{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0537c5e6",
   "metadata": {},
   "source": [
    "# Inner Sydney High, a deep dive: floor space per student\n",
    "\n",
    "Inner Sydney High School (NSW school 8913, Surry Hills) is the exception in a land analysis. Divide a school's grounds\n",
    "(the land it sits on, buildings included) by its students and most Sydney schools land in the tens of square metres;\n",
    "this one lands in single figures. It is a 14-storey school on a small inner-city site, so the land figure says little\n",
    "about the space the students have. This notebook asks the next question: how much floor space does each student have?\n",
    "\n",
    "The shapes in `data/geometry/` were made in [Plotlay](https://www.getplotlay.com): the site from three NSW Cadastre lots,\n",
    "the new building's podium and the four heritage buildings picked from OpenStreetMap outlines, and the tower's roof\n",
    "outline drawn over the NSW aerial photo of 22 January 2021. Students are the NSW Department of Education's headcount,\n",
    "one figure per year. The school opened in stages: Year 7 in 2020, a year group a year, Years 7 to 12 from 2025.\n",
    "\n",
    "Floor space is **estimated**: each building's footprint times its storeys. It is checked against the gross floor area\n",
    "(GFA) approved for the school (SSD 7610), quoted in `data/planning/`. The section \"Read it carefully\" says what the\n",
    "estimate counts and how far to trust it.\n",
    "\n",
    "It reads only the files in `../data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9653203",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', message='.*colormapping.*')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from pyproj import Geod\n",
    "\n",
    "DATA = '../data'\n",
    "pd.set_option('display.width', 160); pd.set_option('display.max_columns', 30)\n",
    "# Plotlay's drafting-film colours\n",
    "FILM, PAPER, INK, INK2, TEXT, MUTED, RED, TEAL, GOLD = '#f2efe6', '#faf8f1', '#1d4f91', '#163c6e', '#1f2a37', '#5b6573', '#cf3b26', '#2f9e8a', '#d9a21b'\n",
    "RULE = (29 / 255, 79 / 255, 145 / 255, .35)\n",
    "PART = {'New building': INK, 'Heritage building': TEAL}\n",
    "plt.rcParams.update({'figure.dpi': 110, 'font.size': 10, 'text.color': TEXT, 'axes.labelcolor': MUTED, 'xtick.color': MUTED, 'ytick.color': MUTED,\n",
    "                     'axes.edgecolor': RULE, 'figure.facecolor': FILM, 'axes.facecolor': FILM, 'savefig.facecolor': FILM,\n",
    "                     'axes.spines.top': False, 'axes.spines.right': False, 'axes.titlecolor': INK2, 'axes.titleweight': 'bold'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc67d13",
   "metadata": {},
   "source": [
    "## Sources and licence\n",
    "\n",
    "School site: NSW Cadastre lots (DCS Spatial Services), CC BY 3.0 AU. Podium and heritage building outlines:\n",
    "© OpenStreetMap contributors, ODbL 1.0, so `data/geometry/buildings.*` is under the ODbL 1.0. Tower outline: drawn over\n",
    "NSW Imagery (DCS Spatial Services), CC BY 3.0 AU. Headcounts: NSW Department of Education, CC BY. Approved floor areas:\n",
    "quoted from the NSW Planning Portal record for SSD 7610 (see `data/planning/README.md`). Our tables: CC BY 4.0. Full\n",
    "credits are in `NOTICE`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea1e766",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13362198",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 buildings; 1 site shape from 3 Cadastre lots; 2020 to 2025 headcounts\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>name</th>\n",
       "      <th>part</th>\n",
       "      <th>storeys</th>\n",
       "      <th>height_m</th>\n",
       "      <th>levels</th>\n",
       "      <th>in_use_from</th>\n",
       "      <th>outline_from</th>\n",
       "      <th>footprint_m2</th>\n",
       "      <th>floor_m2_est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P</td>\n",
       "      <td>New building, podium</td>\n",
       "      <td>New building</td>\n",
       "      <td>6</td>\n",
       "      <td>16.9</td>\n",
       "      <td>Basement, Lower Ground, Ground, Levels 1 to 3</td>\n",
       "      <td>2021</td>\n",
       "      <td>OpenStreetMap way/1178119661 (ODbL)</td>\n",
       "      <td>2102.2</td>\n",
       "      <td>12613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T</td>\n",
