5_univariate_analysis_ndvi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9744e7bb",
   "metadata": {},
   "source": [
    "# Univariate analysis NDVI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "febe111d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from pygam import LinearGAM, s, l\n",
    "from sklearn import linear_model, metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from PIL import Image as im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aeed14f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install pygam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cf24c0",
   "metadata": {},
   "source": [
    "## 1. Modeling vegetation densities for buffer distances of 100 meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e0e7418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>NDVI_100</th>\n",
       "      <th>Greenspace%_100</th>\n",
       "      <th>Canopy%_100</th>\n",
       "      <th>Grass%_100</th>\n",
       "      <th>Shrubs%_100</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.455823</td>\n",
       "      <td>0.992861</td>\n",
       "      <td>2.168372e-17</td>\n",
       "      <td>0.006803</td>\n",
       "      <td>0.848073</td>\n",
       "      <td>POINT (386483.262 417703.544)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.387782</td>\n",
       "      <td>0.521713</td>\n",
       "      <td>2.176871e-01</td>\n",
       "      <td>0.097506</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>POINT (395310.475 388609.235)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.389722</td>\n",
       "      <td>0.979774</td>\n",
       "      <td>5.117090e-17</td>\n",
       "      <td>0.083900</td>\n",
       "      <td>0.891156</td>\n",
       "      <td>POINT (384225.708 417867.819)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.623486</td>\n",
       "      <td>0.771565</td>\n",
       "      <td>8.163265e-02</td>\n",
       "      <td>0.083900</td>\n",
       "      <td>0.612245</td>\n",
       "      <td>POINT (390673.998 416261.747)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.628733</td>\n",
       "      <td>0.886972</td>\n",
       "      <td>6.643991e-01</td>\n",
       "      <td>0.052154</td>\n",
       "      <td>0.163265</td>\n",
       "      <td>POINT (393523.154 393041.484)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NDVI_100  Greenspace%_100   Canopy%_100  Grass%_100  Shrubs%_100  \\\n",
       "0  0.455823         0.992861  2.168372e-17    0.006803     0.848073   \n",
       "1  0.387782         0.521713  2.176871e-01    0.097506     0.190476   \n",
       "2  0.389722         0.979774  5.117090e-17    0.083900     0.891156   \n",
       "3  0.623486         0.771565  8.163265e-02    0.083900     0.612245   \n",
       "4  0.628733         0.886972  6.643991e-01    0.052154     0.163265   \n",
       "\n",
       "                        geometry  \n",
       "0  POINT (386483.262 417703.544)  \n",
       "1  POINT (395310.475 388609.235)  \n",
       "2  POINT (384225.708 417867.819)  \n",
       "3  POINT (390673.998 416261.747)  \n",
       "4  POINT (393523.154 393041.484)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load greenspace metrics at 100m geodataframe\n",
    "gdf_100 = gpd.read_file(\"thesis_project/output/dataframes/greenspace_metrics_100_df_2.json\")\n",
    "gdf_100.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821fbc94",
   "metadata": {},
   "source": [
    "### 1.1. Model 1: greenspace ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24238424",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data/predictors as the NDVI_100\n",
    "df_100 = pd.DataFrame(gdf_100, columns=['NDVI_100'])\n",
    "\n",
    "# target as Greenspace%_100\n",
    "target_100 = pd.DataFrame(gdf_100, columns=['Greenspace%_100'])\n",
    "\n",
    "# define X and Y\n",
    "X = df_100\n",
    "y = target_100['Greenspace%_100']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4851ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9999\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -84351.7622\n",
      "Number of Samples:                         4749 AIC:                                           168709.5242\n",
      "                                                AICc:                                          168709.5293\n",
      "                                                GCV:                                                0.0243\n",
      "                                                Scale:                                              0.0243\n",
      "                                                Pseudo R-Squared:                                   0.6833\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.0158]             1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          3.46e-03     **          \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/161998086.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_100_1.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 1\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_100_1 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_100_1.gridsearch(X_train, y_train)\n",
    "uni_gam_100_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56f27b9",
   "metadata": {},
   "source": [
    "### 1.2. Model 2: greenspace ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "91a31661",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                     10.1105\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -93747.8378\n",
      "Number of Samples:                         4749 AIC:                                           187517.8966\n",
      "                                                AICc:                                          187517.9534\n",
      "                                                GCV:                                                0.0221\n",
      "                                                Scale:                                               0.022\n",
      "                                                Pseudo R-Squared:                                   0.7134\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [15.8489]            20           10.1         1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2437069232.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_100_2.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 2\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_100_2 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_100_2.gridsearch(X_train, y_train)\n",
    "uni_gam_100_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "616e2947",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting best greenspace_100 model\n",
    "for i, term in enumerate(uni_gam_100_2.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_100_2.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_100_2.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Greenspace to NDVI\\nfor buffer zones of 100 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nGreenspace', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_greenspace_100_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_greenspace_100_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_greenspace_100_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b11fd028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, Greenspace%_100 increases by: 0.17756113980713956\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does Greenspace% increase, if NDVI changes from 0.25 to 0.35?\n",
    "green_l0 = uni_gam_100_2.predict(0.20)\n",
    "green_l1 = uni_gam_100_2.predict(0.30)\n",
    "green_sens_low = float(green_l1 - green_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, Greenspace%_100 increases by:', green_sens_low)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "d37ac440",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, Greenspace%_100 increases by: 0.15092372111359387\n"
     ]
    }
   ],
   "source": [
    "# Change in mid NDVI values: how does Greenspace% increase, if NDVI changes from 0.45 to 0.55?\n",
    "green_m0 = uni_gam_100_2.predict(0.40)\n",
    "green_m1 = uni_gam_100_2.predict(0.50)\n",
    "green_sens_mid = float(green_m1 - green_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, Greenspace%_100 increases by:', green_sens_mid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5e088ace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, Greenspace%_100 increases by: 0.062488191175932406\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does Greenspace% increase, if NDVI changes from 0.65 to 0.75?\n",
    "green_h0 = uni_gam_100_2.predict(0.60)\n",
    "green_h1 = uni_gam_100_2.predict(0.70)\n",
    "green_sens_hig = float(green_h1 - green_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, Greenspace%_100 increases by:', green_sens_hig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c6462a",
   "metadata": {},
   "source": [
    "### 1.3. Model 3: tree canopy ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c50b675d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NDVI_100 as the predictor\n",
    "df = pd.DataFrame(gdf_100, columns=['NDVI_100'])\n",
    "\n",
    "# Canopy%_100 as the target\n",
    "target = pd.DataFrame(gdf_100, columns=['Canopy%_100'])\n",
    "\n",
    "# define X and y\n",
    "X = df\n",
    "y = target['Canopy%_100']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_100 = linear_model.LinearRegression()\n",
    "model_100 = lm_100.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "470329b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9948\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -66029.3274\n",
      "Number of Samples:                         4749 AIC:                                           132064.6444\n",
      "                                                AICc:                                          132064.6495\n",
      "                                                GCV:                                                0.0303\n",
      "                                                Scale:                                              0.0303\n",
      "                                                Pseudo R-Squared:                                   0.0508\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.6]                1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2799447770.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_100_3.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 3\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_100_3 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_100_3.gridsearch(X_train, y_train)\n",
