plotly_interactions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup Folders, Tokens, and Dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import plotly.express as px\n",
    "from pathlib import Path\n",
    "from klimainsights.environment.settings import MAPBOX_TOKEN\n",
    "import math\n",
    "\n",
    "datasets_folder = Path('./klimainsights/data')\n",
    "px.set_mapbox_access_token(MAPBOX_TOKEN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature_gdf = gpd.read_file(datasets_folder / \"temperature.geojson\")\n",
    "biodiversity_gdf =  gpd.read_file(datasets_folder / 'biodiversity.geojson')\n",
    "disaster_gdf = gpd.read_file(datasets_folder / 'disaster.geojson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**APP IDEAS**\n",
    "- The initial page will an animated of a short text on the center that catches the viewers' attention. It can be a call to action or a hook to incite curiosity.\n",
    "- Below the Text will be a button to \"Click to Learn More\" or \"Continue\", leading to the Temperature page\n",
    "- All pages will contain a header/nav that contains an offcanvas for easier navigation between pages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_value(string):\n",
    "    return string[:-6] if string.endswith('_value') else string\n",
    "\n",
    "melt_value = temperature_gdf.drop(columns=[col for col in temperature_gdf.columns if 'TempDiff' in col])\n",
    "melt_value.columns = [col.split('_')[0] if '_value' in col else col for col in melt_value.columns]\n",
    "melt_value = melt_value.melt(id_vars=['name', 'geometry', 'admin_div', 'island_group', 'Region'], \n",
    "                            var_name='decade', \n",
    "                            value_name='value')\n",
    "melt_tempdiff = temperature_gdf.drop(columns=[col for col in temperature_gdf.columns if 'value' in col])\n",
    "melt_tempdiff.columns = [col.split('_')[0] if '_TempDiff' in col else col for col in melt_tempdiff.columns]\n",
    "melt_tempdiff = melt_tempdiff.melt(id_vars=['name', 'geometry', 'admin_div', 'island_group', 'Region'], \n",
    "                            var_name='decade', \n",
    "                            value_name='TempDiff')\n",
    "temp_melted_gdf = pd.merge(melt_value, melt_tempdiff, on=['name', 'geometry', 'admin_div', 'island_group', 'Region', 'decade'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(temperature_gdf.shape)\n",
    "temperature_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_melted_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lowest = math.floor(temp_melted_gdf['value'].min())\n",
    "highest = math.ceil(temp_melted_gdf['value'].max())\n",
    "lowest_1960 = math.floor(temperature_gdf['1960s_value'].min())\n",
    "highest_1960 = math.ceil(temperature_gdf['1960s_value'].max())\n",
    "\n",
    "print(f\"All Time Low: {lowest}\")\n",
    "print(f\"Midpoint: {(highest + lowest) / 2:.0f}\")\n",
    "print(f\"All Time High: {highest}\")\n",
    "print(\"- - - - - -\")\n",
    "print(f\"All Time Low 1960s: {lowest_1960}\")\n",
    "print(f\"Midpoint 1960s: {(highest_1960 + lowest_1960) / 2:.0f}\")\n",
    "print(f\"All Time High 1960s: {highest_1960}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_bar_fig(island_value):\n",
    "    island_gdf = temp_melted_gdf[(temp_melted_gdf['island_group'].isin([island_value]) == True)].drop(columns=['geometry'])\n",
    "    # Create the Figure with horizontal orientation\n",
    "    bar_fig = px.bar(island_gdf, y='name', x='value', animation_frame=\"decade\", orientation='h')\n",
    "    bar_fig.update_layout(\n",
    "        height=750,\n",
    "        title=f'Average Temperature per Province<br>in {island_value}',\n",
    "        margin=dict(l=20, r=20, t=75, b=50),\n",
    "        yaxis=dict(\n",
    "            title=\"Province Name\",\n",
    "            tickfont=dict(size=11)  # Adjust tick font size to prevent overlap\n",
    "        ),\n",
    "        xaxis=dict(\n",
    "            title=\"Avg Temperature (°C)\",\n",
    "            range=[0, 35],\n",
    "            tickmode='linear',\n",
    "            dtick=5,\n",
    "            tickfont=dict(size=13),  # Adjust tick font size to prevent overlap\n",
    "            tickangle=0\n",
    "        ),\n",
    "        font=dict(  # Adjust font size for main title\n",
    "            size=12\n",
    "        )\n",
    "    )\n",
    "    hover_template = \"Average Temperature in<br>\" + \\\n",
    "                     \"<b>%{y}</b><br>\" + \\\n",
    "                     \"during the %{customdata[0]}:<br>\" + \\\n",
    "                     \"%{customdata[1]:.2f}°C\"\n",
    "    bar_fig.update_traces(hovertemplate=hover_template,\n",
    "                           customdata=island_gdf[['decade', 'value']])\n",
    "\n",
    "    for frame in bar_fig.frames:\n",
    "        frame.data[0].hovertemplate = hover_template\n",
    "        frame.data[0].customdata = island_gdf[['decade', 'value']]\n",
    "    bar_fig.update_layout(\n",
    "        updatemenus=[{\n",
    "            'direction': 'left',\n",
    "            'pad': {'t': 10, 'b': 10, 'l': 10, 'r': 10},\n",
    "            'showactive': False,\n",
    "            'type': 'buttons',\n",
    "            'x': 0.06,\n",
    "            'xanchor': 'right',\n",
    "            'y': -0.14,\n",
    "            'yanchor': 'top'\n",
    "        }],\n",
    "        sliders=[{\n",
    "            'active': 0,\n",
    "            'x': 0.98,\n",
    "            'y': -0.08,\n",
