{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73e912f2",
   "metadata": {},
   "source": [
    "# One school campus, building by building: University High School, Parkville\n",
    "\n",
    "What does one shape per school hide? One school drawn as its buildings, rolled up to the site, with estimated floor\n",
    "space per student.\n",
    "\n",
    "The other school showcases draw one shape per school and divide its grounds (the land it sits on, buildings included)\n",
    "by its students. That is a site-level figure. This notebook takes one campus and reports it building by building, then\n",
    "rolls the buildings back up to the site, so the two can be compared. Students are February FTE enrolments (Victorian\n",
    "Department of Education), not a headcount.\n",
    "\n",
    "The shapes in `data/geometry/` were made in [Plotlay](https://www.getplotlay.com): the site from the City of Melbourne's\n",
    "two property parcels (University High School and the Elizabeth Blackburn School of Sciences), and each building picked\n",
    "from the council's 2023 building footprints, the parts of one structure joined by `structure_id`. The site's roll-up\n",
    "(how many buildings, their footprint and floor area) was worked out in Plotlay from the buildings that stand inside it.\n",
    "\n",
    "It reads only the files in `../data`. Enrolments are the school's: they are divided at site level only, never split\n",
    "across buildings. Floor space is an estimate from the council's heights, said wherever it is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbd86b4c",
   "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",
    "\n",
    "DATA = '../data'\n",
    "pd.set_option('display.width', 160); pd.set_option('display.max_columns', 30)\n",
    "INK, MUTED = '#1d4f91', '#5b6573'\n",
    "ERA = {'1930, built (CLUE)': '#E69F00', '2014, built (CLUE)': '#56B4E9', 'On the 2014 outlines': '#009E73', 'Shown from the 2018 footprints': '#F0E442', 'Shown from the 2023 footprints': '#0072B2'}\n",
    "plt.rcParams.update({'figure.dpi': 110, 'axes.spines.top': False, 'axes.spines.right': False, 'font.size': 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e23334",
   "metadata": {},
   "source": [
    "## Sources and licence\n",
    "\n",
    "Building outlines 2014 and 2015, building footprints 2018, 2020 and 2023, property boundaries and the Census of Land Use\n",
    "and Employment (CLUE) building information: City of Melbourne Open Data, CC BY 4.0. February FTE enrolments: Victorian\n",
    "Department of Education, All Schools FTE Enrolments (DataVic), CC BY 4.0. Nothing comes from OpenStreetMap or aerial\n",
    "imagery. Full credits are in `NOTICE`. `data/sample/` is sample data and is used only in the last section, labelled so."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a475ed1",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "652dcfaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 buildings; 1 site shape in 2 parts; 2007 to 2026 enrolments\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>structure_id</th>\n",
       "      <th>footprint_m2</th>\n",
       "      <th>height_m</th>\n",
       "      <th>roof</th>\n",
       "      <th>first_mapped</th>\n",
       "      <th>era</th>\n",
       "      <th>storeys_est</th>\n",
       "      <th>storeys_from</th>\n",
       "      <th>floor_m2_est</th>\n",
       "      <th>clue_construction_year</th>\n",
       "      <th>clue_floors</th>\n",
       "      <th>clue_accessibility_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B01</td>\n",
       "      <td>811846</td>\n",
       "      <td>6433.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Flat/Hip</td>\n",
       "      <td>2014</td>\n",
       "      <td>1930, built (CLUE)</td>\n",
       "      <td>3</td>\n",
       "      <td>CLUE floors (cap)</td>\n",
       "      <td>17939</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B02</td>\n",
       "      <td>811844</td>\n",
       "      <td>883.3</td>\n",
       "      <td>13.5</td>\n",
       "      <td>Flat/Gable/Hip</td>\n",
       "      <td>2014</td>\n",
       "      <td>On the 2014 outlines</td>\n",
       "      <td>3</td>\n",
       "      <td>height</td>\n",
       "      <td>1960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B03</td>\n",
       "      <td>811043</td>\n",
       "      <td>835.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>2015</td>\n",
       "      <td>2014, built (CLUE)</td>\n",
       "      <td>3</td>\n",
       "      <td>CLUE floors (cap)</td>\n",
       "      <td>2386</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B04</td>\n",
       "      <td>900635</td>\n",
       "      <td>204.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2023</td>\n",
       "      <td>Shown from the 2023 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B05</td>\n",
       "      <td>900636</td>\n",
       "      <td>202.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2023</td>\n",
       "      <td>Shown from the 2023 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B06</td>\n",
       "      <td>901093</td>\n",
       "      <td>198.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2023</td>\n",
       "      <td>Shown from the 2023 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>199</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B07</td>\n",
       "      <td>900641</td>\n",
       "      <td>197.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2023</td>\n",