    "uni_gam_100_3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fe9f392",
   "metadata": {},
   "source": [
    "### 1.4. Model 4: tree canopy ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cc241b41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                     12.6008\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -69592.9031\n",
      "Number of Samples:                         4749 AIC:                                           139213.0076\n",
      "                                                AICc:                                          139213.0915\n",
      "                                                GCV:                                                 0.029\n",
      "                                                Scale:                                              0.0289\n",
      "                                                Pseudo R-Squared:                                   0.0988\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [3.9811]             20           12.6         1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/3283584014.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_100_4.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 4\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_100_4 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_100_4.gridsearch(X_train, y_train)\n",
    "uni_gam_100_4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "88ac8883",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting best canopy_100 model\n",
    "for i, term in enumerate(uni_gam_100_4.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_100_4.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_100_4.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Tree canopy to NDVI\\nfor buffer zones of 100 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nTree canopy', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_canopy_100_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_canopy_100_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_canopy_100_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "b1a37d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, Canopy%_100 increases by: 0.0497382459229099\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does canopy% increase, if NDVI changes from 0.25 to 0.35?\n",
    "canopy_l0 = uni_gam_100_4.predict(0.20)\n",
    "canopy_l1 = uni_gam_100_4.predict(0.30)\n",
    "canopy_sens_low = float(canopy_l1 - canopy_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, Canopy%_100 increases by:', canopy_sens_low)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "bab50cf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, Canopy%_100 increases by: 0.0437499395868525\n"
     ]
    }
   ],
   "source": [
    "# Change in mid NDVI values: how does canopy% increase, if NDVI changes from 0.45 to 0.55?\n",
    "canopy_m0 = uni_gam_100_4.predict(0.40)\n",
    "canopy_m1 = uni_gam_100_4.predict(0.50)\n",
    "canopy_sens_mid = float(canopy_m1 - canopy_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, Canopy%_100 increases by:', canopy_sens_mid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "bda7350c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, Canopy%_100 increases by: -0.03925067938015117\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does canopy% increase, if NDVI changes from 0.65 to 0.75?\n",
    "canopy_h0 = uni_gam_100_4.predict(0.60)\n",
    "canopy_h1 = uni_gam_100_4.predict(0.70)\n",
    "canopy_sens_hig = float(canopy_h1 - canopy_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, Canopy%_100 increases by:', canopy_sens_hig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775ff828",
   "metadata": {},
   "source": [
    "### 1.5. Model 5: forbs and shrubs ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ce18abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data/predictors as the NDVI_100\n",
    "df_100 = pd.DataFrame(gdf_100, columns=['NDVI_100'])\n",
    "\n",
    "# target as Shrubs%_100\n",
    "target_100 = pd.DataFrame(gdf_100, columns=['Shrubs%_100'])\n",
    "\n",
    "# define X and y\n",
    "X = df_100\n",
    "y = target_100['Shrubs%_100']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_100 = linear_model.LinearRegression()\n",
    "model_100 = lm_100.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "812b1302",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9978\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -26872.0266\n",
      "Number of Samples:                         4749 AIC:                                            53750.0489\n",
      "                                                AICc:                                           53750.0539\n",
      "                                                GCV:                                                0.0672\n",
      "                                                Scale:                                              0.0672\n",
      "                                                Pseudo R-Squared:                                   0.1617\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.2512]             1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          8.41e-01                 \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2907954286.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_100_5.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 5\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_100_5 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_100_5.gridsearch(X_train, y_train)\n",
    "uni_gam_100_5.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695353ab",
   "metadata": {},
   "source": [
    "### 1.6. Model 6: forbs and shrubs ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dcb9579c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      4.6163\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -27514.9148\n",
      "Number of Samples:                         4749 AIC:                                            55041.0622\n",
      "                                                AICc:                                           55041.0779\n",
      "                                                GCV:                                                0.0659\n",
      "                                                Scale:                                              0.0658\n",
      "                                                Pseudo R-Squared:                                   0.1799\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [1000.]              20           4.6          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/201407906.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_100_6.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 6\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_100_6 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_100_6.gridsearch(X_train, y_train)\n",
    "uni_gam_100_6.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "52f66618",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting best model\n",
    "for i, term in enumerate(uni_gam_100_6.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_100_6.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_100_6.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Shrubs density to NDVI\\nfor buffer zones of 100 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nShrubs density', fontsize=18)    \n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_shrubs_100_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_shrubs_100_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_shrubs_100_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1ceffedf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, Shrubs%_100 increases by: 0.09777238149995937\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does Shrubs% increase, if NDVI changes from 0.25 to 0.35?\n",
    "shrubs_l0 = uni_gam_100_6.predict(0.20)\n",
    "shrubs_l1 = uni_gam_100_6.predict(0.30)\n",
    "shrubs_sens_low = float(shrubs_l1 - shrubs_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, Shrubs%_100 increases by:', shrubs_sens_low)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "181798b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, Shrubs%_100 increases by: 0.09264013365186013\n"
     ]
    }
   ],
   "source": [
    "# Change in mid NDVI values: how does Shrubs% increase, if NDVI changes from 0.45 to 0.55?\n",
    "shrubs_m0 = uni_gam_100_6.predict(0.40)\n",
    "shrubs_m1 = uni_gam_100_6.predict(0.50)\n",
    "shrubs_sens_mid = float(shrubs_m1 - shrubs_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, Shrubs%_100 increases by:', shrubs_sens_mid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "3fef61f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, Shrubs%_100 increases by: 0.004204302974184393\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does Shrubs% increase, if NDVI changes from 0.65 to 0.75?\n",
    "shrubs_h0 = uni_gam_100_6.predict(0.60)\n",
    "shrubs_h1 = uni_gam_100_6.predict(0.70)\n",
    "shrubs_sens_hig = float(shrubs_h1 - shrubs_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, Shrubs%_100 increases by:', shrubs_sens_hig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7665ba8c",
   "metadata": {},
   "source": [
    "### 1.7. Model 7: grass ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7e84d41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NDVI_100 as the predictor\n",
    "df = pd.DataFrame(gdf_100, columns=['NDVI_100'])\n",
    "\n",
    "# Grass%_100 as the target\n",
    "target = pd.DataFrame(gdf_100, columns=['Grass%_100'])\n",
    "\n",
    "# define X and y\n",
    "X = df\n",
    "y = target['Grass%_100']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_100 = linear_model.LinearRegression()\n",
    "model_100 = lm_100.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f327ba9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9978\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -81601.5616\n",
      "Number of Samples:                         4749 AIC:                                           163209.1189\n",
      "                                                AICc:                                          163209.1239\n",
      "                                                GCV:                                                0.0251\n",
      "                                                Scale:                                              0.0251\n",
