1 - Sensitivity Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started: Sensitivity Analysis\n",
    "\n",
    "To start analyzing tree diversity, it's important to check if how sensistive your dataset is to the number of trees in the city center geographic boundary. The two diversity (entropy) indices used below are both impacted by the number of members in a sample, so this notebook walks through some basic cleaning and then iterates through counts of trees to generate plots suggested where the diversity indices reach a reasonable equilibrium.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "# library import\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import descartes\n",
    "from tqdm import tqdm\n",
    "import treeParsing as tP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Tree Inventory and City Center Boundary\n",
    "\n",
    "Import your tree data and city center boundary data below. These data may use any geospatial data format (SHP, Geojson, Geopackage) and should be in the same coordinate projection.\n",
    "\n",
    "Your tree data will need the following columns:\n",
    "* Point geographic location\n",
    "* Diameter at breast height (DBH)\n",
    "* Tree Scientific Name\n",
    "* Tree Genus Name\n",
    "* Tree Family Name\n",
    "\n",
    "Your city center geography simply needs to be a single, dissolved geometry representing your city center area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enter the path to your data below ###\n",
    "tree_data_path = 'example_data/trees_paris.gpkg'\n",
    "tree_data = gpd.read_file(tree_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a91db96e48>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enter the path to your data below ###\n",
    "city_center_boundary_path = 'example_data/paris.gpkg'\n",
    "city_center = gpd.read_file(city_center_boundary_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a91ee7c348>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_center.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean Data and Calculate Basal Area\n",
    "\n",
    "To start, we need to remove features missing data and remove the top quantile of data. Removing any missing data and the top quantile helps remove erroneous entries that are too large or too small than what we would expect. If your data has already been cleaned, feel free to skip the second cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enter your column names here ###\n",
    "scientific_name_column = 'Scientific'\n",
    "genus_name_column = 'genus'\n",
    "family_name_column = 'family'\n",
    "diameter_breast_height_column = 'DBH'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Ignore if data is already cleaned ###\n",
    "# Exclude Data Missing DBH\n",
    "tree_data = tree_data[tree_data[diameter_breast_height_column]>0]\n",
    "\n",
    "# Exclude data larger than the 99th quantile (often erroneously large)\n",
    "tree_data = tree_data[tree_data[diameter_breast_height_column]<=tree_data.quantile(0.99).DBH]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Basal Area\n",
    "basal_area_column =  'BA'\n",
    "tree_data[basal_area_column] = tree_data[diameter_breast_height_column]**2 * 0.00007854"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Shannon and Simpson Indices for Count and Basal Area\n",
    "\n",
    "This analysis will sample a random number of trees up to your total number of trees. By default, it will take 10 samples at each count, but a higher number will yield more accurate results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "### For more accurate results increase the iterations_per_count\n",
    "### If this notebook is running too slowly on your computer, reduce the iterations_per_count\n",
    "iterations_per_count = 10\n",
    "increment_exponent = 1.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 132/132 [00:59<00:00,  2.21it/s]\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>count</th>\n",
       "      <th>species_simpson_count</th>\n",
       "      <th>species_simpson_area</th>\n",
       "      <th>genus_simpson_count</th>\n",
       "      <th>genus_simpson_area</th>\n",
       "      <th>family_simpson_count</th>\n",
       "      <th>family_simpson_area</th>\n",
       "      <th>species_shannon_count</th>\n",
       "      <th>species_shannon_area</th>\n",
       "      <th>genus_shannon_count</th>\n",
       "      <th>genus_shannon_area</th>\n",
       "      <th>family_shannon_count</th>\n",
       "      <th>family_shannon_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.375788</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.375788</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.375788</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.563199</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.563199</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.563199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.508233</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.508233</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.508233</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.766421</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.766421</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.766421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.462771</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.462771</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.462771</td>\n",