       "      <td>Shown from the 2023 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>199</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B08</td>\n",
       "      <td>900645</td>\n",
       "      <td>197.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2023</td>\n",
       "      <td>Shown from the 2023 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B09</td>\n",
       "      <td>900644</td>\n",
       "      <td>194.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2023</td>\n",
       "      <td>Shown from the 2023 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B10</td>\n",
       "      <td>811842</td>\n",
       "      <td>71.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>2014</td>\n",
       "      <td>On the 2014 outlines</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B11</td>\n",
       "      <td>811847</td>\n",
       "      <td>38.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>2018</td>\n",
       "      <td>Shown from the 2018 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B12</td>\n",
       "      <td>811845</td>\n",
       "      <td>13.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2018</td>\n",
       "      <td>Shown from the 2018 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B13</td>\n",
       "      <td>811843</td>\n",
       "      <td>12.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2018</td>\n",
       "      <td>Shown from the 2018 footprints</td>\n",
       "      <td>1</td>\n",
       "      <td>height</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    key structure_id  footprint_m2  height_m            roof  first_mapped                             era  storeys_est       storeys_from  floor_m2_est  \\\n",
       "0   B01       811846        6433.7      18.0        Flat/Hip          2014              1930, built (CLUE)            3  CLUE floors (cap)         17939   \n",
       "1   B02       811844         883.3      13.5  Flat/Gable/Hip          2014            On the 2014 outlines            3             height          1960   \n",
       "2   B03       811043         835.8      18.0            Flat          2015              2014, built (CLUE)            3  CLUE floors (cap)          2386   \n",
       "3   B04       900635         204.6       4.0           Gable          2023  Shown from the 2023 footprints            1             height           205   \n",
       "4   B05       900636         202.0       3.8           Gable          2023  Shown from the 2023 footprints            1             height           203   \n",
       "5   B06       901093         198.5       3.7           Gable          2023  Shown from the 2023 footprints            1             height           199   \n",
       "6   B07       900641         197.8       3.7           Gable          2023  Shown from the 2023 footprints            1             height           199   \n",
       "7   B08       900645         197.4       3.8           Gable          2023  Shown from the 2023 footprints            1             height           198   \n",
       "8   B09       900644         194.8       3.9           Gable          2023  Shown from the 2023 footprints            1             height           195   \n",
       "9   B10       811842          71.8       3.0            Flat          2014            On the 2014 outlines            1             height            72   \n",
       "10  B11       811847          38.2       4.5            Flat          2018  Shown from the 2018 footprints            1             height            39   \n",
       "11  B12       811845          13.5       3.0           Gable          2018  Shown from the 2018 footprints            1             height            14   \n",
       "12  B13       811843          12.7       2.5           Gable          2018  Shown from the 2018 footprints            1             height            15   \n",
       "\n",
       "    clue_construction_year  clue_floors  clue_accessibility_rating  \n",
       "0                   1930.0          3.0                        2.0  \n",
       "1                      NaN          NaN                        NaN  \n",
       "2                   2014.0          3.0                        3.0  \n",
       "3                      NaN          NaN                        NaN  \n",
       "4                      NaN          NaN                        NaN  \n",
       "5                      NaN          NaN                        NaN  \n",
       "6                      NaN          NaN                        NaN  \n",
       "7                      NaN          NaN                        NaN  \n",
       "8                      NaN          NaN                        NaN  \n",
       "9                      NaN          NaN                        NaN  \n",
       "10                     NaN          NaN                        NaN  \n",
       "11                     NaN          NaN                        NaN  \n",
       "12                     NaN          NaN                        NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.read_csv(f'{DATA}/buildings/buildings.csv', dtype={'structure_id': str})\n",
    "fy = pd.read_csv(f'{DATA}/buildings/footprint_by_year.csv')\n",
    "enr = pd.read_csv(f'{DATA}/site/enrolments.csv')\n",
    "clue = pd.read_csv(f'{DATA}/site/clue_records.csv')\n",
    "roll = pd.read_csv(f'{DATA}/site/site_rollup.csv')\n",
    "shapes = gpd.read_file(f'{DATA}/geometry/campus_buildings.geojson').to_crs(7855)\n",