      "                                                Pseudo R-Squared:                                   0.1734\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.2512]             1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          9.54e-14     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/822825484.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_100_7.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 7\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_100_7 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_100_7.gridsearch(X_train, y_train)\n",
    "uni_gam_100_7.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c00f1de",
   "metadata": {},
   "source": [
    "### 1.8. Model 8: grass ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7e9cf051",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                     10.1105\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -85312.1923\n",
      "Number of Samples:                         4749 AIC:                                           170646.6055\n",
      "                                                AICc:                                          170646.6623\n",
      "                                                GCV:                                                0.0241\n",
      "                                                Scale:                                               0.024\n",
      "                                                Pseudo R-Squared:                                   0.2089\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [15.8489]            20           10.1         1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/227079347.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_100_8.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 8\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_100_8 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_100_8.gridsearch(X_train, y_train)\n",
    "uni_gam_100_8.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "4007f1a3",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting best grass_100 model\n",
    "for i, term in enumerate(uni_gam_100_8.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_100_8.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_100_8.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Grass density to NDVI\\nfor buffer zones of 100 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nGrass density', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_grass_100_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_grass_100_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_grass_100_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4aff0fc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, Grass%_100 increases by: 0.04838241050848764\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does Grass% increase, if NDVI changes from 0.25 to 0.35?\n",
    "grass_l0 = uni_gam_100_8.predict(0.20)\n",
    "grass_l1 = uni_gam_100_8.predict(0.30)\n",
    "grass_sens_low = float(grass_l1 - grass_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, Grass%_100 increases by:', grass_sens_low)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "142ad7ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, Grass%_100 increases by: 0.017751675654094162\n"
     ]
    }
   ],
   "source": [
    "# Change in mid NDVI values: how does Grass% increase, if NDVI changes from 0.45 to 0.55?\n",
    "grass_m0 = uni_gam_100_8.predict(0.4)\n",
    "grass_m1 = uni_gam_100_8.predict(0.5)\n",
    "grass_sens_mid = float(grass_m1 - grass_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, Grass%_100 increases by:', grass_sens_mid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "72e407ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, Grass%_100 increases by: 0.12305952325462363\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does Grass% increase, if NDVI changes from 0.65 to 0.75?\n",
    "grass_h0 = uni_gam_100_8.predict(0.6)\n",
    "grass_h1 = uni_gam_100_8.predict(0.7)\n",
    "grass_sens_hig = float(grass_h1 - grass_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, Grass%_100 increases by:', grass_sens_hig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f18a166",
   "metadata": {},
   "source": [
    "## 2. Modeling vegetation densities for buffer distances of 300 meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a719d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NDVI_300</th>\n",
       "      <th>Greenspace%_300</th>\n",
       "      <th>Canopy%_300</th>\n",
       "      <th>Grass%_300</th>\n",
       "      <th>Shrubs%_300</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.326412</td>\n",
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       "      <td>POINT (387760.076 393972.512)</td>\n",
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       "      <td>0.503846</td>\n",
       "      <td>0.847073</td>\n",
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       "      <td>0.391561</td>\n",
       "      <td>0.387530</td>\n",
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       "      <td>0.589265</td>\n",
       "      <td>0.831569</td>\n",
       "      <td>0.104542</td>\n",
       "      <td>0.243752</td>\n",
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       "      <td>POINT (391126.815 417220.287)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493672</td>\n",
       "      <td>0.796394</td>\n",
       "      <td>0.127654</td>\n",
       "      <td>0.150497</td>\n",
       "      <td>0.513034</td>\n",
       "      <td>POINT (361327.964 411390.899)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.343043</td>\n",
       "      <td>0.610614</td>\n",
       "      <td>0.275732</td>\n",
       "      <td>0.147272</td>\n",
       "      <td>0.190540</td>\n",
       "      <td>POINT (377099.280 400575.430)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NDVI_300  Greenspace%_300  Canopy%_300  Grass%_300  Shrubs%_300  \\\n",
       "0  0.280478         0.326412     0.145660    0.080623     0.097554   \n",
       "1  0.503846         0.847073     0.062886    0.391561     0.387530   \n",
       "2  0.589265         0.831569     0.104542    0.243752     0.480785   \n",
       "3  0.493672         0.796394     0.127654    0.150497     0.513034   \n",
       "4  0.343043         0.610614     0.275732    0.147272     0.190540   \n",
       "\n",
       "                        geometry  \n",
       "0  POINT (387760.076 393972.512)  \n",
       "1  POINT (361558.182 408801.964)  \n",
       "2  POINT (391126.815 417220.287)  \n",
       "3  POINT (361327.964 411390.899)  \n",
       "4  POINT (377099.280 400575.430)  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load greenspace metrics at 300m geodataframe\n",
    "gdf_300 = gpd.read_file(\"thesis_project/output/dataframes/greenspace_metrics_300_df_2.json\")\n",
    "gdf_300.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea624177",
   "metadata": {},
   "source": [
    "### 2.1. Model 1: greenspace ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "881ff4d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data/predictors as the NDVI_300\n",
    "df_300 = pd.DataFrame(gdf_300, columns=['NDVI_300'])\n",
    "\n",
    "# target as Greenspace%_300\n",
    "target_300 = pd.DataFrame(gdf_300, columns=['Greenspace%_300'])\n",
    "\n",
    "# define X and Y\n",
    "X = df_300\n",
    "y = target_300['Greenspace%_300']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3ab33a0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9998\n",
      "Link Function:                     IdentityLink Log Likelihood:                               -118115.4594\n",
      "Number of Samples:                         4656 AIC:                                           236236.9183\n",
      "                                                AICc:                                          236236.9235\n",
      "                                                GCV:                                                0.0176\n",
      "                                                Scale:                                              0.0175\n",
      "                                                Pseudo R-Squared:                                   0.6762\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.0158]             1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          9.78e-03     **          \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/1216624643.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_300_1.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 1\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_300_1 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_300_1.gridsearch(X_train, y_train)\n",
    "uni_gam_300_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0460b7",
   "metadata": {},
   "source": [
    "### 2.2. Model 2: greenspace ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "abba7ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      7.6496\n",
      "Link Function:                     IdentityLink Log Likelihood:                               -128043.4791\n",
      "Number of Samples:                         4656 AIC:                                           256104.2573\n",
      "                                                AICc:                                          256104.2933\n",
      "                                                GCV:                                                0.0163\n",
      "                                                Scale:                                              0.0163\n",
      "                                                Pseudo R-Squared:                                   0.7002\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [63.0957]            20           7.6          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/3283538479.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_300_2.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 2\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_300_2 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_300_2.gridsearch(X_train, y_train)\n",
    "uni_gam_300_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "2d00d1c0",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting best greenspace_300 model\n",
    "for i, term in enumerate(uni_gam_300_2.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_300_2.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_300_2.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Greenspace to NDVI\\nfor buffer zones of 300 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nGreenspace', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_greenspace_300_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_greenspace_300_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_greenspace_300_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "3daefd81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, Greenspace%_300 increases by: 0.16708961466717198\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does Greenspace% increase, if NDVI changes from 0.25 to 0.35?\n",
