feat: register specialists read pre-built JSON catalogs

step_nycha hung the FSM for 8+ minutes per query because
summary_for_point did 20 polygon×polygon intersections (5 nearest
developments × 4 layers) every time. Refactor to read pre-computed
exposure flags from data/registers/nycha.json — the bake script
already runs that math citywide once. Per-query work is haversine
+ dict lookup, sub-millisecond.

Same pattern for step_doe_schools (reads data/registers/schools.json,
~1ms). Hospitals have no pre-built catalog (~150 entries; would have
required a new build script + LLM-budget bake) so they read the
30 KB hospitals.geojson directly and sample the Cornerstone baked
rasters per hit (~10ms first call, sub-ms after).

Schema change for NYCHA per-query view: drop pct_inside_sandy_2012
and pct_in_dep_extreme_2080 floats (polygon-overlap percentages
weren't in the prebuilt catalog), replace with binary
inside_sandy_2012 + dep_*_class/dep_*_label to match the schools
and hospitals shape. NYCHA developments are now reported with
"centroid is inside Sandy zone" instead of "85% of footprint inside
Sandy zone." Honest given the data we actually have pre-computed.

Updated downstream consumers:
- app/fsm.py step_nycha result dict (n_inside_sandy_2012,
n_in_dep_extreme_2080)
- app/reconcile.py NYCHA briefing prompt (binary statements + DEP
depth class instead of pct interpolation)
- web/sveltekit/src/lib/client/registerAdapter.ts adaptNycha
(inundLabel(inside) instead of inundLabel(undefined, pct))
- web/sveltekit/src/routes/q/[queryId]/+page.svelte (step keys for
not-invoked rendering; map circle props for NYCHA dev pins)

web/main.py:_warm_caches no longer pre-loads the 91 MB Sandy
GeoJSON for NYCHA — the prebuilt JSON catalog has everything the
specialists need at boot.

Verified locally on 4 canonical addresses (Beach Channel, Coney
Island I, Carleton Manor, Pioneer St): all 3 register specialists
return real data in <100ms total, replacing what previously timed
out at 8+ min on the HF Space.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/fsm.py

@@ -646,8 +646,8 @@ def step_nycha(state: State) -> State:
         rec["ok"] = True
         rec["result"] = {
             "n_developments": s["n_developments"],
-            "n_majority_inside_sandy_2012": s["n_majority_inside_sandy_2012"],
-            "n_with_dep_2080_overlap": s["n_with_dep_2080_overlap"],
+            "n_inside_sandy_2012": s["n_inside_sandy_2012"],
+            "n_in_dep_extreme_2080": s["n_in_dep_extreme_2080"],
         }
         return state.update(nycha_developments=s, trace=trace)
     except Exception as e:

app/reconcile.py

@@ -586,27 +586,31 @@ def build_documents(state: dict[str, Any]) -> list[dict]:
         for d in nycha.get("developments", [])[:4]:
             tds = d.get("tds_num")
             body = [
-                "Source: NYC Open Data NYCHA Developments (phvi-damg) "
-                "+ NYC OEM Sandy 2012 Inundation Zone (5xsi-dfpx) "
-                "+ NYC DEP Stormwater Flood Maps + USGS 3DEP DEM.",
+                "Source: pre-computed from NYC Open Data NYCHA Developments "
+                "(phvi-damg) joined to NYC OEM Sandy 2012 Inundation Zone "
+                "(5xsi-dfpx) + NYC DEP Stormwater Flood Maps + USGS 3DEP DEM.",
                 (f"NYCHA development {d.get('development')} (TDS {tds}, "
-                 f"{d.get('borough')}), footprint {d.get('footprint_km2')} km², "
-                 f"{d.get('distance_m')} m from query."),
+                 f"{d.get('borough')}), {d.get('distance_m')} m from query."),
                 (f"Representative-point elevation {d.get('rep_elevation_m')} m, "
                  f"HAND {d.get('rep_hand_m')} m."),
-                (f"{d.get('pct_inside_sandy_2012')}% of footprint inside the "
-                 "2012 Sandy Inundation Zone (empirical)."),
             ]
-            if (d.get("pct_in_dep_extreme_2080") or 0) > 0:
+            if d.get("inside_sandy_2012"):
+                body.append(
+                    "Centroid is inside the 2012 Sandy Inundation Zone "
+                    "(empirical).")
+            else:
+                body.append(
+                    "Centroid is outside the 2012 Sandy Inundation Zone.")
+            c2080 = d.get("dep_extreme_2080_class") or 0
+            if c2080 > 0:
                 body.append(
-                    f"{d.get('pct_in_dep_extreme_2080')}% of footprint inside "
-                    "NYC DEP Extreme-2080 scenario "
-                    f"(of which {d.get('pct_in_dep_extreme_2080_deep')}% in the "
-                    "deepest >4 ft band).")
-            if (d.get("pct_in_dep_moderate_2050") or 0) > 0:
+                    f"DEP Extreme-2080 scenario at this development: "
+                    f"{d.get('dep_extreme_2080_label')} (depth class {c2080}).")
+            c2050 = d.get("dep_moderate_2050_class") or 0
+            if c2050 > 0:
                 body.append(
-                    f"{d.get('pct_in_dep_moderate_2050')}% of footprint inside "
-                    "NYC DEP Moderate-2050 scenario.")
+                    f"DEP Moderate-2050 scenario at this development: "
+                    f"{d.get('dep_moderate_2050_label')} (depth class {c2050}).")
             docs.append(_doc_message(f"nycha_dev_{tds}", body))
 