    "site = gpd.read_file(f'{DATA}/geometry/campus_site.geojson').to_crs(7855)\n",
    "assert b.key.is_unique and set(b.key) == set(shapes.key)\n",
    "print(len(b), 'buildings;', len(site), 'site shape in', int(roll.parts[0]), 'parts;', enr.year.min(), 'to', enr.year.max(), 'enrolments')\n",
    "b[['key', 'structure_id', 'footprint_m2', 'height_m', 'roof', 'first_mapped', 'era', 'storeys_est', 'storeys_from', 'floor_m2_est', 'clue_construction_year', 'clue_floors', 'clue_accessibility_rating']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c8b7fce",
   "metadata": {},
   "source": [
    "## The site as one shape\n",
    "\n",
    "What a one-shape-per-school map knows: the site's area and the school's enrolment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65f321ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 24,233 m² (Plotlay worked out 24,233 m²)\n",
      "2007: 1,209.6 students, 20.0 m² of grounds each\n",
      "2025: 1,983.4 students, 12.2 m² of grounds each\n",
      "Enrolment 2007 to 2026: +65%\n"
     ]
    }
   ],
   "source": [
    "site_m2 = site.geometry.area.sum()\n",
    "fte = enr.set_index('year').fte_students\n",
    "print(f'Site: {site_m2:,.0f} m² (Plotlay worked out {roll.area_m2[0]:,.0f} m²)')\n",
    "for y in (fte.index.min(), 2025):\n",
    "    print(f'{y}: {fte[y]:,.1f} students, {site_m2 / fte[y]:.1f} m² of grounds each')\n",
    "print(f'Enrolment {fte.index.min()} to {fte.index.max()}: {fte.iloc[-1] / fte.iloc[0] - 1:+.0%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32fc272e",
   "metadata": {},
   "source": [
    "The council's land use census (CLUE) has one record for the school's property in every census year, and one for the\n",
    "science school since it opened. It describes a building, not the campus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44c3ef80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>first</th>\n",
       "      <th>last</th>\n",
       "      <th>years</th>\n",
       "      <th>name</th>\n",
       "      <th>built</th>\n",
       "      <th>floors</th>\n",
       "      <th>access</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clue_property_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109209</th>\n",
       "      <td>2002</td>\n",
       "      <td>2024</td>\n",
       "      <td>23</td>\n",
       "      <td>University High School</td>\n",
       "      <td>1930</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674741</th>\n",
       "      <td>2016</td>\n",
       "      <td>2024</td>\n",
       "      <td>9</td>\n",
       "      <td>Building 405 Elizabeth Blackburn</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  first  last  years                              name  built  floors  access\n",
       "clue_property_id                                                                             \n",
       "109209             2002  2024     23            University High School   1930       3     2.0\n",
       "674741             2016  2024      9  Building 405 Elizabeth Blackburn   2014       3     3.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clue.groupby('clue_property_id').agg(first=('census_year', 'min'), last=('census_year', 'max'), years=('census_year', 'nunique'), name=('building_name', 'last'), built=('construction_year', 'last'), floors=('floors', 'last'), access=('accessibility_rating', 'last'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad3163ec",
   "metadata": {},
   "source": [
    "## Building by building\n",
    "\n",
    "The council's 2023 footprints show the buildings on the site. `height_m` is the top of a building's highest part above\n",
    "the lowest base of the structure, as the council measures it (on a sloping site the uphill side reads a little taller).\n",
    "Storeys and floor area are estimated: height divided by 4 m floor to floor, 2 m taken off a pitched roof, never more than\n",
    "the CLUE floors where the census has a record for that building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31150e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x605 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5.5))\n",
    "site.boundary.plot(ax=ax, color=INK, lw=1.5, ls='--')\n",
    "sh = shapes.merge(b[['key', 'era']], on='key')\n",
    "for era, g in sh.groupby('era'): g.plot(ax=ax, color=ERA[era], edgecolor=INK, lw=.6, label=era)\n",
    "for _, r in sh.iterrows():\n",
    "    if r.geometry.area > 150: ax.annotate(r.key, r.geometry.representative_point().coords[0], ha='center', fontsize=8)\n",
    "ax.set_axis_off(); ax.legend(handles=[plt.Rectangle((0, 0), 1, 1, fc=ERA[e]) for e in ERA if e in set(sh.era)], labels=[e for e in ERA if e in set(sh.era)], fontsize=8, loc='lower right')\n",
    "ax.set_title('Buildings by era: the CLUE year built, else the first council layer that shows them'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05437c90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 buildings: footprint 9,484 m² (39% of the site), floor space 23,624 m² (estimated)\n",
      "Floor over footprint: 2.49; floor over grounds: 0.97\n",
      "Share of the floor: B01 76%, B03 10%, B02 8%, B04 1%\n"
     ]
    }
   ],
   "source": [
    "fp_total, floor_total = b.footprint_m2.sum(), b.floor_m2_est.sum()\n",
    "assert abs(fp_total - roll.footprint_m2[0]) < 2 and abs(floor_total - roll.floor_m2_est[0]) < 1   # Plotlay's roll-up\n",