    "green_l0 = uni_gam_300_2.predict(0.2)\n",
    "green_l1 = uni_gam_300_2.predict(0.3)\n",
    "green_sens_low_300 = float(green_l1 - green_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, Greenspace%_300 increases by:', green_sens_low_300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "99e6dfe8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, Greenspace%_300 increases by: 0.14772700545627215\n"
     ]
    }
   ],
   "source": [
    "# Change in mid NDVI values: how does Greenspace% increase, if NDVI changes from 0.45 to 0.55?\n",
    "green_m0 = uni_gam_300_2.predict(0.4)\n",
    "green_m1 = uni_gam_300_2.predict(0.5)\n",
    "green_sens_mid_300 = float(green_m1 - green_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, Greenspace%_300 increases by:', green_sens_mid_300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8a3b269e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, Greenspace%_300 increases by: 0.07458566958684576\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does Greenspace% increase, if NDVI changes from 0.65 to 0.75?\n",
    "green_h0 = uni_gam_300_2.predict(0.6)\n",
    "green_h1 = uni_gam_300_2.predict(0.7)\n",
    "green_sens_hig_300 = float(green_h1 - green_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, Greenspace%_300 increases by:', green_sens_hig_300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199952e1",
   "metadata": {},
   "source": [
    "### 2.3. Model 3: tree canopy ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c5a97c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NDVI_300 as the predictor\n",
    "df = pd.DataFrame(gdf_300, columns=['NDVI_300'])\n",
    "\n",
    "# Canopy%_300 as the target\n",
    "target = pd.DataFrame(gdf_300, columns=['Canopy%_300'])\n",
    "\n",
    "# define X and y\n",
    "X = df\n",
    "y = target['Canopy%_300']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_300 = linear_model.LinearRegression()\n",
    "model_300 = lm_300.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5482b173",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9924\n",
      "Link Function:                     IdentityLink Log Likelihood:                               -119751.0474\n",
      "Number of Samples:                         4656 AIC:                                           239508.0794\n",
      "                                                AICc:                                          239508.0846\n",
      "                                                GCV:                                                0.0173\n",
      "                                                Scale:                                              0.0173\n",
      "                                                Pseudo R-Squared:                                   0.0403\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.6]                1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/1162806293.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_300_3.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 3\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_300_3 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_300_3.gridsearch(X_train, y_train)\n",
    "uni_gam_300_3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5269d0ba",
   "metadata": {},
   "source": [
    "### 2.4. Model 4: tree canopy ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a67e49b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      5.8758\n",
      "Link Function:                     IdentityLink Log Likelihood:                               -124323.8803\n",
      "Number of Samples:                         4656 AIC:                                           248661.5122\n",
      "                                                AICc:                                          248661.5355\n",
      "                                                GCV:                                                0.0168\n",
      "                                                Scale:                                              0.0167\n",
      "                                                Pseudo R-Squared:                                   0.0745\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [251.1886]           20           5.9          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2529495405.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_300_4.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 4\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_300_4 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_300_4.gridsearch(X_train, y_train)\n",
    "uni_gam_300_4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bf29d6a4",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "for i, term in enumerate(uni_gam_300_4.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_300_4.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_300_4.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Tree canopy to NDVI\\nfor buffer zones of 300 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nTree canopy', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_canopy_300_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_canopy_300_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_canopy_300_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "28b9e794",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, Canopy%_300 increases by: 0.042208439088218475\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does Canopy% increase, if NDVI changes from 0.25 to 0.35?\n",
    "canopy_l0 = uni_gam_300_4.predict(0.2)\n",
    "canopy_l1 = uni_gam_300_4.predict(0.3)\n",
    "canopy_sens_low_300 = float(canopy_l1 - canopy_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, Canopy%_300 increases by:', canopy_sens_low_300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "a670b83a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, Canopy%_300 increases by: 0.043816312708352845\n"
     ]
    }
   ],
   "source": [
    "# Change in mid NDVI values: how does Canopy% increase, if NDVI changes from 0.45 to 0.55?\n",
    "canopy_m0 = uni_gam_300_4.predict(0.4)\n",
    "canopy_m1 = uni_gam_300_4.predict(0.5)\n",
    "canopy_sens_mid_300 = float(canopy_m1 - canopy_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, Canopy%_300 increases by:', canopy_sens_mid_300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2919b158",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, Canopy%_300 increases by: -0.04711510591714471\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does Canopy% increase, if NDVI changes from 0.65 to 0.75?\n",
    "canopy_h0 = uni_gam_300_4.predict(0.6)\n",
    "canopy_h1 = uni_gam_300_4.predict(0.7)\n",
    "canopy_sens_hig_300 = float(canopy_h1 - canopy_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, Canopy%_300 increases by:', canopy_sens_hig_300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e8cba5",
   "metadata": {},
   "source": [
    "### 2.5. Model 5: forbs and shrubs ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a58f7093",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data/predictors as the NDVI_300\n",
    "df_300 = pd.DataFrame(gdf_300, columns=['NDVI_300'])\n",
    "\n",
    "# target as Shrubs%_300\n",
    "target_300 = pd.DataFrame(gdf_300, columns=['Shrubs%_300'])\n",
    "\n",
    "# define X and y\n",
    "X = df_300\n",
    "y = target_300['Shrubs%_300']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_300 = linear_model.LinearRegression()\n",
    "model_300 = lm_300.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "acde44da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9992\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -39207.7892\n",
      "Number of Samples:                         4656 AIC:                                            78421.5767\n",
      "                                                AICc:                                           78421.5819\n",
      "                                                GCV:                                                0.0474\n",
      "                                                Scale:                                              0.0474\n",
      "                                                Pseudo R-Squared:                                   0.1753\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.0631]             1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          2.06e-02     *           \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2500949949.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_300_5.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 5\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_300_5 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_300_5.gridsearch(X_train, y_train)\n",
    "uni_gam_300_5.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5149dc",
   "metadata": {},
   "source": [
    "### 2.6. Model 6: forbs and shrubs ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "52b5c456",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      7.6496\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -39581.6776\n",
      "Number of Samples:                         4656 AIC:                                            79180.6545\n",
      "                                                AICc:                                           79180.6904\n",
      "                                                GCV:                                                 0.047\n",
      "                                                Scale:                                              0.0469\n",
      "                                                Pseudo R-Squared:                                   0.1842\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [63.0957]            20           7.6          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/1093988074.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_300_6.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 6\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_300_6 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_300_6.gridsearch(X_train, y_train)\n",
    "uni_gam_300_6.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "24f67f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting\n",