     schools = state.get("doe_schools")

app/registers/_loader.py

@@ -0,0 +1,69 @@
+"""Shared loader for pre-built register JSONs in data/registers/.
+
+Each register specialist (`nycha`, `doe_schools`, `doh_hospitals`,
+`mta_entrances`) has a pre-computed JSON catalog of every Tier 1-3
+exposed asset. The catalog is built once by scripts/build_*_register.py
+running the full polygon-overlap math; per-query specialists used to
+recompute that math against multi-million-polygon GDB layers, which
+on the HF Space CPU made `step_nycha` hang for minutes.
+
+This module provides O(1) cached load + haversine-on-prebuilt-rows
+nearest-N retrieval. Per-query latency drops from minutes to ~ms
+without losing the exposure semantics — the per-asset flags
+(snap.sandy, snap.dep[scen].depth_class, snap.microtopo) were already
+computed during the bake.
+
+Asset classes outside this catalog (truly unexposed assets, tier 0)
+are intentionally not surfaced: a Carleton Manor query that returns
+"no NYCHA developments at risk within 1 mi" is a more useful
+result than "we found 5 inland NYCHA developments with 0% Sandy
+overlap."
+"""
+from __future__ import annotations
+
+import json
+import math
+from functools import lru_cache
+from pathlib import Path
+
+REGISTERS_DIR = Path(__file__).resolve().parents[2] / "data" / "registers"
+
+
+@lru_cache(maxsize=8)
+def load_register(asset_class: str) -> list[dict]:
+    """Return the rows list from data/registers/<asset_class>.json. The
+    caller treats each row as opaque except for the lat/lon fields."""
+    p = REGISTERS_DIR / f"{asset_class}.json"
+    if not p.exists():
+        return []
+    with open(p) as f:
+        d = json.load(f)
+    return list(d.get("rows", []))
+
+
+def haversine_m(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+    R = 6371000.0
+    p1, p2 = math.radians(lat1), math.radians(lat2)
+    dp = math.radians(lat2 - lat1); dl = math.radians(lon2 - lon1)
+    a = math.sin(dp / 2) ** 2 + math.cos(p1) * math.cos(p2) * math.sin(dl / 2) ** 2
+    return 2 * R * math.asin(math.sqrt(a))
+
+
+def nearest_n(asset_class: str, lat: float, lon: float,
+              radius_m: float, n: int) -> list[tuple[float, dict]]:
+    """Return up to N rows within radius_m of (lat, lon), sorted by
+    distance ascending. Each entry is (distance_m, row)."""
+    rows = load_register(asset_class)
+    if not rows:
+        return []
+    candidates: list[tuple[float, dict]] = []
+    for r in rows:
+        rlat = r.get("lat")
+        rlon = r.get("lon")
+        if rlat is None or rlon is None:
+            continue
+        d = haversine_m(lat, lon, float(rlat), float(rlon))
+        if d <= radius_m:
+            candidates.append((d, r))
+    candidates.sort(key=lambda t: t[0])
+    return candidates[:n]

app/registers/doe_schools.py

@@ -127,38 +127,50 @@ def _dep_class(lat: float, lon: float, scenario: str):
 def summary_for_point(lat: float, lon: float,
                        radius_m: float = DEFAULT_RADIUS_M,
                        max_schools: int = DEFAULT_MAX_PER_QUERY) -> dict:
-    near = _schools_near(lat, lon, radius_m)
-    if near.empty:
+    """N nearest tier-1-3 DOE schools to (lat, lon), with pre-computed
+    exposure flags read from data/registers/schools.json. The bake
+    script runs the buffered point-in-polygon math citywide once;
+    per-query work is haversine + dict lookup."""
+    from app.registers._loader import nearest_n
+    hits = nearest_n("schools", lat, lon, radius_m, max_schools)
+    if not hits:
         return {"available": False,
                 "n_schools": 0,
                 "radius_m": radius_m,
                 "schools": []}
 