       "      <td>New building, tower</td>\n",
       "      <td>New building</td>\n",
       "      <td>8</td>\n",
       "      <td>48.3</td>\n",
       "      <td>Levels 4 to 11 (Level 4 mostly an open games t...</td>\n",
       "      <td>2021</td>\n",
       "      <td>Drawn over NSW Imagery, 22 January 2021</td>\n",
       "      <td>993.6</td>\n",
       "      <td>7948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H1</td>\n",
       "      <td>Heritage building 1</td>\n",
       "      <td>Heritage building</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3 storeys (OpenStreetMap)</td>\n",
       "      <td>2020</td>\n",
       "      <td>OpenStreetMap way/570103244 (ODbL)</td>\n",
       "      <td>653.2</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H2</td>\n",
       "      <td>Heritage building 2</td>\n",
       "      <td>Heritage building</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2 storeys (OpenStreetMap)</td>\n",
       "      <td>2020</td>\n",
       "      <td>OpenStreetMap way/570103245 (ODbL)</td>\n",
       "      <td>448.9</td>\n",
       "      <td>898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H3</td>\n",
       "      <td>Heritage building 3</td>\n",
       "      <td>Heritage building</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3 storeys (OpenStreetMap)</td>\n",
       "      <td>2020</td>\n",
       "      <td>OpenStreetMap way/570103241 (ODbL)</td>\n",
       "      <td>340.6</td>\n",
       "      <td>1022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>H4</td>\n",
       "      <td>Heritage building 4</td>\n",
       "      <td>Heritage building</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2 storeys (OpenStreetMap)</td>\n",
       "      <td>2020</td>\n",
       "      <td>OpenStreetMap way/570103246 (ODbL)</td>\n",
       "      <td>225.6</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key                  name               part  storeys  height_m                                             levels  in_use_from  \\\n",
       "0   P  New building, podium       New building        6      16.9      Basement, Lower Ground, Ground, Levels 1 to 3         2021   \n",
       "1   T   New building, tower       New building        8      48.3  Levels 4 to 11 (Level 4 mostly an open games t...         2021   \n",
       "2  H1   Heritage building 1  Heritage building        3      12.0                          3 storeys (OpenStreetMap)         2020   \n",
       "3  H2   Heritage building 2  Heritage building        2       8.0                          2 storeys (OpenStreetMap)         2020   \n",
       "4  H3   Heritage building 3  Heritage building        3      12.0                          3 storeys (OpenStreetMap)         2020   \n",
       "5  H4   Heritage building 4  Heritage building        2       8.0                          2 storeys (OpenStreetMap)         2020   \n",
       "\n",
       "                              outline_from  footprint_m2  floor_m2_est  \n",
       "0      OpenStreetMap way/1178119661 (ODbL)        2102.2         12613  \n",
       "1  Drawn over NSW Imagery, 22 January 2021         993.6          7948  \n",
       "2       OpenStreetMap way/570103244 (ODbL)         653.2          1960  \n",
       "3       OpenStreetMap way/570103245 (ODbL)         448.9           898  \n",
       "4       OpenStreetMap way/570103241 (ODbL)         340.6          1022  \n",
       "5       OpenStreetMap way/570103246 (ODbL)         225.6           451  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.read_csv(f'{DATA}/buildings/buildings.csv')\n",
    "enr = pd.read_csv(f'{DATA}/site/enrolments.csv')\n",
    "fiu = pd.read_csv(f'{DATA}/site/floor_in_use.csv')\n",
    "roll = pd.read_csv(f'{DATA}/site/site_rollup.csv')\n",
    "gfa = pd.read_csv(f'{DATA}/planning/approved_floor_areas.csv').set_index('component').m2\n",
    "shapes = gpd.read_file(f'{DATA}/geometry/buildings.geojson')\n",
    "site = gpd.read_file(f'{DATA}/geometry/school_site.geojson')\n",
    "assert b.key.is_unique and set(b.key) == set(shapes.key)\n",
    "print(len(b), 'buildings;', len(site), 'site shape from', len(roll.lots[0].split()), 'Cadastre lots;', enr.year.min(), 'to', enr.year.max(), 'headcounts')\n",
    "b[['key', 'name', 'part', 'storeys', 'height_m', 'levels', 'in_use_from', 'outline_from', 'footprint_m2', 'floor_m2_est']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3749920f",
