrColors = {nbr_color_map_json};
  var poiStyles = {poi_styles_json};
  var slopeColors = {slope_colors_json};

  // --- Map ---
  var map = L.map('map').setView([47.68, -122.11], 13);
  L.tileLayer('https://{{s}}.tile.openstreetmap.org/{{z}}/{{x}}/{{y}}.png', {{
    attribution: '&copy; <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a>',
    maxZoom: 19
  }}).addTo(map);

  // --- Helper: create a circle-icon marker ---
  function makeIcon(symbol, color) {{
    return L.divIcon({{
      className: 'poi-icon',
      html: '<div style="background:' + color + ';color:#fff;border-radius:50%;width:22px;height:22px;' +
            'display:flex;align-items:center;justify-content:center;font-size:11px;font-weight:bold;' +
            'border:2px solid #fff;box-shadow:0 1px 4px rgba(0,0,0,0.4);">' + symbol + '</div>',
      iconSize: [22, 22],
      iconAnchor: [11, 11],
      popupAnchor: [0, -13]
    }});
  }}

  // --- Neighborhoods layer ---
  var neighborhoodsLayer = L.geoJSON(neighborhoodsData, {{
    style: function(feature) {{
      var name = feature.properties.NAME;
      return {{
        color: '#333',
        weight: 2,
        fillColor: nbrColors[name] || '#ccc',
        fillOpacity: 0.3
      }};
    }},
    onEachFeature: function(feature, layer) {{
      var p = feature.properties;
      layer.bindPopup(
        '<strong>' + p.NAME + '</strong> (' + p.ABBEVIATIO + ')<br>' +
        'Area: ' + p.Area_Acres.toLocaleString() + ' acres'
      );
    }}
  }}).addTo(map);

  // --- Parcels layer (slope classification) ---
  var parcelsLayer = L.geoJSON(parcelsData, {{
    style: function(feature) {{
      var cls = feature.properties.slope_class || 'Gentle';
      return {{
        color: '#666',
        weight: 0.5,
        fillColor: slopeColors[cls] || '#ccc',
        fillOpacity: 0.5
      }};
    }},
    onEachFeature: function(feature, layer) {{
      var p = feature.properties;
      var popup = '<strong>Parcel: ' + (p.PIN || 'N/A') + '</strong><br>' +
        'Area: ' + (p.Area_Acres || 'N/A') + ' acres<br>' +
        'Mean Slope: ' + (p.mean_slope_pct || 'N/A') + '%<br>' +
        'Slope Class: ' + (p.slope_class || 'N/A');
      layer.bindPopup(popup);
    }}
  }});

  // --- Roads layer ---
  var roadsLayer = L.geoJSON(roadsData, {{
    style: function(feature) {{
      var cls = feature.properties.Classification;
      return {{
        color: roadColors[cls] || '#888',
        weight: roadWeights[cls] || 1.5,
        opacity: 0.85
      }};
    }},
    onEachFeature: function(feature, layer) {{
      var p = feature.properties;
      var name = p.StreetName || 'Unnamed';
      var popup = '<strong>' + name + '</strong><br>' +
        'Classification: ' + p.Classification + '<br>' +
        'Speed Limit: ' + (p.MaxSpeedLi || 'N/A') + ' mph<br>' +
        'From: ' + (p.FromStreet || 'N/A') + '<br>' +
        'To: ' + (p.ToStreet || 'N/A') + '<br>' +
        'Width: ' + (p.StreetWidt || 'N/A') + ' ft';
      layer.bindPopup(popup);
    }}
  }}).addTo(map);

  // --- Schools layer ---
  var schoolsLayer = L.layerGroup();
  var sIcon = makeIcon(poiStyles.schools.symbol, poiStyles.schools.color);
  schoolsData.forEach(function(s) {{
    L.marker([s.lat, s.lng], {{ icon: sIcon }})
      .bindPopup('<strong>' + s.name + '</strong><br>School')
      .addTo(schoolsLayer);
  }});
  schoolsLayer.addTo(map);

  // --- Helper: render star rating as HTML ---
  function renderStars(rating) {{
    if (!rating) return '';
    var full = Math.floor(rating);
    var half = (rating - full) >= 0.3 && (rating - full) <= 0.7;
    var empty = 5 - full - (half ? 1 : 0);
    var html = '<span style="color:#f5a623;font-size:14px;letter-spacing:1px;">';
    for (var i = 0; i < full; i++) html += '&#9733;';
    if (half) html += '&#9734;';
    for (var j = 0; j < empty; j++) html += '&#9734;';
    html += '</span> <strong>' + rating + '</strong>';
    return html;
  }}