-    near = near.head(max_schools)
     findings: list[SchoolFinding] = []
-    for _, row in near.iterrows():
-        slat, slon = float(row["lat"]), float(row["lon"])
-        elev = _sample_raster(DATA / "nyc_dem_30m.tif", slat, slon)
-        hand = _sample_raster(DATA / "hand.tif", slat, slon)
-        in_sandy = _inside_sandy(slat, slon)
-        d80c, d80l = _dep_class(slat, slon, "dep_extreme_2080")
-        d50c, d50l = _dep_class(slat, slon, "dep_moderate_2050")
-        boronum = str(row.get("boronum", ""))
+    for distance_m, row in hits:
+        snap = row.get("snap") or {}
+        dep = snap.get("dep") or {}
+        microtopo = snap.get("microtopo") or {}
+
+        def _depth(scen: str) -> tuple[int | None, str | None]:
+            d = dep.get(scen) or {}
+            cls = d.get("depth_class")
+            lbl = d.get("depth_label")
+            return (int(cls) if cls is not None else None,
+                    str(lbl) if lbl else None)
+
+        d80c, d80l = _depth("dep_extreme_2080")
+        d50c, d50l = _depth("dep_moderate_2050")
+        elev = microtopo.get("point_elev_m")
+        hand = microtopo.get("aoi_hand_m") or microtopo.get("hand_m")
+
         findings.append(SchoolFinding(
-            loc_code=str(row["loc_code"]),
-            loc_name=str(row["loc_name"]),
-            address=str(row["address"]).strip(),
-            borough=BORO_NAME.get(boronum, boronum),
-            bin=str(row["bin"]),
-            bbl=str(row["bbl"]),
-            managed_by=MANAGED_BY_LABEL.get(str(row["managed_by"]),
-                                              str(row["managed_by"])),
-            school_lat=round(slat, 5),
-            school_lon=round(slon, 5),
-            distance_m=round(float(row["distance_m"]), 1),
-            elevation_m=round(elev, 2) if elev is not None else None,
-            hand_m=round(hand, 2) if hand is not None else None,
-            inside_sandy_2012=in_sandy,
+            loc_code=str(row.get("loc_code", "")),
+            loc_name=str(row.get("name", "")),
+            address=str(row.get("address", "")).strip(),
+            borough=str(row.get("borough", "")),
+            bin=str(row.get("bin", "")),
+            bbl=str(row.get("bbl", "")),
+            managed_by="DOE-managed",
+            school_lat=round(float(row["lat"]), 5),
+            school_lon=round(float(row["lon"]), 5),
+            distance_m=round(distance_m, 1),
+            elevation_m=round(float(elev), 2) if elev is not None else None,
+            hand_m=round(float(hand), 2) if hand is not None else None,
+            inside_sandy_2012=bool(snap.get("sandy")),
             dep_extreme_2080_class=d80c,
             dep_extreme_2080_label=d80l,
             dep_moderate_2050_class=d50c,
@@ -176,9 +188,10 @@ def summary_for_point(lat: float, lon: float,
         "n_inside_sandy_2012": n_in_sandy,
         "n_in_dep_extreme_2080": n_dep_2080,
         "schools": [vars(f) for f in findings],
-        "citation": ("NYC DOE Locations Points + NYC OEM Sandy 2012 "
-                     "Inundation Zone (5xsi-dfpx) + NYC DEP Stormwater "
-                     "Flood Maps + USGS 3DEP DEM"),
+        "citation": ("Pre-computed from NYC DOE Locations Points joined "
+                     "to Sandy 2012 Inundation Zone (5xsi-dfpx) + "
+                     "NYC DEP Stormwater Flood Maps + USGS 3DEP DEM. "
+                     "See data/registers/schools.json."),
     }
 
 

app/registers/doh_hospitals.py

@@ -121,9 +121,49 @@ def _dep_class(lat: float, lon: float, scenario: str):
     return dep_class_buffered(lat, lon, BUFFER_DOH_HOSPITAL_M, scenario)
 
 
+_DEPTH_LABEL = {
+    0: "outside",
+    1: "Nuisance (>4 in to 1 ft)",
+    2: "Deep & Contiguous (1-4 ft)",
+    3: "Deep Contiguous (>4 ft)",
+}
+
+
+def _exposure_at(lat: float, lon: float) -> tuple[bool, dict]:
+    """Use baked Cornerstone rasters for fast per-point exposure lookup.
+    Returns (inside_sandy, {scen: (depth_class, depth_label)}). Falls
+    back to the legacy buffered GDB join if rasters absent."""
+    try:
+        import geopandas as gpd
+        from shapely.geometry import Point
+
+        from app.flood_layers import dep_stormwater, sandy_inundation
+        pt = (gpd.GeoDataFrame(geometry=[Point(lon, lat)], crs="EPSG:4326")
+              .to_crs("EPSG:2263").iloc[0].geometry)
+        in_sandy = sandy_inundation.inside_raster(pt)
+        deps = {}
+        for scen in ("dep_extreme_2080", "dep_moderate_2050"):
+            cls = dep_stormwater.join_raster(pt, scen)
+            deps[scen] = (cls, _DEPTH_LABEL.get(cls, "outside"))
+        return in_sandy, deps
+    except Exception:
+        log.exception("raster exposure lookup failed; falling back")
+        in_sandy = _inside_sandy(lat, lon)
+        d80c, d80l = _dep_class(lat, lon, "dep_extreme_2080")
+        d50c, d50l = _dep_class(lat, lon, "dep_moderate_2050")
+        return in_sandy, {
+            "dep_extreme_2080": (d80c, d80l),
+            "dep_moderate_2050": (d50c, d50l),
+        }
+
+
 def summary_for_point(lat: float, lon: float,
                        radius_m: float = DEFAULT_RADIUS_M,
                        max_hospitals: int = DEFAULT_MAX_PER_QUERY) -> dict:
+    """N nearest hospitals to (lat, lon), with exposure flags computed
+    via Cornerstone baked rasters. Hospitals have no pre-built register
+    (small enough at ~150 entries to not need one), so we read the
+    full GeoJSON and sample the rasters per-hit. Sub-ms per query."""
     near = _hospitals_near(lat, lon, radius_m)
     if near.empty:
         return {"available": False,
@@ -137,9 +177,9 @@ def summary_for_point(lat: float, lon: float,
         hlat, hlon = float(row["lat"]), float(row["lon"])
         elev = _sample_raster(DATA / "nyc_dem_30m.tif", hlat, hlon)
         hand = _sample_raster(DATA / "hand.tif", hlat, hlon)
-        in_sandy = _inside_sandy(hlat, hlon)
-        d80c, d80l = _dep_class(hlat, hlon, "dep_extreme_2080")
-        d50c, d50l = _dep_class(hlat, hlon, "dep_moderate_2050")
+        in_sandy, deps = _exposure_at(hlat, hlon)
+        d80c, d80l = deps["dep_extreme_2080"]
+        d50c, d50l = deps["dep_moderate_2050"]
         findings.append(HospitalFinding(
             fac_id=str(row["fac_id"]),
             facility_name=str(row["facility_name"]),

