{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c5b18c52",
   "metadata": {},
   "source": [
    "# Where the canopy runs out\n",
    "\n",
    "The City of Melbourne records each of its 82,064 trees with a species, a planting year and a useful life expectancy:\n",
    "the years it has left as a healthy, safe tree, in bands (under 10 years, 11 to 20, 21 to 30, 31 to 40, over 41). The\n",
    "council also maps its tree canopy from aerial imagery, as outlines. Neither says which streets and blocks lose their\n",
    "shade, and when.\n",
    "\n",
    "Three things were added in [Plotlay](https://www.getplotlay.com):\n",
    "\n",
    "1. each tree's share of the 2021 canopy outline its trunk stands in (prepared in Python, `data/trees/trees.csv`);\n",
    "2. the 603 census blocks and the 1,809 road segments that hold a council tree, taken from the council's files and keyed\n",
    "   by `block_id` and `seg_id`, with the trees placed in them by position;\n",
    "3. formula columns for the shade now and in each decade, and the 13 precincts rolling the blocks up.\n",
    "\n",
    "This notebook reads only the files in `../data` and recomputes every number on the page, top to bottom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45900902",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "DATA = '../data'\n",
    "pd.set_option('display.width', 140)\n",
    "INK, RED, MUTED = '#1d4f91', '#cf3b26', '#5b6573'\n",
    "YEARS = [2025, 2035, 2045, 2055, 2065]\n",
    "plt.rcParams.update({'figure.dpi': 110, 'axes.spines.top': False, 'axes.spines.right': False, 'font.size': 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70924b01",
   "metadata": {},
   "source": [
    "## Sources and licence\n",
    "\n",
    "Trees (with species and dimensions), tree canopies 2021, CLUE blocks, CLUE small areas and road corridors: City of\n",
    "Melbourne Open Data, under CC BY 4.0. The full credits are in `NOTICE`. Nothing here comes from OpenStreetMap, and no\n",
    "imagery was traced."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e258870",
   "metadata": {},
   "source": [
    "## The trees\n",
    "\n",
    "`trees.csv` has one row per council tree: its useful life band, the year its useful life ends by (`end_by`, the top of\n",
    "the band counted from 2025, the dataset's last update), and `canopy_m2`, its share of the 2021 canopy.\n",
    "\n",
    "A tree's share is the part of the canopy outline its trunk stands in (or the nearest outline within 3 m) that is nearer\n",
    "to it than to any other council tree in that outline, and within 15 m of its trunk. Only Mature and Semi-mature trees\n",
    "claim canopy: a newly planted tree gives no measured shade yet. Trees with no band (17,410; the portal's value column\n",
    "says 50 for them, which is a fill) are \"Not assessed\": they are counted as standing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf13e0b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82,064 trees; 59,440 with canopy; 274.0 ha of shade\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ule_band\n",
       "> 41 years       22604\n",
       "21 - 30 years    21115\n",
       "Not assessed     17410\n",
       "31 - 40 years    15989\n",
       "11 - 20 years     4283\n",
       "< 10 years         663\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trees = pd.read_csv(f'{DATA}/trees/trees.csv', dtype={'com_id': str})\n",
    "print(f'{len(trees):,} trees; {(trees.canopy_m2 > 0).sum():,} with canopy; {trees.canopy_m2.sum() / 1e4:,.1f} ha of shade')\n",
    "trees.ule_band.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f14b6d1",
   "metadata": {},
   "source": [
    "## The shade standing at each decade\n",
    "\n",
    "Each tree is taken as gone by the top of its band: a tree with \"11 - 20 years\" left is gone by 2045 at the latest. It\n",
    "could go sooner, so these are the latest dates, and if the council assessed a tree before 2025 its date comes sooner\n",
    "still. New plantings are not counted: this is the shade today's trees give."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6e63310",
   "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>standing_ha</th>\n",
       "      <th>lost_ha</th>\n",
       "      <th>lost_pct</th>\n",
       "      <th>trees_ending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>274.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>272.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2045</th>\n",
       "      <td>252.2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2055</th>\n",