  // --- Restaurants layer ---
  var restaurantsLayer = L.layerGroup();
  var rIcon = makeIcon(poiStyles.restaurants.symbol, poiStyles.restaurants.color);
  restaurantsData.forEach(function(r) {{
    var popup = '<strong>' + r.name + '</strong><br>';
    if (r.rating) popup += renderStars(r.rating) + '<br>';
    popup += 'Restaurant';
    if (r.cuisine) popup += '<br>Cuisine: ' + r.cuisine;
    L.marker([r.lat, r.lng], {{ icon: rIcon }})
      .bindPopup(popup)
      .addTo(restaurantsLayer);
  }});
  restaurantsLayer.addTo(map);

  // --- Parks layer ---
  var parksLayer = L.layerGroup();
  var pIcon = makeIcon(poiStyles.parks.symbol, poiStyles.parks.color);
  parksData.forEach(function(p) {{
    L.marker([p.lat, p.lng], {{ icon: pIcon }})
      .bindPopup('<strong>' + p.name + '</strong><br>Park')
      .addTo(parksLayer);
  }});
  parksLayer.addTo(map);

  // --- Layer control ---
  L.control.layers(null, {{
    'Neighborhoods': neighborhoodsLayer,
    'Parcels (Slope)': parcelsLayer,
    'Roads': roadsLayer,
    'Schools': schoolsLayer,
    'Restaurants': restaurantsLayer,
    'Parks': parksLayer
  }}).addTo(map);

  // --- Legend ---
  var legend = L.control({{ position: 'bottomright' }});
  legend.onAdd = function() {{
    var div = L.DomUtil.create('div', 'legend');
    var html = '<h4>Neighborhoods</h4>';
    for (var name in nbrColors) {{
      html += '<div class="legend-item">' +
        '<div class="legend-color" style="background:' + nbrColors[name] + '"></div>' +
        name + '</div>';
    }}
    html += '<hr><h4>Parcel Slope Grade</h4>';
    var slopeOrder = ['Gentle', 'Moderate', 'Steep', 'Very Steep'];
    var slopeLabels = ['Gentle (0-9%)', 'Moderate (9-15%)', 'Steep (15-30%)', 'Very Steep (>30%)'];
    for (var si = 0; si < slopeOrder.length; si++) {{
      html += '<div class="legend-item">' +
        '<div class="legend-color" style="background:' + slopeColors[slopeOrder[si]] + '"></div>' +
        slopeLabels[si] + '</div>';
    }}
    html += '<hr><h4>Road Classification</h4>';
    var order = ['SR 520/Highway','Principal Arterial','Minor Arterial','Collector','Local','Private/Alley'];
    for (var i = 0; i < order.length; i++) {{
      var cls = order[i];
      var w = roadWeights[cls] || 1.5;
      html += '<div class="legend-item">' +
        '<div class="legend-line" style="border-top:' + w + 'px solid ' + roadColors[cls] + '"></div>' +
        cls + '</div>';
    }}
    html += '<hr><h4>Points of Interest</h4>';
    var poiOrder = ['schools', 'restaurants', 'parks'];
    for (var j = 0; j < poiOrder.length; j++) {{
      var key = poiOrder[j];
      var st = poiStyles[key];
      html += '<div class="legend-item">' +
        '<div class="legend-icon" style="background:' + st.color + ';">' + st.symbol + '</div>' +
        st.label + '</div>';
    }}
    div.innerHTML = html;
    return div;
  }};
  legend.addTo(map);
}})();
</script>
</body>
</html>"""
    return html


def main():
    roads = load_roads()
    neighborhoods = load_neighborhoods()
    parcels_gj = load_parcels()

    roads_gj = to_geojson_dict(roads)
    neighborhoods_gj = to_geojson_dict(neighborhoods)

    print(f"Roads GeoJSON: {len(json.dumps(roads_gj)):,} chars")
    print(f"Neighborhoods GeoJSON: {len(json.dumps(neighborhoods_gj)):,} chars")
    print(f"Parcels GeoJSON: {len(json.dumps(parcels_gj)):,} chars")

    print(f"POIs from OpenStreetMap: {len(RAW_SCHOOLS)} schools, {len(RAW_RESTAURANTS)} restaurants, {len(RAW_PARKS)} parks")

    # Filter to only points inside neighborhood boundaries
    nbr_union = neighborhoods.union_all()
    schools = filter_pois_inside(RAW_SCHOOLS, nbr_union)
    restaurants = filter_pois_inside(RAW_RESTAURANTS, nbr_union)
    parks = filter_pois_inside(RAW_PARKS, nbr_union)
    print(f"  Inside neighborhoods: {len(schools)} schools, {len(restaurants)} restaurants, {len(parks)} parks")

    html = build_html(roads_gj, neighborhoods_gj, parcels_gj, schools, restaurants, parks)
    OUTPUT_HTML.write_text(html, encoding="utf-8")
    print(f"\nWrote {OUTPUT_HTML} ({len(html):,} bytes)")


if __name__ == "__main__":
    main()