    "print(f'{len(b)} buildings: footprint {fp_total:,.0f} m² ({fp_total / site_m2:.0%} of the site), floor space {floor_total:,.0f} m² (estimated)')\n",
    "print(f'Floor over footprint: {floor_total / fp_total:.2f}; floor over grounds: {floor_total / site_m2:.2f}')\n",
    "share = (b.set_index('key').floor_m2_est / floor_total).sort_values(ascending=False)\n",
    "print('Share of the floor:', ', '.join(f'{k} {v:.0%}' for k, v in share.head(4).items()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d30aa9e",
   "metadata": {},
   "source": [
    "## Grounds are not floor space (site level, 2025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "758d0b2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x308 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "grounds (the site)         12.2\n",
       "footprints                  4.8\n",
       "floor space (estimated)    11.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = 2025\n",
    "per = pd.Series({'grounds (the site)': site_m2, 'footprints': fp_total, 'floor space (estimated)': floor_total}) / fte[y]\n",
    "ax = per.plot.barh(color=[INK, '#7fb0d8', '#E69F00'], figsize=(8, 2.8)); ax.invert_yaxis()\n",
    "ax.set_xlabel(f'm² per student, {y}')\n",
    "for i, v in enumerate(per): ax.text(v + .2, i, f'{v:.1f}', va='center')\n",
    "plt.show(); per.round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b91b8b4",
   "metadata": {},
   "source": [
    "## When the buildings appeared\n",
    "\n",
    "Each building's footprint on the council's snapshots (blank: not on that layer). The six single-storey buildings of\n",
    "about 200 m² are on the 2023 footprints (captured January 2022) and not on the 2020 ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f913559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year\n",
      "2014     3\n",
      "2015     4\n",
      "2018     7\n",
      "2020     7\n",
      "2023    13\n",
      "Name: buildings shown, dtype: int64\n",
      "New between the 2020 and 2023 footprints: 6 buildings, 1,195 m²; enrolment 1,662 in 2020, 1,983 in 2025\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wide = fy.pivot(index='key', columns='year', values='footprint_m2')\n",
    "print(wide.notna().sum().rename('buildings shown'))\n",
    "new = wide[wide[2020].isna() & wide[2023].notna()]\n",
    "print(f'New between the 2020 and 2023 footprints: {len(new)} buildings, {new[2023].sum():,.0f} m²; enrolment {fte[2020]:,.0f} in 2020, {fte[2025]:,.0f} in 2025')\n",
    "fig, ax = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n",
    "ax[0].plot(fte.index, fte.values, color=INK, marker='o'); ax[0].set_ylabel('students (FTE)'); ax[0].set_ylim(0, None)\n",
    "ax[1].bar(wide.columns, wide.sum(), color='#E69F00'); ax[1].set_ylabel('footprint m²')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6606e51",
   "metadata": {},
   "source": [
    "## Accessibility, where it is recorded\n",
    "\n",
    "CLUE rates the accessibility of the buildings it records (0 not rated to 3 high). On this campus that is two of the\n",
    "thirteen buildings; the rest have no record at all, which is itself the finding: a site-level record cannot say which\n",
    "building a wheelchair user can get into."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a79e17e",
   "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>key</th>\n",
       "      <th>clue_building_name</th>\n",
       "      <th>clue_accessibility_rating</th>\n",
       "      <th>clue_accessibility_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B01</td>\n",
       "      <td>University High School</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Moderate level of accessibility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B02</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B03</td>\n",
       "      <td>Building 405 Elizabeth Blackburn</td>\n",
       "      <td>3.0</td>\n",
       "      <td>High level of accessibility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B04</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B05</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B06</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B07</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B08</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B09</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B10</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B11</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B12</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B13</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "      <td>no CLUE record</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    key                clue_building_name clue_accessibility_rating          clue_accessibility_type\n",
       "0   B01            University High School                       2.0  Moderate level of accessibility\n",
       "1   B02                    no CLUE record            no CLUE record                   no CLUE record\n",
       "2   B03  Building 405 Elizabeth Blackburn                       3.0      High level of accessibility\n",
       "3   B04                    no CLUE record            no CLUE record                   no CLUE record\n",
       "4   B05                    no CLUE record            no CLUE record                   no CLUE record\n",
       "5   B06                    no CLUE record            no CLUE record                   no CLUE record\n",
       "6   B07                    no CLUE record            no CLUE record                   no CLUE record\n",