   "metadata": {},
   "source": [
    "## The grounds\n",
    "\n",
    "The site's area, worked out two ways: geodesic (on the GRS80 ellipsoid) and in GDA2020 Australian Albers (EPSG:9473), an\n",
    "equal-area projection. (GDA2020 Lambert, EPSG:7845, is conformal and would not keep areas true.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc9786b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grounds: 5,687.3 m² in EPSG:9473, 5,687.3 m² geodesic (difference 0.0000%)\n",
      "Plotlay worked out 5,687.2 m²; the roll-up table says 5,687 m²\n",
      "2025: 1,161 students, 4.9 m² of grounds each\n"
     ]
    }
   ],
   "source": [
    "geod = Geod(ellps='GRS80')\n",
    "site_geo = sum(abs(geod.geometry_area_perimeter(g)[0]) for g in site.geometry)\n",
    "site_alb = site.to_crs(9473).geometry.area.sum()\n",
    "grounds = site_alb\n",
    "print(f'Grounds: {site_alb:,.1f} m² in EPSG:9473, {site_geo:,.1f} m² geodesic (difference {abs(site_alb / site_geo - 1):.4%})')\n",
    "print(f'Plotlay worked out {site.area_m2.sum():,.1f} m²; the roll-up table says {roll.grounds_m2[0]:,.0f} m²')\n",
    "hc = enr.set_index('year').students\n",
    "last = hc.index.max()\n",
    "print(f'{last}: {hc[last]:,} students, {grounds / hc[last]:.1f} m² of grounds each')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5be55b",
   "metadata": {},
   "source": [
    "That is the figure that makes the school the exception. In the Sydney schools showcase the median grounds per student in\n",
    "2025 was 34.7 m² across 14 compared high schools and 30.3 m² across 49 primary schools. Those schools mostly sit on one\n",
    "or two storeys over a playground; this one stacks 14 storeys on a small inner-city site. Land per student\n",
    "measures how much ground a school has, not how much room its students have, so for a vertical school the useful figure is\n",
    "floor space. The rest of this notebook works that out for this one school. It does not rank or compare its floor space\n",
    "against any other school."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9050ad3",
   "metadata": {},
   "source": [
    "## The buildings\n",
    "\n",
    "Six shapes. The new building is two of them: the podium **P** (the OpenStreetMap outline of the whole new building at\n",
    "ground level, Basement to Level 3, 6 storeys) and the tower **T** (its roof outline, Levels 4 to 11, 8 storeys) drawn\n",
    "on top of the podium. The four heritage buildings **H1** to **H4** are the former Cleveland Street school buildings,\n",
    "refurbished, with their storeys from OpenStreetMap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccfc9532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standing on another shape: T on P\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 825x715 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alb = shapes.to_crs(9473).set_index('key')\n",
    "pts = site.to_crs(9473)\n",
    "# which shapes stand on another one (most of their area inside it)\n",
    "on = {k: [o for o in alb.index if o != k and alb.geometry[k].intersection(alb.geometry[o]).area > .8 * alb.geometry[k].area] for k in alb.index}\n",
    "stacked = sorted(k for k, v in on.items() if v)\n",
    "print('Standing on another shape:', ', '.join(f'{k} on {on[k][0]}' for k in stacked))\n",
    "fig, ax = plt.subplots(figsize=(7.5, 6.5))\n",
    "pts.boundary.plot(ax=ax, color=INK, lw=1.5, ls='--')\n",
    "for k, r in alb.join(b.set_index('key')[['part']]).iterrows():\n",
    "    if k in stacked: continue\n",
    "    gpd.GeoSeries([r.geometry]).plot(ax=ax, color=PART[r.part], alpha=.55, edgecolor=INK2, lw=.8)\n",
    "for k in stacked:\n",
    "    gpd.GeoSeries([alb.geometry[k]]).plot(ax=ax, facecolor='none', edgecolor=RED, lw=1.6, hatch='///')\n",
    "for k, r in alb.iterrows():\n",
    "    g = r.geometry\n",
    "    for o in stacked:   # label a shape where nothing stands on it\n",
    "        if k in on[o]: g = g.difference(alb.geometry[o])\n",
    "    x, y = g.representative_point().coords[0]\n",
    "    ax.annotate(k, (x, y), ha='center', va='center', fontsize=10, fontweight='bold', color=RED if k in stacked else PAPER)\n",
    "ax.set_axis_off()\n",
    "ax.legend(handles=[plt.Rectangle((0, 0), 1, 1, fc=PART['New building'], alpha=.55), plt.Rectangle((0, 0), 1, 1, fc=PART['Heritage building'], alpha=.55),\n",
