🛰️

myst.yml

@@ -32,6 +32,7 @@ project:
         - file: reading/07-ibis-4.ipynb
         - file: reading/spatial-1.ipynb
         - file: reading/spatial-2.ipynb
+        - file: reading/spatial-3.ipynb
 
 site:
   template: book-theme

reading/spatial-4.ipynb

@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2124260a-f39e-4f6d-9469-fbac6c72a864",
+   "metadata": {},
+   "source": [
+    "# Zonal Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "63c059eb-dc3b-47f9-a544-e0f9ff3b7b75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from exactextract import exact_extract\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c21844bd-685b-4907-9396-30a8e44a85ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exact_extract?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5e46e88c-ab93-4c36-8a48-1a777668726f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>city</th>\n",
+       "      <th>fill</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A1</td>\n",
+       "      <td>A</td>\n",
+       "      <td>New Haven</td>\n",
+       "      <td>#76a865</td>\n",
+       "      <td>0.709624</td>\n",
+       "      <td>MULTIPOLYGON (((-72.90000 41.36885, -72.90325 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A2</td>\n",
+       "      <td>A</td>\n",
+       "      <td>New Haven</td>\n",
+       "      <td>#76a865</td>\n",
+       "      <td>0.761685</td>\n",
+       "      <td>MULTIPOLYGON (((-72.89401 41.36331, -72.89256 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>New Haven</td>\n",
+       "      <td>#76a865</td>\n",
+       "      <td>0.665130</td>\n",
+       "      <td>MULTIPOLYGON (((-72.90981 41.35949, -72.90284 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A4</td>\n",
+       "      <td>A</td>\n",
+       "      <td>New Haven</td>\n",
+       "      <td>#76a865</td>\n",
+       "      <td>0.751388</td>\n",
+       "      <td>MULTIPOLYGON (((-72.90284 41.35795, -72.89990 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A5</td>\n",
+       "      <td>A</td>\n",
+       "      <td>New Haven</td>\n",
+       "      <td>#76a865</td>\n",
+       "      <td>0.742981</td>\n",
+       "      <td>MULTIPOLYGON (((-72.89801 41.34930, -72.89883 ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  label grade       city     fill      mean  \\\n",
+       "0    A1     A  New Haven  #76a865  0.709624   \n",
+       "1    A2     A  New Haven  #76a865  0.761685   \n",
+       "2    A3     A  New Haven  #76a865  0.665130   \n",
+       "3    A4     A  New Haven  #76a865  0.751388   \n",
+       "4    A5     A  New Haven  #76a865  0.742981   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  MULTIPOLYGON (((-72.90000 41.36885, -72.90325 ...  \n",
+       "1  MULTIPOLYGON (((-72.89401 41.36331, -72.89256 ...  \n",
+       "2  MULTIPOLYGON (((-72.90981 41.35949, -72.90284 ...  \n",
+       "3  MULTIPOLYGON (((-72.90284 41.35795, -72.89990 ...  \n",
+       "4  MULTIPOLYGON (((-72.89801 41.34930, -72.89883 ...  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "city_stats = exact_extract(\"ndvi.tif\", \n",
+    "                           \"new_haven.parquet\", \n",
+    "                           [\"mean\"], \n",
+    "                           include_geom = True,\n",
+    "                           include_cols=[\"label\", \"grade\", \"city\", \"fill\"],\n",
+    "                           output=\"pandas\")\n",
+    "\n",
+    "city_stats.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9d6361b-c9a9-48e2-9b3f-818e88951d0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city_stats.to_parquet(\"new_haven_stats.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e37afb42-dd1f-44ec-8180-99397dadd9ab",
+   "metadata": {},
+   "source": [
+    "We can bring our data back into ibis,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dc44e75d-c9f5-476b-8a09-b77833aafb23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>grade</th>\n",
+       "      <th>ndvi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.547228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0.730811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>D</td>\n",
+       "      <td>0.486641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B</td>\n",
+       "      <td>0.609324</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  grade      ndvi\n",
+       "0     C  0.547228\n",
+       "1     A  0.730811\n",
+       "2     D  0.486641\n",
+       "3     B  0.609324"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import ibis\n",
+    "from ibis import _\n",
+    "con = ibis.duckdb.connect(extensions=[\"spatial\"])\n",
+    "\n",
+    "city = con.read_parquet(\"new_haven_stats.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cce332f-65f4-4916-86e3-e9b7e82c9e1f",
+   "metadata": {},
+   "source": [
+    " and we are finally ready to summarize the data to determine if the spectre of redlining remains: on average, are A grades still greener than D grades?  Construct the rest of the `ibis` code to compute the average NDVI by grade."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fea87402-e985-41b2-9999-e7749f28e6be",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

reading/spatial-5.ipynb

@@ -0,0 +1,67 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7ecc9fa6-a5ed-4bdb-9861-ca0f7ba6d990",
+   "metadata": {},
+   "source": [
+    "# Mapping and publishing\n",
+    "\n",
+    "In this section we take a look at adding our computed NDVI metrics to our map, along with methods for publishing our maps as interactive web applications."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad08a14b-1abe-4955-b0d3-7688b7ca05eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ibis import _\n",
+    "con = ibis.duckdb.connect(extensions=[\"spatial\"])\n",
+    "\n",
+    "city = (con\n",
+    "        .read_geo(\"/vsicurl/https://dsl.richmond.edu/panorama/redlining/static/mappinginequality.gpkg\")\n",
+    "        .filter(_.city == \"New Haven\")\n",
+    "        .execute()\n",
+    "       )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79338e3f-227b-4555-a58e-b6df628d1389",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import leafmap.maplibregl as leafmap\n",
+    "\n",
+    "m = leafmap.Map(style = \"voyager\")\n",
+    "m.add_cog_layer(\"https://github.com/espm-157/static-maps/raw/refs/heads/main/ndvi.tif\", colormap_name = \"greens\", nodata=1)\n",
+    "m.add_gdf(city, \"fill\",  paint = { \"fill-color\": [\"get\", \"fill\"], \"fill-opacity\": 0.8})\n",
+    "m\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}