    "            'xanchor': 'right',\n",
    "            'yanchor': 'top',\n",
    "            'transition': {'duration': 300, 'easing': 'cubic-in-out'},\n",
    "            'pad': {'t': 10, 'b': 10, 'l': 10, 'r': 10},\n",
    "            'currentvalue': {\n",
    "                'font': {'size': 15},\n",
    "                'prefix': 'Decade:',\n",
    "                'visible': True,\n",
    "                'xanchor': 'right',\n",
    "            },\n",
    "            'visible': True,\n",
    "        }]\n",
    "    )\n",
    "\n",
    "    highest_temp_1960 = island_gdf[island_gdf['decade'] == '1960s']['value'].max()  # Highest temperature for the decade 1960\n",
    "    bar_fig.add_shape(  # Line representing highest temperature in the 1960s\n",
    "        type=\"line\",\n",
    "        x0=highest_temp_1960,\n",
    "        y0=-1,\n",
    "        x1=highest_temp_1960,\n",
    "        y1=island_gdf.name.nunique()-0.5,\n",
    "        line=dict(\n",
    "            color=\"red\",\n",
    "            width=1,\n",
    "            dash=\"dash\",\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    bar_fig.add_annotation(\n",
    "        x=highest_temp_1960 + 1,\n",
    "        y=island_gdf.name.nunique() / 1.3,  # Adjust the y position of the label\n",
    "        text=\"Highest Temp in the 1960s\",\n",
    "        showarrow=True,\n",
    "        arrowhead=1,\n",
    "        ax=0,\n",
    "        ay=0,  # Adjust the arrow position\n",
    "        font=dict(\n",
    "            color=\"red\",\n",
    "            size=12\n",
    "        ),\n",
    "        align=\"left\",\n",
    "        textangle=90  # Tilt the text 90 degrees\n",
    "    )\n",
    "\n",
    "    return bar_fig\n",
    "\n",
    "bar_fig = update_bar_fig('Mindanao')\n",
    "bar_fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TEMP 2:  The second visualization for the temperature data is a **Chloropleth Map** that shows the Average Temperature per Province across all of the decades from 1960s to 2020s.\n",
    "#### Interactions:\n",
    "- Across the Decades Animation\n",
    "\n",
    "<em>Note: the reason that only four decades are used (1960s, 1980s, 2000s, and 2020s) is because using more decades/dimensions would make the load time of the figure significantly higher reaching up to an hour. This visualization takes approximately 2-3 minutes to load in this notebook. </em>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def update_map_fig(n_intervals):\n",
    "#     temp_sliced = temp_gdf[(temp_gdf['decade'].isin(['1960s', '1980s', '2000s', '2020s']) == True)].reset_index()\n",
    "#     map_fig = px.choropleth_mapbox(temp_sliced,\n",
    "#                                     geojson=temp_sliced.geometry,\n",
    "#                                     locations=temp_sliced.index,\n",
    "#                                     color='value',\n",
    "#                                     color_continuous_scale='turbo',\n",
    "#                                     range_color=[25, 32.5],\n",
    "#                                     mapbox_style='streets',\n",
    "#                                     zoom=5,\n",
    "#                                     center={\"lat\": 12.8797, \"lon\": 121.7740},\n",
    "#                                     opacity=0.6,\n",
    "#                                     animation_frame=\"decade\"\n",
    "#                                     )\n",
    "#     map_fig.update_layout(\n",
    "#         coloraxis_colorbar=dict(title=f\"Average<br>Temperature(°C)\", yanchor=\"top\", xanchor='left',\n",
    "#                                 y=1, x=0, ticks=\"outside\", ticklabelposition=\"outside left\", thickness=10, title_font_color='#0c232c',\n",
    "#                                 tickvals=[i for i in range(25, 33)],\n",
    "#                                 tickmode='array',\n",
    "#                                 ticksuffix='°C',\n",
    "#                                 tickfont=dict(\n",
    "#                                     size=12,\n",
    "#                                     color='#0c232c')\n",
    "#                                 )\n",
    "#     )\n",
    "#     map_fig.update_layout(\n",
    "#         margin=dict(l=0, r=0, t=0, b=0),\n",
    "#         updatemenus=[{\n",
    "#             'buttons': [\n",
    "#                 {'visible': True}\n",
    "#             ],\n",
    "#             'direction': 'left',\n",
    "#             'pad': {'t': 0, 'b': 0, 'l': 0, 'r': 0},\n",
    "#             'showactive': False,\n",
    "#             'type': 'buttons',\n",
    "#             'x': 0.15,\n",
    "#             'xanchor': 'right',\n",
    "#             'y': 0.21,\n",
    "#             'yanchor': 'top'\n",
    "#         }],\n",
    "#         sliders=[{\n",
    "#             'active': 0,\n",
    "#             'x': 0.07,\n",
    "#             'y': 0.19,\n",
    "#             'len': 0.5,\n",
    "#             'xanchor': 'left',\n",
    "#             'yanchor': 'top',\n",
    "#             'currentvalue': {\n",
    "#                 'font': {'size': 16},\n",
    "#                 'prefix': 'Decade:',\n",
    "#                 'visible': True,\n",
    "#                 'xanchor': 'right'\n",
    "#             },\n",
    "#             'transition': {'duration': 1000, 'easing': 'cubic-in-out'},\n",
    "#             'pad': {'t': 0, 'b': 0, 'l': 0, 'r': 0}\n",
    "#         }]\n",
    "#     )\n",
    "#     hover_template = '<b>%{customdata[0]}</b><br>Average Temp: %{customdata[1]:.2f}°C<extra></extra>'\n",
