Chapter6_Species_Distribution_Modeling.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/osgeokr/GEE-PAM-Book/blob/main/Chapter6_Species_Distribution_Modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ee622536-a80f-420f-ab8c-86c5ab5f89e8",
      "metadata": {
        "id": "ee622536-a80f-420f-ab8c-86c5ab5f89e8"
      },
      "source": [
        "# 6장. 종 분포 모델링: GEE를 활용한 서식지 예측 및 분석"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6.1. 종 분포 모델 소개"
      ],
      "metadata": {
        "id": "mJXxCqFy0oH2"
      },
      "id": "mJXxCqFy0oH2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.1.1. 종 분포 모델이란?\n",
        "\n",
        "종 분포 모델링(SDM: Species Distribution Modeling)은 종의 실제 또는 잠재적인 지리적 분포를 추정하는 가장 일반적인 방법론으로 해당 종에 적합한 환경 조건을 특성화하고, 그런 다음 어느 지역에 이러한 적합한 환경이 분포되어 있는지 식별하는 것입니다.\n",
        "\n",
        "SDM은 최근 수년간 보전계획의 중요한 구성요소로 부상하였으며, 이를 위해 다양한 모델링 기법이 개발되어 왔습니다. Google Earth Engine에서 SDM을 구현하면 대규모 환경 데이터에 쉽게 접근할 수 있고, 강력한 컴퓨팅 능력과 머신러닝 알고리즘 지원으로 신속한 모델링이 가능해집니다. 이번 장에서는 Google Earth Engine을 활용하여 종 분포 모델링을 구현해 보겠습니다."
      ],
      "metadata": {
        "id": "cOJLUPyK0u7B"
      },
      "id": "cOJLUPyK0u7B"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.1.2. 모델에 필요한 데이터\n",
        "\n",
        "SDM은 일반적으로 알려진 종의 위치 기록과 환경변수 간의 관계를 활용하여 모집단이 유지될 수 있는 환경 조건을 식별합니다. 즉, 두 가지 유형의 모델 입력 데이터가 필요합니다: 1) 알려진 종의 위치 기록; 그리고 2) 다양한 환경변수. 이러한 데이터는 알고리즘에 입력되어 종의 출현과 관련된 환경조건을 식별하는데 사용됩니다."
      ],
      "metadata": {
        "id": "zSFpOF0t1W6Y"
      },
      "id": "zSFpOF0t1W6Y"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6.1.3. 종 분포 모델링의 작업 흐름\n",
        "\n",
        "보전생물학자인 Dr. Ramiro D. Crego는 GEE JavaScript Code Editor를 통해 종 분포 모델링을 구현하고, 그 연구결과를 공개했습니다. 이 책에서 소개할 SDM의 작업 흐름은 그가 공유한 [JavaScript 소스코드](https://smithsonian.github.io/SDMinGEE/)를 Python 언어로 변환 및 수정 보완한 것입니다.\n",
        "\n",
        "이 책에서 다루는 Google Earth Engine을 이용한 종 분포 모델링의 작업 흐름은 다음과 같습니다: 1) 종 출현 데이터 수집 및 처리, 2) 관심 영역 정의, 3) GEE 환경 변수 추가, 4) 임의-비출현 데이터 생성, 5) 모델 적합 및 예측, 6) 변수 중요도 및 정확도 평가.\n",
        "\n",
        "<p align=\"center\"><img src=\"https://github.com/osgeokr/GEE-PAM-Book/blob/main/img/sdm_workflow.png?raw=1\" width = \"800px\"></img></p>"
      ],
      "metadata": {
        "id": "Y5iHVxP61nId"
      },
      "id": "Y5iHVxP61nId"
    },
    {
      "cell_type": "markdown",
      "id": "149a3fdc-0650-4241-89d7-b3303ce996bd",
      "metadata": {
        "id": "149a3fdc-0650-4241-89d7-b3303ce996bd"
      },
      "source": [
        "## 6.2. GEE를 활용한 서식지 예측 및 분석"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GEE 기반 종 분포 모델링은 \"팔색조(Pitta nympha)\"를 사례로 정리해 보겠습니다. 팔색조는 우리나라에서는 드문 여름철새이자 나그네새로, 최근 한반도의 기후 온난화 등으로 인해 분포 지역이 확대되고 있습니다. 팔색조는 희귀종으로 멸종위기 야생생물 Ⅱ급, 천연기념물 제204호, 국가적색목록 평가: 지역절멸(RE) 및 IUCN 범주: 취약(VU) 등급 종입니다.\n",
        "\n",
        "팔색조의 보전계획 수립을 위한 SDM은 충분히 가치있어 보입니다. 이제 GEE를 통해 서식지 예측 및 분석을 진행해 보겠습니다."
