{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets for use with libpysal\n",
    "As of version 4.2, libpysal has refactored the `examples` package to:\n",
    "\n",
    "- reduce the size of the source installation\n",
    "- allow the use of remote datasets from the [Center for Spatial Data Science at the Unversity of Chicago](https://spatial.uchicago.edu/), and other remotes\n",
    "\n",
    "This notebook highlights the new functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backwards compatibility is maintained"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work. \n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from libpysal.examples import get_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/serge/para/1_projects/code-pysal-libpysal/libpysal/libpysal/examples/mexico/mexicojoin.dbf'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_path(\"mexicojoin.dbf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a `builtin` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import libpysal\n",
    "\n",
    "dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['POLY_ID',\n",
       " 'AREA',\n",
       " 'CODE',\n",
       " 'NAME',\n",
       " 'PERIMETER',\n",
       " 'ACRES',\n",
       " 'HECTARES',\n",
       " 'PCGDP1940',\n",
       " 'PCGDP1950',\n",
       " 'PCGDP1960',\n",
       " 'PCGDP1970',\n",
       " 'PCGDP1980',\n",
       " 'PCGDP1990',\n",
       " 'PCGDP2000',\n",
       " 'HANSON03',\n",
       " 'HANSON98',\n",
       " 'ESQUIVEL99',\n",
       " 'INEGI',\n",
       " 'INEGI2',\n",
       " 'MAXP',\n",
       " 'GR4000',\n",
       " 'GR5000',\n",
       " 'GR6000',\n",
       " 'GR7000',\n",
       " 'GR8000',\n",
       " 'GR9000',\n",
       " 'LPCGDP40',\n",
       " 'LPCGDP50',\n",
       " 'LPCGDP60',\n",
       " 'LPCGDP70',\n",
       " 'LPCGDP80',\n",
       " 'LPCGDP90',\n",
       " 'LPCGDP00',\n",
       " 'TEST']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbf.header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `available` is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, `available` will report on what datasets are available, either as builtin or remotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from libpysal.examples import available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99 datasets available, 29 installed, 70 remote.\n"
     ]
    }
   ],
   "source": [
    "libpysal.examples.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are 98 total datasets available for use with PySAL. On an initial install (i.e., `examples` has not been used yet), 27 of these are builtin datasets and 71 are remote. The latter can be downloaded and installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading Remote Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Name</th>\n",
       "      <th>Description</th>\n",
       "      <th>Installed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10740</td>\n",
       "      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AirBnB</td>\n",
       "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Atlanta, GA region homicide counts and rates</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Baltimore house sales prices and hedonics</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bostonhsg</td>\n",
       "      <td>Boston housing and neighborhood data</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name                                        Description  Installed\n",
       "0      10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
       "1     AirBnB  Airbnb rentals, socioeconomics, and crime in C...      False\n",
       "2    Atlanta       Atlanta, GA region homicide counts and rates      False\n",
       "3  Baltimore          Baltimore house sales prices and hedonics      False\n",
       "4  Bostonhsg               Boston housing and neighborhood data      False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remote `AirBnB` can be installed by calling `load_example`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading AirBnB to /home/serge/.local/share/pysal/AirBnB\n"
     ]
    }
   ],
   "source": [
    "airbnb = libpysal.examples.load_example(\"AirBnB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99 datasets available, 30 installed, 69 remote.\n"
     ]
    }
   ],
   "source": [