    "                   plt.Rectangle((0, 0), 1, 1, fc='none', ec=RED, hatch='///'), plt.Line2D([], [], color=INK, ls='--')],\n",
    "          labels=['New building (podium)', 'Heritage building', 'Tower, standing on the podium', 'Site (three Cadastre lots)'], fontsize=8, loc='lower left', frameon=False)\n",
    "ax.set_title('The site and its six building shapes', loc='left'); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f4127b7",
   "metadata": {},
   "source": [
    "### Floor space, building by building (estimated)\n",
    "\n",
    "Each building's floor estimate is its footprint times its storeys. The footprints are Plotlay's (`footprint_m2`); the\n",
    "same shapes measured here in EPSG:9473 are shown beside them as a check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3bc337a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Floor space, estimated: 24,893 m² (new building 20,562 m², heritage buildings 4,331 m²)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x352 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>footprint_m2</th>\n",
       "      <th>footprint_check_m2</th>\n",
       "      <th>storeys</th>\n",
       "      <th>floor_m2_est</th>\n",
       "      <th>floor_check_m2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P</td>\n",
       "      <td>2102.2</td>\n",
       "      <td>2102.2</td>\n",
       "      <td>6</td>\n",
       "      <td>12613</td>\n",
       "      <td>12613.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T</td>\n",
       "      <td>993.6</td>\n",
       "      <td>993.6</td>\n",
       "      <td>8</td>\n",
       "      <td>7948</td>\n",
       "      <td>7949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H1</td>\n",
       "      <td>653.2</td>\n",
       "      <td>653.2</td>\n",
       "      <td>3</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H2</td>\n",
       "      <td>448.9</td>\n",
       "      <td>448.9</td>\n",
       "      <td>2</td>\n",
       "      <td>898</td>\n",
       "      <td>898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H3</td>\n",
       "      <td>340.6</td>\n",
       "      <td>340.6</td>\n",
       "      <td>3</td>\n",
       "      <td>1022</td>\n",
       "      <td>1022.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>H4</td>\n",
       "      <td>225.6</td>\n",
       "      <td>225.6</td>\n",
       "      <td>2</td>\n",
       "      <td>451</td>\n",
       "      <td>451.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  footprint_m2  footprint_check_m2  storeys  floor_m2_est  floor_check_m2\n",
       "0   P        2102.2              2102.2        6         12613         12613.0\n",
       "1   T         993.6               993.6        8          7948          7949.0\n",
       "2  H1         653.2               653.2        3          1960          1960.0\n",
       "3  H2         448.9               448.9        2           898           898.0\n",
       "4  H3         340.6               340.6        3          1022          1022.0\n",
       "5  H4         225.6               225.6        2           451           451.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b['footprint_check_m2'] = b.key.map(alb.geometry.area).round(1)\n",
    "b['floor_check_m2'] = (b.footprint_m2 * b.storeys).round()\n",
    "floor_total = b.floor_check_m2.sum()\n",
    "new = b[b.part == 'New building'].floor_check_m2.sum(); her = b[b.part == 'Heritage building'].floor_check_m2.sum()\n",
    "print(f'Floor space, estimated: {floor_total:,.0f} m² (new building {new:,.0f} m², heritage buildings {her:,.0f} m²)')\n",
    "ax = b.set_index('key').floor_check_m2.plot.barh(color=b.part.map(PART), figsize=(8, 3.2)); ax.invert_yaxis()\n",
    "for i, (v, s, f) in enumerate(zip(b.floor_check_m2, b.storeys, b.footprint_m2)): ax.text(v + 120, i, f'{f:,.0f} m² x {s} = {v:,.0f} m²', va='center', fontsize=8, color=MUTED)\n",
    "ax.set_xlim(0, b.floor_check_m2.max() * 1.35); ax.set_xlabel('floor m² (estimated)'); ax.set_ylabel('')\n",
    "ax.set_title('Footprint times storeys, by building', loc='left'); plt.show()\n",
    "b[['key', 'footprint_m2', 'footprint_check_m2', 'storeys', 'floor_m2_est', 'floor_check_m2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ae87c6",
   "metadata": {},
   "source": [
    "### Rolled up to the site\n",
    "\n",