    "#     map_fig.update_traces(hovertemplate=hover_template,\n",
    "#                            customdata=temp_sliced[['name', 'value']])\n",
    "#     for frame in map_fig.frames:\n",
    "#         frame.data[0].hovertemplate = hover_template\n",
    "#         frame.data[0].customdata = temp_sliced[['name', 'value']]\n",
    "\n",
    "#     return map_fig\n",
    "\n",
    "# map_fig = update_map_fig(1)\n",
    "# map_fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_map_fig(decade_value):\n",
    "    map_fig = px.choropleth_mapbox(temperature_gdf,\n",
    "                                    geojson=temperature_gdf.geometry,\n",
    "                                    locations=temperature_gdf.index,\n",
    "                                    color=decade_value,\n",
    "                                    color_continuous_scale='turbo',\n",
    "                                    range_color=[25, 33],\n",
    "                                    mapbox_style='streets',\n",
    "                                    zoom=5,\n",
    "                                    center={\"lat\": 12.8797, \"lon\": 122.7740},\n",
    "                                    opacity=0.6,\n",
    "                                    )\n",
    "    map_fig.update_layout(\n",
    "        coloraxis_colorbar=dict(title=f\"{remove_value(decade_value)}<br>Average<br>Temperature(°C)\", yanchor=\"top\", xanchor='left',\n",
    "                                y=1, x=0, ticks=\"outside\", ticklabelposition=\"outside left\", thickness=10, title_font_color='#0c232c',\n",
    "                                tickvals=[i for i in range(25, 33)],\n",
    "                                tickmode='array',\n",
    "                                ticksuffix='°C',\n",
    "                                tickfont=dict(\n",
    "                                    size=12,\n",
    "                                    color='#0c232c')\n",
    "                                )\n",
    "    )\n",
    "    map_fig.update_layout(\n",
    "        margin=dict(l=0, r=0, t=0, b=0),\n",
    "    )\n",
    "    hover_template = '<b>%{customdata[0]}</b><br>during the '+ remove_value(decade_value) +'<br>Average Temp: %{customdata[1]:.2f}°C<extra></extra>'\n",
    "    map_fig.update_traces(hovertemplate=hover_template,\n",
    "                           customdata=temperature_gdf[['name', decade_value]])\n",
    "\n",
    "    return map_fig\n",
    "\n",
    "map_fig = update_map_fig('1970s' + '_value')\n",
    "map_fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TEMP 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_line(division, click_data):\n",
    "    curr_div = ''\n",
    "    data = click_data\n",
    "    if division == 'Region':\n",
    "        curr_div = 'Region'\n",
    "    elif division == 'Province':\n",
    "        curr_div = 'name'\n",
    "    else:\n",
    "        return\n",
    "\n",
    "    island_gdf = temp_melted_gdf[(temp_melted_gdf[curr_div].isin([data]) == True)].drop(columns=['geometry']).sort_values(by=['name', 'decade'], ascending=True, ignore_index=True)\n",
    "\n",
    "    line_fig = px.line(island_gdf, x='decade', y='value', color='name')\n",
    "    line_fig.update_layout(\n",
    "        autosize=True,  \n",
    "        height=700,\n",
    "        title='Change in Avg Temperature in ' + data,\n",
    "        yaxis=dict(\n",
    "            range=[25, 32],\n",
    "            tickmode='linear',\n",
    "            dtick=0.5\n",
    "        ),\n",
    "        margin=dict(l=20, r=20, t=100, b=100),\n",
    "        updatemenus=[{\n",
    "            'direction': 'left',  \n",
    "            'pad': {'t': 0, 'b': 0, 'l': 0, 'r': 0},  \n",
    "            'showactive': False,\n",
    "            'type': 'buttons',\n",
    "            'x': 0.06,  \n",
    "            'xanchor': 'right',\n",
    "            'y': -0.46,  \n",
    "            'yanchor': 'top'\n",
    "        }],\n",
    "        xaxis_tickangle=-45 \n",
    "    )\n",
    "    hover_template = '<br>Average Temperature in<br>the %{x}:<br><b>%{y:.2f}°C</b><extra></extra>'\n",
    "    line_fig.update_traces(hovertemplate=hover_template)\n",
    "\n",
    "    return line_fig\n",
    "\n",
    "done = update_line('Province', 'Abra')\n",
    "done.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TEMP 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_bar1960_fig(island_value):\n",
    "    island_gdf = temp_melted_gdf[(temp_melted_gdf['island_group'].isin([island_value])) & (temp_melted_gdf['decade'].isin(['1960s']) == False)].drop(columns=['geometry'])\n",
    "    # Create the Figure with horizontal orientation\n",
    "    bar1960_fig = px.bar(island_gdf, y='name', x='TempDiff', animation_frame=\"decade\", orientation='h')\n",
    "    bar1960_fig.update_layout(\n",
    "        height=750,\n",
    "        title=f'Average Temperature Increase Relative to 1960s<br>Across {island_value} Provinces',\n",
    "        margin=dict(l=20, r=20, t=75, b=50),\n",
    "        yaxis=dict(\n",
    "            title=\"Province Name\",\n",
    "            tickfont=dict(size=11)  # Adjust tick font size to prevent overlap\n",
    "        ),\n",
    "        xaxis=dict(\n",
    "            title=\"Temperature (°C)\",\n",
    "            range=[0, 1.5],\n",
    "            tickmode='linear',\n",
    "            dtick=0.3,\n",
    "            tickfont=dict(size=13),  # Adjust tick font size to prevent overlap\n",
    "            tickangle=0\n",