       "7   B08                    no CLUE record            no CLUE record                   no CLUE record\n",
       "8   B09                    no CLUE record            no CLUE record                   no CLUE record\n",
       "9   B10                    no CLUE record            no CLUE record                   no CLUE record\n",
       "10  B11                    no CLUE record            no CLUE record                   no CLUE record\n",
       "11  B12                    no CLUE record            no CLUE record                   no CLUE record\n",
       "12  B13                    no CLUE record            no CLUE record                   no CLUE record"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[['key', 'clue_building_name', 'clue_accessibility_rating', 'clue_accessibility_type']].astype(object).fillna('no CLUE record')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afb02f91",
   "metadata": {},
   "source": [
    "## Sample data: facilities on the same buildings\n",
    "\n",
    "Everything from here on reads `data/sample/`, which is sample data: the figures are not about University High School\n",
    "or any real building. They show what a facilities team links once each building is a keyed shape: the asset register,\n",
    "risk, work orders, planned maintenance, essential safety measures, the asbestos register, meters and the capital plan,\n",
    "in the shape a CAFM or CMMS exports them. `data/sample/README.md` is the data dictionary and says how each column was\n",
    "drawn. Nothing above uses these tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "27c75deb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample data: 226 assets, 40 at high or extreme risk; 36 work orders open, 17 overdue; planned maintenance on time 86.9%; essential safety measures overdue 7 of 50; backlog A$3,922,000\n"
     ]
    }
   ],
   "source": [
    "S = f'{DATA}/sample'\n",
    "assets = pd.read_csv(f'{S}/assets.csv'); risk = pd.read_csv(f'{S}/asset_risk.csv')\n",
    "wo = pd.read_csv(f'{S}/work_orders.csv', parse_dates=['raised', 'target', 'completed'])\n",
    "pm = pd.read_csv(f'{S}/pm_compliance.csv'); esm = pd.read_csv(f'{S}/esm_register.csv'); asb = pd.read_csv(f'{S}/asbestos_register.csv')\n",
    "cap = pd.read_csv(f'{S}/capital_works.csv'); summary = pd.read_csv(f'{S}/building_summary.csv'); space = pd.read_csv(f'{S}/space_use.csv')\n",
    "for t in (assets, risk, wo, pm, esm, asb, cap, summary, space): assert (t.data_status == 'Sample data').all()\n",
    "assert set(summary.key) == set(b.key) and set(assets.building.dropna()) <= set(b.key)\n",
    "ar = assets.merge(risk[['asset_id', 'likelihood_1to5', 'consequence_1to5', 'risk_score', 'risk_rating', 'risk_drivers']], on='asset_id')\n",
    "open_wo = wo[~wo.status.isin(['Completed', 'Cancelled, missed'])]\n",
    "print(f\"Sample data: {len(assets)} assets, {(risk.risk_score >= 12).sum()} at high or extreme risk; {len(open_wo)} work orders open, {(wo.overdue == 'Yes').sum()} overdue;\",\n",
    "      f\"planned maintenance on time {100 * pm.on_time.sum() / pm.scheduled.sum():.1f}%; essential safety measures overdue {(esm.status == 'Overdue').sum()} of {len(esm)}; backlog A${cap.estimate_aud.sum():,.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462aa5f1",
   "metadata": {},
   "source": [
    "### The register inside the buildings\n",
    "\n",
    "Each asset is a point. A spatial join puts it in the building whose outline holds it: the same roll-up Plotlay does\n",
    "from the assets layer into the buildings layer. The two hydrants in the grounds fall in no building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b8fb53d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the grounds: 2 assets\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x605 with 2 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>assets</th>\n",
       "      <th>high_or_extreme</th>\n",
       "      <th>highest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B01</th>\n",
       "      <td>109</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B02</th>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B03</th>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B04</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B05</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B06</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B07</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B08</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B09</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B10</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     assets  high_or_extreme  highest\n",
       "key                                  \n",
       "B01     109               26       20\n",
       "B02      26                6       20\n",
       "B03      50                7       20\n",
       "B04       6                0        3\n",
       "B05       6                0        6\n",
       "B06       6                0        6\n",
       "B07       6                0        3\n",
       "B08       6                0        6\n",
       "B09       6                0        3\n",
       "B10       2                0        6\n",
       "B11       1                0        6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = gpd.GeoDataFrame(ar, geometry=gpd.points_from_xy(ar.lng, ar.lat), crs=4326).to_crs(7855)\n",
    "joined = gpd.sjoin(pts, shapes[['key', 'geometry']], how='left', predicate='within')\n",