    "for i, term in enumerate(uni_gam_300_6.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_300_6.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_300_6.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Shrubs density to NDVI\\nfor buffer zones of 300 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nShrubs density', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_shrubs_300_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_shrubs_300_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_shrubs_300_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "86816a81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, shrubs%_300 increases by: 0.0849148035086395\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does shrubs% increase, if NDVI changes from 0.25 to 0.35?\n",
    "shrubs_l0 = uni_gam_300_6.predict(0.2)\n",
    "shrubs_l1 = uni_gam_300_6.predict(0.3)\n",
    "shrubs_sens_low_300 = float(shrubs_l1 - shrubs_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, shrubs%_300 increases by:', shrubs_sens_low_300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "3262dbe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, shrubs%_300 increases by: 0.055604244987157214\n"
     ]
    }
   ],
   "source": [
    "# Change in mid NDVI values: how does shrubs% increase, if NDVI changes from 0.45 to 0.55?\n",
    "shrubs_m0 = uni_gam_300_6.predict(0.4)\n",
    "shrubs_m1 = uni_gam_300_6.predict(0.5)\n",
    "shrubs_sens_mid_300 = float(shrubs_m1 - shrubs_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, shrubs%_300 increases by:', shrubs_sens_mid_300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "cd32ccb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, shrubs%_300 increases by: 0.015122301316151776\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does shrubs% increase, if NDVI changes from 0.65 to 0.75?\n",
    "shrubs_h0 = uni_gam_300_6.predict(0.6)\n",
    "shrubs_h1 = uni_gam_300_6.predict(0.7)\n",
    "shrubs_sens_hig_300 = float(shrubs_h1 - shrubs_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, shrubs%_300 increases by:', shrubs_sens_hig_300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5af5c02",
   "metadata": {},
   "source": [
    "### 2.7. Model 7: grass ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4754fece",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NDVI_300 as the predictor\n",
    "df = pd.DataFrame(gdf_300, columns=['NDVI_300'])\n",
    "\n",
    "# Grass%_300 as the target\n",
    "target = pd.DataFrame(gdf_300, columns=['Grass%_300'])\n",
    "\n",
    "# define X and y\n",
    "X = df\n",
    "y = target['Grass%_300']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_300 = linear_model.LinearRegression()\n",
    "model_300 = lm_300.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cc8e5b84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n",
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/277399172.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_300_7.summary()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9992\n",
      "Link Function:                     IdentityLink Log Likelihood:                               -204349.6362\n",
      "Number of Samples:                         4656 AIC:                                           408705.2708\n",
      "                                                AICc:                                           408705.276\n",
      "                                                GCV:                                                0.0105\n",
      "                                                Scale:                                              0.0105\n",
      "                                                Pseudo R-Squared:                                   0.2563\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.0631]             1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    }
   ],
   "source": [
    "# Model 7\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_300_7 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_300_7.gridsearch(X_train, y_train)\n",
    "uni_gam_300_7.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4cf5be",
   "metadata": {},
   "source": [
    "### 2.8. Model 8: grass ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "00f7a619",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      7.6496\n",
      "Link Function:                     IdentityLink Log Likelihood:                               -207895.5869\n",
      "Number of Samples:                         4656 AIC:                                            415808.473\n",
      "                                                AICc:                                          415808.5089\n",
      "                                                GCV:                                                0.0104\n",
      "                                                Scale:                                              0.0103\n",
      "                                                Pseudo R-Squared:                                     0.27\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [63.0957]            20           7.6          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/68270352.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_300_8.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 8\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_300_8 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_300_8.gridsearch(X_train, y_train)\n",
    "uni_gam_300_8.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "008d6fd7",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "for i, term in enumerate(uni_gam_300_8.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_300_8.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_300_8.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Grass density to NDVI\\nfor buffer zones of 300 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nGrass density', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_grass_300_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_grass_300_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_grass_300_best_plot_2.bmp')\n",
    "\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "2fdc55ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, Grass%_300 increases by: 0.04291982509477453\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does Grass% increase, if NDVI changes from 0.25 to 0.35?\n",
    "grass_l0 = uni_gam_300_8.predict(0.2)\n",
    "grass_l1 = uni_gam_300_8.predict(0.3)\n",
    "grass_sens_low_300 = float(grass_l1 - grass_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, Grass%_300 increases by:', grass_sens_low_300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "6da7b121",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, Grass%_300 increases by: 0.04066750806211014\n"
     ]
    }
   ],
   "source": [
    "# Change in mid NDVI values: how does Grass% increase, if NDVI changes from 0.45 to 0.55?\n",
    "grass_m0 = uni_gam_300_8.predict(0.4)\n",
    "grass_m1 = uni_gam_300_8.predict(0.5)\n",
    "grass_sens_mid_300 = float(grass_m1 - grass_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, Grass%_300 increases by:', grass_sens_mid_300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "dba30d14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, Grass%_300 increases by: 0.10783165280396259\n"
     ]
    }
   ],
   "source": [
    "# Change in hig NDVI values: how does Grass% increase, if NDVI changes from 0.65 to 0.75?\n",
    "grass_h0 = uni_gam_300_8.predict(0.6)\n",
    "grass_h1 = uni_gam_300_8.predict(0.7)\n",
    "grass_sens_hig_300 = float(grass_h1 - grass_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, Grass%_300 increases by:', grass_sens_hig_300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e319e6",
   "metadata": {},
   "source": [
    "## 3. Modeling vegetation densities for buffer distances of 500 meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "056870cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>NDVI_500</th>\n",
       "      <th>Greenspace%_500</th>\n",
       "      <th>Canopy%_500</th>\n",
       "      <th>Grass%_500</th>\n",
       "      <th>Shrubs%_500</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.407077</td>\n",
       "      <td>0.982575</td>\n",
       "      <td>0.006176</td>\n",
       "      <td>0.028919</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>POINT (401901.359 408671.581)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.441260</td>\n",
       "      <td>0.575357</td>\n",
       "      <td>0.167827</td>\n",
       "      <td>0.131850</td>\n",
       "      <td>0.277816</td>\n",
       "      <td>POINT (368835.367 407479.786)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.312300</td>\n",
       "      <td>0.395262</td>\n",
       "      <td>0.212136</td>\n",
       "      <td>0.095873</td>\n",
       "      <td>0.087050</td>\n",
       "      <td>POINT (377751.293 401260.842)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.283220</td>\n",
       "      <td>0.355313</td>\n",
       "      <td>0.098324</td>\n",
       "      <td>0.070777</td>\n",
       "      <td>0.188119</td>\n",
       "      <td>POINT (391535.548 395524.756)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.552286</td>\n",
       "      <td>0.770303</td>\n",
       "      <td>0.265954</td>\n",
       "      <td>0.094795</td>\n",
       "      <td>0.409666</td>\n",
       "      <td>POINT (369836.790 402258.456)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NDVI_500  Greenspace%_500  Canopy%_500  Grass%_500  Shrubs%_500  \\\n",
       "0  0.407077         0.982575     0.006176    0.028919     0.947064   \n",
       "1  0.441260         0.575357     0.167827    0.131850     0.277816   \n",
       "2  0.312300         0.395262     0.212136    0.095873     0.087050   \n",
       "3  0.283220         0.355313     0.098324    0.070777     0.188119   \n",
       "4  0.552286         0.770303     0.265954    0.094795     0.409666   \n",
       "\n",
       "                        geometry  \n",
       "0  POINT (401901.359 408671.581)  \n",
       "1  POINT (368835.367 407479.786)  \n",
       "2  POINT (377751.293 401260.842)  \n",
       "3  POINT (391535.548 395524.756)  \n",
       "4  POINT (369836.790 402258.456)  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load greenspace metrics at 500m geodataframe\n",
    "gdf_500 = gpd.read_file(\"thesis_project/output/dataframes/greenspace_metrics_500_df_2.json\")\n",
    "gdf_500.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442e67bf",
   "metadata": {},
   "source": [
    "### 3.1. Model 1: greenspace ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "150814b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NDVI_500 as the predictor\n",
