peterdesmet/geese-species-observations-per-year.png

## geese-species-observations-per-year.png

      
    Raw
  

              geese-species-observations-per-year.png
            
          
## ias-species-observations-per-year.png

      
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              ias-species-observations-per-year.png
            
          
## species-observations-per-year.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Species observations for a country per year (using GBIF occurrence facets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import pygbif\n",
    "import requests\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define a list of species we want counts for. I'm using feral geese and coots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_species_list = ['Branta canadensis', 'Branta leucopsis', 'Anser anser', 'Alopochen aegyptiaca', 'Fulica atra', 'this name should get ignored']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we look up their GBIF `speciesKey`s. Note: these is the key for the accepted species name, so if your list contains infraspecific names or synonyms, you'll get the **accepted species** back. If you don't want this, use the `usageKey`, which is id for that specific name (not it's accepted species)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_species_key(name):\n",
    "    gbif_match = pygbif.species.name_backbone(name)\n",
    "    try:\n",
    "        return gbif_match['speciesKey'] # Found a valid match\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "my_species_dict = {}\n",
    "for species_name in my_species_list:\n",
    "    species_key = get_species_key(species_name)\n",
    "    if species_key:\n",
    "        my_species_dict[species_key] = species_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2474377: 'Fulica atra',\n",
       " 2498036: 'Anser anser',\n",
       " 2498252: 'Alopochen aegyptiaca',\n",
       " 5232437: 'Branta canadensis',\n",
       " 5232464: 'Branta leucopsis'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_species_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll use GBIF occurrence facets to get `HumanObservation` counts per year for these species, for a certain country. Here I use `BE` for Belgium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_count_per_year_dict(country_code, species_key):\n",
    "    request = 'http://api.gbif.org/v1/occurrence/search?country=' + str(country_code) + '&taxon_key=' + str(species_key) + '&basis_of_record=HUMAN_OBSERVATION&facet=YEAR&YEAR.facetLimit=300&limit=0'\n",
    "    response = json.loads(requests.get(request).text)\n",
    "    return response['facets'][0]['counts'] # Counts per year (or None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_year_count_df(country_code, species_dict):\n",
    "    year_counts = {}\n",
    "    \n",
    "    for key, value in species_dict.items():\n",
    "        species_key = str(key)\n",
    "        scientific_name = str(value) # This will be our index\n",
    "        \n",
    "        # Get counts (in column count) per year (in column name)\n",
    "        temp = pd.DataFrame(get_count_per_year_dict(country_code, species_key))\n",
    "        if len(temp) > 0:\n",
    "            year_counts[scientific_name] = temp.set_index('name') # {scientificName: df, scientificName: df}\n",
    "    \n",
    "    # 1. Concatenate all the dfs into one, (scientificName) column by column\n",
    "    # 2. Transpose, to get year columns and scientificName rows\n",
    "    # 3. Cast all counts to int, with NaN as 0\n",
    "    df = pd.concat(year_counts, axis=1).transpose().fillna(0.0).astype(int)\n",
    "    \n",
    "    # Drop the remaining column['name'] with values 'count'\n",
    "    df.index = df.index.droplevel(1)\n",
    "    \n",
    "    # Not sure why, but the transformations above also got our columns and rows sorted.\n",
    "    # Otherwise, we could use:\n",
    "    # df = df.sort_index(axis=0).sort_index(axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "species_counts_df = build_year_count_df('BE', my_species_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1970</th>\n",
       "      <th>1974</th>\n",
       "      <th>1985</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>...</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alopochen aegyptiaca</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>54</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>857</td>\n",
       "      <td>951</td>\n",
       "      <td>1062</td>\n",
       "      <td>1226</td>\n",
       "      <td>1227</td>\n",
       "      <td>1673</td>\n",
       "      <td>2011</td>\n",
       "      <td>1370</td>\n",
       "      <td>833</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anser anser</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>107</td>\n",
       "      <td>98</td>\n",
       "      <td>...</td>\n",
       "      <td>1459</td>\n",
