Install_geosnap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to install geosnap\n",
    "\n",
    "Command lines below are to install the geosnap package and additional packages that you need to run geosnap.\n",
    "This installation instruction is for CyberGISX.\n",
    "For jupyter notebook users see the website below for installation instructions\n",
    "Learn more about geosnap at https://github.com/spatialucr/geosnap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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     ]
    }
   ],
   "source": [
    "!pip install geosnap"
   ]
  },
  {
   "cell_type": "code",
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    }
   ],
   "source": [
    "!pip install contextily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "Installing collected packages: scikit-plot\n",
      "Successfully installed scikit-plot-0.3.7\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting hdbscan\n",
      "  Downloading hdbscan-0.8.26.tar.gz (4.7 MB)\n",
      "\u001b[K     |████████████████████████████████| 4.7 MB 10.4 MB/s eta 0:00:01�█████████████▏                 | 2.1 MB 10.4 MB/s eta 0:00:01\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h    Preparing wheel metadata ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: cython>=0.27 in /opt/conda/lib/python3.7/site-packages (from hdbscan) (0.29.15)\n",
      "Requirement already satisfied: scikit-learn>=0.17 in /opt/conda/lib/python3.7/site-packages (from hdbscan) (0.22.2.post1)\n",
      "Requirement already satisfied: numpy>=1.16.0 in /opt/conda/lib/python3.7/site-packages (from hdbscan) (1.18.1)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from hdbscan) (1.14.0)\n",
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from hdbscan) (0.14.1)\n",
      "Requirement already satisfied: scipy>=0.9 in /opt/conda/lib/python3.7/site-packages (from hdbscan) (1.4.1)\n",
      "Building wheels for collected packages: hdbscan\n",
      "  Building wheel for hdbscan (PEP 517) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for hdbscan: filename=hdbscan-0.8.26-cp37-cp37m-linux_x86_64.whl size=2401369 sha256=b5632ebf6fb24f0995bd50213bcf1aed7433f821396a3b60bbb418e569501e54\n",
      "  Stored in directory: /home/jovyan/.cache/pip/wheels/12/bb/54/7401fdc7883b5975dba5514a185d75d7a0986472df4957637b\n",
      "Successfully built hdbscan\n",
      "Installing collected packages: hdbscan\n",
      "Successfully installed hdbscan-0.8.26\n"
     ]
    }
   ],
   "source": [
    "!pip install hdbscan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To use LTDB data, you need to download the two datasets below and excute below.\n",
    "\n",
    "Download LTDB data at https://s4.ad.brown.edu/projects/diversity/Researcher/LTDB.htm and create the folder, “downloads”.\n",
    "On the webpage above, <strong>CLICK<a href=\"https://s4.ad.brown.edu/projects/diversity/Researcher/LTBDDload/Default.aspx\"> HERE</a> for the download page</strong> under the section  <strong>\"USE the LTDB\"</strong>. Provide your email address and check for all agreements. On the next page, Two drop-down boxes “Select a file type:” and “Select a year” with a button, “Download Standard Data Files” are available. Download both “Full” and “Sample” for “All Years”. Then, you are going to have two datasets, “LTDB_Std_All_fullcount.zip” and “LTDB_Std_All_Sample.zip”\n",
    "\n",
    "In the source code, the command lines below should be executed only for the first run to write LTDB data to your disk. This process takes a few minutes. But you do not need to repeat it from the second run. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Hashing: 100%|██████████| 300M/300M [00:01<00:00, 264MB/s] \n"
     ]
    }
   ],
   "source": [
    "from geosnap.io import store_ltdb\n",
    "\n",
    "# if the archives were in my downloads folder, the paths might be something like this\n",
    "sample = \"downloads/LTDB_Std_All_Sample.zip\"\n",
    "full = \"downloads/LTDB_Std_All_fullcount.zip\"\n",
    "\n",
    "# uncomment to run\n",
    "store_ltdb(sample=sample, fullcount=full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from geosnap import Community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/geosnap/_data.py:303: UserWarning: streaming remote data. Use `geosnap.io.store_census() to store the data locally for better performance\n",
      "  warn('streaming remote data. Use `geosnap.io.store_census() to store the data locally for better performance')\n",
      "/opt/conda/lib/python3.7/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n",
      "/opt/conda/lib/python3.7/site-packages/geosnap/_data.py:334: UserWarning: streaming remote data. Use `geosnap.io.store_census() to store the data locally for better performance\n",
      "  warn('streaming remote data. Use `geosnap.io.store_census() to store the data locally for better performance')\n",
      "/opt/conda/lib/python3.7/site-packages/geosnap/_data.py:364: UserWarning: streaming remote data. Use `geosnap.io.store_census() to store the data locally for better performance\n",
      "  warn('streaming remote data. Use `geosnap.io.store_census() to store the data locally for better performance')\n"
     ]
    }
   ],
   "source": [
    "# this will create a new community using data from Washington DC (which is fips code 11)\n",
    "dc = Community.from_census(state_fips=\"11\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>geoid</th>\n",
       "      <th>n_mexican_pop</th>\n",
       "      <th>n_cuban_pop</th>\n",
       "      <th>n_puerto_rican_pop</th>\n",