      ],
      "metadata": {
        "id": "iecf28q85osO"
      },
      "id": "iecf28q85osO"
    },
    {
      "cell_type": "markdown",
      "source": [
        "실습에 앞서 몇 가지 Python 라이브러리를 추가 설치합니다. 이 명령은 해당 라이브러리를 업그레이드(`-U`)하면서 설치합니다. `-q` 옵션은 설치 과정에서의 상세한 메시지를 출력하지 않도록 하는 '조용한(quiet)' 모드입니다. 만약 Google Colab에서 실습을 진행한다면 `pycrs`만 추가 설치해 주시면 됩니다."
      ],
      "metadata": {
        "id": "rwt_fPpv6cgd"
      },
      "id": "rwt_fPpv6cgd"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "49fa81f9-75e7-4973-a952-5bdeacafee8e",
      "metadata": {
        "id": "49fa81f9-75e7-4973-a952-5bdeacafee8e"
      },
      "outputs": [],
      "source": [
        "# 라이브러리 설치\n",
        "# %pip install -U -q pyarrow\n",
        "# %pip install -U -q geopandas\n",
        "# %pip install -U -q statsmodels\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Python 라이브러리들을 호출합니다. import 문은 모듈의 전체 내용을 가져오고, from import 문은 모듈에서 특정 객체만 가져올 수 있습니다."
      ],
      "metadata": {
        "id": "Jl9XTHC_7o7u"
      },
      "id": "Jl9XTHC_7o7u"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "25548343-b156-4d9b-9800-8e846be17999",
      "metadata": {
        "id": "25548343-b156-4d9b-9800-8e846be17999"
      },
      "outputs": [],
      "source": [
        "# 라이브러리 호출\n",
        "import ee\n",
        "import geemap\n",
        "\n",
        "import geemap.colormaps as cm\n",
        "import pandas as pd, geopandas as gpd\n",
        "import numpy as np, matplotlib.pyplot as plt\n",
        "import os, requests, math, random\n",
        "\n",
        "from ipyleaflet import TileLayer\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Google Earth Engine의 API(ee 라이브러리)를 인증 및 초기화합니다."
      ],
      "metadata": {
        "id": "wBmjyMgb8BBn"
      },
      "id": "wBmjyMgb8BBn"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "4a242c6d-828c-4e68-9fa5-cf67a4481600",
      "metadata": {
        "id": "4a242c6d-828c-4e68-9fa5-cf67a4481600",
        "outputId": "a4fb77ca-96b1-4c87-ae5e-86a6ca2d2833",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "ee.Authenticate()\n",
        "ee.Initialize(project='ee-foss4g')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35acc316-28de-4c5b-86da-b0383d0c1e2b",
      "metadata": {
        "id": "35acc316-28de-4c5b-86da-b0383d0c1e2b"
      },
      "source": [
        "###  6.2.1. 종 출현 데이터 수집 및 처리"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이제 팔색조의 종 출현 데이터를 수집해 보겠습니다. 현재 관심 종의 출현 데이터를 가지고 있지 않더라도, 생물다양성 정보를 제공하는 GBIF API를 통해 특정 생물종에 관한 관측 데이터를 확보할 수 있습니다.\n",
        "\n",
        "GBIF는 Global Biodiversity Information Facility의 약자로, 전 세계 생물 다양성 정보를 관리하는 국제적인 기구입니다. 과학 연구, 환경 보호 및 정책 결정에 도움을 주는 생물종 분포 데이터를 제공하는 중요한 역할을 합니다. [GBIF API](https://techdocs.gbif.org/en/openapi/)는 GBIF에서 제공하는 생물종 분포 데이터에 접근할 수 있는 API로,  이를 활용하면 데이터를 검색, 필터링 및 다운로드할 수 있으며 생물종과 관련된 다양한 정보를 얻을 수 있습니다.\n",
        "\n",
        "아래 코드에서 변수 species_name은 팔색조의 학명을, country_code 변수는 대한민국의 국가코드를 할당했습니다. base_url 변수에는 GBIF API 주소가 저장됩니다. params는 API 요청에 사용될 파라미터들을 담고 있는 딕셔너리입니다.\n",
        "\n",
        "* scientificName: 검색 대상 종의 학명을 설정합니다.\n",
        "* country_code:  검색을 특정 국가로 한정합니다.\n",
        "* hasCoordinate: 좌표가 있는 데이터만(true) 검색하도록 합니다.\n",
        "* basisOfRecord\": 인간에 의한 관측(HUMAN_OBSERVATION) 기록만 택합니다.\n",
        "* limit: 최대 10,000개의 결과를 반환하도록 설정합니다."