    "per = joined.groupby('key').agg(assets=('asset_id', 'count'), high_or_extreme=('risk_score', lambda s: (s >= 12).sum()), highest=('risk_score', 'max'))\n",
    "check = per.merge(summary.set_index('key')[['assets', 'risk_high_or_extreme']].add_suffix('_table'), left_index=True, right_index=True)\n",
    "assert (check.assets == check.assets_table).all() and (check.high_or_extreme == check.risk_high_or_extreme_table).all()\n",
    "print('In the grounds:', joined.key.isna().sum(), 'assets')\n",
    "fig, ax = plt.subplots(figsize=(10, 5.5))\n",
    "sh = shapes.merge(summary[['key', 'risk_high_or_extreme']], on='key')\n",
    "sh.plot(ax=ax, column='risk_high_or_extreme', cmap='Oranges', edgecolor=INK, lw=.6, legend=True, legend_kwds={'label': 'Assets at high or extreme risk (Sample data)', 'shrink': .6})\n",
    "col = {'Low': '#cfd8e3', 'Medium': '#f3c46b', 'High': '#e07a3a', 'Extreme': '#a32a17'}\n",
    "for r, g in pts.groupby('risk_rating'): g.plot(ax=ax, color=col[r], markersize={'Low': 4, 'Medium': 7, 'High': 12, 'Extreme': 30}[r], edgecolor=INK, lw=.3, label=r)\n",
    "ax.legend(title='Asset risk (Sample data)', loc='lower right', fontsize=8); ax.set_axis_off(); ax.set_title('Sample data: where the risk sits'); plt.show()\n",
    "per"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d23c3fac",
   "metadata": {},
   "source": [
    "### Risk: likelihood times consequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3100004b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "risk_rating\n",
      "Low        114\n",
      "Medium      72\n",
      "High        37\n",
      "Extreme      3\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset_id</th>\n",
       "      <th>asset_type</th>\n",
       "      <th>building</th>\n",
       "      <th>risk_score</th>\n",
       "      <th>risk_rating</th>\n",
       "      <th>risk_drivers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>FIP-002</td>\n",
       "      <td>Fire indicator panel (FIP)</td>\n",
       "      <td>B03</td>\n",
       "      <td>20</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>Past expected life; Poor condition; Life safet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>FD-005</td>\n",
       "      <td>Fire door</td>\n",
       "      <td>B01</td>\n",
       "      <td>20</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>Past expected life; Poor condition; Life safet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>FD-008</td>\n",
       "      <td>Fire door</td>\n",
       "      <td>B02</td>\n",
       "      <td>20</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>Past expected life; Poor condition; Life safet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>BFP-001</td>\n",
       "      <td>Backflow prevention device</td>\n",
       "      <td>B01</td>\n",
       "      <td>16</td>\n",
       "      <td>High</td>\n",
       "      <td>Past expected life; Poor condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>SL-001</td>\n",
       "      <td>Static line</td>\n",
       "      <td>B01</td>\n",
       "      <td>16</td>\n",
       "      <td>High</td>\n",
       "      <td>Past expected life; Poor condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>FD-007</td>\n",
       "      <td>Fire door</td>\n",
       "      <td>B02</td>\n",
       "      <td>16</td>\n",
       "      <td>High</td>\n",
       "      <td>Past expected life; Poor condition; Life safet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>RAP-011</td>\n",
       "      <td>Roof anchor point</td>\n",
       "      <td>B03</td>\n",
       "      <td>16</td>\n",
       "      <td>High</td>\n",
       "      <td>Poor condition; Service overdue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSB-001</td>\n",
       "      <td>Main switchboard (MSB)</td>\n",
       "      <td>B01</td>\n",
       "      <td>15</td>\n",
       "      <td>High</td>\n",
       "      <td>Poor condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>RAP-002</td>\n",
       "      <td>Roof anchor point</td>\n",
       "      <td>B01</td>\n",
       "      <td>15</td>\n",
       "      <td>High</td>\n",
       "      <td>Poor condition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>EXT-016</td>\n",
       "      <td>Fire extinguisher</td>\n",
       "      <td>B01</td>\n",
       "      <td>15</td>\n",
       "      <td>High</td>\n",
       "      <td>Past expected life; Poor condition; Life safet...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    asset_id                  asset_type building  risk_score risk_rating                                       risk_drivers\n",
       "140  FIP-002  Fire indicator panel (FIP)      B03          20     Extreme  Past expected life; Poor condition; Life safet...\n",
       "48    FD-005                   Fire door      B01          20     Extreme  Past expected life; Poor condition; Life safet...\n",
       "122   FD-008                   Fire door      B02          20     Extreme  Past expected life; Poor condition; Life safet...\n",
       "96   BFP-001  Backflow prevention device      B01          16        High                 Past expected life; Poor condition\n",
       "106   SL-001                 Static line      B01          16        High                 Past expected life; Poor condition\n",
       "121   FD-007                   Fire door      B02          16        High  Past expected life; Poor condition; Life safet...\n",