    "libpysal.examples.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we see that the number of remotes as declined by one and the number of installed has increased by 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying to load an example that doesn't exist will return None and alert the user:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example not available: dataset42\n"
     ]
    }
   ],
   "source": [
    "libpysal.examples.load_example(\"dataset42\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting remote urls\n",
    "\n",
    "If the url, rather than the dataset, is needed this can be obtained on a remote with `get_url`. \n",
    "As the `Baltimore` dataset has not yet been downloaded in this example, we can grab it's url:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balt_url = libpysal.examples.get_url(\"Baltimore\")\n",
    "balt_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explaining a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "taz\n",
      "===\n",
      "\n",
      "Dataset used for regionalization\n",
      "--------------------------------\n",
      "\n",
      "* taz.dbf: attribute data. (k=14)\n",
      "* taz.shp: Polygon shapefile. (n=4109)\n",
      "* taz.shx: spatial index.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "libpysal.examples.explain(\"taz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "taz = libpysal.examples.load_example(\"taz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/serge/.local/share/pysal/taz/taz-master/taz.dbf',\n",
       " '/home/serge/.local/share/pysal/taz/taz-master/README.md',\n",
       " '/home/serge/.local/share/pysal/taz/taz-master/taz.shx',\n",
       " '/home/serge/.local/share/pysal/taz/taz-master/taz.shp']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taz.get_file_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"350\"\n",
       "            src=\"https://geodacenter.github.io/data-and-lab//baltim/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f233fa968a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "libpysal.examples.explain(\"Baltimore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Baltimore to /home/serge/.local/share/pysal/Baltimore\n"
     ]
    }
   ],
   "source": [
    "balt = libpysal.examples.load_example(\"Baltimore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Name</th>\n",
       "      <th>Description</th>\n",
       "      <th>Installed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10740</td>\n",
       "      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AirBnB</td>\n",
       "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Atlanta, GA region homicide counts and rates</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Baltimore house sales prices and hedonics</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bostonhsg</td>\n",
       "      <td>Boston housing and neighborhood data</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>taz</td>\n",
       "      <td>Traffic Analysis Zones in So. California</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>tokyo</td>\n",
       "      <td>Tokyo Mortality data</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>us_income</td>\n",
       "      <td>Per-capita income for the lower 48 US states 1...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>virginia</td>\n",
       "      <td>Virginia counties shapefile</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>wmat</td>\n",
       "      <td>Datasets used for spatial weights testing</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name                                        Description  Installed\n",
       "0       10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
       "1      AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
       "2     Atlanta       Atlanta, GA region homicide counts and rates      False\n",
       "3   Baltimore          Baltimore house sales prices and hedonics       True\n",
       "4   Bostonhsg               Boston housing and neighborhood data      False\n",
       "..        ...                                                ...        ...\n",
       "94        taz           Traffic Analysis Zones in So. California       True\n",
       "95      tokyo                               Tokyo Mortality data       True\n",
       "96  us_income  Per-capita income for the lower 48 US states 1...       True\n",
       "97   virginia                        Virginia counties shapefile       True\n",
       "98       wmat          Datasets used for spatial weights testing       True\n",
       "\n",
       "[99 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "libpysal.examples.available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with an example dataset\n",