      ],
      "metadata": {
        "id": "PryA7kPV8hy4"
      },
      "id": "PryA7kPV8hy4"
    },
    {
      "cell_type": "code",
      "source": [
        "def get_gbif_species_data(species_name, country_code=\"KR\"):\n",
        "    \"\"\"\n",
        "    GBIF API를 활용하여 특정 생물종의 관측 데이터를 조회하고 데이터프레임으로 반환합니다.\n",
        "\n",
        "    Parameters:\n",
        "    species_name (str): 조회할 생물종의 학명.\n",
        "    country_code (str): 관측 데이터를 조회할 국가의 코드 (기본값: \"KR\").\n",
        "\n",
        "    Returns:\n",
        "    pd.DataFrame: 관측 데이터를 포함하는 pandas 데이터프레임.\n",
        "    \"\"\"\n",
        "    base_url = \"https://api.gbif.org/v1/occurrence/search\"\n",
        "    params = {\n",
        "        \"scientificName\": species_name,\n",
        "        \"country\": country_code,\n",
        "        \"hasCoordinate\": \"true\",\n",
        "        \"basisOfRecord\": \"HUMAN_OBSERVATION\",\n",
        "        \"limit\": 10000,\n",
        "    }\n",
        "\n",
        "    try:\n",
        "        response = requests.get(base_url, params=params)\n",
        "        response.raise_for_status()  # 응답 에러가 있을 경우 예외를 발생시킵니다.\n",
        "        data = response.json()\n",
        "        occurrences = data.get(\"results\", [])\n",
        "\n",
        "        if occurrences:  # 데이터가 있는 경우\n",
        "            df = pd.json_normalize(occurrences)\n",
        "            return df\n",
        "        else:\n",
        "            print(\"No data found for the given species and country code.\")\n",
        "            return pd.DataFrame()  # 비어있는 데이터프레임 반환\n",
        "    except requests.RequestException as e:\n",
        "        print(f\"Request failed: {e}\")\n",
        "        return pd.DataFrame()  # 예외 발생 시 비어있는 데이터프레임 반환"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "GaktdobEr3Kq",
        "outputId": "37b1f9ed-5bbc-4afe-fb29-1a62230b76f5"
      },
      "id": "GaktdobEr3Kq",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "앞서 설정한 파라미터를 사용하여 GBIF API를 통해 팔색조(Pitta nympha)의 관측기록을 조회하고, 조회된 결과를 DataFrame으로 불러와 첫 번째 행을 확인해 봅니다. DataFrame은 테이블 형식의 데이터를 다루기 위한 데이터 구조로 행과 열로 이루어져 있습니다. 필요하다면 DataFrame을 CSV 파일로 저장하고 다시 읽어올 수도 있습니다."