    "        ),\n",
    "        font=dict(  # Adjust font size for main title\n",
    "            size=12\n",
    "        )\n",
    "    )\n",
    "    hover_template = \"Avg Temperature Increase<br>\" + \\\n",
    "                     \"Relative to 1960s<br>\" + \\\n",
    "                     \"<b>in %{y}</b><br>\" + \\\n",
    "                     \"during the %{customdata[0]}:<br>\" + \\\n",
    "                     \"%{customdata[1]:.2f}°C\"\n",
    "    bar1960_fig.update_traces(hovertemplate=hover_template,\n",
    "                           customdata=island_gdf[['decade', 'value']])\n",
    "\n",
    "    for frame in bar1960_fig.frames:\n",
    "        frame.data[0].hovertemplate = hover_template\n",
    "        frame.data[0].customdata = island_gdf[['decade', 'value']]\n",
    "    bar1960_fig.update_layout(\n",
    "        updatemenus=[{\n",
    "            'direction': 'left',\n",
    "            'pad': {'t': 10, 'b': 10, 'l': 10, 'r': 10},\n",
    "            'showactive': False,\n",
    "            'type': 'buttons',\n",
    "            'x': 0.06,\n",
    "            'xanchor': 'right',\n",
    "            'y': -0.14,\n",
    "            'yanchor': 'top'\n",
    "        }],\n",
    "        sliders=[{\n",
    "            'active': 0,\n",
    "            'x': 0.98,\n",
    "            'y': -0.08,\n",
    "            'xanchor': 'right',\n",
    "            'yanchor': 'top',\n",
    "            'transition': {'duration': 300, 'easing': 'cubic-in-out'},\n",
    "            'pad': {'t': 10, 'b': 10, 'l': 10, 'r': 10},\n",
    "            'currentvalue': {\n",
    "                'font': {'size': 15},\n",
    "                'prefix': 'Decade:',\n",
    "                'visible': True,\n",
    "                'xanchor': 'right',\n",
    "            },\n",
    "            'visible': True,\n",
    "        }]\n",
    "    )\n",
    "\n",
    "    return bar1960_fig\n",
    "\n",
    "test_fig = update_bar1960_fig('Mindanao')\n",
    "test_fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**APP IDEAS**\n",
    "- The temperature page will be in two columns 3:9\n",
    "- The right one is the map TEMP 2\n",
    "- The left one is a header and story explaining the map\n",
    "- below the text will be a modal button \"See More\" that opens a modal\n",
    "- The modal contains two columns, the left one is a text header and story, the right one is TEMP 1 bar graph\n",
    "- The app will have a footer that has two buttons at its ends, \"< BACK\" and \"NEXT >\"\n",
    "- Back will lead to the initial page and Next will lead to the Biodiversity Page"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Biodiversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(biodiversity_gdf.shape)\n",
    "biodiversity_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = biodiversity_gdf[biodiversity_gdf['island_group'] == 'Luzon'].sort_values(by='total_species', ascending=True).reset_index().drop(columns='index')\n",
    "filtered_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = filtered_data['name'].iloc[-1]\n",
    "data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIO 1: The first visualization for the biodiversity data is a **Chloropleth Map** that shows the number of [Total/CR/EN/VU] unique species per Province.\n",
    "#### Interactions:\n",
    "- DropDown/RadioButtons that switch between \"Total, CR, EN and VU\" through Dash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "land_gdf = biodiversity_gdf[(biodiversity_gdf['area_type'].isin(['Land']))]\n",
    "\n",
    "fig = px.choropleth_mapbox(land_gdf,\n",
    "                            geojson=land_gdf.geometry,\n",
    "                            locations=land_gdf.index,\n",
    "                            color='Critically Endangered', # Change this based on dropdown\n",
    "                            color_continuous_scale='balance',\n",
    "                            range_color=[land_gdf['Critically Endangered'].min(), land_gdf['Critically Endangered'].max()],  \n",
    "                            mapbox_style='streets',  \n",
    "                            zoom=5,  \n",
    "                            center={\"lat\": 12.8797, \"lon\": 122.7740}, \n",
    "                            opacity=0.6,\n",
    "                            )\n",
    "\n",
    "fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))\n",
    "\n",
    "fig.update_layout(coloraxis_colorbar=dict(title=\"Critically Endangered<br>Species\",yanchor=\"top\",xanchor='right',\n",
    "                            y=1, x=1, ticks=\"outside\", thickness=10, title_font_color='#0c232c',  \n",
    "                            ticksuffix=' Species',\n",
    "                            tickfont=dict(\n",
    "                            size=12,\n",
    "                            color='#0c232c')\n",
    "                        )\n",
    "                    )\n",
    "\n",
    "hover_template = '<b>%{customdata[0]}</b><br>Unique Species: %{customdata[1]:.0f}<extra></extra>'\n",
    "\n",
    "fig.update_traces(hovertemplate=hover_template,\n",
    "                  customdata=land_gdf[['name', 'Critically Endangered']])\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIO 2: The second visualization for the biodiversity data is a DRILLDOWN operation on the previous **Chloropleth Map** that shows the number of [Total/CR/EN/VU] unique species per Province. In addition to this, the drilldown will be based on Island Groups\n",
    "#### Interactions:\n",