    "The site's footprint counts the podium and the heritage buildings, not the tower again: the tower stands on the podium,\n",
    "so its ground is already counted. Plotlay's roll-up of the shapes inside the site adds every shape, tower included; the\n",
    "difference is the tower's footprint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfd7b812",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site footprint: 3,770 m², 66% of the grounds (every shape added: 4,764.1 m², less the tower 993.6 m²)\n",
      "Floor over grounds: 4.38 (floor space, estimated, over the site area)\n",
      "Floor over the site footprint: 6.60\n"
     ]
    }
   ],
   "source": [
    "fp_site = b[~b.key.isin(stacked)].footprint_m2.sum()\n",
    "fp_all = site.footprint_m2.sum()\n",
    "print(f'Site footprint: {fp_site:,.0f} m², {fp_site / grounds:.0%} of the grounds (every shape added: {fp_all:,.1f} m², less the tower {b.set_index(\"key\").footprint_m2[stacked].sum():,.1f} m²)')\n",
    "print(f'Floor over grounds: {floor_total / grounds:.2f} (floor space, estimated, over the site area)')\n",
    "print(f'Floor over the site footprint: {floor_total / fp_site:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46e3ee75",
   "metadata": {},
   "source": [
    "## Floor space per student, year by year\n",
    "\n",
    "The heritage buildings were in use from 2020; the new building from Term 1 2021. Floor in use each year is the floor\n",
    "estimate of the buildings in use that year. `data/site/floor_in_use.csv` carries the same figure from the plan; it is\n",
    "compared below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8314a2f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Floor in use against the table: largest difference 0.02%\n",
      "At capacity (1,200 students): 20.7 m² of floor space each (estimated)\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>students</th>\n",
       "      <th>year_groups</th>\n",
       "      <th>floor_in_use_m2</th>\n",
       "      <th>floor_in_use_table_m2</th>\n",
       "      <th>floor_per_student</th>\n",
       "      <th>grounds_per_student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>147</td>\n",
       "      <td>Year 7</td>\n",
       "      <td>4331.0</td>\n",
       "      <td>4330</td>\n",
       "      <td>29.5</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>358</td>\n",
       "      <td>Years 7 and 8</td>\n",
       "      <td>24893.0</td>\n",
       "      <td>24888</td>\n",
       "      <td>69.5</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>591</td>\n",
       "      <td>Years 7 to 9</td>\n",
       "      <td>24893.0</td>\n",
       "      <td>24888</td>\n",
       "      <td>42.1</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>797</td>\n",
       "      <td>Years 7 to 10</td>\n",
       "      <td>24893.0</td>\n",
       "      <td>24888</td>\n",
       "      <td>31.2</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>999</td>\n",
       "      <td>Years 7 to 11</td>\n",
       "      <td>24893.0</td>\n",
       "      <td>24888</td>\n",
       "      <td>24.9</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>1161</td>\n",
       "      <td>Years 7 to 12</td>\n",
       "      <td>24893.0</td>\n",
       "      <td>24888</td>\n",
       "      <td>21.4</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      students    year_groups  floor_in_use_m2  floor_in_use_table_m2  floor_per_student  grounds_per_student\n",
       "2020       147         Year 7           4331.0                   4330               29.5                 38.7\n",
       "2021       358  Years 7 and 8          24893.0                  24888               69.5                 15.9\n",
       "2022       591   Years 7 to 9          24893.0                  24888               42.1                  9.6\n",
       "2023       797  Years 7 to 10          24893.0                  24888               31.2                  7.1\n",
       "2024       999  Years 7 to 11          24893.0                  24888               24.9                  5.7\n",
       "2025      1161  Years 7 to 12          24893.0                  24888               21.4                  4.9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yrs = hc.index\n",
    "use = pd.Series({y: b[b.in_use_from <= y].floor_check_m2.sum() for y in yrs}, name='floor_in_use_m2')\n",
    "tbl = fiu.set_index('year').floor_in_use_m2_est\n",