      ],
      "metadata": {
        "id": "MyppJfvR9m_I"
      },
      "id": "MyppJfvR9m_I"
    },
    {
      "cell_type": "code",
      "source": [
        "# Fairy nympha 데이터 호출\n",
        "df = get_gbif_species_data(\"Pitta nympha\")\n",
        "# df.to_csv(\"pitta_nympha_data.csv\", index=False) DataFrame을 CSV로 저장\n",
        "# df = pd.read_csv(\"pitta_nympha_data.csv\")\n",
        "df.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "kYmu-wshsW5K",
        "outputId": "10ac5d72-52e0-48f7-a8ba-8ac29d92c602"
      },
      "id": "kYmu-wshsW5K",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          key                            datasetKey  \\\n",
              "0  4126765284  50c9509d-22c7-4a22-a47d-8c48425ef4a7   \n",
              "\n",
              "                       publishingOrgKey                       installationKey  \\\n",
              "0  28eb1a3f-1c15-4a95-931a-4af90ecb574d  997448a8-f762-11e1-a439-00145eb45e9a   \n",
              "\n",
              "                 hostingOrganizationKey publishingCountry     protocol  \\\n",
              "0  28eb1a3f-1c15-4a95-931a-4af90ecb574d                US  DWC_ARCHIVE   \n",
              "\n",
              "                     lastCrawled                     lastParsed  crawlId  ...  \\\n",
              "0  2024-01-23T16:28:21.693+00:00  2024-01-25T09:13:47.069+00:00      431  ...   \n",
              "\n",
              "  nomenclaturalCode fieldNotes  behavior  verbatimElevation  \\\n",
              "0               NaN        NaN       NaN                NaN   \n",
              "\n",
              "   higherClassification  extensions.http://rs.tdwg.org/ac/terms/Multimedia  \\\n",
              "0                   NaN                                                NaN   \n",
              "\n",
              "   distanceFromCentroidInMeters  associatedTaxa  lifeStage  occurrenceRemarks  \n",
              "0                           NaN             NaN        NaN                NaN  \n",
              "\n",
              "[1 rows x 110 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f15e39b-c6d6-41ce-ae5a-83419fae2b8b\" class=\"colab-df-container\">\n",
              "    <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>key</th>\n",
              "      <th>datasetKey</th>\n",
              "      <th>publishingOrgKey</th>\n",
              "      <th>installationKey</th>\n",
              "      <th>hostingOrganizationKey</th>\n",
              "      <th>publishingCountry</th>\n",
              "      <th>protocol</th>\n",
              "      <th>lastCrawled</th>\n",
              "      <th>lastParsed</th>\n",
              "      <th>crawlId</th>\n",
              "      <th>...</th>\n",
              "      <th>nomenclaturalCode</th>\n",
              "      <th>fieldNotes</th>\n",
              "      <th>behavior</th>\n",
              "      <th>verbatimElevation</th>\n",
              "      <th>higherClassification</th>\n",
              "      <th>extensions.http://rs.tdwg.org/ac/terms/Multimedia</th>\n",
              "      <th>distanceFromCentroidInMeters</th>\n",
              "      <th>associatedTaxa</th>\n",
              "      <th>lifeStage</th>\n",
              "      <th>occurrenceRemarks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4126765284</td>\n",
              "      <td>50c9509d-22c7-4a22-a47d-8c48425ef4a7</td>\n",
              "      <td>28eb1a3f-1c15-4a95-931a-4af90ecb574d</td>\n",
              "      <td>997448a8-f762-11e1-a439-00145eb45e9a</td>\n",
              "      <td>28eb1a3f-1c15-4a95-931a-4af90ecb574d</td>\n",
              "      <td>US</td>\n",
              "      <td>DWC_ARCHIVE</td>\n",
              "      <td>2024-01-23T16:28:21.693+00:00</td>\n",
              "      <td>2024-01-25T09:13:47.069+00:00</td>\n",
              "      <td>431</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 110 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f15e39b-c6d6-41ce-ae5a-83419fae2b8b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3f15e39b-c6d6-41ce-ae5a-83419fae2b8b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f15e39b-c6d6-41ce-ae5a-83419fae2b8b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이번에는 DataFrame을 지리정보를 포함하는 열(geometry)을 가진 GeoDataFrame으로 변환하고 첫번째 행을 확인해 봅니다. GeoDataFrame은 GeoPackage 파일로 저장하고 다시 읽어올 수 있습니다. GeoPackage (*.gpkg) 파일은 QGIS와 같은 지리공간 소프트웨어에서도 사용 가능합니다."
