Lifecycle/may 2026 release

.claude/skills/conflate-snapshots/SKILL.md

@@ -12,7 +12,7 @@ upload for web consumption.
 
 - Rated OSM snapshot (`osm_snapshot_rated.parquet`) at `versions.snapshot_osm` — produced by [skills/full-data-pull](../full-data-pull/SKILL.md) step 3.
 - Overture snapshot (`overture_snapshot.parquet`) at `versions.snapshot_overture`.
-- OSM history parquets (`osm_versions.parquet`, `osm_changes.parquet`) at `versions.osm_data` — produced by [skills/model-history-pipeline](../model-history-pipeline/SKILL.md). Required by the change-detection step in stage 4.
+- OSM history parquets (`osm_versions.parquet`, `osm_changes.parquet`) at `versions.osm_data` — **regenerated each month** by [skills/full-data-pull](../full-data-pull/SKILL.md) step 2 (via `scripts/osm_data/download_history.py`). The full re-fit pipeline at [skills/model-history-pipeline](../model-history-pipeline/SKILL.md) is only invoked when re-fitting λ. Required by the change-detection step in stage 4.
 - **Fresh Source Cooperative temp credentials** in `.env.json` at the repo root. Tokens expire in ~1 hour.
 
 > ⚠️ **Credential refresh check.** Source Cooperative uses short-lived AWS

.claude/skills/full-data-pull/SKILL.md

@@ -5,14 +5,15 @@ description: Use when the user wants to refresh the independent POI snapshots (O
 
 # Full data pull
 
-Downloads the snapshot sources (50 US states + DC + 5 inhabited territories: PR, VI, GU, MP, AS) and applies the rating model to OSM so conflation can run.
+Downloads the snapshot sources (50 US states + DC + 5 inhabited territories: PR, VI, GU, MP, AS), refreshes the OSM history that drives ghost reconstruction, and applies the rating model to OSM so conflation can run.
 
 ## Prerequisites
 
 - conda env `openpois` active.
 - For OSM: `osmium` in env bin (resolved automatically via `Path(sys.executable).parent / "osmium"`).
 - Boundary cache at `directories.boundary` (auto-downloads on first use).
 - A fitted model exists for the OSM rating step (see [skills/model-history-pipeline](../model-history-pipeline/SKILL.md)).
+- For OSM history: a fresh Geofabrik OAuth cookie file at `download.osm.history_cookie_file` (Netscape format; any OSM account works). See [docs/data-sources.md](../../docs/data-sources.md#osm-history-geofabrik-full-history-pbfs).
 
 ## Steps
 
@@ -21,23 +22,30 @@ Downloads the snapshot sources (50 US states + DC + 5 inhabited territories: PR,
    versions:
      snapshot_osm: "YYYYMMDD"
      snapshot_overture: "YYYYMMDD"
+     osm_data: "YYYYMMDD"     # bumps each month — history is refreshed for ghosts
+     ghost_osm: "YYYYMMDD"    # pinned to osm_data; bumps in lockstep
    ```
-   See [docs/data-versioning.md](../../docs/data-versioning.md).
+   `model_output` does **not** bump unless you're re-fitting λ from scratch (see [skills/model-history-pipeline](../model-history-pipeline/SKILL.md)). See [docs/data-versioning.md](../../docs/data-versioning.md).
 
 2. **Run the downloads** (independent — order doesn't matter, can run in parallel):
 
    ```bash
    python scripts/osm_snapshot/download.py     # 4 Geofabrik PBFs → osm_snapshot.parquet
    python scripts/overture/download.py         # DuckDB over S3   → overture_snapshot.parquet
+   python scripts/osm_data/download_history.py # 4 internal OSH PBFs → osm_versions.parquet + osm_changes.parquet
    ```
-   The snapshot loader pulls 4 extracts in sequence: `us`, `pr`, `usvi`, `american_oceania`. Per-source details, auth, and schema quirks are in [docs/data-sources.md](../../docs/data-sources.md).
+   Each loader pulls 4 extracts in sequence: `us`, `pr`, `usvi`, `american_oceania`. Per-source details, auth, and schema quirks are in [docs/data-sources.md](../../docs/data-sources.md).
 