       "180  RAP-011           Roof anchor point      B03          16        High                    Poor condition; Service overdue\n",
       "0    MSB-001      Main switchboard (MSB)      B01          15        High                                     Poor condition\n",
       "99   RAP-002           Roof anchor point      B01          15        High                                     Poor condition\n",
       "26   EXT-016           Fire extinguisher      B01          15        High  Past expected life; Poor condition; Life safet..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = risk.pivot_table(index='likelihood_1to5', columns='consequence_1to5', values='asset_id', aggfunc='count', fill_value=0).sort_index(ascending=False)\n",
    "print(risk.risk_rating.value_counts().reindex(['Low', 'Medium', 'High', 'Extreme']))\n",
    "ar.sort_values(['risk_score', 'consequence_1to5'], ascending=False).head(10)[['asset_id', 'asset_type', 'building', 'risk_score', 'risk_rating', 'risk_drivers']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d0fef9c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>consequence_1to5</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>likelihood_1to5</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "consequence_1to5  1   2   3  4  5\n",
       "likelihood_1to5                  \n",
       "5                 1   2   7  2  0\n",
       "4                 3  13  20  4  1\n",
       "3                 1   7  12  4  2\n",
       "2                 4  10  27  9  2\n",
       "1                 8  29  46  8  4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27aa8296",
   "metadata": {},
   "source": [
    "### Work orders\n",
    "\n",
    "Open now by priority and how long they have been open, and raised each month, reactive against planned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b9da44c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age_days    up to a week  8 to 30 days  31 to 90 days  over 90 days  All\n",
      "priority                                                                \n",
      "P2 Urgent              1             1              0             2    4\n",
      "P3 Routine             5             3              2             5   15\n",
      "P4 Planned            11             5              0             1   17\n",
      "All                   17             9              2             8   36\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x385 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = pd.cut(open_wo.age_days, [-1, 7, 30, 90, 10_000], labels=['up to a week', '8 to 30 days', '31 to 90 days', 'over 90 days'])\n",
    "print(pd.crosstab(open_wo.priority, bins, margins=True))\n",
    "monthly = wo.groupby([wo.raised.dt.to_period('M'), 'wo_type']).size().unstack(fill_value=0)\n",
    "ax = monthly.plot.bar(figsize=(10, 3.5), color={'Reactive': '#e0884c', 'Planned': '#8fb3d6'}); ax.set_xlabel(''); ax.set_ylabel('work orders raised'); ax.set_title('Sample data: raised each month'); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d42a9f6",
   "metadata": {},
   "source": [
    "### Planned maintenance and essential safety measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "103f2b2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "2025-11    91.3\n",
      "2025-12    80.0\n",
      "2026-01    90.0\n",
      "2026-02    87.5\n",
      "2026-03    86.4\n",
      "2026-04    84.2\n",
      "2026-05    90.9\n",
      "2026-06    86.0\n",
      "2026-07    84.4\n",
      "2026-08    88.9\n",
      "2026-09    90.9\n",
      "2026-10    86.8\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>status</th>\n",
       "      <th>Current</th>\n",
       "      <th>Due within 30 days</th>\n",
       "      <th>Overdue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>building</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B01</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B02</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B03</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B04</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B05</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B06</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B07</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B08</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B09</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B10</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "status    Current  Due within 30 days  Overdue\n",
       "building                                      \n",
       "B01             7                   3        1\n",
       "B02             5                   0        1\n",
       "B03             6                   3        2\n",
       "B04             2                   1        0\n",
       "B05             1                   1        1\n",
       "B06             0                   2        1\n",
       "B07             2                   1        0\n",
       "B08             2                   0        1\n",
       "B09             2                   1        0\n",
       "B10             1                   1        0\n",
       "B11             1                   1        0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmm = pm.groupby('month')[['scheduled', 'on_time']].sum(); pmm['on_time_pct'] = (100 * pmm.on_time / pmm.scheduled).round(1)\n",
    "print(pmm.on_time_pct.to_string())\n",
    "pd.crosstab(esm.building, esm.status)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5934481d",