    "- A button that triggers the drilldown\n",
    "- DropDown/RadioButtons that switch between \"Total, CR, EN and VU\" (from the previous map BIO 1)\n",
    "- DropDown/RadioButtons that switch between \"Luzon, Visayas, and Mindanao\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Island Group\n",
    "land_gdf = biodiversity_gdf[(biodiversity_gdf['area_type'].isin(['Land'])) & (biodiversity_gdf['island_group'].isin(['Luzon']))]\n",
    "\n",
    "# Createe the figure\n",
    "fig = px.choropleth_mapbox(land_gdf,\n",
    "                            geojson=land_gdf.geometry,\n",
    "                            locations=land_gdf.index,\n",
    "                            color='Critically Endangered', # Change this based on dropdown\n",
    "                            color_continuous_scale='balance',\n",
    "                            range_color=[land_gdf['Critically Endangered'].min(), land_gdf['Critically Endangered'].max()],  \n",
    "                            mapbox_style='streets',  \n",
    "                            zoom=6,  \n",
    "                            center={\"lat\": 12.8797, \"lon\": 122.7740}, \n",
    "                            opacity=0.6,\n",
    "                            )\n",
    "\n",
    "fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))\n",
    "\n",
    "fig.update_layout(coloraxis_colorbar=dict(title=\"Critically Endangered<br>Species\",yanchor=\"top\",xanchor='right',\n",
    "                            y=1, x=1, ticks=\"outside\", thickness=10, title_font_color='#0c232c',  \n",
    "                            ticksuffix=' Species',\n",
    "                            tickfont=dict(\n",
    "                            size=12,\n",
    "                            color='#0c232c')\n",
    "                        )\n",
    "                    )\n",
    "\n",
    "hover_template = '<b>%{customdata[0]}</b><br>Unique Species: %{customdata[1]:.0f}<extra></extra>'\n",
    "\n",
    "fig.update_traces(hovertemplate=hover_template,\n",
    "                  customdata=land_gdf[['name', 'Critically Endangered']])\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIO 3: The third visualization for the biodiversity data is ALSO a DRILLDOWN operation on the first chloropleth map and is a **Horizontal Stacked Bar Chart** that shows the number of [CR/EN/VU] unique species per Province. In addition to this, the drilldown will be based on Island Groups\n",
    "#### Interactions:\n",
    "- A button that triggers the drilldown (same button as BIO 2)\n",
    "- DropDown/RadioButtons that switch between \"Luzon, Visayas, and Mindanao\" (same button as BIO 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = biodiversity_gdf[biodiversity_gdf['island_group'] == 'Luzon'].sort_values(by='total_species', ascending=True, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Island Group\n",
    "land_gdf = biodiversity_gdf[(biodiversity_gdf['area_type'].isin(['Land'])) & (biodiversity_gdf['island_group'].isin(['Luzon']))].sort_values(by='total_species', ascending=True, ignore_index=True)\n",
    "\n",
    "# Create stacked bar plot using Plotly Express\n",
    "bar_fig = px.bar(land_gdf,\n",
    "             x={'Vulnerable':\"Vulnerable\", 'Endangered':\"Endangered\", 'Critically Endangered':\"Critical\"},  \n",
    "             y='name',  \n",
    "             title='Number of Unique Species<br>at Risk in Luzon',\n",
    "             labels={'Vulnerable':\"Vulnerable\", 'Endangered':\"Endangered\", 'Critically Endangered':\"Critical\"},  \n",
    "             orientation='h',  \n",
    "             height=750,  \n",
    "             color_discrete_sequence=['yellow', 'orange', 'red'], \n",
    "             )\n",
    "\n",
    "bar_fig.update_layout(\n",
    "    xaxis_title='Unique Species', \n",
    "    yaxis_title='Province',\n",
    "    legend_title='IUCN Category',\n",
    "    legend=dict(\n",
    "        x=0.99,\n",
    "        y=0.01,\n",
    "        xanchor='right',\n",
    "        yanchor='bottom',\n",
    "        traceorder='normal',\n",
    "        font=dict(\n",
    "            family='Arial',\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "        bgcolor='rgba(255, 255, 255, 0.6)',\n",
    "        bordercolor='rgba(0, 0, 0, 0.6)',\n",
    "        borderwidth=1,\n",
    "    ),\n",
    "    margin=dict(l=0, r=0, t=70, b=60)\n",
    ")\n",
    "\n",
    "hover_template = '<b>%{y}</b><br>Unique Species: %{x}<extra></extra>'\n",
    "\n",
    "bar_fig.update_traces(hovertemplate=hover_template,\n",
    "                  customdata=land_gdf[['name']])\n",
    "\n",
    "bar_fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIO 4: The fourth visualization for the biodiversity data is a **Chloropleth Map** that shows the number of [Total/CR/EN/VU] unique species per sea.\n",
    "#### Interactions:\n",
    "- DropDown/RadioButtons that switch between \"Total, CR, EN and VU\" through Dash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_gdf = biodiversity_gdf[(biodiversity_gdf['area_type'].isin(['Sea']))]\n",
    "\n",
    "fig = px.choropleth_mapbox(sea_gdf,\n",
    "                            geojson=sea_gdf.geometry,\n",
    "                            locations=sea_gdf.index,\n",
    "                            color='Critically Endangered', # Change this based on dropdown\n",
    "                            color_continuous_scale='sunsetdark',\n",
    "                            range_color=[sea_gdf['Critically Endangered'].min(), sea_gdf['Critically Endangered'].max()],  \n",