app/registers/nycha.py

@@ -59,13 +59,15 @@ class DevelopmentFinding:
     centroid_lat: float
     centroid_lon: float
     distance_m: float
-    footprint_km2: float
     rep_elevation_m: float | None
     rep_hand_m: float | None
-    pct_inside_sandy_2012: float
-    pct_in_dep_extreme_2080: float       # any-depth (class>=1)
-    pct_in_dep_extreme_2080_deep: float  # class==3 only ("Deep Contiguous")
-    pct_in_dep_moderate_2050: float
+    inside_sandy_2012: bool
+    dep_extreme_2080_class: int          # 0=outside, 1/2/3 = depth class
+    dep_extreme_2080_label: str
+    dep_moderate_2050_class: int
+    dep_moderate_2050_label: str
+    dep_moderate_current_class: int
+    dep_moderate_current_label: str
 
 
 @lru_cache(maxsize=1)
@@ -192,64 +194,85 @@ def _dep_overlap(geom_2263, scenario: str) -> tuple[float, float]:
     return pct_any, pct_deep
 
 
+_DEPTH_LABEL = {
+    0: "outside",
+    1: "Nuisance (>4 in to 1 ft)",
+    2: "Deep & Contiguous (1-4 ft)",
+    3: "Deep Contiguous (>4 ft)",
+}
+
+
 def summary_for_point(lat: float, lon: float,
                        radius_m: float = DEFAULT_RADIUS_M,
                        max_developments: int = DEFAULT_MAX_PER_QUERY) -> dict:
-    near, _ = _developments_near(lat, lon, radius_m)
-    if near.empty:
+    """Return the N nearest tier-1-3 NYCHA developments to (lat, lon)
+    within radius_m, with their pre-computed exposure flags from the
+    register catalog at data/registers/nycha.json.
+
+    The catalog is the source of truth for which developments are
+    flood-exposed (the bake script ran the polygon-overlap math once,
+    citywide). Per-query work is haversine + dict lookup — sub-ms even
+    on the HF Space CPU. Developments outside the tier-1-3 catalog
+    (truly unexposed inland sites) are intentionally not surfaced;
+    "no NYCHA developments at risk within 1 mi" is the honest answer
+    for low-exposure queries.
+    """
+    from app.registers._loader import nearest_n
+    hits = nearest_n("nycha", lat, lon, radius_m, max_developments)
+    if not hits:
         return {"available": False,
                 "n_developments": 0,
                 "radius_m": radius_m,
                 "developments": []}
 
-    near = near.head(max_developments)
-    sandy_2263 = _load_sandy_2263()
-
     findings: list[DevelopmentFinding] = []
-    for _, row in near.iterrows():
-        geom = row.geometry
-        # Representative interior point gives a more meaningful elevation
-        # than the centroid for irregular development footprints.
-        rep = geom.representative_point()
-        # Re-project the rep point to 4326 for raster sampling
-        import geopandas as gpd
-        rep_4326 = gpd.GeoSeries([rep], crs="EPSG:2263").to_crs("EPSG:4326").iloc[0]
-        rep_lat, rep_lon = rep_4326.y, rep_4326.x
-
-        elev = _sample_raster(DATA / "nyc_dem_30m.tif", rep_lat, rep_lon)
-        hand = _sample_raster(DATA / "hand.tif", rep_lat, rep_lon)
-        pct_sandy = _overlap_pct(geom, sandy_2263)
-        pct_2080_any, pct_2080_deep = _dep_overlap(geom, "dep_extreme_2080")
-        pct_2050_any, _ = _dep_overlap(geom, "dep_moderate_2050")
+    for distance_m, row in hits:
+        snap = row.get("snap") or {}
+        dep = snap.get("dep") or {}
+        microtopo = snap.get("microtopo") or {}
+
+        def _dep_class(scen: str) -> int:
+            d = dep.get(scen) or {}
+            return int(d.get("depth_class") or 0)
+
+        c2080 = _dep_class("dep_extreme_2080")
+        c2050 = _dep_class("dep_moderate_2050")
+        ccur  = _dep_class("dep_moderate_current")
+
+        elev = microtopo.get("point_elev_m")
+        hand = microtopo.get("aoi_hand_m") or microtopo.get("hand_m")
 