       "      <td>116.8</td>\n",
       "      <td>157.2</td>\n",
       "      <td>57.4</td>\n",
       "      <td>26061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2065</th>\n",
       "      <td>58.6</td>\n",
       "      <td>215.5</td>\n",
       "      <td>78.6</td>\n",
       "      <td>42050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      standing_ha  lost_ha  lost_pct  trees_ending\n",
       "2025        274.0      0.0       0.0             0\n",
       "2035        272.6      1.4       0.5           663\n",
       "2045        252.2     21.8       8.0          4946\n",
       "2055        116.8    157.2      57.4         26061\n",
       "2065         58.6    215.5      78.6         42050"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = trees.canopy_m2.sum()\n",
    "standing = {y: trees[trees.end_by.isna() | (trees.end_by > y)].canopy_m2.sum() for y in YEARS}\n",
    "table = pd.DataFrame({'standing_ha': {y: v / 1e4 for y, v in standing.items()}})\n",
    "table['lost_ha'] = (total - pd.Series(standing)) / 1e4\n",
    "table['lost_pct'] = 100 * (total - pd.Series(standing)) / total\n",
    "table['trees_ending'] = [int((trees.end_by <= y).sum()) for y in YEARS]\n",
    "table.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f0829da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 770x352 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = table.standing_ha.plot.bar(color=[INK, INK, INK, '#8aa3c4', '#8aa3c4'], figsize=(7, 3.2), rot=0)\n",
    "ax.set_ylabel('ha of shade standing'); ax.set_title(\"Council trees' shade, if each is gone by the top of its band\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75c5620",
   "metadata": {},
   "source": [
    "## Which species carry the shade that ends by 2045"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49d65cee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elms of every kind: 8.0 ha, 37% of the shade ending by 2045\n"
     ]
    },
    {
     "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>ha</th>\n",
       "      <th>trees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>common_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>English Elm</th>\n",
       "      <td>5.28</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London Plane</th>\n",
       "      <td>2.29</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>River red gum</th>\n",
       "      <td>1.47</td>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dutch Elm</th>\n",
       "      <td>0.88</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huntingdon Elm</th>\n",
       "      <td>0.65</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sugar gum</th>\n",
       "      <td>0.56</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moreton Bay Fig</th>\n",
       "      <td>0.56</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spotted Gum</th>\n",
       "      <td>0.49</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smooth-Leaved Elm</th>\n",
       "      <td>0.46</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yellow Box</th>\n",
       "      <td>0.30</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ha  trees\n",
       "common_name                   \n",
       "English Elm        5.28    435\n",
       "London Plane       2.29    252\n",
       "River red gum      1.47    698\n",
       "Dutch Elm          0.88     97\n",
       "Huntingdon Elm     0.65     74\n",
       "Sugar gum          0.56     44\n",
       "Moreton Bay Fig    0.56     37\n",
       "Spotted Gum        0.49    106\n",
       "Smooth-Leaved Elm  0.46     38\n",
       "Yellow Box         0.30    162"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soon = trees[trees.end_by <= 2045]\n",
    "sp = soon.groupby('common_name').agg(ha=('canopy_m2', lambda s: s.sum() / 1e4), trees=('com_id', 'count')).sort_values('ha', ascending=False)\n",
    "elm = soon[soon.genus == 'Ulmus'].canopy_m2.sum()\n",
    "print(f'Elms of every kind: {elm / 1e4:.1f} ha, {100 * elm / soon.canopy_m2.sum():.0f}% of the shade ending by 2045')\n",
    "sp.head(10).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eabf7b4f",
   "metadata": {},
   "source": [
    "## The blocks, as keyed and worked out in Plotlay\n",
    "\n",
    "`blocks.geojson` is Plotlay's export of the Blocks layer: each block keyed by its `block_id`, with the formula columns\n",
    "`canopy_2025` to `canopy_2065` (`coalesce(sum(canopy_m2, year = 2045), 0)` over the trees placed in it),\n",