    "                            mapbox_style='streets',  \n",
    "                            zoom=5,  \n",
    "                            center={\"lat\": 12.8797, \"lon\": 122.7740}, \n",
    "                            opacity=0.6,\n",
    "                            )\n",
    "\n",
    "fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))\n",
    "\n",
    "fig.update_layout(coloraxis_colorbar=dict(title=\"Critically Endangered<br>Species\",yanchor=\"top\",xanchor='right',\n",
    "                            y=1, x=1, ticks=\"outside\", thickness=10, title_font_color='#0c232c',  \n",
    "                            ticksuffix=' Species',\n",
    "                            tickfont=dict(\n",
    "                            size=12,\n",
    "                            color='#0c232c')\n",
    "                        )\n",
    "                    )\n",
    "\n",
    "hover_template = '<b>%{customdata[0]}</b><br>Unique Species: %{customdata[1]:.0f}<extra></extra>'\n",
    "\n",
    "fig.update_traces(hovertemplate=hover_template,\n",
    "                  customdata=sea_gdf[['name', 'Critically Endangered']])\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIO 5: The fifth visualization for the biodiversity data is a **Horizontal Stacked Bar Chart** that shows the number of [CR/EN/VU] unique species per Sea.\n",
    "#### Interactions:\n",
    "- A button that shows the visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_gdf = biodiversity_gdf[(biodiversity_gdf['area_type'].isin(['Sea']))]\n",
    "# Create stacked bar plot using Plotly Express\n",
    "fig = px.bar(sea_gdf,\n",
    "             x=['Vulnerable', 'Endangered', 'Critically Endangered'],  \n",
    "             y='name',  \n",
    "             title='Number of Unique Endangered Species in Sea',\n",
    "             labels={'Vulnerable', 'Endangered', 'Critically Endangered'},  \n",
    "             orientation='h',  \n",
    "             height=600,  \n",
    "             color_discrete_sequence=['yellow', 'orange', 'red'], \n",
    "             )\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title='Number of Unique Species', \n",
    "    yaxis_title='Province',\n",
    "    legend_title='Category',\n",
    ")\n",
    "\n",
    "hover_template = '<b>%{customdata[0]}</b><br>Unique Species: %{x}<extra></extra>'\n",
    "\n",
    "fig.update_traces(hovertemplate=hover_template,\n",
    "                  customdata=sea_gdf[['name']])\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**APP IDEAS**\n",
    "- There is toggle that switches the page to show Land Data or Sea Data\n",
    "- Bio 1, Bio 2, and Bio 3 is part of the Land and will have interactions that are connected to each other\n",
    "- Land data will somehow be related to the temp data\n",
    "- Bio 4, and Bio 5 is part of the Sea and will havee interactions that are also connected to each other\n",
    "- Will also be compared to temp data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Disaster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(disaster_gdf.shape)\n",
    "disaster_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DISASTER 1: The first visualization for the disaster data is a **Chloropleth Map** that shows the number of [Disaster Type] per Province.\n",
    "#### Interactions:\n",
    "- DropDown/RadioButtons that switch between Disaster Types (Total, Storm, Flood, Earthquake, Volcanic, Mass Movement, Drought) through Dash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.choropleth_mapbox(disaster_gdf,\n",
    "                            geojson=disaster_gdf.geometry,\n",
    "                            locations=disaster_gdf.index,\n",
    "                            color='Total Disaster Count', # Change based on dropdown value\n",
    "                            color_continuous_scale='reds',\n",
    "                            range_color=[disaster_gdf['Total Disaster Count'].min(), disaster_gdf['Total Disaster Count'].max()],  \n",
    "                            mapbox_style='streets',  \n",
    "                            zoom=5,  \n",
    "                            center={\"lat\": disaster_gdf.geometry.centroid.y.mean(), \n",
    "                                    \"lon\": disaster_gdf.geometry.centroid.x.mean()}, \n",
    "                            opacity=0.6,\n",
    "                            )\n",
    "\n",
    "fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))\n",
    "\n",
    "fig.update_layout(coloraxis_colorbar=dict(title=\"Total Disaster<br>Count\",yanchor=\"top\",xanchor='right',\n",
    "                            y=1, x=1, ticks=\"outside\", thickness=10, title_font_color='#0c232c',  \n",
    "                            # ticksuffix=' disasters',\n",
    "                            tickfont=dict(\n",
    "                            size=12,\n",
    "                            color='#0c232c')\n",
    "                        )\n",
    "                    )\n",
    "\n",
    "hover_template = '<b>%{customdata[0]}</b><br>%{customdata[1]:.0f} disasters<extra></extra>'\n",
    "\n",
    "fig.update_traces(hovertemplate=hover_template,\n",
    "                  customdata=disaster_gdf[['Area Name', 'Total Disaster Count']])\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DISASTER 2: The second visualization for the disaster data is a **Horizontal Bar Chart** that shows the number of [Disaster Type] per Province per [Island Group].\n",
    "#### Interactions:\n",