         findings.append(DevelopmentFinding(
-            development=str(row["developmen"]),
-            tds_num=str(row["tds_num"]),
-            borough=str(row["borough"]),
-            centroid_lat=round(float(row["clat"]), 5),
-            centroid_lon=round(float(row["clon"]), 5),
-            distance_m=round(float(row["distance_m"]), 1),
-            footprint_km2=round(geom.area / 10.7639 / 1_000_000, 4),  # sq-ft -> km²
-            rep_elevation_m=round(elev, 2) if elev is not None else None,
-            rep_hand_m=round(hand, 2) if hand is not None else None,
-            pct_inside_sandy_2012=pct_sandy,
-            pct_in_dep_extreme_2080=pct_2080_any,
-            pct_in_dep_extreme_2080_deep=pct_2080_deep,
-            pct_in_dep_moderate_2050=pct_2050_any,
+            development=str(row.get("name", "")),
+            tds_num=str(row.get("tds_num", "")),
+            borough=str(row.get("borough", "")),
+            centroid_lat=round(float(row["lat"]), 5),
+            centroid_lon=round(float(row["lon"]), 5),
+            distance_m=round(distance_m, 1),
+            rep_elevation_m=round(float(elev), 2) if elev is not None else None,
+            rep_hand_m=round(float(hand), 2) if hand is not None else None,
+            inside_sandy_2012=bool(snap.get("sandy")),
+            dep_extreme_2080_class=c2080,
+            dep_extreme_2080_label=_DEPTH_LABEL.get(c2080, "outside"),
+            dep_moderate_2050_class=c2050,
+            dep_moderate_2050_label=_DEPTH_LABEL.get(c2050, "outside"),
+            dep_moderate_current_class=ccur,
+            dep_moderate_current_label=_DEPTH_LABEL.get(ccur, "outside"),
         ))
 
-    n_majority_sandy = sum(1 for f in findings if f.pct_inside_sandy_2012 >= 50)
-    n_any_2080 = sum(1 for f in findings if f.pct_in_dep_extreme_2080 > 0)
+    n_in_sandy = sum(1 for f in findings if f.inside_sandy_2012)
+    n_in_2080 = sum(1 for f in findings if f.dep_extreme_2080_class > 0)
     return {
         "available": True,
         "n_developments": len(findings),
         "radius_m": radius_m,
-        "n_majority_inside_sandy_2012": n_majority_sandy,
-        "n_with_dep_2080_overlap": n_any_2080,
+        "n_inside_sandy_2012": n_in_sandy,
+        "n_in_dep_extreme_2080": n_in_2080,
         "developments": [vars(f) for f in findings],
-        "citation": ("NYC Open Data NYCHA Developments (phvi-damg) + "
-                     "NYC OEM Sandy 2012 Inundation Zone (5xsi-dfpx) + "
-                     "NYC DEP Stormwater Flood Maps + USGS 3DEP DEM"),
+        "citation": ("Pre-computed from NYC Open Data NYCHA Developments "
+                     "(phvi-damg) joined to Sandy 2012 Inundation Zone "
+                     "(5xsi-dfpx) + NYC DEP Stormwater Flood Maps + "
+                     "USGS 3DEP DEM. See data/registers/nycha.json."),
     }
 
 

web/main.py

@@ -145,18 +145,22 @@ def _warm_caches():
         dep_stormwater.load(scen)
     print("[startup] flood layers ready", flush=True)
     if os.environ.get("RIPRAP_NYCHA_REGISTERS", "0").lower() in ("1", "true", "yes"):
-        print("[startup] pre-warming NYCHA registers (may take 60–120 s)...", flush=True)
+        print("[startup] pre-loading register catalogs...", flush=True)
         try:
-            from app.registers import doe_schools as _r_schools
+            # NYCHA + DOE schools read from pre-built JSON catalogs at
+            # data/registers/{nycha,schools}.json — sub-ms per query.
+            from app.registers._loader import load_register
+            n_nycha = len(load_register("nycha"))
+            n_schools = len(load_register("schools"))
+            print(f"[startup] catalogs ready: nycha={n_nycha} rows, "
+                  f"schools={n_schools} rows", flush=True)
+            # DOH hospitals has no pre-built catalog (~150 entries; we
+            # read the GeoJSON directly and sample baked rasters per hit).
             from app.registers import doh_hospitals as _r_hospitals
-            from app.registers import nycha as _r_nycha
-            _r_nycha._load_nycha()
-            _r_nycha._load_sandy_2263()
-            _r_schools._load_schools()
             _r_hospitals._load_hospitals()
-            print("[startup] NYCHA registers ready", flush=True)
+            print("[startup] hospitals geojson loaded", flush=True)
         except Exception as _e:
-            print(f"[startup] NYCHA register warm failed (non-fatal): {_e}", flush=True)
+            print(f"[startup] register warm failed (non-fatal): {_e}", flush=True)
     print("[startup] warming RAG (Granite Embedding 278M + 5 PDFs)...", flush=True)
     # RAG warm loads sentence-transformers, which on some HF Space rebuilds
     # has hit transformers-lazy-import edge cases (CodeCarbonCallback). The

web/sveltekit/build/200.html

@@ -6,17 +6,17 @@
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="description" content="Riprap — citation-grounded NYC flood-exposure briefings." />
     <title>Riprap — flood-exposure briefing</title>
-    <link href="/_app/immutable/entry/start.D9d5J30B.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/BRRh3tKo.js" rel="modulepreload">
+    <link href="/_app/immutable/entry/start.CJsf30jm.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/D30NfLoU.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/BTUA7_xE.js" rel="modulepreload">
-		<link href="/_app/immutable/entry/app.C74wsJjh.js" rel="modulepreload">
+		<link href="/_app/immutable/entry/app.COZFJ2px.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CXQd8Y6F.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CWw6qgC_.js" rel="modulepreload">
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 		<link href="/_app/immutable/chunks/CW0zSL4D.js" rel="modulepreload">
-		<link href="/_app/immutable/nodes/0.WjwQ9Jo9.js" rel="modulepreload">
+		<link href="/_app/immutable/nodes/0.C7EsZXea.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/DxQlA7U2.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/B-yumwKg.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/3HH2ymBC.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/DCD6_LXk.js" rel="modulepreload">
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 		<link href="/_app/immutable/chunks/DixtWtwq.js" rel="modulepreload">
@@ -28,15 +28,15 @@
     <div style="display: contents">
 			<script>
 				{
-					__sveltekit_yivtrn = {
+					__sveltekit_whg5iw = {
 						base: ""
 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("/_app/immutable/entry/start.D9d5J30B.js"),
-						import("/_app/immutable/entry/app.C74wsJjh.js")
+						import("/_app/immutable/entry/start.CJsf30jm.js"),
+						import("/_app/immutable/entry/app.COZFJ2px.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element);
 					});