    "`lost_by_2045` and `pct_lost_by_2045`. Below, the same numbers are worked out again from the trees, placed in the\n",
    "blocks by GeoPandas, and compared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f2d6633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "largest difference, Plotlay against GeoPandas: 153.3 m² of shade, 0.5 m² lost\n"
     ]
    }
   ],
   "source": [
    "blocks = gpd.read_file(f'{DATA}/geometry/blocks.geojson')\n",
    "for c in ['canopy_2025', 'canopy_2045', 'lost_by_2045', 'pct_lost_by_2045']: blocks[c] = pd.to_numeric(blocks[c])\n",
    "g = gpd.GeoDataFrame(trees, geometry=gpd.points_from_xy(trees.lng, trees.lat), crs=4326)\n",
    "j = gpd.sjoin(g, blocks[['key', 'geometry']], predicate='within')\n",
    "j['lost'] = j.canopy_m2.where(j.end_by <= 2045, 0)\n",
    "again = j.groupby('key').agg(canopy=('canopy_m2', 'sum'), lost=('lost', 'sum'))\n",
    "cmp = blocks.set_index('key')[['canopy_2025', 'lost_by_2045']].join(again).fillna(0)\n",
    "print('largest difference, Plotlay against GeoPandas:', round(float((cmp.canopy_2025 - cmp.canopy).abs().max()), 1), 'm² of shade,',\n",
    "      round(float((cmp.lost_by_2045 - cmp.lost).abs().max()), 1), 'm² lost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87c30a66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Blocks losing the most shade by 2045 (m²)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>key</th>\n",
       "      <th>area_name</th>\n",
       "      <th>canopy_2025</th>\n",
       "      <th>lost_by_2045</th>\n",
       "      <th>pct_lost_by_2045</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>931</td>\n",
       "      <td>Parkville</td>\n",
       "      <td>437455.3</td>\n",
       "      <td>30871</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>852</td>\n",
       "      <td>Melbourne (Remainder)</td>\n",
       "      <td>232371.0</td>\n",
       "      <td>18659</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>866</td>\n",
       "      <td>South Yarra</td>\n",
       "      <td>129440.2</td>\n",
       "      <td>17459</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>619</td>\n",
       "      <td>East Melbourne</td>\n",
       "      <td>127232.9</td>\n",
       "      <td>14391</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>274</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>102944.4</td>\n",
       "      <td>9611</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>269</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>44417.3</td>\n",
       "      <td>7690</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>273</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>14040.8</td>\n",
       "      <td>5746</td>\n",
       "      <td>40.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>653</td>\n",
       "      <td>East Melbourne</td>\n",
       "      <td>29767.3</td>\n",
       "      <td>5234</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>271</td>\n",
       "      <td>Parkville</td>\n",
       "      <td>14512.1</td>\n",
       "      <td>2436</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>554</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>20398.0</td>\n",
       "      <td>2269</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     key              area_name  canopy_2025  lost_by_2045  pct_lost_by_2045\n",
       "570  931              Parkville     437455.3         30871               7.1\n",
       "139  852  Melbourne (Remainder)     232371.0         18659               8.0\n",
       "583  866            South Yarra     129440.2         17459              13.5\n",
       "481  619         East Melbourne     127232.9         14391              11.3\n",
       "3    274                Carlton     102944.4          9611               9.3\n",
       "4    269                Carlton      44417.3          7690              17.3\n",
       "235  273                Carlton      14040.8          5746              40.9\n",
       "70   653         East Melbourne      29767.3          5234              17.6\n",
       "377  271              Parkville      14512.1          2436              16.8\n",
       "95   554             Kensington      20398.0          2269              11.1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Blocks losing the most shade by 2045 (m²)')\n",
