{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b3f637b",
   "metadata": {},
   "source": [
    "# A campus drawn out, building by building: TAFE NSW Richmond\n",
    "\n",
    "What if a campus's buildings are in no map data at all? OpenStreetMap has one building on this campus, the state's\n",
    "footprints are not open, and the land title is a lot shared with the university next door. The buildings in\n",
    "`data/geometry/` were drawn in [Plotlay](https://www.getplotlay.com) over NSW aerial imagery (CC BY 3.0 AU), each with\n",
    "where its outline came from and how accurate it is likely to be. This notebook checks those, compares the outlines\n",
    "with Microsoft's building outlines (made by software from aerial photos), checks Plotlay's roll-up to the site, and reads the sample facilities\n",
    "tables in their own section at the end.\n",
    "\n",
    "It reads only the files in `../data`. Accuracy depends on the imagery the outlines were drawn over."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6e5feacd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', message='.*colormapping.*')\n",
    "import pandas as pd, geopandas as gpd, matplotlib.pyplot as plt\n",
    "DATA = '../data'\n",
    "pd.set_option('display.width', 160); pd.set_option('display.max_columns', 30)\n",
    "INK = '#1d4f91'\n",
    "plt.rcParams.update({'figure.dpi': 110, 'axes.spines.top': False, 'axes.spines.right': False, 'font.size': 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afb0711",
   "metadata": {},
   "source": [
    "## Sources and licence\n",
    "\n",
    "NSW Imagery, (c) State of New South Wales (Spatial Services), CC BY 3.0 AU: the outlines were drawn over it. Two\n",
    "outlines are Microsoft Global ML Building Footprints (CDLA Permissive 2.0) as they are. Nothing comes from\n",
    "OpenStreetMap. `data/sample/` is sample data and is used only in the last section, labelled so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9ab750a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38 buildings drawn; campus 32.3 ha\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>outline_source</th>\n",
       "      <th>outline_accuracy_m</th>\n",
       "      <th>buildings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Microsoft footprint, picked as it is (a comple...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Traced over NSW Imagery (not in the Microsoft ...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Traced over NSW Imagery, started from the Micr...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      outline_source  outline_accuracy_m  buildings\n",
       "0  Microsoft footprint, picked as it is (a comple...                 2.5          2\n",
       "1  Traced over NSW Imagery (not in the Microsoft ...                 0.5          5\n",
       "2  Traced over NSW Imagery, started from the Micr...                 0.5         31"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.read_csv(f'{DATA}/buildings/buildings.csv')\n",
    "shapes = gpd.read_file(f'{DATA}/geometry/campus_buildings.geojson').to_crs(7856)\n",
    "site = gpd.read_file(f'{DATA}/geometry/campus_site.geojson').to_crs(7856)\n",
    "roll = pd.read_csv(f'{DATA}/site/site_rollup.csv')\n",
    "assert b.key.is_unique and set(b.key) == set(shapes.key)\n",
    "print(len(b), 'buildings drawn;', f\"campus {site.area.sum() / 10000:.1f} ha\")\n",
    "b.groupby(['outline_source', 'outline_accuracy_m']).size().rename('buildings').reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74efeae6",
   "metadata": {},
   "source": [
    "## The outlines against the table\n",
    "\n",
    "Each shape's area worked out again from the geometry (MGA zone 56) against the footprint the table records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "005073a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "largest difference 0.13% (the table carries the area as drawn); footprint in all 18,114 m², the table 18,109 m²\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 990x770 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = shapes.assign(area=shapes.area).merge(b[['key', 'footprint_m2', 'outline_accuracy_m']], on='key')\n",
    "s['diff_pct'] = (s.area / s.footprint_m2 - 1) * 100\n",
    "print(f\"largest difference {s.diff_pct.abs().max():.2f}% (the table carries the area as drawn); footprint in all {s.area.sum():,.0f} m², the table {b.footprint_m2.sum():,.0f} m²\")\n",
    "fig, ax = plt.subplots(figsize=(9, 7))\n",
    "site.boundary.plot(ax=ax, color='#c9a227', lw=1.5, ls='--')\n",
    "cols = {0.5: '#56B4E9', 2.5: '#CC79A7'}\n",
    "s.plot(ax=ax, color=[cols.get(a, '#E69F00') for a in s.outline_accuracy_m], edgecolor=INK, lw=.5)\n",
    "for _, r in s.iterrows():\n",
    "    if r.area > 300: ax.annotate(r.key, r.geometry.representative_point().coords[0], ha='center', fontsize=7)\n",
    "ax.set_axis_off(); ax.set_title('The buildings by accuracy: ±0.5 m drawn over the imagery, ±2.5 m picked as Microsoft drew them'); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64faad7c",
   "metadata": {},
   "source": [
    "## How far Microsoft's outlines were off\n",
    "\n",
    "For the buildings Microsoft's footprints had, how far each outline moved when it was drawn onto the roof edges, and\n",
    "how much the two overlapped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3566ed33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 buildings started from Microsoft's footprint: median offset 2.23 m, median overlap 0.67\n",
      "5 buildings were in no open data at all\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 880x770 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ms = b.dropna(subset=['microsoft_offset_m'])\n",
    "print(f\"{len(ms)} buildings started from Microsoft's footprint: median offset {ms.microsoft_offset_m.median():.2f} m, median overlap {ms.microsoft_overlap.median():.2f}\")\n",
    "print(f\"{(b.in_microsoft_footprints == 'no').sum()} buildings were in no open data at all\")\n",
    "ax = ms.sort_values('microsoft_offset_m').plot.barh(x='key', y='microsoft_offset_m', color=INK, legend=False, figsize=(8, 7))\n",
    "ax.set_xlabel('metres'); ax.set_ylabel(''); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ff2e49",
   "metadata": {},
   "source": [
    "## The roll-up to the site\n",
    "\n",
    "Plotlay counted the buildings inside the campus and added up their footprint; the same with a spatial join here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eac7ad79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38 of 38 buildings inside the campus; Plotlay's roll-up: 38, 18,109 m²\n"
     ]
    }
   ],
   "source": [
    "inside = gpd.sjoin(shapes.set_geometry(shapes.representative_point()), site, predicate='within')\n",