web/sveltekit/build/_app/immutable/chunks/{B-yumwKg.js → 3HH2ymBC.js}

@@ -1 +1 @@
-import{s as e,p as r}from"./BRRh3tKo.js";const t={get error(){return r.error},get params(){return r.params},get status(){return r.status},get url(){return r.url}};e.updated.check;const a=t;export{a as p};
+import{s as e,p as r}from"./D30NfLoU.js";const t={get error(){return r.error},get params(){return r.params},get status(){return r.status},get url(){return r.url}};e.updated.check;const a=t;export{a as p};

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n=be(),u=c(n);ve(u,l=>N(t,l),()=>d(t)),k(2),o(n),m(e,n),M()}var Se=w(`<section class="land-section svelte-1anw2jf"><div class="land-section-head svelte-1anw2jf"><span class="section-label">What you'll get back</span> <span class="land-section-meta svelte-1anw2jf">A grounded paragraph with citations, not a chatbot answer.</span></div> <div class="land-preview-grid svelte-1anw2jf"><div class="land-preview-pane land-preview-pane-excerpt svelte-1anw2jf"><div class="land-preview-eyebrow svelte-1anw2jf">Briefing excerpt</div> <p class="land-preview-body svelte-1anw2jf">The lot sits inside the FEMA <span class="land-preview-cite svelte-1anw2jf">1% AE flood zone <sup class="svelte-1anw2jf">[c3]</sup></span>,
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web/sveltekit/build/_app/immutable/nodes/{3.D-7M_E8S.js → 3.7DONSlfO.js}

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+import{d as ge,c as he,a as v,s as l,e as xe,f as p}from"../chunks/CWw6qgC_.js";import{p as ye,ai as we,f as $e,a as ke,aj as qe,o as e,a5 as F,ak as je,al as d,a4 as I,c as a,s,r as t,t as M}from"../chunks/BTUA7_xE.js";import{i as O}from"../chunks/Bd-v_9Ud.js";import{e as Se}from"../chunks/25_y8TFd.js";import{h as Fe}from"../chunks/cDW0xQNP.js";import{p as Ie}from"../chunks/3HH2ymBC.js";import{B as Me,T as Oe}from"../chunks/BatqQaKj.js";import{l as Te}from"../chunks/DxQlA7U2.js";const Pe=!1,Re=!1,Ue=Object.freeze(Object.defineProperty({__proto__:null,prerender:Pe,ssr:Re},Symbol.toStringTag,{value:"Module"}));var Ae=p(`<div class="empty svelte-uialbm"><h1 class="svelte-uialbm">No briefing snapshot found</h1> <p>Run a briefing first at <a href="/" class="svelte-uialbm">riprap home</a>; once it finishes,
       use <strong>export PDF</strong> from the header to open this view.
       Snapshots are stored per-browser and persist between runs of the same query.</p></div>`),Be=p('<div class="curl svelte-uialbm"> </div>'),De=p('<li class="svelte-uialbm"><span class="cn svelte-uialbm"> </span> <span class="cglyph svelte-uialbm"><!></span> <span class="csrc svelte-uialbm"> </span> <span class="cvint svelte-uialbm"> </span> <div class="ctitle svelte-uialbm"> </div> <!> <div class="cdocid svelte-uialbm">doc_id <code> </code></div></li>'),Ge=p('<section class="print-citations svelte-uialbm"><h2 class="svelte-uialbm">Citations</h2> <ol class="svelte-uialbm"></ol></section>'),ze=p('<article class="print-doc svelte-uialbm"><header class="print-head svelte-uialbm"><div class="print-head-top svelte-uialbm"><span class="wordmark svelte-uialbm">riprap</span> <span class="meta"> </span></div> <h1 class="print-title svelte-uialbm"> </h1> <div class="print-sub svelte-uialbm">intent <strong> </strong> </div></header> <div class="print-controls no-print svelte-uialbm"><button type="button" class="svelte-uialbm">print / save as PDF</button> <span class="hint svelte-uialbm"> </span></div> <!> <!> <footer class="print-foot svelte-uialbm"> </footer></article>'),Le=p('<div class="empty svelte-uialbm"><p>Loading…</p></div>');function Ve(Q,U){ye(U,!0);let V=d(()=>Ie.params.queryId??""),i=I(null),T=I(!1),P=I(!1);we(()=>{const r=Te(e(V));if(!r){F(T,!0);return}F(i,r,!0),requestAnimationFrame(()=>{requestAnimationFrame(()=>{typeof window<"u"&&(window.print(),F(P,!0))})})});function X(){typeof window<"u"&&window.print()}let R=d(()=>e(i)?Object.values(e(i).citations).sort((r,n)=>r.n-n.n):[]),A=d(()=>e(i)?new Date(e(i).generatedAt).toISOString().slice(0,10):"");var B=he();Fe("uialbm",r=>{qe(()=>{var n;je.title=`Riprap briefing — ${((n=e(i))==null?void 0:n.queryText)??"export"??""}`})});var Y=$e(B);{var Z=r=>{var n=Ae();v(r,n)},ee=r=>{var n=ze(),c=a(n),u=a(c),D=s(a(u),2),ae=a(D);t(D),t(u);var m=s(u,2),se=a(m,!0);t(m);var G=s(m,2),f=s(a(G)),re=a(f,!0);t(f);var ie=s(f);t(G),t(c);var b=s(c,2),z=a(b),L=s(z,2),ne=a(L,!0);t(L),t(b);var N=s(b,2);Me(N,{get blocks(){return e(i).blocks},get citations(){return e(i).citations},streaming:!1});var C=s(N,2);{var le=_=>{var g=Ge(),W=s(a(g),2);Se(W,21,()=>e(R),h=>h.id,(h,o)=>{var x=De(),y=a(x),ve=a(y);t(y);var w=s(y,2),pe=a(w);Oe(pe,{get tier(){return e(o).tier},size:9,get color(){return`var(--tier-${e(o).tier??""})`}}),t(w);var $=s(w,2),de=a($,!0);t($);var k=s($,2),ce=a(k);t(k);var q=s(k,2),ue=a(q,!0);t(q);var H=s(q,2);{var me=j=>{var S=Be(),_e=a(S,!0);t(S),M(()=>l(_e,e(o).url)),v(j,S)},fe=d(()=>e(o).url&&e(o).url.startsWith("http"));O(H,j=>{e(fe)&&j(me)})}var J=s(H,2),K=s(a(J)),be=a(K,!0);t(K),t(J),t(x),M(()=>{l(ve,`[${e(o).n??""}]`),l(de,e(o).source),l(ce,`v. ${e(o).vintage??""}`),l(ue,e(o).title),l(be,e(o).docId)}),v(h,x)}),t(W),t(g),v(_,g)};O(C,_=>{e(R).length&&_(le)})}var E=s(C,2),oe=a(E);t(E),t(n),M(()=>{l(ae,`flood-exposure briefing · v0.4.2 · ${e(A)??""}`),l(se,e(i).queryText),l(re,e(i).intent??"briefing"),l(ie,` · ${e(i).specialists??""} specialists
         · ${e(i).attempts??1??""} reconcile${(e(i).attempts??1)===1?"":"s"}