    "blocks.sort_values('lost_by_2045', ascending=False)[['key', 'area_name', 'canopy_2025', 'lost_by_2045', 'pct_lost_by_2045']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d95d2834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "262 blocks have 2,000 m² of council-tree shade or more; 9 lose a quarter of it or more by 2045\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>key</th>\n",
       "      <th>area_name</th>\n",
       "      <th>canopy_2025</th>\n",
       "      <th>lost_by_2045</th>\n",
       "      <th>pct_lost_by_2045</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>605</td>\n",
       "      <td>East Melbourne</td>\n",
       "      <td>2946.1</td>\n",
       "      <td>1382</td>\n",
       "      <td>46.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>273</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>14040.8</td>\n",
       "      <td>5746</td>\n",
       "      <td>40.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>219</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>2265.9</td>\n",
       "      <td>908</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>660</td>\n",
       "      <td>Melbourne (Remainder)</td>\n",
       "      <td>6090.2</td>\n",
       "      <td>2236</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>131</td>\n",
       "      <td>Docklands</td>\n",
       "      <td>2541.3</td>\n",
       "      <td>933</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>266</td>\n",
       "      <td>Carlton</td>\n",
       "      <td>2560.3</td>\n",
       "      <td>779</td>\n",
       "      <td>30.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>616</td>\n",
       "      <td>East Melbourne</td>\n",
       "      <td>2915.4</td>\n",
       "      <td>754</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>1112</td>\n",
       "      <td>Docklands</td>\n",
       "      <td>2511.2</td>\n",
       "      <td>644</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>649</td>\n",
       "      <td>East Melbourne</td>\n",
       "      <td>6912.8</td>\n",
       "      <td>1759</td>\n",
       "      <td>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>907</td>\n",
       "      <td>Parkville</td>\n",
       "      <td>2188.2</td>\n",
       "      <td>493</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      key              area_name  canopy_2025  lost_by_2045  pct_lost_by_2045\n",
       "68    605         East Melbourne       2946.1          1382              46.9\n",
       "235   273                Carlton      14040.8          5746              40.9\n",
       "428   219                Carlton       2265.9           908              40.1\n",
       "527   660  Melbourne (Remainder)       6090.2          2236              36.7\n",
       "275   131              Docklands       2541.3           933              36.7\n",
       "225   266                Carlton       2560.3           779              30.4\n",
       "73    616         East Melbourne       2915.4           754              25.9\n",
       "463  1112              Docklands       2511.2           644              25.6\n",
       "471   649         East Melbourne       6912.8          1759              25.4\n",
       "370   907              Parkville       2188.2           493              22.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big = blocks[blocks.canopy_2025 >= 2000]\n",
    "print(f'{len(big)} blocks have 2,000 m² of council-tree shade or more; {(big.pct_lost_by_2045 >= 25).sum()} lose a quarter of it or more by 2045')\n",
    "big.sort_values('pct_lost_by_2045', ascending=False)[['key', 'area_name', 'canopy_2025', 'lost_by_2045', 'pct_lost_by_2045']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "313b9adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x770 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = blocks.to_crs(7855).plot(column='pct_lost_by_2045', cmap='YlOrRd', vmin=0, vmax=50, figsize=(8, 7), legend=True,\n",
    "    legend_kwds={'label': '% of shade ending by 2045', 'shrink': .6}, edgecolor='white', linewidth=.2,\n",
    "    missing_kwds={'color': '#eee'})\n",
    "ax.set_axis_off(); ax.set_title('Share of each block’s council-tree shade ending by 2045')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5f0bf9",
   "metadata": {},
   "source": [
    "## The streets\n",
    "\n",
    "`streets.geojson` is the Streets layer: each road corridor segment that holds a council tree, keyed by `seg_id`, with\n",
    "the same columns. Segments are added up into streets by the name at the start of the council's description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0448d58c",
   "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>canopy</th>\n",