      ],
      "metadata": {
        "id": "DH9D7rpV-CR2"
      },
      "id": "DH9D7rpV-CR2"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c74e7ddc-fb4d-4c9f-be48-87f3ef03b7df",
      "metadata": {
        "id": "c74e7ddc-fb4d-4c9f-be48-87f3ef03b7df",
        "outputId": "6b6273ab-1b14-4dc2-b9cd-508611fe004d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        species  year  month                    geometry\n",
              "0  Pitta nympha  2023      5  POINT (126.72514 33.20314)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6187fef1-702d-4f71-b592-8664e0bce044\" class=\"colab-df-container\">\n",
              "    <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>species</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pitta nympha</td>\n",
              "      <td>2023</td>\n",
              "      <td>5</td>\n",
              "      <td>POINT (126.72514 33.20314)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6187fef1-702d-4f71-b592-8664e0bce044')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6187fef1-702d-4f71-b592-8664e0bce044 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6187fef1-702d-4f71-b592-8664e0bce044');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# DataFrame을 GeoDataFrame으로 변환\n",
        "gdf = gpd.GeoDataFrame(\n",
        "    df,\n",
        "    geometry=gpd.points_from_xy(df.decimalLongitude,\n",
        "                                df.decimalLatitude),\n",
        "    crs=\"EPSG:4326\"\n",
        ")[[\"species\", \"year\", \"month\", \"geometry\"]]\n",
        "\"\"\"\n",
        "# GeoDataFrame을 GeoPackage로 변환 (pycrs 모듈 필요)\n",
        "%pip install -U -q pycrs\n",
        "gdf.to_file(\"pitta_nympha_data.gpkg\", driver=\"GPKG\")\n",
        "gdf = gpd.read_file(\"pitta_nympha_data.gpkg\")\n",
        "\"\"\"\n",
        "gdf.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "e1gLgTzB-Jhc"
      },
      "id": "e1gLgTzB-Jhc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "이번에는 GeoDataFrame에서 연도(year)와 월별(month) 데이터 분포를 시각화하여 그래프로 표시하고, 그래프를 이미지 파일로 저장하는 함수를 만들었습니다. 이렇게 하면 종 출현 데이터의 시계열 패턴 및 계절적 변동을 파악할 수 있으며, 데이터의 이상치나 품질 문제를 신속하게 확인할 수 있습니다."
      ],
      "metadata": {
        "id": "cSAidSFr-PY6"
      },
      "id": "cSAidSFr-PY6"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "2fe7983e-1a71-4881-aa81-8ed761051d3f",
      "metadata": {
        "id": "2fe7983e-1a71-4881-aa81-8ed761051d3f",
        "outputId": "0124da93-dcd3-413f-ee7d-1ae17e89020f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 연도(year)와 월별(month) 데이터 분포 시각화\n",
        "def plot_data_distribution(gdf, h_size=12):\n",
        "\n",
        "    plt.figure(figsize=(h_size, h_size-8))\n",
        "\n",
        "    # 연도별 데이터 분포 그래프 (왼쪽)\n",
        "    plt.subplot(1, 2, 1)\n",
        "    year_counts = gdf['year'].value_counts().sort_index()\n",
        "    plt.bar(year_counts.index, year_counts.values)\n",
        "    plt.xlabel('Year')\n",
        "    plt.ylabel('Count')\n",
        "    plt.title('Yearly Data Distribution')\n",
        "\n",
        "    # 막대 그래프 위에 데이터 개수 표시\n",
        "    for i, count in enumerate(year_counts.values):\n",
        "        plt.text(year_counts.index[i], count, str(count), ha='center', va='bottom')\n",
        "\n",
        "    # 월별 데이터 분포 그래프 (오른쪽)\n",
        "    plt.subplot(1, 2, 2)\n",
        "    month_counts = gdf['month'].value_counts().sort_index()\n",
        "    plt.bar(month_counts.index, month_counts.values)\n",
        "    plt.xlabel('Month')\n",
        "    plt.ylabel('Count')\n",
        "    plt.title('Monthly Data Distribution')\n",
        "\n",
        "    # 막대 그래프 위에 데이터 개수 표시\n",
        "    for i, count in enumerate(month_counts.values):\n",
        "        plt.text(month_counts.index[i], count, str(count), ha='center', va='bottom')\n",
        "\n",
        "    # x 축의 눈금을 정수 형식으로 설정\n",
        "    plt.xticks(month_counts.index, map(int, month_counts.index))\n",