    **Gotcha — interrupted snapshot runs**: all 4 extracts share `~/data/openpois/snapshots/osm/<v>/parse_chunks/`. If a run dies between extracts, leftover chunks from extract N may be silently mistaken for extract N+1's parsed output on resume (the parser short-circuits on existing chunks). Before resuming an interrupted snapshot run, nuke the work dir:
    ```bash
    rm -rf ~/data/openpois/snapshots/osm/{version}/parse_chunks/
    ```
    This forces a clean re-parse of whichever extract was in flight; completed extracts (which write their own per-extract intermediate parquet next to the final output) are still skipped.
 
+   **Gotcha — `download_history.py` is for ghost regeneration only**: do **not** re-run `scripts/osm_data/format_tabular.py` or `scripts/models/osm_turnover.py` in the monthly cycle — those are part of the model-fit pipeline, which stays pinned to `versions.model_output`. The monthly history refresh only feeds `build_ghosts.py` (invoked by `make conflate`).
+
+   **Gotcha — per-territory 404 tolerance**: if Geofabrik stops publishing a territory's `*-internal.osh.pbf`, the loader logs a warning, skips that extract, and continues. The territory's POIs still flow through downstream stages but the rater falls back to the global-mean δ for its `shared_label`s.
+
 3. **Apply the rating model to OSM** → `osm_snapshot_rated.parquet`:
    ```bash
    python scripts/osm_snapshot/apply_model.py