    "per = pd.DataFrame({'students': hc, 'year_groups': enr.set_index('year').year_groups, 'floor_in_use_m2': use,\n",
    "                    'floor_in_use_table_m2': tbl, 'floor_per_student': (use / hc).round(1), 'grounds_per_student': (grounds / hc).round(1)})\n",
    "print(f'Floor in use against the table: largest difference {(tbl / use - 1).abs().max():.2%}')\n",
    "cap = int(roll.capacity[0])\n",
    "print(f'At capacity ({cap:,} students): {floor_total / cap:.1f} m² of floor space each (estimated)')\n",
    "per"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9423afcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x572 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(9, 5.2), sharex=True, gridspec_kw={'height_ratios': [1, 1.3]})\n",
    "ax[0].bar(per.index, per.students, color=INK, width=.6)\n",
    "for y, v in per.students.items(): ax[0].text(y, v - 25, f'{v:,}', ha='center', va='top', fontsize=8, color=PAPER)\n",
    "ax[0].axhline(cap, color=RED, lw=1, ls='--'); ax[0].text(per.index.min() - .4, cap + 30, f'built for up to {cap:,}', fontsize=8, color=RED)\n",
    "ax[0].set_ylim(0, cap * 1.18); ax[0].set_ylabel('students (headcount)')\n",
    "ax[1].plot(per.index, per.floor_per_student, color=INK, marker='o', label='floor space (estimated)')\n",
    "ax[1].plot(per.index, per.grounds_per_student, color=GOLD, marker='s', label='grounds')\n",
    "for y, v in per.floor_per_student.items(): ax[1].annotate(f'{v:.1f}', (y, v), textcoords='offset points', xytext=(0, -14 if per.grounds_per_student[y] > v else 7), ha='center', fontsize=8, color=INK)\n",
    "ax[1].set_ylim(0, per.floor_per_student.max() * 1.2); ax[1].set_ylabel('m² per student'); ax[1].legend(frameon=False, fontsize=8)\n",
    "ax[0].set_title('A staged opening: one year group a year', loc='left'); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450ec084",
   "metadata": {},
   "source": [
    "The 2021 peak is the staged opening, not a measure of comfort: the whole new building opened to two year groups. The\n",
    "figure falls each year as a year group is added, and 2025 is the first year with all six."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd9929de",
   "metadata": {},
   "source": [
    "## The estimate against the approved floor areas\n",
    "\n",
    "The approved plans (SSD 7610, the GFA area schedule) list the gross floor area by part. GFA is measured from the inside\n",
    "of the outer walls and leaves out stairs, lifts, plant and open areas; this schedule counts covered outdoor terraces as\n",
    "GFA in their own line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66563c85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approved GFA: 18,154.0 m² in all, of which 5,538.3 m² covered outdoor terraces; enclosed 12,615.7 m²\n",
      "Estimate over approved GFA: 1.37 times the total, 1.97 times the enclosed GFA\n",
      "New building: estimate 20,562 m² against 14,740.9 m² approved (enclosed and terraces), 1.39 times\n",
      "Heritage buildings: estimate 4,331 m² against 3,413.1 m² approved, 1.27 times\n",
      "2025, per student: 21.4 m² estimated; approved GFA 15.6 m² in all, 10.9 m² enclosed\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x286 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = {'new': gfa['New building, enclosed GFA'], 'terraces': gfa['Covered outdoor terraces counted as GFA'], 'heritage': gfa['Heritage buildings 1 to 3, GFA'],\n",
    "     'total': gfa['Total GFA on the schedule'], 'enclosed': gfa['Enclosed GFA (new and heritage), derived']}\n",
    "y = last\n",
    "print(f'Approved GFA: {G[\"total\"]:,.1f} m² in all, of which {G[\"terraces\"]:,.1f} m² covered outdoor terraces; enclosed {G[\"enclosed\"]:,.1f} m²')\n",
    "print(f'Estimate over approved GFA: {floor_total / G[\"total\"]:.2f} times the total, {floor_total / G[\"enclosed\"]:.2f} times the enclosed GFA')\n",
    "print(f'New building: estimate {new:,.0f} m² against {G[\"new\"] + G[\"terraces\"]:,.1f} m² approved (enclosed and terraces), {new / (G[\"new\"] + G[\"terraces\"]):.2f} times')\n",
    "print(f'Heritage buildings: estimate {her:,.0f} m² against {G[\"heritage\"]:,.1f} m² approved, {her / G[\"heritage\"]:.2f} times')\n",
    "print(f'{y}, per student: {floor_total / hc[y]:.1f} m² estimated; approved GFA {G[\"total\"] / hc[y]:.1f} m² in all, {G[\"enclosed\"] / hc[y]:.1f} m² enclosed')\n",