    "\n",
    "`explain` will render maps for an example if available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"350\"\n",
       "            src=\"https://geodacenter.github.io/data-and-lab//tampa1/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f22606903e0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from libpysal.examples import explain\n",
    "\n",
    "explain(\"Tampa1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Tampa1 to /home/serge/.local/share/pysal/Tampa1\n"
     ]
    }
   ],
   "source": [
    "from libpysal.examples import load_example\n",
    "\n",
    "tampa1 = load_example(\"Tampa1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tampa1.installed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mif',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mif',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.dbf',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mid',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj',\n",
       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n",
       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n",
       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n",
       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n",
       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/._TampaMSA']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tampa1.get_file_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tampa_counties_shp = tampa1.load(\"tampa_counties.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7f22604b2810>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tampa_counties_shp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tampa_df = geopandas.read_file(tampa1.get_path(\"tampa_counties.shp\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "tampa_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Remotes\n",
    "\n",
    "In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rio_Grande_do_Sul\n",
      "======================\n",
      "\n",
      "Cities of the Brazilian State of Rio Grande do Sul\n",
      "-------------------------------------------------------\n",
      "\n",
      "* 43MUE250GC_SIR.dbf: attribute data (k=2)\n",
      "* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)\n",
      "* 43MUE250GC_SIR.shx: spatial index\n",
      "* 43MUE250GC_SIR.cpg: encoding file \n",
      "* 43MUE250GC_SIR.prj: projection information \n",
      "* map_RS_BR.dbf: attribute data (k=3)\n",
      "* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)\n",
      "* map_RS_BR.prj: projection information\n",
      "* map_RS_BR.shx: spatial index\n",
      "\n",
      "\n",
      "\n",
      "Source: Renan Xavier Cortes \n",
      "Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "libpysal.examples.explain(\"Rio Grande do Sul\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `explain` function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Rio Grande do Sul to /home/serge/.local/share/pysal/Rio_Grande_do_Sul\n"
     ]
    }
   ],
   "source": [
    "rio = libpysal.examples.load_example(\"Rio Grande do Sul\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AirBnB': <libpysal.examples.base.Example at 0x7f2265395550>,\n",
       " 'Atlanta': <libpysal.examples.base.Example at 0x7f22653954f0>,\n",
       " 'Baltimore': <libpysal.examples.base.Example at 0x7f2265395580>,\n",
       " 'Bostonhsg': <libpysal.examples.base.Example at 0x7f2264367800>,\n",
       " 'Buenosaires': <libpysal.examples.base.Example at 0x7f22643677d0>,\n",
       " 'Charleston1': <libpysal.examples.base.Example at 0x7f2264367860>,\n",
       " 'Charleston2': <libpysal.examples.base.Example at 0x7f2264367890>,\n",
       " 'Chicago Health': <libpysal.examples.base.Example at 0x7f22643678c0>,\n",
       " 'Chicago commpop': <libpysal.examples.base.Example at 0x7f22643678f0>,\n",
       " 'Chicago parcels': <libpysal.examples.base.Example at 0x7f2264367920>,\n",
       " 'Chile Labor': <libpysal.examples.base.Example at 0x7f2264367950>,\n",
       " 'Chile Migration': <libpysal.examples.base.Example at 0x7f2264367980>,\n",
       " 'Cincinnati': <libpysal.examples.base.Example at 0x7f22643679b0>,\n",
       " 'Cleveland': <libpysal.examples.base.Example at 0x7f22643679e0>,\n",
       " 'Columbus': <libpysal.examples.base.Example at 0x7f2264367a10>,\n",
       " 'Elections': <libpysal.examples.base.Example at 0x7f2264367a40>,\n",