    "df = pd.DataFrame(gdf_500, columns=['NDVI_500'])\n",
    "\n",
    "# Greenspace%_300 as the target\n",
    "target = pd.DataFrame(gdf_500, columns=['Greenspace%_500'])\n",
    "\n",
    "# define X and y\n",
    "X = df\n",
    "y = target['Greenspace%_500']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_500 = linear_model.LinearRegression()\n",
    "model_500 = lm_500.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d79127c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9999\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -68708.8381\n",
      "Number of Samples:                         2316 AIC:                                           137423.6759\n",
      "                                                AICc:                                          137423.6863\n",
      "                                                GCV:                                                0.0152\n",
      "                                                Scale:                                              0.0152\n",
      "                                                Pseudo R-Squared:                                   0.6684\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.004]              1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          4.74e-01                 \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2474993179.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_500_1.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 1\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_500_1 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_500_1.gridsearch(X_train, y_train)\n",
    "uni_gam_500_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412d77c1",
   "metadata": {},
   "source": [
    "### 3.2. Model 2: greenspace ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "595a8e06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      8.8225\n",
      "Link Function:                     IdentityLink Log Likelihood:                                 -74296.051\n",
      "Number of Samples:                         2316 AIC:                                            148611.747\n",
      "                                                AICc:                                          148611.8392\n",
      "                                                GCV:                                                0.0142\n",
      "                                                Scale:                                              0.0141\n",
      "                                                Pseudo R-Squared:                                   0.6935\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [15.8489]            20           8.8          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/3753528684.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_500_2.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 2\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_500_2 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_500_2.gridsearch(X_train, y_train)\n",
    "uni_gam_500_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e4ec89e0",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "for i, term in enumerate(uni_gam_500_2.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_500_2.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_500_2.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Greenspace to NDVI\\nfor buffer zones of 500 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nGreenspace', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_greenspace_500_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_greenspace_500_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_greenspace_500_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "6fb0a348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, Greenspace%_500 increases by: 0.1405613918601712\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does Greenspace% increase, if NDVI changes from 0.25 to 0.35?\n",
    "green_l0 = uni_gam_500_2.predict(0.2)\n",
    "green_l1 = uni_gam_500_2.predict(0.3)\n",
    "green_sens_low_500 = float(green_l1 - green_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, Greenspace%_500 increases by:', green_sens_low_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c3d945c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, Greenspace%_500 increases by: 0.13795851063273912\n"
     ]
    }
   ],
   "source": [
    "# Change in medium NDVI values: how does Greenspace% increase, if NDVI changes from 0.45 to 0.55?\n",
    "green_m0 = uni_gam_500_2.predict(0.4)\n",
    "green_m1 = uni_gam_500_2.predict(0.5)\n",
    "green_sens_mid_500 = float(green_m1 - green_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, Greenspace%_500 increases by:', green_sens_mid_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "73f655eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, Greenspace%_500 increases by: 0.08164082798287908\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does Greenspace% increase, if NDVI changes from 0.65 to 0.75?\n",
    "green_h0 = uni_gam_500_2.predict(0.6)\n",
    "green_h1 = uni_gam_500_2.predict(0.7)\n",
    "green_sens_hig_500 = float(green_h1 - green_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, Greenspace%_500 increases by:', green_sens_hig_500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5653389d",
   "metadata": {},
   "source": [
    "### 3.3. Model 3: tree canopy ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "68ee1650",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NDVI_500 as the predictor\n",
    "df = pd.DataFrame(gdf_500, columns=['NDVI_500'])\n",
    "\n",
    "# Greenspace%_300 as the target\n",
    "target = pd.DataFrame(gdf_500, columns=['Canopy%_500'])\n",
    "\n",
    "# define X and y\n",
    "X = df\n",
    "y = target['Canopy%_500']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_500 = linear_model.LinearRegression()\n",
    "model_500 = lm_500.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3ebb7231",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      1.9812\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -82627.5544\n",
      "Number of Samples:                         2316 AIC:                                           165261.0713\n",
      "                                                AICc:                                          165261.0815\n",
      "                                                GCV:                                                0.0128\n",
      "                                                Scale:                                              0.0128\n",
      "                                                Pseudo R-Squared:                                   0.0309\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.6]                1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2840720071.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_500_3.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 3\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_500_3 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_500_3.gridsearch(X_train, y_train)\n",
    "uni_gam_500_3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8902f230",
   "metadata": {},
   "source": [
    "### 3.4. Model 4: tree canopy ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ef109c53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      6.8256\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -84808.3044\n",
      "Number of Samples:                         2316 AIC:                                           169632.2598\n",
      "                                                AICc:                                          169632.3197\n",
      "                                                GCV:                                                0.0125\n",
      "                                                Scale:                                              0.0124\n",
      "                                                Pseudo R-Squared:                                   0.0582\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [63.0957]            20           6.8          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/3479331719.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_500_4.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 4\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_500_4 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_500_4.gridsearch(X_train, y_train)\n",
    "uni_gam_500_4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "caa32aa0",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "for i, term in enumerate(uni_gam_500_4.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_500_4.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_500_4.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Tree canopy to NDVI\\nfor buffer zones of 500 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nTree canopy', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_canopy_500_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_canopy_500_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_canopy_500_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "98e8fb83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, canopy%_500 increases by: 0.047297824935435315\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does Canopy% increase, if NDVI changes from 0.25 to 0.35?\n",
    "canopy_l0 = uni_gam_500_4.predict(0.2)\n",
    "canopy_l1 = uni_gam_500_4.predict(0.3)\n",
    "canopy_sens_low_500 = float(canopy_l1 - canopy_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, canopy%_500 increases by:', canopy_sens_low_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b2b333d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, canopy%_500 increases by: 0.04245781300167431\n"
     ]
    }
   ],
   "source": [
    "# Change in medium NDVI values: how does canopy% increase, if NDVI changes from 0.45 to 0.55?\n",
    "canopy_m0 = uni_gam_500_4.predict(0.4)\n",
    "canopy_m1 = uni_gam_500_4.predict(0.5)\n",
    "canopy_sens_mid_500 = float(canopy_m1 - canopy_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, canopy%_500 increases by:', canopy_sens_mid_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "976911ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, canopy%_500 increases by: -0.011118977468192959\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does canopy% increase, if NDVI changes from 0.65 to 0.75?\n",
    "canopy_h0 = uni_gam_500_4.predict(0.6)\n",
    "canopy_h1 = uni_gam_500_4.predict(0.7)\n",