    "- DropDown/RadioButtons that switch between Disaster Types (Total, Storm, Flood, Earthquake, Volcanic, Mass Movement, Drought) through Dash (Same button as Disaster 1 visualization)\n",
    "- DropDown/RadioButtons that switch between Island Groups (Luzon Visayas Mindanao)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DISASTER 3: The third visualization for the disaster data is a **Chloropleth Map** that shows the number of [Disaster Type] per REGION.\n",
    "#### Interactions:\n",
    "- DropDown/RadioButtons that switch between Disaster Types (Total, Storm, Flood, Earthquake, Volcanic, Mass Movement, Drought) through Dash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Region_gdf = disaster_gdf.copy()\n",
    "Region_tot_ave = Region_gdf.drop(columns=['geometry']).groupby('Region').sum()\n",
    "\n",
    "def region_count(disaster_type,col_name):\n",
    "    Region_gdf[col_name] = np.nan #Create a new column that contains the number of disasters per region\n",
    "\n",
    "    for i1,r1 in Region_gdf.iterrows():\n",
    "        for i2,r2 in Region_tot_ave.iterrows():\n",
    "            if r1['Region'] == i2:\n",
    "                Region_gdf.at[i1, col_name] = r2[disaster_type]\n",
    "            else:\n",
    "                continue\n",
    "\n",
    "# Total Disasters per Region\n",
    "region_count('Total Disaster Count','Region_tot')\n",
    "# Storm per Region\n",
    "region_count('Storm Count','Region_storm')\n",
    "# Flood per Region\n",
    "region_count('Flood Count','Region_flood')\n",
    "# Earthquake per Region\n",
    "region_count('Earthquake Count','Region_earth')\n",
    "# Volcanic Activity per Region\n",
    "region_count('Volcanic Activity Count','Region_vol')\n",
    "# Mass Movement per Region\n",
    "region_count('Mass Movement Count','Region_mass')\n",
    "# Drought per Region\n",
    "region_count('Drought Count','Region_drought')\n",
    "Region_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_map(division, disaster_type):\n",
    "    curr_division = ''\n",
    "    curr_disaster = ''\n",
    "    if division == 'Region':\n",
    "        curr_division = 'Region'\n",
    "        match disaster_type:\n",
    "            case 'Total Disaster':\n",
    "                curr_disaster = 'Region_tot'\n",
    "            case 'Storm':\n",
    "                curr_disaster = 'Region_storm'\n",
    "            case 'Flood':\n",
    "                curr_disaster = 'Region_flood'\n",
    "            case 'Earthquake':\n",
    "                curr_disaster = 'Region_earth'\n",
    "            case 'Volcanic Activity':\n",
    "                curr_disaster = 'Region_vol'\n",
    "            case 'Mass Movement':\n",
    "                curr_disaster = 'Region_mass'\n",
    "            case 'Drought':\n",
    "                curr_disaster = 'Region_drought'\n",
    "            case _:\n",
    "                return\n",
    "    elif division == 'Province':\n",
    "        curr_division = 'Area Name'\n",
    "        match disaster_type:\n",
    "            case 'Total Disaster':\n",
    "                curr_disaster = 'Total Disaster Count'\n",
    "            case 'Storm':\n",
    "                curr_disaster = 'Storm Count'\n",
    "            case 'Flood':\n",
    "                curr_disaster = 'Flood Count'\n",
    "            case 'Earthquake':\n",
    "                curr_disaster = 'Earthquake Count'\n",
    "            case 'Volcanic Activity':\n",
    "                curr_disaster = 'Volcanic Activity Count'\n",
    "            case 'Mass Movement':\n",
    "                curr_disaster = 'Mass Movement Count'\n",
    "            case 'Drought':\n",
    "                curr_disaster = 'Drought Count'\n",
    "            case _:\n",
    "                return\n",
    "    else:\n",
    "        return\n",
    "\n",
    "    map_fig = px.choropleth_mapbox(Region_gdf,\n",
    "                                geojson=Region_gdf.geometry,\n",
    "                                locations=Region_gdf.index,\n",
    "                                color=curr_disaster, # Change based on dropdown value\n",
    "                                color_continuous_scale='amp',\n",
    "                                range_color=[Region_gdf[curr_disaster].min(), Region_gdf[curr_disaster].max()],  \n",
    "                                mapbox_style='streets',  \n",
    "                                zoom=5,  \n",
    "                                center={\"lat\": 12.8797, \"lon\": 122.7740}, \n",
    "                                opacity=0.6,\n",
    "                                )\n",
    "\n",
    "    map_fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))\n",
    "\n",
    "    map_fig.update_layout(coloraxis_colorbar=dict(title= disaster_type + \"<br>Count\",yanchor=\"top\",xanchor='left',\n",
    "                                y=1, x=0, ticks=\"outside\", ticklabelposition=\"outside left\", thickness=10, title_font_color='#0c232c',  \n",
    "                                tickfont=dict(\n",
    "                                size=12,\n",
    "                                color='#0c232c')\n",
    "                            )\n",
    "                        )\n",
    "\n",
    "    hover_template = '<b>%{customdata[0]}</b><br>' + disaster_type + ' Count: %{customdata[1]:.0f}<extra></extra>'\n",
    "\n",
    "    map_fig.update_traces(hovertemplate=hover_template,\n",
    "                    customdata=Region_gdf[[curr_division, curr_disaster]])\n",
    "\n",
    "    return map_fig\n",
    "\n",