       "      <th>n_total_housing_units</th>\n",
       "      <th>n_vacant_housing_units</th>\n",
       "      <th>n_occupied_housing_units</th>\n",
       "      <th>n_owner_occupied_housing_units</th>\n",
       "      <th>n_renter_occupied_housing_units</th>\n",
       "      <th>n_white_persons</th>\n",
       "      <th>...</th>\n",
       "      <th>p_irish_born_pop</th>\n",
       "      <th>p_italian_born_pop</th>\n",
       "      <th>p_poverty_rate_children</th>\n",
       "      <th>p_poverty_rate_hispanic</th>\n",
       "      <th>p_russian_born_pop</th>\n",
       "      <th>p_scandanavian_born_pop</th>\n",
       "      <th>p_scandanavian_pop</th>\n",
       "      <th>n_total_pop_sample</th>\n",
       "      <th>p_female_labor_force</th>\n",
       "      <th>p_black_persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11001001600</td>\n",
       "      <td>4409.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1624.0</td>\n",
       "      <td>1458.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>114423.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11001001500</td>\n",
       "      <td>5430.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2309.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2231.0</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>4694102.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11001001701</td>\n",
       "      <td>2868.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1287.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>618.0</td>\n",
       "      <td>31727.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11001001801</td>\n",
       "      <td>694.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35813.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11001001702</td>\n",
       "      <td>2516.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1043.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>48917.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 195 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         geoid  n_mexican_pop  n_cuban_pop  n_puerto_rican_pop  \\\n",
       "0  11001001600         4409.0          2.0                 7.0   \n",
       "1  11001001500         5430.0         11.0                15.0   \n",
       "2  11001001701         2868.0          5.0                22.0   \n",
       "3  11001001801          694.0          5.0                28.0   \n",
       "4  11001001702         2516.0          6.0                12.0   \n",
       "\n",
       "   n_total_housing_units  n_vacant_housing_units  n_occupied_housing_units  \\\n",
       "0                 1667.0                    43.0                    1624.0   \n",
       "1                 2309.0                    78.0                    2231.0   \n",
       "2                 1287.0                    50.0                    1237.0   \n",
       "3                   10.0                     1.0                       9.0   \n",
       "4                 1086.0                    43.0                    1043.0   \n",
       "\n",
       "   n_owner_occupied_housing_units  n_renter_occupied_housing_units  \\\n",
       "0                          1458.0                            166.0   \n",
       "1                          1898.0                            333.0   \n",
       "2                           619.0                            618.0   \n",
       "3                             0.0                              9.0   \n",
       "4                           712.0                            331.0   \n",
       "\n",
       "   n_white_persons  ...  p_irish_born_pop  p_italian_born_pop  \\\n",
       "0         114423.0  ...               NaN                 NaN   \n",
       "1        4694102.0  ...               NaN                 NaN   \n",
       "2          31727.0  ...               NaN                 NaN   \n",
       "3          35813.0  ...               NaN                 NaN   \n",
       "4          48917.0  ...               NaN                 NaN   \n",
       "\n",
       "   p_poverty_rate_children  p_poverty_rate_hispanic  p_russian_born_pop  \\\n",
       "0                      NaN                      NaN                 NaN   \n",
       "1                      NaN                      NaN                 NaN   \n",
       "2                      NaN                      NaN                 NaN   \n",
       "3                      NaN                      NaN                 NaN   \n",
       "4                      NaN                      NaN                 NaN   \n",
       "\n",
       "   p_scandanavian_born_pop  p_scandanavian_pop  n_total_pop_sample  \\\n",
       "0                      NaN                 NaN                 NaN   \n",
       "1                      NaN                 NaN                 NaN   \n",
       "2                      NaN                 NaN                 NaN   \n",
       "3                      NaN                 NaN                 NaN   \n",
       "4                      NaN                 NaN                 NaN   \n",
       "\n",
       "   p_female_labor_force  p_black_persons  \n",
       "0                   NaN              NaN  \n",
       "1                   NaN              NaN  \n",
       "2                   NaN              NaN  \n",
       "3                   NaN              NaN  \n",
       "4                   NaN              NaN  \n",
       "\n",
       "[5 rows x 195 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc.gdf.head()"
   ]
  }
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