    "canopy_sens_hig_500 = float(canopy_h1 - canopy_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, canopy%_500 increases by:', canopy_sens_hig_500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6493010",
   "metadata": {},
   "source": [
    "### 3.5. Model 5: forbs and shrubs ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b3aa53b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NDVI_500 as the predictor\n",
    "df = pd.DataFrame(gdf_500, columns=['NDVI_500'])\n",
    "\n",
    "# Shrubs%_500 as the target\n",
    "target = pd.DataFrame(gdf_500, columns=['Shrubs%_500'])\n",
    "\n",
    "# define X and y\n",
    "X = df\n",
    "y = target['Shrubs%_500']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_500 = linear_model.LinearRegression()\n",
    "model_500 = lm_500.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "89d5e340",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                       1.998\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -23525.4931\n",
      "Number of Samples:                         2316 AIC:                                            47056.9821\n",
      "                                                AICc:                                           47056.9925\n",
      "                                                GCV:                                                0.0402\n",
      "                                                Scale:                                              0.0401\n",
      "                                                Pseudo R-Squared:                                   0.1777\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.0631]             1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          1.28e-02     *           \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/405054797.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_500_5.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 5\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_500_5 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_500_5.gridsearch(X_train, y_train)\n",
    "uni_gam_500_5.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a69599b4",
   "metadata": {},
   "source": [
    "### 3.6. Model 6: forbs and shrubs ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "280653f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      8.8225\n",
      "Link Function:                     IdentityLink Log Likelihood:                                -23940.5195\n",
      "Number of Samples:                         2316 AIC:                                            47900.6838\n",
      "                                                AICc:                                           47900.7761\n",
      "                                                GCV:                                                0.0396\n",
      "                                                Scale:                                              0.0394\n",
      "                                                Pseudo R-Squared:                                   0.1953\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [15.8489]            20           8.8          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2335926396.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_500_6.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 6\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_500_6 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_500_6.gridsearch(X_train, y_train)\n",
    "uni_gam_500_6.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "cb9915a7",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "for i, term in enumerate(uni_gam_500_6.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_500_6.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_500_6.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Shrubs density to NDVI\\nfor buffer zones of 500 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nShrubs density', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_shrubs_500_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_shrubs_500_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_shrubs_500_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "7aef5eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, shrubs%_500 increases by: 0.05029990817324037\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does shrubs% increase, if NDVI changes from 0.25 to 0.35?\n",
    "shrubs_l0 = uni_gam_500_6.predict(0.2)\n",
    "shrubs_l1 = uni_gam_500_6.predict(0.3)\n",
    "shrubs_sens_low_500 = float(shrubs_l1 - shrubs_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, shrubs%_500 increases by:', shrubs_sens_low_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "5c004ae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, shrubs%_500 increases by: 0.026466985867567072\n"
     ]
    }
   ],
   "source": [
    "# Change in medium NDVI values: how does shrubs% increase, if NDVI changes from 0.45 to 0.55?\n",
    "shrubs_m0 = uni_gam_500_6.predict(0.4)\n",
    "shrubs_m1 = uni_gam_500_6.predict(0.5)\n",
    "shrubs_sens_mid_500 = float(shrubs_m1 - shrubs_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, shrubs%_500 increases by:', shrubs_sens_mid_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ef026c90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, shrubs%_500 increases by: -0.002568085141203269\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does shrubs% increase, if NDVI changes from 0.65 to 0.75?\n",
    "shrubs_h0 = uni_gam_500_6.predict(0.6)\n",
    "shrubs_h1 = uni_gam_500_6.predict(0.7)\n",
    "shrubs_sens_hig_500 = float(shrubs_h1 - shrubs_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, shrubs%_500 increases by:', shrubs_sens_hig_500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0fb116",
   "metadata": {},
   "source": [
    "### 3.7. Model 7: grass ~ linear(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f7bc5b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NDVI_500 as the predictor\n",
    "df = pd.DataFrame(gdf_500, columns=['NDVI_500'])\n",
    "\n",
    "# Grass%_500 as the target\n",
    "target = pd.DataFrame(gdf_500, columns=['Grass%_500'])\n",
    "\n",
    "# define X and y\n",
    "X = df\n",
    "y = target['Grass%_500']\n",
    "\n",
    "# split independent and dependent variables into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# fit the linear model\n",
    "lm_500 = linear_model.LinearRegression()\n",
    "model_500 = lm_500.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0839f3ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                       1.998\n",
      "Link Function:                     IdentityLink Log Likelihood:                                 -158512.13\n",
      "Number of Samples:                         2316 AIC:                                            317030.256\n",
      "                                                AICc:                                          317030.2664\n",
      "                                                GCV:                                                0.0069\n",
      "                                                Scale:                                              0.0069\n",
      "                                                Pseudo R-Squared:                                   0.2992\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "l(0)                              [0.0631]             1            1.0          1.11e-16     ***         \n",
      "intercept                                              1            1.0          1.04e-14     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/972376635.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_500_7.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 7\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_500_7 = LinearGAM(l(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_500_7.gridsearch(X_train, y_train)\n",
    "uni_gam_500_7.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dd896cf",
   "metadata": {},
   "source": [
    "### 3.8. Model 8: grass ~ spline(NDVI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "876b2947",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      5.2216\n",
      "Link Function:                     IdentityLink Log Likelihood:                               -158651.7219\n",
      "Number of Samples:                         2316 AIC:                                            317315.887\n",
      "                                                AICc:                                          317315.9259\n",
      "                                                GCV:                                                0.0069\n",
      "                                                Scale:                                              0.0069\n",
      "                                                Pseudo R-Squared:                                   0.3018\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [251.1886]           20           5.2          1.11e-16     ***         \n",
      "intercept                                              1            0.0          1.11e-16     ***         \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6g/kgz5rml118s6lqw25w5sv5mr0000gn/T/ipykernel_1007/2750512757.py:6: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  uni_gam_500_8.summary()\n"
     ]
    }
   ],
   "source": [
    "# Model 8\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "uni_gam_500_8 = LinearGAM(s(0)).fit(X_train, y_train)\n",
    "\n",
    "uni_gam_500_8.gridsearch(X_train, y_train)\n",
    "uni_gam_500_8.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "96c1d48c",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "for i, term in enumerate(uni_gam_500_7.terms):\n",
    "    if term.isintercept:\n",
    "        continue\n",
    "\n",
    "    XX = uni_gam_500_7.generate_X_grid(term=i)\n",
    "    pdep, confi = uni_gam_500_7.partial_dependence(term=i, X=XX, width=0.95)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(XX[:, term.feature], pdep)\n",
    "    plt.hlines(y=0, xmin=0, xmax=1, linewidth=1, color='black', linestyle='--')\n",
    "    plt.plot(XX[:, term.feature], confi, c='r', ls='--')\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim([-0.65, 0.55])\n",
    "    # set title, xlabel and ylabel\n",
    "    plt.title('Sensitivity of Grass density to NDVI\\nfor buffer zones of 500 m', fontsize=18)\n",
    "    plt.xlabel('NDVI', fontsize=18)\n",
    "    plt.ylabel('Change relative to mean\\nGrass density', fontsize=18)\n",
    "    # save the figure\n",
    "    plt.savefig('thesis_project/visualizations/modelling/univariate/gam_grass_500_best_plot_2.png', dpi=300, bbox_inches='tight')\n",