CHANGELOG.md

@@ -0,0 +1,83 @@
+# Changelog
+
+## 2026-05-21-v0
+
+### Snapshot inputs
+
+| Source                 | Value                                       |
+| ---------------------- | ------------------------------------------- |
+| OSM snapshot date      | 2026-05-21                                  |
+| Overture release       | `2026-05-20.0` (pinned)                     |
+| OSM snapshot rows      | 8,799,633                                   |
+| Overture snapshot rows | 13,458,763                                  |
+| Boundary footprint     | US + all territories (PR, USVI, GU, MP, AS) |
+
+### Conflated output
+
+| Metric                       | This run    | Prior        | Δ                       |
+| ---------------------------- | ----------- | ------------ | ----------------------- |
+| Total rows                   | 17,989,377  | 17,788,585   | +200,792 (+1.13%)       |
+| Matched OSM × Overture       | 2,696,484   | 2,677,091    | +19,393 (+0.72%)        |
+| OSM-only                     | 6,103,149   | 6,031,413    | +71,736 (+1.19%)        |
+| Overture-only                | 9,189,744   | 9,080,081    | +109,663 (+1.21%)       |
+| Shadow-matched (CD penalty)  | 47,925      | n/a          | new — first run with change detection |
+| Shared labels                | 93          | 93           | unchanged set           |
+
+### Methods changes vs. prior release
+
+- **Change detection (new).** Post-conflation pass that reconstructs "ghost" POIs from OSM history (deleted or renamed nodes) and uses them to penalize unmatched Overture POIs that shadow-match a ghost. Penalty multiplies the Overture row's `conf_mean` by the per-`shared_label` δ from the fitted turnover model. 47,925 rows penalized this run. Adds audit columns to every conflated row: `shadow_matched`, `shadow_ghost_id`, `shadow_event_type`, `shadow_event_timestamp`, `shadow_score`, `shadow_distance_m`, `original_conf_mean`. **PR #29**; design in `docs/change-detection.md`.
+- **US territory expansion.** Spatial footprint widened from CONUS + PR to include all US territories (PR, USVI, GU, MP, AS). Affects both snapshots and the conflation domain. **PR #31**.
+- **Wider metadata propagation.** Additional OSM and Overture metadata fields now flow through to the conflated parquet (website, wikidata, wikipedia, etc.). **PR #30**.
+- **PMTiles re-tuned.** Zoom range narrowed to Z10–Z14 with `--drop-densest-as-needed`, so feature drops cascade through low zooms instead of failing tile builds. Site updated with zoom-aware point styling. **PR #33**.
+- **Covering bbox in partitioned parquet.** GeoParquet 1.1 `bbox` struct column emitted via `write_covering_bbox=True`, enabling DuckDB row-group pruning on viewport queries. **PR #32**.
+- **Overture release pinned.** `download.overture.release_date` set to `2026-05-20.0` (was `null` = auto-detect latest). Future runs against the same pin are deterministic.
+- **Pipeline memory hardening (uncommitted on `lifecycle/may-2026-release`).** Both `apply_change_detection.py` and the partitioned-write helper hit the 24 GB WSL cap on nationwide inputs. The CD writer now mutates in place and streams the output parquet in row-group chunks via `pyarrow.parquet.ParquetWriter`; the geohash partition writer drops one full-partition copy (numpy `argsort` + `iloc` instead of pandas `sort_values`) and streams large partitions in chunks. See `src/openpois/conflation/change_detection.py` and `src/openpois/io/geohash_partition.py`.
+
+### Taxonomy changes
+
+**Overture crosswalk** (`src/openpois/conflation/data/taxonomy_crosswalk_overture_maps.csv`, uncommitted on `lifecycle/may-2026-release`): 7 new entries under `services_and_business.family_service`, previously unmapped and dropped from the partitioned output.
+
+| Overture sub-category         | Maps to            |
+| ----------------------------- | ------------------ |
+| `funeral_service`             | Other Professional |
+| `adoption_service`            | Other Professional |
+| `family_service_center`       | Other Professional |
+| `nanny_service`               | Other Professional |
+| `genealogist`                 | Other Professional |
+| `elder_care_planning`         | Other Professional |
+| `mobility_equipment_service`  | Other Shop         |
+
+This is the proximate cause of the +22,715 row jump (+8.45%) in **Other Professional**.
+
+No OSM-side taxonomy changes since 2026-04-23.
+
+### Top label-level row-count changes
+
+| Shared label        | This run    | Prior       | Δ rows    | Δ %     | Δ matched |
+| ------------------- | ----------- | ----------- | --------- | ------- | --------- |
+| Specialty Store     | 1,026,395   | 917,422     | +108,973  | +11.88% | +753      |
+| Other Amenity       | 3,858,315   | 3,819,068   | +39,247   | +1.03%  | +3,124    |
+| Clothing Store      | 317,177     | 288,506     | +28,671   | +9.94%  | +779      |
+| Other Professional  | 291,500     | 268,785     | +22,715   | +8.45%  | 0         |
+| Other Healthcare    | 995,881     | 1,016,112   | −20,231   | −1.99%  | +54       |
+| (unlabeled)         | 701,209     | 719,862     | −18,653   | −2.59%  | +1,506    |
+| Car Dealer          | 182,314     | 164,517     | +17,797   | +10.82% | +521      |
+| Restaurant          | 718,472     | 702,020     | +16,452   | +2.34%  | +1,092    |
+| Supermarket         | 193,777     | 179,783     | +13,994   | +7.78%  | +361      |
+| Recreation          | 1,302,776   | 1,293,338   | +9,438    | +0.73%  | −510      |
+
+Drivers:
+- Most positive movers (Specialty Store, Clothing Store, Car Dealer, Supermarket, Bakery, Charging Station) track Overture's snapshot growth (+2.5% overall) landing in shared labels with moderate base counts.
+- **Other Professional** also reflects the new `family_service` crosswalk entries above.
+- **Other Healthcare** dropping by ~20k against a larger Overture snapshot is worth a closer look — likely an Overture taxonomy reshuffle inside `health_and_medical` upstream. Flagged for QA, not blocking.
+
+### Version pins
+
+| Key                       | This run                 | Prior                    |
+| ------------------------- | ------------------------ | ------------------------ |
+| `versions.conflation`     | 20260521                 | 20260423                 |
+| `versions.snapshot_osm`   | 20260521                 | 20260417                 |
+| `versions.snapshot_overture` | 20260521              | 20260423                 |
+| `versions.osm_data`       | 20260521                 | 20260515                 |
+| `versions.ghost_osm`      | 20260521                 | 20260515                 |
+| `versions.model_output`   | 20260422_by_shared_label | 20260422_by_shared_label (unchanged — model not refit this cycle) |