   "metadata": {},
   "source": [
    "### Meters from the BMS trend log\n",
    "\n",
    "Hourly kWh per building meter (point names such as `B02-EM-01.kWh`), four weeks of term. The share used outside\n",
    "8 am to 4 pm on school days, and B02's week as a heat map: it does not switch off at night."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a329832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x330 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "key\n",
       "B02    61.9\n",
       "B05    40.3\n",
       "B04    40.1\n",
       "B08    39.4\n",
       "B07    39.0\n",
       "B06    38.2\n",
       "B03    36.9\n",
       "B09    35.6\n",
       "B01    33.8\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr = pd.read_csv(f'{S}/meter_trends.csv', parse_dates=['timestamp'])\n",
    "e = tr[tr.unit == 'kWh'].assign(key=lambda d: d.point.str[:3], wd=lambda d: d.timestamp.dt.day_of_week, hr=lambda d: d.timestamp.dt.hour)\n",
    "day = (e.wd < 5) & (e.hr >= 8) & (e.hr < 16)\n",
    "after = (e[~day].groupby('key').value.sum() / e.groupby('key').value.sum() * 100).round(1)\n",
    "assert (after - summary.set_index('key').after_hours_share_pct.dropna()).abs().max() < 0.11\n",
    "heat = e[e.key == 'B02'].pivot_table(index='wd', columns='hr', values='value', aggfunc='mean')\n",
    "fig, ax = plt.subplots(figsize=(10, 3)); im = ax.imshow(heat, aspect='auto', cmap='Oranges'); ax.set_yticks(range(7), ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])\n",
    "ax.set_xlabel('hour'); ax.set_title('Sample data: B02 mean kWh by hour and day'); plt.colorbar(im, ax=ax, shrink=.8); plt.show()\n",
    "after.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962bd02e",
   "metadata": {},
   "source": [
    "### The capital plan and the asbestos register"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2eb566af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "risk      Low  Medium\n",
      "building             \n",
      "B01         5       2\n",
      "B02         3       0\n",
      "B10         1       0\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>priority</th>\n",
       "      <th>1 Within a year</th>\n",
       "      <th>2 In 2 to 3 years</th>\n",
       "      <th>3 In 4 to 5 years</th>\n",
       "      <th>4 In 6 to 10 years</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>building</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B01</th>\n",
       "      <td>35000</td>\n",
       "      <td>500000</td>\n",
       "      <td>2615000</td>\n",
       "      <td>0</td>\n",
       "      <td>3150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B02</th>\n",
       "      <td>18000</td>\n",
       "      <td>231000</td>\n",
       "      <td>322000</td>\n",
       "      <td>0</td>\n",
       "      <td>571000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B03</th>\n",
       "      <td>53000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B04</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26000</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B05</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21000</td>\n",
       "      <td>21000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B06</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19000</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B07</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29000</td>\n",
       "      <td>29000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B08</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27000</td>\n",
       "      <td>27000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B09</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26000</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>106000</td>\n",
       "      <td>731000</td>\n",
       "      <td>2937000</td>\n",
       "      <td>148000</td>\n",
       "      <td>3922000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "priority  1 Within a year  2 In 2 to 3 years  3 In 4 to 5 years  4 In 6 to 10 years      All\n",
       "building                                                                                    \n",
       "B01                 35000             500000            2615000                   0  3150000\n",
       "B02                 18000             231000             322000                   0   571000\n",
       "B03                 53000                  0                  0                   0    53000\n",
       "B04                     0                  0                  0               26000    26000\n",
       "B05                     0                  0                  0               21000    21000\n",
       "B06                     0                  0                  0               19000    19000\n",
       "B07                     0                  0                  0               29000    29000\n",
       "B08                     0                  0                  0               27000    27000\n",
       "B09                     0                  0                  0               26000    26000\n",
       "All                106000             731000            2937000              148000  3922000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(asb.groupby(['building', 'risk']).size().unstack(fill_value=0))\n",
    "cap.pivot_table(index='building', columns='priority', values='estimate_aud', aggfunc='sum', fill_value=0, margins=True)"
   ]
  }
 ],
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