        "\n",
        "    # 그래프 출력\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('data_distribution_plot.png')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "f5b68331-7426-4c3b-ba9a-e320ccb8db27",
      "metadata": {
        "id": "f5b68331-7426-4c3b-ba9a-e320ccb8db27",
        "outputId": "f72a88e4-912a-4975-b7cf-a07b40da52fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_data_distribution(gdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1995년 데이터는 표본이 매우 적고 다른 연도의 데이터와의 간격이 크며, 8월과 9월도 표본이 적고 계절적 특성이 다른 시기의 데이터입니다. 이를 제외하는 것은 모델의 안정성과 예측력을 향상시키는데 기여할 것입니다.\n",
        "\n",
        "그러나 데이터를 제외하는 것이 모델의 일반화 능력을 향상시킬 수는 있지만, 연구 목적에 중요한 정보를 잃어버릴 수도 있기 때문에 이런 결정은 주의 깊게 이루어져야 합니다."
      ],
      "metadata": {
        "id": "mb0K6xWn-jEU"
      },
      "id": "mb0K6xWn-jEU"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "892d9b13-8910-4094-a0b2-53b354c5e80e",
      "metadata": {
        "id": "892d9b13-8910-4094-a0b2-53b354c5e80e",
        "outputId": "0b09b41d-4ee0-466e-e018-69764582f155",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 연도와 월별 데이터 필터링\n",
        "filtered_gdf = gdf[\n",
        "    (~gdf['year'].eq(1995)) &\n",
        "    (~gdf['month'].between(8, 9))\n",
        "]\n",
        "\n",
        "plot_data_distribution(filtered_gdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "또한 히트맵(heatmap)을 사용하면 연도와 월별 종 출현 빈도를 한눈에 파악할 수 있으며, 데이터의 시간적 변화와 패턴을 직관적으로 시각화할 수 있습니다."
      ],
      "metadata": {
        "id": "dDAP1bTL-tNm"
      },
      "id": "dDAP1bTL-tNm"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "60bb0dba-4ac6-4e7d-940b-4b2a0ff643e7",
      "metadata": {
        "id": "60bb0dba-4ac6-4e7d-940b-4b2a0ff643e7",
        "outputId": "e3d33f3c-9c91-4f52-b345-f531b0073ba0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 연도(year)와 월별(month) 데이터 분포 히트맵\n",
        "def plot_heatmap(gdf, h_size=8):\n",
        "\n",
        "    statistics = gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)\n",
        "\n",
        "    # 히트맵\n",
        "    plt.figure(figsize=(h_size, h_size-6))\n",
        "    heatmap = plt.imshow(statistics.values, cmap=\"YlOrBr\", origin=\"upper\", aspect=\"auto\")\n",
        "\n",
        "    # 각 픽셀 위에 수치 표시\n",
        "    for i in range(len(statistics.index)):\n",
        "        for j in range(len(statistics.columns)):\n",
        "            plt.text(j, i, statistics.values[i, j], ha=\"center\", va=\"center\", color=\"black\")\n",
        "\n",
        "    plt.colorbar(heatmap, label=\"Count\")\n",
        "    plt.title(\"Monthly Species Count by Year\")\n",
        "    plt.xlabel(\"Year\")\n",
        "    plt.ylabel(\"Month\")\n",
        "    plt.xticks(range(len(statistics.columns)), statistics.columns)\n",
        "    plt.yticks(range(len(statistics.index)), statistics.index)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('heatmap_plot.png')\n",
        "    plt.show()\n",
        "    print(gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "d5e51554-0772-4ef4-bbd3-a0351c3495b3",
      "metadata": {
        "id": "d5e51554-0772-4ef4-bbd3-a0351c3495b3",
        "outputId": "8606c7e0-915b-437a-f7fa-69c7fc78c536",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x200 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "year   2012  2013  2014  2015  2016  2017  2018  2019  2020  2021  2022  2023\n",
            "month                                                                        \n",
            "5         0     0     5     0     2     1     5     4     5    22     8     1\n",
            "6         0     2     6     1     1     6     0     6    13    35    20     3\n",
            "7         1     0     0     1     0     1     1     7     1    10     8     0\n"
          ]
        }
      ],
      "source": [
        "plot_heatmap(filtered_gdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이제 필터링된 GeoDataFrame을 Google Earth Engine 객체로 변환합니다."
