docs: address Lonboard quickstart review

Mirror tutorial changes to address Kyle Barron's feedback:

- Replace the bespoke quadkey() helper with mercantile.tile +
  mercantile.quadkey; pass the string quadkey to the STAC search
  (preserves leading zeros).
- Swap dask_geopandas for the canonical deltalake + geopandas pattern
  used by the existing ms-buildings example notebook, building
  storage_options with sas_token from the asset's credential field.
- Update install list to match: drop dask-geopandas, add deltalake,
  geopandas, mercantile; keep adlfs for Azure access.

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

Author: aboydnw

quickstarts/lonboard.ipynb

@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "2719b6d3",
    "metadata": {
     "execution": {
@@ -40,27 +40,8 @@
      "shell.execute_reply": "2026-06-01T23:09:16.913769Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.2\u001b[0m\r\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install --quiet lonboard pystac-client planetary-computer dask-geopandas adlfs"
-   ]
+   "outputs": [],
+   "source": "%pip install --quiet lonboard pystac-client planetary-computer geopandas deltalake adlfs mercantile"
   },
   {
    "cell_type": "markdown",
@@ -101,17 +82,11 @@
    "cell_type": "markdown",
    "id": "626369d5",
    "metadata": {},
-   "source": [
-    "## Find the building-footprints partition for Portland\n",
-    "\n",
-    "The `ms-buildings` collection is partitioned by [quadkey](https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system). Compute the zoom-9 quadkey for a Portland coordinate and fetch the STAC item whose partition covers it.\n",
-    "\n",
-    "**Expected result:** one matching item and its GeoParquet `data` asset."
-   ]
+   "source": "## Find the building-footprints partition for Portland\n\nThe `ms-buildings` collection is partitioned by [quadkey](https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system). Use `mercantile` to convert a Portland coordinate to a zoom-9 quadkey, then fetch the STAC item whose partition covers it.\n\n**Expected result:** one matching item and its Delta Table `data` asset."
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "2e668d74",
    "metadata": {
     "execution": {
@@ -121,60 +96,18 @@
      "shell.execute_reply": "2026-06-01T23:09:19.138497Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('021230223', 'UnitedStates_21230223_2023-04-25')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "\n",
-    "def quadkey(lat, lon, zoom):\n",
-    "    n = 2 ** zoom\n",
-    "    x = int((lon + 180.0) / 360.0 * n)\n",
-    "    y = int((1.0 - math.asinh(math.tan(math.radians(lat))) / math.pi) / 2.0 * n)\n",
-    "    digits = []\n",
-    "    for i in range(zoom, 0, -1):\n",
-    "        bit = 1 << (i - 1)\n",
-    "        digits.append(str((1 if x & bit else 0) + (2 if y & bit else 0)))\n",
-    "    return \"\".join(digits)\n",
-    "\n",
-    "\n",
-    "qk = quadkey(45.52, -122.66, 9)\n",
-    "item = next(catalog.search(\n",
-    "    collections=[\"ms-buildings\"],\n",
-    "    query={\n",
-    "        \"msbuildings:region\": {\"eq\": \"UnitedStates\"},\n",
-    "        \"msbuildings:quadkey\": {\"eq\": int(qk)},\n",
-    "    },\n",
-    ").items())\n",
-    "asset = item.assets[\"data\"]\n",
-    "qk, item.id"
-   ]
+   "outputs": [],
+   "source": "import mercantile\n\ntile = mercantile.tile(-122.66, 45.52, 9)\nquadkey = mercantile.quadkey(*tile)\n\nitem = next(catalog.search(\n    collections=[\"ms-buildings\"],\n    query={\n        \"msbuildings:region\": {\"eq\": \"UnitedStates\"},\n        \"msbuildings:quadkey\": {\"eq\": quadkey},\n    },\n).items())\nasset = item.assets[\"data\"]\nquadkey, item.id"
   },
   {
    "cell_type": "markdown",
    "id": "1bc86af0",
    "metadata": {},
-   "source": [
-    "## Load the footprints into a GeoDataFrame\n",
-    "\n",
-    "The asset is a Delta/Parquet partition on Azure Blob. `dask_geopandas.read_parquet` reads it with the asset's `table:storage_options` (account + SAS), then `.compute()` materializes a GeoDataFrame. Clip to the Portland metro for a focused view.\n",
-    "\n",
-    "**Expected result:** a few hundred thousand building polygons."
-   ]
+   "source": "## Load the footprints into a GeoDataFrame\n\nThe asset is a Delta Table partition on Azure Blob. Open it with `deltalake`, enumerate the parquet files in the partition, then read each one with `geopandas`. Clip to the Portland metro for a focused view.\n\n**Expected result:** a few hundred thousand building polygons."
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "8078f767",
    "metadata": {
     "execution": {
@@ -184,29 +117,8 @@
      "shell.execute_reply": "2026-06-01T23:09:34.561197Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "324600"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import dask_geopandas\n",
-    "\n",
-    "gdf = dask_geopandas.read_parquet(\n",
-    "    asset.href,\n",
-    "    storage_options=asset.extra_fields[\"table:storage_options\"],\n",
-    ").compute()\n",
-    "gdf = gdf.cx[-122.85:-122.45, 45.42:45.62]\n",
-    "\n",
-    "len(gdf)"
-   ]
+   "outputs": [],
+   "source": "import geopandas as gpd\nimport pandas as pd\nfrom deltalake import DeltaTable\n\nstorage_options = {\n    \"account_name\": asset.extra_fields[\"table:storage_options\"][\"account_name\"],\n    \"sas_token\": asset.extra_fields[\"table:storage_options\"][\"credential\"],\n}\ntable = DeltaTable(asset.href, storage_options=storage_options)\ngdf = pd.concat([\n    gpd.read_parquet(uri, storage_options=storage_options)\n    for uri in table.file_uris()\n])\ngdf = gdf.cx[-122.85:-122.45, 45.42:45.62]\n\nlen(gdf)"
   },
   {
    "cell_type": "markdown",
@@ -347,4 +259,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}