    "    im.open('thesis_project/visualizations/modelling/univariate/gam_grass_500_best_plot_2.png').save('thesis_project/visualizations/modelling/univariate/gam_grass_500_best_plot_2.bmp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "14a955a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.25 to 0.35, grass%_500 increases by: 0.04654250928086066\n"
     ]
    }
   ],
   "source": [
    "# Change in low NDVI values: how does grass% increase, if NDVI changes from 0.25 to 0.35?\n",
    "grass_l0 = uni_gam_500_7.predict(0.25)\n",
    "grass_l1 = uni_gam_500_7.predict(0.35)\n",
    "grass_sens_low_500 = float(grass_l1 - grass_l0)\n",
    "print('If NDVI goes from 0.25 to 0.35, grass%_500 increases by:', grass_sens_low_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e116e05d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.45 to 0.55, grass%_500 increases by: 0.04654250928086068\n"
     ]
    }
   ],
   "source": [
    "# Change in medium NDVI values: how does grass% increase, if NDVI changes from 0.45 to 0.55?\n",
    "grass_m0 = uni_gam_500_7.predict(0.45)\n",
    "grass_m1 = uni_gam_500_7.predict(0.55)\n",
    "grass_sens_mid_500 = float(grass_m1 - grass_m0)\n",
    "print('If NDVI goes from 0.45 to 0.55, grass%_500 increases by:', grass_sens_mid_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d08caffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If NDVI goes from 0.65 to 0.75, grass%_500 increases by: 0.04654250928086062\n"
     ]
    }
   ],
   "source": [
    "# Change in high NDVI values: how does grass% increase, if NDVI changes from 0.65 to 0.75?\n",
    "grass_h0 = uni_gam_500_7.predict(0.65)\n",
    "grass_h1 = uni_gam_500_7.predict(0.75)\n",
    "grass_sens_hig_500 = float(grass_h1 - grass_h0)\n",
    "print('If NDVI goes from 0.65 to 0.75, grass%_500 increases by:', grass_sens_hig_500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5b7c1b",
   "metadata": {},
   "source": [
    "## 4. Prediction sensitivity analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd57d5a",
   "metadata": {},
   "source": [
    "### 4.1. Sensitivity of vegetation types to increments in mean NDVI for a buffer zone of 100 m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "d3881cc6",
   "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>Increment in mean NDVI</th>\n",
       "      <th>Greenspace%_100</th>\n",
       "      <th>Canopy%_100</th>\n",
       "      <th>Shrubs%_100</th>\n",
       "      <th>Grass%_100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low-range (0.2 to 0.3)</td>\n",
       "      <td>0.17756</td>\n",
       "      <td>0.04974</td>\n",
       "      <td>0.09777</td>\n",
       "      <td>0.04838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mid-range (0.4 to 0.5)</td>\n",
       "      <td>0.15092</td>\n",
       "      <td>0.04375</td>\n",
       "      <td>0.09264</td>\n",
       "      <td>0.01775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High-range (0.6 to 0.7)</td>\n",
       "      <td>0.06249</td>\n",
       "      <td>-0.03925</td>\n",
       "      <td>0.00420</td>\n",
       "      <td>0.12306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Increment in mean NDVI  Greenspace%_100  Canopy%_100  Shrubs%_100  \\\n",
       "0   Low-range (0.2 to 0.3)          0.17756      0.04974      0.09777   \n",
       "1   Mid-range (0.4 to 0.5)          0.15092      0.04375      0.09264   \n",
       "2  High-range (0.6 to 0.7)          0.06249     -0.03925      0.00420   \n",
       "\n",
       "   Grass%_100  \n",
       "0     0.04838  \n",
       "1     0.01775  \n",
       "2     0.12306  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vegetation metrics sensitivity to changes in NDVI\n",
    "data_100 = {'Increment in mean NDVI': ['Low-range (0.2 to 0.3)', 'Mid-range (0.4 to 0.5)', 'High-range (0.6 to 0.7)'], \n",
    "        'Greenspace%_100': [green_sens_low, green_sens_mid, green_sens_hig],\n",
    "        'Canopy%_100': [canopy_sens_low, canopy_sens_mid, canopy_sens_hig], \n",
    "        'Shrubs%_100': [shrubs_sens_low, shrubs_sens_mid, shrubs_sens_hig],\n",
    "        'Grass%_100': [grass_sens_low, grass_sens_mid, grass_sens_hig]}  \n",
    "  \n",
    "# create DataFrame  \n",
    "df_sens_100 = pd.DataFrame(data_100)\n",
    "df_sens_100 = df_sens_100.round(5)\n",
    "df_sens_100.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f477c27d",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# hide axes\n",
    "fig.patch.set_visible(False)\n",
    "ax.axis('off')\n",
    "ax.axis('tight')\n",
    "\n",
    "ax.table(cellText=df_sens_100.values, colLabels=df_sens_100.columns, loc='center')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# save the figure\n",
    "plt.savefig('thesis_project/visualizations/modelling/univariate/sensitivity_100_table_2.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dfe56f6",
   "metadata": {},
   "source": [
    "### 4.2. Sensitivity of vegetation types to increments in mean NDVI for a buffer zone of 300 m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "7e8c3d6a",
   "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>Increment in mean NDVI</th>\n",
       "      <th>Greenspace%_300</th>\n",
       "      <th>Canopy%_300</th>\n",
       "      <th>Shrubs%_300</th>\n",
       "      <th>Grass%_300</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low-range (0.2 to 0.3)</td>\n",
       "      <td>0.1671</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0849</td>\n",
       "      <td>0.0429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mid-range (0.4 to 0.5)</td>\n",
       "      <td>0.1477</td>\n",
       "      <td>0.0438</td>\n",
       "      <td>0.0556</td>\n",
       "      <td>0.0407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High-range (0.6 to 0.7)</td>\n",
       "      <td>0.0746</td>\n",
       "      <td>-0.0471</td>\n",
       "      <td>0.0151</td>\n",
       "      <td>0.1078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Increment in mean NDVI  Greenspace%_300  Canopy%_300  Shrubs%_300  \\\n",
       "0   Low-range (0.2 to 0.3)           0.1671       0.0422       0.0849   \n",
       "1   Mid-range (0.4 to 0.5)           0.1477       0.0438       0.0556   \n",
       "2  High-range (0.6 to 0.7)           0.0746      -0.0471       0.0151   \n",
       "\n",
       "   Grass%_300  \n",
       "0      0.0429  \n",
       "1      0.0407  \n",
       "2      0.1078  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vegetation metrics sensitivity to changes in NDVI\n",
    "data_300 = {'Increment in mean NDVI': ['Low-range (0.2 to 0.3)', 'Mid-range (0.4 to 0.5)', 'High-range (0.6 to 0.7)'], \n",
    "        'Greenspace%_300': [green_sens_low_300, green_sens_mid_300, green_sens_hig_300],\n",
    "        'Canopy%_300': [canopy_sens_low_300, canopy_sens_mid_300, canopy_sens_hig_300], \n",
    "        'Shrubs%_300': [shrubs_sens_low_300, shrubs_sens_mid_300, shrubs_sens_hig_300],\n",
    "        'Grass%_300': [grass_sens_low_300, grass_sens_mid_300, grass_sens_hig_300]}  \n",
    "  \n",
    "# create DataFrame  \n",
    "df_sens_300 = pd.DataFrame(data_300)\n",
    "df_sens_300 = df_sens_300.round(4)\n",
    "df_sens_300.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1c677c59",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# hide axes\n",
    "fig.patch.set_visible(False)\n",
    "ax.axis('off')\n",
    "ax.axis('tight')\n",
    "\n",
    "ax.table(cellText=df_sens_300.values, colLabels=df_sens_300.columns, loc='center')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# save the figure\n",
    "plt.savefig('thesis_project/visualizations/modelling/univariate/sensitivity_300_table_2.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983bbab1",
   "metadata": {},
   "source": [
    "### 4.3. Sensitivity of vegetation types to increments in mean NDVI for a buffer zone of 500 m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "467b27b2",
   "metadata": {
    "scrolled": true
   },
   "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>Increment in mean NDVI</th>\n",
       "      <th>Greenspace%_500</th>\n",
       "      <th>Canopy%_500</th>\n",
       "      <th>Shrubs%_500</th>\n",
       "      <th>Grass%_500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low-range (0.2 to 0.3)</td>\n",
       "      <td>0.1406</td>\n",
       "      <td>0.0473</td>\n",
       "      <td>0.0503</td>\n",
       "      <td>0.0465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mid-range (0.4 to 0.5)</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.0465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High-range (0.6 to 0.7)</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>-0.0111</td>\n",
       "      <td>-0.0026</td>\n",
       "      <td>0.0465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Increment in mean NDVI  Greenspace%_500  Canopy%_500  Shrubs%_500  \\\n",
       "0   Low-range (0.2 to 0.3)           0.1406       0.0473       0.0503   \n",
       "1   Mid-range (0.4 to 0.5)           0.1380       0.0425       0.0265   \n",
       "2  High-range (0.6 to 0.7)           0.0816      -0.0111      -0.0026   \n",
       "\n",
       "   Grass%_500  \n",
       "0      0.0465  \n",
       "1      0.0465  \n",
       "2      0.0465  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vegetation metrics sensitivity to changes in NDVI\n",
    "data_500 = {'Increment in mean NDVI': ['Low-range (0.2 to 0.3)', 'Mid-range (0.4 to 0.5)', 'High-range (0.6 to 0.7)'], \n",
    "        'Greenspace%_500': [green_sens_low_500, green_sens_mid_500, green_sens_hig_500],\n",
    "        'Canopy%_500': [canopy_sens_low_500, canopy_sens_mid_500, canopy_sens_hig_500], \n",
    "        'Shrubs%_500': [shrubs_sens_low_500, shrubs_sens_mid_500, shrubs_sens_hig_500],\n",
    "        'Grass%_500': [grass_sens_low_500, grass_sens_mid_500, grass_sens_hig_500]}  \n",
    "  \n",
    "# create DataFrame  \n",
    "df_sens_500 = pd.DataFrame(data_500)\n",
    "df_sens_500 = df_sens_500.round(4)\n",
    "df_sens_500.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2296412f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "# plotting\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# hide axes\n",
    "fig.patch.set_visible(False)\n",
    "ax.axis('off')\n",
    "ax.axis('tight')\n",
    "\n",
    "ax.table(cellText=df_sens_500.values, colLabels=df_sens_500.columns, loc='center')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# save the figure\n",
    "plt.savefig('thesis_project/visualizations/modelling/univariate/sensitivity_500_table_2.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}