web/sveltekit/build/_app/version.json

@@ -1 +1 @@
-{"version":"1778242964734"}
+{"version":"1778247242661"}

web/sveltekit/build/index.html

@@ -6,21 +6,21 @@
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="description" content="Riprap — citation-grounded NYC flood-exposure briefings." />
     <title>Riprap — flood-exposure briefing</title>
-    <link href="./_app/immutable/entry/start.D9d5J30B.js" rel="modulepreload">
-		<link href="./_app/immutable/chunks/BRRh3tKo.js" rel="modulepreload">
+    <link href="./_app/immutable/entry/start.CJsf30jm.js" rel="modulepreload">
+		<link href="./_app/immutable/chunks/D30NfLoU.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/BTUA7_xE.js" rel="modulepreload">
-		<link href="./_app/immutable/entry/app.C74wsJjh.js" rel="modulepreload">
+		<link href="./_app/immutable/entry/app.COZFJ2px.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/CXQd8Y6F.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/CWw6qgC_.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/Bd-v_9Ud.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/CW0zSL4D.js" rel="modulepreload">
-		<link href="./_app/immutable/nodes/0.WjwQ9Jo9.js" rel="modulepreload">
+		<link href="./_app/immutable/nodes/0.C7EsZXea.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/DxQlA7U2.js" rel="modulepreload">
-		<link href="./_app/immutable/chunks/B-yumwKg.js" rel="modulepreload">
+		<link href="./_app/immutable/chunks/3HH2ymBC.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/DCD6_LXk.js" rel="modulepreload">
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 		<link href="./_app/immutable/chunks/DixtWtwq.js" rel="modulepreload">
-		<link href="./_app/immutable/nodes/2.jyZRUUOQ.js" rel="modulepreload">
+		<link href="./_app/immutable/nodes/2._YbGDT13.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/cDW0xQNP.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/25_y8TFd.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/D907np-5.js" rel="modulepreload">
@@ -37,15 +37,15 @@
 			
 			<script>
 				{
-					__sveltekit_yivtrn = {
+					__sveltekit_whg5iw = {
 						base: new URL(".", location).pathname.slice(0, -1)
 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("./_app/immutable/entry/start.D9d5J30B.js"),
-						import("./_app/immutable/entry/app.C74wsJjh.js")
+						import("./_app/immutable/entry/start.CJsf30jm.js"),
+						import("./_app/immutable/entry/app.COZFJ2px.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element, {
 							node_ids: [0, 2],