      ],
      "metadata": {
        "id": "ZU61yjLs-6lM"
      },
      "id": "ZU61yjLs-6lM"
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "97b4666d-a9b2-4746-8cac-07a9daa055a1",
      "metadata": {
        "id": "97b4666d-a9b2-4746-8cac-07a9daa055a1",
        "outputId": "1449e2ea-0269-48ec-98cd-6bb7236f4c42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# GeoDataFrame을 Earth Engine 객체로 변환\n",
        "data_raw = geemap.geopandas_to_ee(filtered_gdf)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이어서 종 분포 모델 결과의 래스터 픽셀 크기는 1km 해상도로 정의하겠습니다."
      ],
      "metadata": {
        "id": "f8qNKjHj_F6q"
      },
      "id": "f8qNKjHj_F6q"
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "63c61692-02f6-4044-bbf5-66550d9962fc",
      "metadata": {
        "id": "63c61692-02f6-4044-bbf5-66550d9962fc",
        "outputId": "6e497c15-1c79-4b41-8554-56d95ed2ffa1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 공간 해상도 설정(m)\n",
        "GrainSize = 1000"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "동일한 1km 해상도의 래스터 픽셀 내에 여러 개의 출현 지점이 있을 경우, 이들은 같은 지리적 위치에 있어 동일한 환경 조건을 공유할 가능성이 높습니다. 분석 시, 이러한 데이터를 그대로 사용하면 데이터로 인해 분석 결과에 편향이 발생할 수 있습니다.\n",
        "\n",
        "즉, 지리적 샘플링 편향(geographic sampling bias)의 잠재적 영향을 제한해야 합니다. 각 1km 픽셀 내에서 하나의 위치만을 남겨 나머지는 모두 제거함으로써 모델이 환경 조건을 더 객관적으로 반영하도록 조정하겠습니다."
      ],
      "metadata": {
        "id": "0rUFKvkD_aMx"
      },
      "id": "0rUFKvkD_aMx"
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "a8c5683b-51bb-460a-b8cf-1f9e5600d111",
      "metadata": {
        "id": "a8c5683b-51bb-460a-b8cf-1f9e5600d111",
        "outputId": "e910bcc5-142f-44f4-c668-6c9007479cd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original data size: 176\n",
            "Final data size: 111\n"
          ]
        }
      ],
      "source": [
        "def remove_duplicates(data, GrainSize):\n",
        "    # 선택한 공간 해상도(1km)에서 픽셀 당 출현 기록 하나만 무작위 선택\n",
        "    random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n",
        "    rand_point_vals = random_raster.sampleRegions(collection=ee.FeatureCollection(data), scale=10, geometries=True)\n",
        "    return rand_point_vals.distinct('random')\n",
        "\n",
        "Data = remove_duplicates(data_raw, GrainSize)\n",
        "\n",
        "# 선택 전, 선택 후\n",
        "print('Original data size:', data_raw.size().getInfo())\n",
        "print('Final data size:', Data.size().getInfo())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "지리적 샘플링 편향의 전처리 전(파란색)과 후(빨간색)를 가시화한 결과는 다음과 같습니다. 두 개 레이어를 비교하기 쉽도록 팔색조 출현 좌표가 많은 [한라산국립공원](https://www.protectedplanet.net/10931)으로 지도를 이동했습니다."