config.yaml

@@ -1,15 +1,16 @@
 # Versioned directories (used with config.get_dir_path())
 versions:
-  osm_data: "20260515"
+  osm_data: "20260521"
   model_output: "20260422_by_shared_label"
-  snapshot_osm: "20260417"
-  snapshot_overture: "20260423"
-  conflation: "20260423"
-  source_coop: "2026-04-23-v0"   # Source Cooperative upload folder (YYYY-MM-DD-v<IDX>); bump v<IDX> only for same-day re-uploads
+  snapshot_osm: "20260521"
+  snapshot_overture: "20260521"
+  conflation: "20260521"
+  source_coop: "2026-05-21-v0"   # Source Cooperative upload folder (YYYY-MM-DD-v<IDX>); bump v<IDX> only for same-day re-uploads
   # Ghost POI dataset reconstructed from OSM history (one row per
   # detected previous-state event). Pinned to the same value as
-  # ``osm_data`` since it is derived from the same history parquets.
-  ghost_osm: "20260515"
+  # ``osm_data`` since it is derived from the same history parquets,
+  # and regenerated together with the monthly snapshot refresh.
+  ghost_osm: "20260521"
 
 # Settings for downloading data
 download:
@@ -74,7 +75,7 @@ download:
       'website','wikidata','wikipedia'
     ]
   overture:
-    release_date: null  # null = auto-detect latest
+    release_date: "2026-05-20.0"  # pin for determinism; null = auto-detect latest
     s3_bucket: "overturemaps-us-west-2"
     s3_region: "us-west-2"
     # DuckDB resource caps for the per-part S3 scans and the final polygon

scripts/publish/upload_to_source_coop.py

@@ -79,6 +79,14 @@ def parse_args() -> argparse.Namespace:
             "default because these rarely change."
         ),
     )
+    parser.add_argument(
+        "--skip-changelog", action = "store_true",
+        help = (
+            "Skip uploading CHANGELOG.md to the repo top level. Default "
+            "is to upload it on every run so the public copy stays in "
+            "sync with the latest per-release deltas."
+        ),
+    )
     parser.add_argument(
         "--skip-latest-mirror", action = "store_true",
         help = (
@@ -204,6 +212,25 @@ def main() -> None:
             f"deleted {summary['deleted']} stale object(s)."
         )
 
+    # -------------------------------------------------------------------------
+    # Top-level CHANGELOG.md (per-release deltas, updated every run by default)
+    # -------------------------------------------------------------------------
+    if not args.skip_changelog:
+        changelog_path = CONFIG_PATH.parent / "CHANGELOG.md"
+        if changelog_path.exists():
+            upload_bytes(
+                client = client,
+                data = changelog_path.read_bytes(),
+                bucket = bucket,
+                key = f"{repo_prefix}/CHANGELOG.md",
+                content_type = "text/markdown; charset=utf-8",
+                dry_run = args.dry_run,
+            )
+        else:
+            print(
+                f"Skipping CHANGELOG.md upload — {changelog_path} not found."
+            )
+
     # -------------------------------------------------------------------------
     # Top-level README + LICENSE (opt-in)
     # -------------------------------------------------------------------------

src/openpois/conflation/change_detection.py

@@ -647,42 +647,91 @@ def apply_shadow_match(
         new_conf_lower[target_global] = np.nan
         new_conf_upper[target_global] = np.nan
 