       " 'Grid100': <libpysal.examples.base.Example at 0x7f2264367a70>,\n",
       " 'Groceries': <libpysal.examples.base.Example at 0x7f2264367aa0>,\n",
       " 'Guerry': <libpysal.examples.base.Example at 0x7f2264367ad0>,\n",
       " 'Health+': <libpysal.examples.base.Example at 0x7f2264367b60>,\n",
       " 'Health Indicators': <libpysal.examples.base.Example at 0x7f2264367b90>,\n",
       " 'Hickory1': <libpysal.examples.base.Example at 0x7f2264367b00>,\n",
       " 'Hickory2': <libpysal.examples.base.Example at 0x7f2264367b30>,\n",
       " 'Home Sales': <libpysal.examples.base.Example at 0x7f2264367bc0>,\n",
       " 'Houston': <libpysal.examples.base.Example at 0x7f2264367bf0>,\n",
       " 'Juvenile': <libpysal.examples.base.Example at 0x7f2264367c20>,\n",
       " 'Lansing1': <libpysal.examples.base.Example at 0x7f2264367c50>,\n",
       " 'Lansing2': <libpysal.examples.base.Example at 0x7f2264367c80>,\n",
       " 'Laozone': <libpysal.examples.base.Example at 0x7f2264367cb0>,\n",
       " 'LasRosas': <libpysal.examples.base.Example at 0x7f2264367ce0>,\n",
       " 'Liquor Stores': <libpysal.examples.base.Example at 0x7f2264367d10>,\n",
       " 'Malaria': <libpysal.examples.base.Example at 0x7f2264367d40>,\n",
       " 'Milwaukee1': <libpysal.examples.base.Example at 0x7f2264367d70>,\n",
       " 'Milwaukee2': <libpysal.examples.base.Example at 0x7f2264367da0>,\n",
       " 'NCOVR': <libpysal.examples.base.Example at 0x7f2264367dd0>,\n",
       " 'Natregimes': <libpysal.examples.base.Example at 0x7f2264367e00>,\n",
       " 'NDVI': <libpysal.examples.base.Example at 0x7f2264367e30>,\n",
       " 'Nepal': <libpysal.examples.base.Example at 0x7f2264367e60>,\n",
       " 'NYC': <libpysal.examples.base.Example at 0x7f2264367e90>,\n",
       " 'NYC Earnings': <libpysal.examples.base.Example at 0x7f2264367ec0>,\n",
       " 'NYC Education': <libpysal.examples.base.Example at 0x7f2264367ef0>,\n",
       " 'NYC Neighborhoods': <libpysal.examples.base.Example at 0x7f2264367f20>,\n",
       " 'NYC Socio-Demographics': <libpysal.examples.base.Example at 0x7f2264367f50>,\n",
       " 'Ohiolung': <libpysal.examples.base.Example at 0x7f2264367fb0>,\n",
       " 'Orlando1': <libpysal.examples.base.Example at 0x7f2264367f80>,\n",
       " 'Orlando2': <libpysal.examples.base.Example at 0x7f2264367fe0>,\n",
       " 'Oz9799': <libpysal.examples.base.Example at 0x7f2264390080>,\n",
       " 'Phoenix ACS': <libpysal.examples.base.Example at 0x7f2264390050>,\n",
       " 'Pittsburgh': <libpysal.examples.base.Example at 0x7f22643900b0>,\n",
       " 'Police': <libpysal.examples.base.Example at 0x7f22643900e0>,\n",
       " 'Sacramento1': <libpysal.examples.base.Example at 0x7f2264390110>,\n",
       " 'Sacramento2': <libpysal.examples.base.Example at 0x7f2264390140>,\n",
       " 'SanFran Crime': <libpysal.examples.base.Example at 0x7f2264390170>,\n",
       " 'Savannah1': <libpysal.examples.base.Example at 0x7f22643901a0>,\n",
       " 'Savannah2': <libpysal.examples.base.Example at 0x7f22643901d0>,\n",
       " 'Scotlip': <libpysal.examples.base.Example at 0x7f2264390200>,\n",
       " 'Seattle1': <libpysal.examples.base.Example at 0x7f2264390230>,\n",
       " 'Seattle2': <libpysal.examples.base.Example at 0x7f2264390260>,\n",
       " 'SIDS': <libpysal.examples.base.Example at 0x7f2264390290>,\n",
       " 'SIDS2': <libpysal.examples.base.Example at 0x7f22643902c0>,\n",
       " 'Snow': <libpysal.examples.base.Example at 0x7f22643902f0>,\n",
       " 'South': <libpysal.examples.base.Example at 0x7f2264390320>,\n",
       " 'Spirals': <libpysal.examples.base.Example at 0x7f2264390350>,\n",
       " 'StLouis': <libpysal.examples.base.Example at 0x7f2264390380>,\n",
       " 'Tampa1': <libpysal.examples.base.Example at 0x7f22643903b0>,\n",
       " 'US SDOH': <libpysal.examples.base.Example at 0x7f22643903e0>,\n",
       " 'Rio Grande do Sul': <libpysal.examples.base.Example at 0x7f2264390440>,\n",
       " 'nyc_bikes': <libpysal.examples.base.Example at 0x7f22643904a0>,\n",
       " 'taz': <libpysal.examples.base.Example at 0x7f2264390410>,\n",
       " 'clearwater': <libpysal.examples.base.Example at 0x7f2264390530>,\n",
       " 'newHaven': <libpysal.examples.base.Example at 0x7f2264390560>,\n",
       " 'chicagoSDOH': <libpysal.examples.base.Example at 0x7f22643904d0>}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "libpysal.examples.remote_datasets.datasets  # a listing of all remotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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