       "      <th>lost_m2</th>\n",
       "      <th>segments</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>street</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>St Kilda Road</th>\n",
       "      <td>50809.8</td>\n",
       "      <td>5259</td>\n",
       "      <td>23</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swanston Street</th>\n",
       "      <td>21638.8</td>\n",
       "      <td>2385</td>\n",
       "      <td>25</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Punt Road</th>\n",
       "      <td>9254.1</td>\n",
       "      <td>2170</td>\n",
       "      <td>20</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dryburgh Street</th>\n",
       "      <td>7333.1</td>\n",
       "      <td>1967</td>\n",
       "      <td>16</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rathdowne Street</th>\n",
       "      <td>10076.4</td>\n",
       "      <td>1876</td>\n",
       "      <td>15</td>\n",
       "      <td>18.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simpson Street</th>\n",
       "      <td>4608.2</td>\n",
       "      <td>1606</td>\n",
       "      <td>10</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Street</th>\n",
       "      <td>6370.4</td>\n",
       "      <td>1580</td>\n",
       "      <td>4</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cardigan Street</th>\n",
       "      <td>9904.8</td>\n",
       "      <td>1537</td>\n",
       "      <td>14</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upfield Railway</th>\n",
       "      <td>6281.6</td>\n",
       "      <td>1313</td>\n",
       "      <td>2</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royal Parade</th>\n",
       "      <td>50802.3</td>\n",
       "      <td>1275</td>\n",
       "      <td>18</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicholson Street</th>\n",
       "      <td>4615.7</td>\n",
       "      <td>1273</td>\n",
       "      <td>11</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Avenue</th>\n",
       "      <td>14658.9</td>\n",
       "      <td>1221</td>\n",
       "      <td>6</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   canopy  lost_m2  segments   pct\n",
       "street                                            \n",
       "St Kilda Road     50809.8     5259        23  10.4\n",
       "Swanston Street   21638.8     2385        25  11.0\n",
       "Punt Road          9254.1     2170        20  23.4\n",
       "Dryburgh Street    7333.1     1967        16  26.8\n",
       "Rathdowne Street  10076.4     1876        15  18.6\n",
       "Simpson Street     4608.2     1606        10  34.9\n",
       "George Street      6370.4     1580         4  24.8\n",
       "Cardigan Street    9904.8     1537        14  15.5\n",
       "Upfield Railway    6281.6     1313         2  20.9\n",
       "Royal Parade      50802.3     1275        18   2.5\n",
       "Nicholson Street   4615.7     1273        11  27.6\n",
       "The Avenue        14658.9     1221         6   8.3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streets = gpd.read_file(f'{DATA}/geometry/streets.geojson')\n",
    "for c in ['canopy_2025', 'lost_by_2045']: streets[c] = pd.to_numeric(streets[c])\n",
    "streets['street'] = streets.seg_descr.str.replace(r'^Intersection of ', '', regex=True).str.split(r' between | from | and |,').str[0].str.strip()\n",
    "by = streets.groupby('street').agg(canopy=('canopy_2025', 'sum'), lost_m2=('lost_by_2045', 'sum'), segments=('key', 'count'))\n",
    "by['pct'] = 100 * by.lost_m2 / by.canopy\n",
    "by.sort_values('lost_m2', ascending=False).head(12).round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26e9983",
   "metadata": {},
   "source": [
    "## The precincts, rolled up in Plotlay\n",
    "\n",
    "`precincts.geojson`: the 13 census small areas, with roll-up columns (`sum(canopy_2025) of Blocks inside`; a block\n",
    "belongs to the precinct holding its centre)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "424ed073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blocks rolled up: 602\n"
     ]
    },
    {
     "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>key</th>\n",
       "      <th>canopy_2025</th>\n",
       "      <th>lost_by_2045</th>\n",
       "      <th>pct_lost_by_2045</th>\n",
       "      <th>blocks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>East Melbourne</td>\n",
       "      <td>296258.6</td>\n",
       "      <td>34266</td>\n",
       "      <td>11.6</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Carlton</td>\n",