-    # -- Stitch into output --------------------------------------------
-    out = conflated.copy()
-    out["conf_mean"] = new_conf_mean
-    out["conf_lower"] = new_conf_lower
-    out["conf_upper"] = new_conf_upper
-    out["shadow_matched"] = shadow_matched
-    out["shadow_ghost_id"] = shadow_ghost_id
-    out["shadow_event_type"] = shadow_event_type
-    out["shadow_event_timestamp"] = shadow_event_timestamp.values
-    out["shadow_score"] = shadow_score
-    out["shadow_distance_m"] = shadow_distance_m
-    out["original_conf_mean"] = original_conf_mean
+    # -- Summary scalars are derived now, while pre- and post-penalty
+    # conf_mean views are both still alive. Lengths are captured here
+    # so we can free the heavy intermediates before the parquet write.
+    mean_penalty_factor = (
+        float(
+            (new_conf_mean[shadow_matched]
+             / np.where(
+                 original_conf_mean[shadow_matched] == 0, 1,
+                 original_conf_mean[shadow_matched],
+             )
+            ).mean()
+        )
+        if shadow_matched.any() else float("nan")
+    )
+    n_ghosts_in = int(len(ghosts))
+    n_shadow_matches = int(len(matches))
+
+    # On nationwide data the original "copy then write" path peaked
+    # past the 24 GB WSL cap (≈18M-row shapely-geometry GDF + a full
+    # .copy() + the pyarrow Table materialized inside to_parquet +
+    # the 9M-row unmatched_ov subset). Free the scratch state before
+    # the write peak.
+    del ghosts, matches
+    if "unmatched_ov" in locals():
+        del unmatched_ov  # noqa: F821 -- only bound in the else branch
+    gc.collect()
+
+    # Mutate conflated in place rather than allocating a full copy:
+    # the un-penalized baseline isn't needed after this point and the
+    # audit data is held in standalone numpy arrays.
+    conflated["conf_mean"] = new_conf_mean
+    conflated["conf_lower"] = new_conf_lower
+    conflated["conf_upper"] = new_conf_upper
+    conflated["shadow_matched"] = shadow_matched
+    conflated["shadow_ghost_id"] = shadow_ghost_id
+    conflated["shadow_event_type"] = shadow_event_type
+    conflated["shadow_event_timestamp"] = shadow_event_timestamp.values
+    conflated["shadow_score"] = shadow_score
+    conflated["shadow_distance_m"] = shadow_distance_m
+    conflated["original_conf_mean"] = original_conf_mean
+
+    # Pandas copied each array on assignment, so the standalone
+    # references are now redundant -- drop them before the write.
+    del (
+        new_conf_mean, new_conf_lower, new_conf_upper,
+        shadow_matched, shadow_ghost_id, shadow_event_type,
+        shadow_event_timestamp, shadow_score, shadow_distance_m,
+        original_conf_mean,
+    )
+    gc.collect()
 
     output_path = Path(output_path)
     output_path.parent.mkdir(parents = True, exist_ok = True)
     if verbose:
         print(f"Writing {output_path} ...")
-    out.to_parquet(output_path, compression = "zstd")
+
+    # Stream the write in row-group chunks. The default
+    # GeoDataFrame.to_parquet materializes a full pyarrow Table
+    # alongside the live GDF, doubling peak memory; on 18M-row
+    # nationwide inputs that exceeds the 24 GB WSL cap.
+    from geopandas.io.arrow import _geopandas_to_arrow
+
+    chunk_rows = 2_000_000
+    sample_tbl = _geopandas_to_arrow(conflated.iloc[:1])
+    schema = sample_tbl.schema
+    del sample_tbl
+    with pq.ParquetWriter(
+        str(output_path), schema, compression = "zstd",
+    ) as writer:
+        for start in range(0, n, chunk_rows):
+            end = min(start + chunk_rows, n)
+            chunk_tbl = _geopandas_to_arrow(
+                conflated.iloc[start:end]
+            )
+            writer.write_table(chunk_tbl)
+            del chunk_tbl
+            gc.collect()
 
     summary = {
         "n_total": int(n),
         "n_unmatched_overture": int(len(ov_global_idx)),
-        "n_ghosts": int(len(ghosts)),
-        "n_shadow_matches": int(len(matches)),
+        "n_ghosts": n_ghosts_in,
+        "n_shadow_matches": n_shadow_matches,
         "n_survivor_dropped": int(n_survivor_dropped),
-        "mean_penalty_factor": (
-            float(
-                (new_conf_mean[shadow_matched]
-                 / np.where(
-                     original_conf_mean[shadow_matched] == 0, 1,
-                     original_conf_mean[shadow_matched],
-                 )
-                ).mean()
-            )
-            if shadow_matched.any() else float("nan")
-        ),
+        "mean_penalty_factor": mean_penalty_factor,
     }
 
     # Confirm read-back schema integrity.