       "      <td>1.213008</td>\n",
       "      <td>0.714235</td>\n",
       "      <td>1.039721</td>\n",
       "      <td>0.714235</td>\n",
       "      <td>1.039721</td>\n",
       "      <td>0.714235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.557136</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.554082</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.554082</td>\n",
       "      <td>1.470808</td>\n",
       "      <td>0.963328</td>\n",
       "      <td>1.332179</td>\n",
       "      <td>0.893326</td>\n",
       "      <td>1.332179</td>\n",
       "      <td>0.893326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  species_simpson_count  species_simpson_area  genus_simpson_count  \\\n",
       "0      1               0.000000              0.000000             0.000000   \n",
       "1      2               0.500000              0.375788             0.500000   \n",
       "2      3               0.666667              0.508233             0.666667   \n",
       "3      4               0.687500              0.462771             0.625000   \n",
       "4      5               0.760000              0.557136             0.720000   \n",
       "\n",
       "   genus_simpson_area  family_simpson_count  family_simpson_area  \\\n",
       "0            0.000000              0.000000             0.000000   \n",
       "1            0.375788              0.500000             0.375788   \n",
       "2            0.508233              0.666667             0.508233   \n",
       "3            0.462771              0.625000             0.462771   \n",
       "4            0.554082              0.720000             0.554082   \n",
       "\n",
       "   species_shannon_count  species_shannon_area  genus_shannon_count  \\\n",
       "0              -0.000000             -0.000000            -0.000000   \n",
       "1               0.693147              0.563199             0.693147   \n",
       "2               1.098612              0.766421             1.098612   \n",
       "3               1.213008              0.714235             1.039721   \n",
       "4               1.470808              0.963328             1.332179   \n",
       "\n",
       "   genus_shannon_area  family_shannon_count  family_shannon_area  \n",
       "0           -0.000000             -0.000000            -0.000000  \n",
       "1            0.563199              0.693147             0.563199  \n",
       "2            0.766421              1.098612             0.766421  \n",
       "3            0.714235              1.039721             0.714235  \n",
       "4            0.893326              1.332179             0.893326  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate counts for Sample Size\n",
    "i = 1\n",
    "sample_size_list = []\n",
    "\n",
    "while i < len(tree_data):\n",
    "    sample_size_list.append(round(i))   \n",
    "    i = (i+1)**increment_exponent\n",
    "sample_size_list.append(len(tree_data))\n",
    "\n",
    "# List of results\n",
    "entropy_data = []\n",
    "\n",
    "# Loop through increasing sizes of tree samples until you hit the size of the tree data\n",
    "for count in tqdm(sample_size_list):    \n",
    "    # Repeat for the number of iterations\n",
    "    for i in range(0,iterations_per_count):\n",
    "        # Sample the trees\n",
    "        sample = tree_data.sample(count)\n",
    "        \n",
    "        # Derive counts, areas, for species, genus, and family\n",
    "        species_count = sample[[scientific_name_column, basal_area_column]].groupby(scientific_name_column).count().reset_index()\n",
    "        species_area = sample[[scientific_name_column, basal_area_column]].groupby(scientific_name_column).sum().reset_index()\n",
    "        \n",
    "        genus_count = sample[[genus_name_column, basal_area_column]].groupby(genus_name_column).count().reset_index()\n",
    "        genus_area = sample[[genus_name_column, basal_area_column]].groupby(genus_name_column).sum().reset_index()\n",
    "        \n",
    "        family_count = sample[[family_name_column, basal_area_column]].groupby(family_name_column).count().reset_index()\n",
    "        family_area = sample[[family_name_column, basal_area_column]].groupby(family_name_column).sum().reset_index()\n",
    "        \n",
    "        \n",
    "        # Calculate Percentages by count and area\n",
    "        species_count[\"Pct\"] = species_count[basal_area_column]/sum(species_count[basal_area_column])\n",
    "        species_area[\"Pct\"]  = species_area[basal_area_column]/sum(species_area[basal_area_column])\n",
    "        \n",
    "        genus_count[\"Pct\"] = genus_count[basal_area_column]/sum(genus_count[basal_area_column])\n",
    "        genus_area[\"Pct\"] = genus_area[basal_area_column]/sum(genus_area[basal_area_column])\n",
    "        \n",
    "        family_count[\"Pct\"] = family_count[basal_area_column]/sum(family_count[basal_area_column])\n",
    "        family_area[\"Pct\"] = family_area[basal_area_column]/sum(family_area[basal_area_column])\n",