    "assert len(inside) == int(roll.buildings[0]) == len(b)\n",
    "print(f\"{len(inside)} of {len(b)} buildings inside the campus; Plotlay's roll-up: {int(roll.buildings[0])}, {roll.footprint_m2[0]:,.0f} m²\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310777be",
   "metadata": {},
   "source": [
    "## Sample data: asset outcomes, building by building\n",
    "\n",
    "Everything from here on reads `data/sample/`, which is sample data: the figures are not about TAFE NSW Richmond or\n",
    "any real building. `data/sample/README.md` is the data dictionary. Nothing above uses these tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd9fb7c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample data: 372 assets, 70 at high or extreme risk; 33 work orders open, 9 overdue; planned maintenance on time 88.1%; safety measures overdue 8 of 131; backlog A$3,054,000\n"
     ]
    }
   ],
   "source": [
    "S = f'{DATA}/sample'\n",
    "assets = pd.read_csv(f'{S}/assets.csv'); risk = pd.read_csv(f'{S}/asset_risk.csv'); wo = pd.read_csv(f'{S}/work_orders.csv')\n",
    "pm = pd.read_csv(f'{S}/pm_compliance.csv'); esm = pd.read_csv(f'{S}/esm_register.csv'); cap = pd.read_csv(f'{S}/capital_works.csv'); summary = pd.read_csv(f'{S}/building_summary.csv')\n",
    "for t in (assets, risk, wo, pm, esm, cap, summary): assert (t.data_status == 'Sample data').all()\n",
    "open_wo = wo[~wo.status.isin(['Completed', 'Cancelled, missed'])]\n",
    "print(f\"Sample data: {len(assets)} assets, {(risk.risk_score >= 12).sum()} at high or extreme risk; {len(open_wo)} work orders open, {(wo.overdue == 'Yes').sum()} overdue;\",\n",
    "      f\"planned maintenance on time {100 * pm.on_time.sum() / pm.scheduled.sum():.1f}%; safety measures overdue {(esm.status == 'Overdue').sum()} of {len(esm)}; backlog A${cap.estimate_aud.sum():,.0f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d28dd286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the grounds: 2 assets; the roll-up into the buildings matches the summary table\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>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>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>42</td>\n",
       "      <td>63</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "consequence_1to5   1   2   3   4  5\n",
       "likelihood_1to5                    \n",
       "5                  0   8   1   0  0\n",
       "4                  5  38  46  12  0\n",
       "3                  8  24  35  10  1\n",
       "2                  3  13  31   6  2\n",
       "1                 15  42  63   8  1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = gpd.GeoDataFrame(assets.merge(risk[['asset_id', 'risk_score', 'risk_rating']], on='asset_id'), geometry=gpd.points_from_xy(assets.lng, assets.lat), crs=4326).to_crs(7856)\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 x: (x >= 12).sum()))\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; the roll-up into the buildings matches the summary table')\n",
    "risk.pivot_table(index='likelihood_1to5', columns='consequence_1to5', values='asset_id', aggfunc='count', fill_value=0).sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "484e2691",
   "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>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>All</th>\n",
       "      <td>455000</td>\n",
       "      <td>2252000</td>\n",
       "      <td>288000</td>\n",
       "      <td>59000</td>\n",
       "      <td>3054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B23</th>\n",
       "      <td>361000</td>\n",
       "      <td>266000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>627000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B36</th>\n",
       "      <td>61000</td>\n",
       "      <td>338000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>399000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B32</th>\n",
       "      <td>14000</td>\n",
       "      <td>267000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>281000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B05</th>\n",
       "      <td>0</td>\n",
       "      <td>269000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>269000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B17</th>\n",
       "      <td>0</td>\n",
       "      <td>268000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>268000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B18</th>\n",
       "      <td>0</td>\n",
       "      <td>225000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B14</th>\n",
       "      <td>2000</td>\n",
       "      <td>173000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B35</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108000</td>\n",
       "      <td>0</td>\n",
       "      <td>108000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B07</th>\n",
       "      <td>0</td>\n",
       "      <td>84000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B33</th>\n",
       "      <td>0</td>\n",
       "      <td>81000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B29</th>\n",
       "      <td>0</td>\n",
       "      <td>61000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61000</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",
       "All                455000            2252000             288000               59000  3054000\n",
       "B23                361000             266000                  0                   0   627000\n",
       "B36                 61000             338000                  0                   0   399000\n",
       "B32                 14000             267000                  0                   0   281000\n",
       "B05                     0             269000                  0                   0   269000\n",
       "B17                     0             268000                  0                   0   268000\n",
       "B18                     0             225000                  0                   0   225000\n",
       "B14                  2000             173000                  0                   0   175000\n",
       "B35                     0                  0             108000                   0   108000\n",
       "B07                     0              84000                  0                   0    84000\n",
       "B33                     0              81000                  0                   0    81000\n",
       "B29                     0              61000                  0                   0    61000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cap.pivot_table(index='building', columns='priority', values='estimate_aud', aggfunc='sum', fill_value=0, margins=True).sort_values('All', ascending=False).head(12)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}