    "cmp = pd.Series({'Floor space, estimated\\n(every storey over each footprint)': floor_total, 'Approved GFA, total\\n(incl. covered terraces)': G['total'], 'Approved GFA, enclosed': G['enclosed']})\n",
    "ax = (cmp / hc[y]).plot.barh(color=[INK, GOLD, TEAL], figsize=(8, 2.6)); ax.invert_yaxis()\n",
    "for i, v in enumerate(cmp / hc[y]): ax.text(v + .3, i, f'{v:.1f} m²', va='center', fontsize=9)\n",
    "ax.set_xlim(0, (cmp / hc[y]).max() * 1.2); ax.set_xlabel(f'm² per student, {y}')\n",
    "ax.set_title('Three ways to count the floor', loc='left'); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e84a890",
   "metadata": {},
   "source": [
    "## Read it carefully\n",
    "\n",
    "- **The method.** Floor space is each building's footprint times its storeys: a gross floor plate figure. It counts\n",
    "  every storey over the whole footprint, including the open undercroft, covered and open terraces (Level 4 is mostly an\n",
    "  open games terrace), stairs, lifts, plant rooms and the walls themselves. That is why it is larger than the approved\n",
    "  GFA, by the ratios worked out above. Read it as an upper bound of the space\n",
    "  students use; the approved enclosed GFA per student is the conservative figure.\n",
    "- **The podium** uses the OpenStreetMap outline of the whole new building at ground level for all six of its storeys\n",
    "  (Basement to Level 3). Floors that step in, and the basement's actual extent, are not known from the outline.\n",
    "- **The tower** is a roof outline drawn over an aerial photo. A tall building is shown a little displaced in an aerial\n",
    "  photo, so the outline is approximate; it is the figure most likely to move between runs, and a few per cent of its\n",
    "  footprint is a few hundred square metres of floor over eight storeys.\n",
    "- **The heritage buildings** take their storeys from OpenStreetMap (`building:levels`); their heights in the data are\n",
    "  storeys times 4 m, an estimate. The approved schedule numbers three heritage buildings while OpenStreetMap draws four\n",
    "  outlines, so the heritage comparison is approximate.\n",
    "- **Students** are the February headcount, not full-time equivalent, for the school as a whole. Nothing here splits\n",
    "  students across buildings.\n",
    "- **Accuracy** depends on the drawings, imagery and outlines used. Where a figure has to stand up for a planning or\n",
    "  contract purpose, use the approved plans and a measured survey."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e555d84e",
   "metadata": {},
   "source": [
    "## Checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d61cbcfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All checks pass.\n"
     ]
    }
   ],
   "source": [
    "fps = (use / hc).round(1)\n",
    "assert abs(site_alb / site_geo - 1) < .001, 'EPSG:9473 and geodesic areas agree'\n",
    "assert abs(site_alb - roll.grounds_m2[0]) < 1.5 and abs(site_alb - site.area_m2.sum()) < 1.5, 'grounds as Plotlay worked them out'\n",
    "assert stacked == ['T'] and on['T'] == ['P'], 'only the tower stands on another shape, the podium'\n",
    "assert ((b.footprint_check_m2 / b.footprint_m2 - 1).abs() < .01).all(), 'footprints match the shapes'\n",
    "assert ((b.floor_m2_est - b.floor_check_m2).abs() <= 1).all(), 'floor estimate is footprint times storeys'\n",
    "assert 24500 <= floor_total <= 25600, floor_total\n",
    "assert abs(floor_total - roll.floor_m2_est[0]) <= 1 and abs(fp_site - roll.footprint_m2[0]) <= 2, 'the site roll-up'\n",
    "assert abs(fp_all - fp_site - b.set_index('key').footprint_m2['T']) < 2, 'every-shape roll-up less the tower'\n",
    "assert list(yrs) == list(range(yrs.min(), yrs.max() + 1)) and hc[last] == roll.students_2025[0]\n",
    "assert ((tbl / use - 1).abs() < .03).all(), 'the floor in use table'\n",
    "assert 21 <= fps[2025] <= 22.2, fps[2025]\n",
    "assert fps.idxmax() == 2021 and fps.loc[2021:].is_monotonic_decreasing, 'the staged opening'\n",
    "assert abs(G['new'] + G['terraces'] + G['heritage'] - G['total']) < .2 and abs(G['new'] + G['heritage'] - G['enclosed']) < .2, 'the GFA schedule adds up'\n",
    "assert G['enclosed'] < G['total'] < floor_total, 'the estimate counts more than the GFA'\n",
    "print('All checks pass.')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
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