    "dis_map = update_map('Province', 'Storm')\n",
    "dis_map.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_disaster_bar(division, disaster_type, island_group):\n",
    "    curr_division = ''\n",
    "    curr_disaster = ''\n",
    "    if division == 'Region':\n",
    "        curr_division = 'Region'\n",
    "        match disaster_type:\n",
    "            case 'Total Disaster':\n",
    "                curr_disaster = 'Region_tot'\n",
    "            case 'Storm':\n",
    "                curr_disaster = 'Region_storm'\n",
    "            case 'Flood':\n",
    "                curr_disaster = 'Region_flood'\n",
    "            case 'Earthquake':\n",
    "                curr_disaster = 'Region_earth'\n",
    "            case 'Volcanic Activity':\n",
    "                curr_disaster = 'Region_vol'\n",
    "            case 'Mass Movement':\n",
    "                curr_disaster = 'Region_mass'\n",
    "            case 'Drought':\n",
    "                curr_disaster = 'Region_drought'\n",
    "            case _:\n",
    "                return\n",
    "        island_disaster = Region_gdf[Region_gdf['Island Group'] == island_group].drop(columns=['geometry', 'Area Name']).groupby(['Island Group', 'Region']).sum().reset_index().sort_values(by=curr_disaster, ascending=True, ignore_index=True)\n",
    "    elif division == 'Province':\n",
    "        curr_division = 'Area Name'\n",
    "        match disaster_type:\n",
    "            case 'Total Disaster':\n",
    "                curr_disaster = 'Total Disaster Count'\n",
    "            case 'Storm':\n",
    "                curr_disaster = 'Storm Count'\n",
    "            case 'Flood':\n",
    "                curr_disaster = 'Flood Count'\n",
    "            case 'Earthquake':\n",
    "                curr_disaster = 'Earthquake Count'\n",
    "            case 'Volcanic Activity':\n",
    "                curr_disaster = 'Volcanic Activity Count'\n",
    "            case 'Mass Movement':\n",
    "                curr_disaster = 'Mass Movement Count'\n",
    "            case 'Drought':\n",
    "                curr_disaster = 'Drought Count'\n",
    "            case _:\n",
    "                return\n",
    "        island_disaster = Region_gdf[(Region_gdf['Island Group'] == island_group)].drop(columns=['geometry']).sort_values(by=curr_disaster, ascending=True, ignore_index=True)\n",
    "    else:\n",
    "        return\n",
    "    # Create stacked bar plot using Plotly Express\n",
    "\n",
    "    x = curr_disaster\n",
    "    y = curr_division\n",
    "    bar_fig = px.bar(island_disaster,\n",
    "                x=x,  \n",
    "                y=y,  \n",
    "                title=disaster_type + \" in \" + island_group + \" per \" + division,\n",
    "                orientation='h',  \n",
    "                height=750,  \n",
    "                color_discrete_sequence=['lightblue'], \n",
    "                )\n",
    "    bar_fig.update_layout(\n",
    "        xaxis_title=disaster_type, \n",
    "        yaxis_title=division,\n",
    "    )\n",
    "    hover_template = '<b>%{customdata[0]}</b><br>' + disaster_type + ' Count: %{x}<extra></extra>'\n",
    "    bar_fig.update_traces(hovertemplate=hover_template,\n",
    "                    customdata=island_disaster[[curr_division]])\n",
    "    \n",
    "    return bar_fig\n",
    "\n",
    "test = update_disaster_bar('Region', 'Total Disaster', 'Luzon')\n",
    "test.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DISASTER 4: The fourth visualization for the disaster data is a **Horizontal Bar Chart** that shows the number of [Disaster Type] per REGION per [Island Group].\n",
    "#### Interactions:\n",
    "- DropDown/RadioButtons that switch between Disaster Types (Total, Storm, Flood, Earthquake, Volcanic, Mass Movement, Drought) through Dash (Same button as Disaster 3 visualization)\n",
    "- DropDown/RadioButtons that switch between Island Groups (Luzon Visayas Mindanao)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "luzon_reg_disaster = Region_gdf[Region_gdf['Island Group'] == 'Luzon'].groupby('Region')['Region_tot'].sum().sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Island Group\n",
    "luzon_reg_disaster = Region_gdf[Region_gdf['Island Group'] == 'Luzon'].groupby('Region')['Region_tot'].sum().sort_values(ascending=True)\n",
    "\n",
    "# Create stacked bar plot using Plotly Express\n",
    "fig = px.bar(x=luzon_reg_disaster.values,  \n",
    "             y=luzon_reg_disaster.index,  \n",
    "             title='Total Disasters in Luzon per Region',\n",
    "             orientation='h',  \n",
    "             height=600,  \n",
    "             color_discrete_sequence=['blue'], \n",
    "             )\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title='Total Disasters', \n",
    "    yaxis_title='Region',\n",
    ")\n",
    "\n",
    "hover_template = '<b>%{y}</b><br>%{x} disasters<extra></extra>'\n",
    "\n",
    "fig.update_traces(hovertemplate=hover_template,\n",
    "                  customdata=luzon_reg_disaster.reset_index().values)\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**APP IDEAS**\n",
    "- There is toggle that switches the visualizations to rollup or drilldown between visualizing by province or by region\n",
    "- Disaster 1 and 2 interactions will be connected\n",
    "- Disasteer 3 and 4 interactions will be connected\n",
    "- Header and stories to be provided as text "
   ]
  }
 ],
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