    "        \n",
    "        # Calculate Shannon Indices\n",
    "        species_shannon_count = tP.ShannonEntropy(list(species_count[\"Pct\"]))\n",
    "        species_shannon_area = tP.ShannonEntropy(list(species_area[\"Pct\"]))\n",
    "        \n",
    "        genus_shannon_count = tP.ShannonEntropy(list(genus_count[\"Pct\"]))\n",
    "        genus_shannon_area = tP.ShannonEntropy(list(genus_area[\"Pct\"]))\n",
    "        \n",
    "        family_shannon_count = tP.ShannonEntropy(list(family_count[\"Pct\"]))\n",
    "        family_shannon_area = tP.ShannonEntropy(list(family_area[\"Pct\"]))\n",
    "        \n",
    "        # Calculate Simpson Indices\n",
    "        species_simpson_count = tP.simpson_di(list(species_count[scientific_name_column]), list(species_count[basal_area_column]))\n",
    "        species_simpson_area = tP.simpson_di(list(species_area[scientific_name_column]),list(species_area[basal_area_column]))\n",
    "        \n",
    "        genus_simpson_count = tP.simpson_di(list(genus_count[genus_name_column]), list(genus_count[basal_area_column]))\n",
    "        genus_simpson_area = tP.simpson_di(list(genus_area[genus_name_column]), list(genus_area[basal_area_column]))\n",
    "        \n",
    "        family_simpson_count = tP.simpson_di(list(family_count[family_name_column]), list(family_count[basal_area_column]))\n",
    "        family_simpson_area = tP.simpson_di(list(family_area[family_name_column]), list(family_area[basal_area_column]))\n",
    "        \n",
    "        # Append to results\n",
    "        entropy_data.append({\n",
    "            'count': count,\n",
    "            'species_simpson_count': species_simpson_count,\n",
    "            'species_simpson_area': species_simpson_area,\n",
    "            'genus_simpson_count': genus_simpson_count,\n",
    "            'genus_simpson_area': genus_simpson_area,\n",
    "            'family_simpson_count': family_simpson_count,\n",
    "            'family_simpson_area': family_simpson_area,\n",
    "            'species_shannon_count': species_shannon_count,\n",
    "            'species_shannon_area': species_shannon_area,\n",
    "            'genus_shannon_count': genus_shannon_count,\n",
    "            'genus_shannon_area': genus_shannon_area,\n",
    "            'family_shannon_count': family_shannon_count,\n",
    "            'family_shannon_area': family_shannon_area\n",
    "        })\n",
    "        \n",
    "# Load results into a Dataframe and get median vlalues\n",
    "results_df = pd.DataFrame(entropy_data)\n",
    "median_df = results_df.groupby('count').median().reset_index()\n",
    "median_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chart Shannon Index by Basal Area\n",
    "\n",
    "Our results are now loaded into a DataFrame. The below functions and cells will map out each combination (Shannon Index and Simpson Index by Area and Count). Each plot will include a vertical line indicating the count of trees in your tree inventory contained within your city center geography. If the vertical line falls after the slope of the species, genus, and family lines have leveled off, it is reasonable to continueto the second notebook, which conducts the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_center_tree_count = len(gpd.sjoin(tree_data, city_center))\n",
    "plt.rcParams[\"figure.figsize\"] = [14, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateFigure(df, index, measure):\n",
    "    x = df['count']\n",
    "    y = df[f'species_{index}_{measure}']\n",
    "    y2 = df[f'genus_{index}_{measure}']\n",
    "    y3 = df[f'family_{index}_{measure}']\n",
    "\n",
    "    axes = plt.gca()\n",
    "    axes.set_xlim([1,city_center_tree_count*4])\n",
    "    \n",
    "    if index == \"shannon\":\n",
    "        axes.set_ylim([0,4.5])\n",
    "    else: \n",
    "        axes.set_ylim([0,1])\n",
    "        \n",
    "    plt.xscale('symlog')\n",
    "    plt.xlabel('Random Sample Size (Log Scale)')\n",
    "\n",
    "    plt.ylabel(f'{index.capitalize()} Index ({measure.capitalize()})')\n",
    "\n",
    "    plt.scatter(x, y, s=30, c='red', alpha=1, marker=\"x\", label='Species')\n",
    "    plt.scatter(x, y2, s=30, c='orange', alpha=1, marker=\"+\", label='Genus')\n",
    "    plt.scatter(x, y3, s=30, c='green', alpha=1, marker=\"_\", label='Family')\n",
    "    plt.legend(loc=\"upper left\")\n",
    "\n",
    "    plt.axvline(x=city_center_tree_count, linewidth=1, color='black')\n",
    "    plt.text(city_center_tree_count+1000,0.1,'Trees in City Center',horizontalalignment='left', rotation=90)\n",
    "\n",
    "    plt.title(f'{index.capitalize()} Index by {measure.capitalize()}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generateFigure(median_df, 'shannon', 'area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generateFigure(median_df, 'shannon', 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generateFigure(median_df, 'simpson', 'area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generateFigure(median_df, 'simpson', 'count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "All looks good? Continue to Notebook 2: City Center Diversity Analysis"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}