       "      <td>1527</td>\n",
       "      <td>1518</td>\n",
       "      <td>1856</td>\n",
       "      <td>1961</td>\n",
       "      <td>2434</td>\n",
       "      <td>2697</td>\n",
       "      <td>1839</td>\n",
       "      <td>945</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Branta canadensis</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>53</td>\n",
       "      <td>114</td>\n",
       "      <td>...</td>\n",
       "      <td>1037</td>\n",
       "      <td>1203</td>\n",
       "      <td>1241</td>\n",
       "      <td>1513</td>\n",
       "      <td>1430</td>\n",
       "      <td>1841</td>\n",
       "      <td>2197</td>\n",
       "      <td>1472</td>\n",
       "      <td>874</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Branta leucopsis</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>269</td>\n",
       "      <td>280</td>\n",
       "      <td>272</td>\n",
       "      <td>362</td>\n",
       "      <td>546</td>\n",
       "      <td>692</td>\n",
       "      <td>734</td>\n",
       "      <td>597</td>\n",
       "      <td>384</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fulica atra</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>1192</td>\n",
       "      <td>1411</td>\n",
       "      <td>1588</td>\n",
       "      <td>...</td>\n",
       "      <td>2751</td>\n",
       "      <td>2901</td>\n",
       "      <td>2943</td>\n",
       "      <td>2840</td>\n",
       "      <td>2626</td>\n",
       "      <td>3018</td>\n",
       "      <td>3041</td>\n",
       "      <td>1663</td>\n",
       "      <td>371</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1970  1974  1985  1987  1988  1989  1991  1992  1993  \\\n",
       "Alopochen aegyptiaca     0     0     0     0     0     0     0    16    54   \n",
       "Anser anser              0     0     1     1     1     0    29    90   107   \n",
       "Branta canadensis        0     0     0     0     0     0     7    19    53   \n",
       "Branta leucopsis         0     0     0     0     0     0     7    21    26   \n",
       "Fulica atra              1     1     1     0     0     1   480  1192  1411   \n",
       "\n",
       "                      1994  ...   2006  2007  2008  2009  2010  2011  2012  \\\n",
       "Alopochen aegyptiaca    86  ...    857   951  1062  1226  1227  1673  2011   \n",
       "Anser anser             98  ...   1459  1527  1518  1856  1961  2434  2697   \n",
       "Branta canadensis      114  ...   1037  1203  1241  1513  1430  1841  2197   \n",
       "Branta leucopsis        36  ...    269   280   272   362   546   692   734   \n",
       "Fulica atra           1588  ...   2751  2901  2943  2840  2626  3018  3041   \n",
       "\n",
       "                      2013  2014  2015  \n",
       "Alopochen aegyptiaca  1370   833   500  \n",
       "Anser anser           1839   945   668  \n",
       "Branta canadensis     1472   874   566  \n",
       "Branta leucopsis       597   384   295  \n",
       "Fulica atra           1663   371    78  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_counts_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's now plot this data, using [@stijnvanhoey's example](https://github.com/stijnvanhoey/open_data_showcases/blob/master/gent_cijfers/wijkmigratie_in_beeld.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "species_counts_subset_df = species_counts_df.ix[:,'2000':] # ix[row_range, column_range]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hjx4MGDCA4OBgw/R79+5x4sSJDBtqnU7H3r17qVKlymvF8/Pz488//2TDhg107tw5zfv1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HbvXxj/5IGmVy39eO1L99Gd8f/wuoDu/F1UtpZoelSHWAUl8CUxa08lrSwfEh1L9KpjGNd0ByMj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\npRUvYbtmNk8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114aa8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "sns.heatmap(species_counts_subset_df, ax=ax, annot=True, fmt='d',\n",
    "            cmap='RdPu')\n",
    "\n",
    "# Have x axis and label on top\n",
    "ax.xaxis.tick_top()\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Set labels (I should try to figure out how to transform the df to get this automatically)\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Species')\n",
    "\n",
    "# Add a title\n",
    "plt.suptitle('GBIF-registered feral geese and coot observations per year for Belgium')\n",
    "plt.subplots_adjust(top=0.85)\n",
    "\n",
    "# Add some padding on the left\n",
    "plt.gcf().subplots_adjust(left=0.3)\n",
    "# plt.tight_layout() # Doesn't take into account suptitle\n",
    "\n",
    "plt.savefig('geese-species-observations-per-year.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there you have it! Note that the observation counts in this figure are not corrected (for sampling effort, lack of data, etc.), so don't make any species trends conclusions. :-)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}