web/sveltekit/build/q/sample.html

@@ -6,17 +6,17 @@
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="description" content="Riprap — citation-grounded NYC flood-exposure briefings." />
     <title>Riprap — flood-exposure briefing</title>
-    <link href="../_app/immutable/entry/start.D9d5J30B.js" rel="modulepreload">
-		<link href="../_app/immutable/chunks/BRRh3tKo.js" rel="modulepreload">
+    <link href="../_app/immutable/entry/start.CJsf30jm.js" rel="modulepreload">
+		<link href="../_app/immutable/chunks/D30NfLoU.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/BTUA7_xE.js" rel="modulepreload">
-		<link href="../_app/immutable/entry/app.C74wsJjh.js" rel="modulepreload">
+		<link href="../_app/immutable/entry/app.COZFJ2px.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/CXQd8Y6F.js" rel="modulepreload">
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 		<link href="../_app/immutable/chunks/Bd-v_9Ud.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/CW0zSL4D.js" rel="modulepreload">
-		<link href="../_app/immutable/nodes/0.WjwQ9Jo9.js" rel="modulepreload">
+		<link href="../_app/immutable/nodes/0.C7EsZXea.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/DxQlA7U2.js" rel="modulepreload">
-		<link href="../_app/immutable/chunks/B-yumwKg.js" rel="modulepreload">
+		<link href="../_app/immutable/chunks/3HH2ymBC.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/DCD6_LXk.js" rel="modulepreload">
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 		<link href="../_app/immutable/chunks/DixtWtwq.js" rel="modulepreload">
@@ -38,15 +38,15 @@
 			
 			<script>
 				{
-					__sveltekit_yivtrn = {
+					__sveltekit_whg5iw = {
 						base: new URL("..", location).pathname.slice(0, -1)
 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("../_app/immutable/entry/start.D9d5J30B.js"),
-						import("../_app/immutable/entry/app.C74wsJjh.js")
+						import("../_app/immutable/entry/start.CJsf30jm.js"),
+						import("../_app/immutable/entry/app.COZFJ2px.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element, {
 							node_ids: [0, 5],

web/sveltekit/src/lib/client/registerAdapter.ts

@@ -107,17 +107,18 @@ function adaptNycha(s: BaseFinding): RegisterData | null {
   if (!s.available) return null;
   const list = (s.developments ?? []) as BaseFinding[];
   const rows: RegisterRow[] = list.map((d) => {
-    const pctSandy = d.pct_inside_sandy_2012 as number | null | undefined;
-    const pct2080 = d.pct_in_dep_extreme_2080 as number | null | undefined;
+    const inSandy = d.inside_sandy_2012 as boolean | undefined;
+    const depLbl = d.dep_extreme_2080_label as string | null | undefined;
+    const depCls = d.dep_extreme_2080_class as number | null | undefined;
     return {
       name: `${d.development ?? '?'}${d.borough ? ` · ${d.borough}` : ''}`,
       elev: feetLabel(d.rep_elevation_m as number | null | undefined),
       ada: false, // NYCHA developments don't carry an ADA flag
       fema: '—',
-      sandy: inundLabel(undefined, pctSandy),
-      dep: depLabel(undefined, undefined, pct2080),
+      sandy: inundLabel(inSandy),
+      dep: depLabel(depLbl, depCls),
       asset: 'nycha',
-      primaryTier: pctSandy && pctSandy > 0 ? 'empirical' : 'modeled'
+      primaryTier: inSandy ? 'empirical' : 'modeled'
     };
   });
   return {

web/sveltekit/src/routes/q/[queryId]/+page.svelte

@@ -138,7 +138,7 @@
       prithvi_eo_live: ['scene_date', 'pct_water_500m'],
       microtopo_lidar: ['elev_m', 'pct_200m', 'relief_m'],
       mta_entrance_exposure: ['n_entrances', 'n_inside_sandy_2012', 'n_in_dep_extreme_2080'],
-      nycha_development_exposure: ['n_developments', 'n_majority_inside_sandy_2012'],
+      nycha_development_exposure: ['n_developments', 'n_inside_sandy_2012', 'n_in_dep_extreme_2080'],
       doe_school_exposure: ['n_schools', 'n_inside_sandy_2012'],
       doh_hospital_exposure: ['n_hospitals', 'n_inside_sandy_2012'],
       floodnet_forecast: ['sensor_id', 'distance_m', 'forecast_28d', 'accelerating'],
@@ -207,16 +207,14 @@
     }
     const ny = fr['nycha_developments'] as Record<string, unknown> | null | undefined;
     if (ny && Array.isArray(ny['developments'])) {
-      // NYCHA findings carry centroid_lat/lon; the actual development
-      // polygon is not in the SSE payload (geometry is not serialized
-      // through the dataclass). Render as larger filled circles whose
-      // color encodes whether ≥50% of the footprint is inside Sandy.
-      // Polygon-fill rendering is a morning tightening once
-      // geometry_geojson lands in the dataclass.
+      // NYCHA findings carry centroid_lat/lon; the development polygon
+      // is not serialized in the SSE payload. Render as larger filled
+      // circles colored by Sandy-zone membership (binary, from the
+      // pre-built catalog at data/registers/nycha.json).
       for (const d of ny['developments'] as Record<string, unknown>[]) {
         const lat = Number(d['centroid_lat']); const lon = Number(d['centroid_lon']);
         if (!Number.isFinite(lat) || !Number.isFinite(lon)) continue;
-        const pct = Number(d['pct_inside_sandy_2012'] ?? 0);
+        const inSandy = Boolean(d['inside_sandy_2012']);
         features.push({
           type: 'Feature',
           geometry: { type: 'Point', coordinates: [lon, lat] },
@@ -224,8 +222,7 @@
             kind: 'nycha',
             name: String(d['development'] ?? '?'),
             doc_id: `nycha_dev_${d['tds_num'] ?? ''}`,
-            inside_sandy_2012: pct >= 50,
-            pct_inside_sandy: pct
+            inside_sandy_2012: inSandy
           }
         });
       }