       "      <td>332017.3</td>\n",
       "      <td>38115</td>\n",
       "      <td>11.5</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>South Yarra</td>\n",
       "      <td>177936.7</td>\n",
       "      <td>19750</td>\n",
       "      <td>11.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Docklands</td>\n",
       "      <td>79516.8</td>\n",
       "      <td>6940</td>\n",
       "      <td>8.7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kensington</td>\n",
       "      <td>214731.9</td>\n",
       "      <td>17530</td>\n",
       "      <td>8.2</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melbourne (Remainder)</td>\n",
       "      <td>390372.5</td>\n",
       "      <td>28660</td>\n",
       "      <td>7.3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North Melbourne</td>\n",
       "      <td>215217.1</td>\n",
       "      <td>15237</td>\n",
       "      <td>7.1</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Parkville</td>\n",
       "      <td>588415.8</td>\n",
       "      <td>41998</td>\n",
       "      <td>7.1</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Port Melbourne</td>\n",
       "      <td>42048.3</td>\n",
       "      <td>2095</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>West Melbourne (Residential)</td>\n",
       "      <td>84738.0</td>\n",
       "      <td>3842</td>\n",
       "      <td>4.5</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Melbourne (CBD)</td>\n",
       "      <td>185613.9</td>\n",
       "      <td>6213</td>\n",
       "      <td>3.3</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West Melbourne (Industrial)</td>\n",
       "      <td>42660.8</td>\n",
       "      <td>925</td>\n",
       "      <td>2.2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Southbank</td>\n",
       "      <td>78153.2</td>\n",
       "      <td>1694</td>\n",
       "      <td>2.2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             key  canopy_2025  lost_by_2045  pct_lost_by_2045  blocks\n",
       "10                East Melbourne     296258.6         34266              11.6      37\n",
       "7                        Carlton     332017.3         38115              11.5      71\n",
       "0                    South Yarra     177936.7         19750              11.1      17\n",
       "6                      Docklands      79516.8          6940               8.7      17\n",
       "11                    Kensington     214731.9         17530               8.2     117\n",
       "2          Melbourne (Remainder)     390372.5         28660               7.3      15\n",
       "3                North Melbourne     215217.1         15237               7.1      94\n",
       "8                      Parkville     588415.8         41998               7.1      34\n",
       "5                 Port Melbourne      42048.3          2095               5.0      18\n",
       "12  West Melbourne (Residential)      84738.0          3842               4.5      48\n",
       "1                Melbourne (CBD)     185613.9          6213               3.3      83\n",
       "4    West Melbourne (Industrial)      42660.8           925               2.2      11\n",
       "9                      Southbank      78153.2          1694               2.2      40"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prec = gpd.read_file(f'{DATA}/geometry/precincts.geojson')\n",
    "for c in ['canopy_2025', 'lost_by_2045', 'pct_lost_by_2045', 'blocks']: prec[c] = pd.to_numeric(prec[c])\n",
    "print('blocks rolled up:', int(prec.blocks.sum()))\n",
    "prec.sort_values('pct_lost_by_2045', ascending=False)[['key', 'canopy_2025', 'lost_by_2045', 'pct_lost_by_2045', 'blocks']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1ed3ab",
   "metadata": {},
   "source": [
    "## Read it carefully\n",
    "\n",
    "- The bands are the council's assessments. The dataset does not say when each tree was assessed, so every date here is\n",
    "  counted from 2025 and is the latest the band allows.\n",
    "- Canopy is the 2021 outline. Trees planted since have no measured shade, and a tree's crown may have grown or shrunk.\n",
    "- A Voronoi split gives a small tree beside a big one an equal claim on the outline between them. Totals for a block or\n",
    "  a street are steadier than any one tree's figure.\n",
    "- Canopy in outlines holding no council tree (private gardens, trees the council does not manage) is left out."
   ]
  }
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