australia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d7196425",
   "metadata": {},
   "source": [
    "## Australia\n",
    "\n",
    "This notebook gives a basic demonstration of the shapefiles found at:  \n",
    "`/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/australia/`\n",
    "\n",
    "The Australian Bureau of Statistics (ABS) maintains the Australian Statistical Geography Standard (ASGS).\n",
    "The latest release is\n",
    "[Edition 3](https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3),\n",
    "which includes a \"Australia - 2021 - Shapefile\" that can be downloaded\n",
    "[here](https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/digital-boundary-files).\n",
    "\n",
    "In order to produce the shapefiles demonstrated below,\n",
    "the `processing_scripts/process_australia.py` script applies some minor changes\n",
    "(it removes some redundant rows and columns) to the original ABS shapefiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24678edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79cbf8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gp.read_file('/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/australia/australia.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e82650f1",
   "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>AUS_CODE21</th>\n",
       "      <th>AUS_NAME21</th>\n",
       "      <th>AREASQKM21</th>\n",
       "      <th>LOCI_URI21</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7.688095e+06</td>\n",
       "      <td>http://linked.data.gov.au/dataset/asgsed3/AUS/AUS</td>\n",
       "      <td>MULTIPOLYGON (((96.91524 -12.15313, 96.91523 -...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AUS_CODE21 AUS_NAME21    AREASQKM21  \\\n",
       "0        AUS  Australia  7.688095e+06   \n",
       "\n",
       "                                          LOCI_URI21  \\\n",
       "0  http://linked.data.gov.au/dataset/asgsed3/AUS/AUS   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((96.91524 -12.15313, 96.91523 -...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f036a4f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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