      ],
      "metadata": {
        "id": "jsBCGGD9_qNn"
      },
      "id": "jsBCGGD9_qNn"
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "4f56291b-a215-4d99-8d68-c886a98ddafb",
      "metadata": {
        "id": "4f56291b-a215-4d99-8d68-c886a98ddafb",
        "outputId": "ed25e057-1b47-43c6-b80b-78450daa55ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "34897ce609d84bd8bb49f2dbfdb444a0",
            "f7043b1d9c3c48fc8b87b44712cd55ce",
            "482cf3f9b1b24be5969e3e9d7ba13359",
            "d50a36965010473fbc33c5c08789c754",
            "23ce7bfe2c03476ab233ab6fe0a343c5",
            "274cccbd5bfe4d7c9526a1771443c1d7",
            "41d7b5ae6d544795852288fc127f83f8",
            "293d02f5ba4e4be8bcdc1e850ecb713d",
            "2620bb91f7fe4ea18efbe57d7e00335a",
            "6f98c46298d441e3b40c4c658e07c754",
            "664ebb5831dd462888a81526d3e60b08",
            "e544af921e314f00a46d2fb9416c9994",
            "e5a821b29c2d4fe09c7b02ac68464d24",
            "26f1e97296364f039ac78eba85b1a193",
            "151742d6122148b29c5acc0851149a6e",
            "06a4e51b79a74269906950774b7675a6",
            "46c69ce2a9e6450da9fbd39d6689f3b2",
            "0c36e741c438483bb18a48b3f4cb445e",
            "55fd7be0f4ea413881d317a91ea2bf37",
            "07c496a120fc416e8c1e1bb30ebb86cb",
            "97c315b820b047ac819efdaaf86d0af0",
            "f6d89b4f82a54a8c8553297a76a0fd71",
            "d731805205734333a95f475096060ea0",
            "27d6e359081c4e84b07a5fc422172cbc",
            "5493ed6833594b529b1dd229b70f6d09",
            "44085fd87c3143a0b2b700848f765beb",
            "3648cfa1df90494699dab548a73f612a",
            "8ed7d07f5c7b4cd983d4690cfb96534c"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[33.516, 126.712], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDat…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34897ce609d84bd8bb49f2dbfdb444a0"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "# 지리적 샘플링 편향의 전처리 전(파란색)과 후(빨간색) 가시화\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "\n",
        "Vworld_Base = TileLayer(\n",
        "    url='https://xdworld.vworld.kr/2d/Base/service/{z}/{x}/{y}.png',\n",
        "    name='Vworld Base',\n",
        "    attribution='Vworld',\n",
        "    shown=True\n",
        ")\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n",
        "Map.addLayer(random_raster, {'min': 0, 'max': 1, 'palette': ['black', 'white'], 'opacity': 0.5}, 'Random Raster')\n",
        "Map.addLayer(data_raw, {'color': 'blue'}, 'Original data')\n",
        "Map.addLayer(Data, {'color': 'red'}, 'Final data')\n",
        "Map.setCenter(126.712, 33.516, 15) # 17은 지도의 확대 레벨을 의미\n",
        "# Map.centerObject(protected_area, 9)\n",
        "Map"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bdc090c1-153e-44c9-b7f3-70608ed009c8",
      "metadata": {
        "id": "bdc090c1-153e-44c9-b7f3-70608ed009c8"
      },
      "source": [
        "### 6.2.2. 관심 영역 정의\n",
        "\n",
        "\"관심 영역(AOI: Area Of Interest)\"는 연구자가 분석하려는 지리적 영역을 가리키는 용어입니다. 연구지역과 비슷한 의미를 가집니다.\n",
        "\n",
        "여기서는 출현지점 레이어 지오메트리의 바운딩 박스(경계 상자)를 가져와서 그 주변에 50킬로미터 크기의 버퍼를 생성(최대 1,000미터의 오차 허용)하였습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9aede96f-0dc0-4100-af71-04a1ef53509a",
      "metadata": {
        "id": "9aede96f-0dc0-4100-af71-04a1ef53509a"
      },
      "outputs": [],
      "source": [
        "# AOI 정의\n",
        "AOI = Data.geometry().bounds().buffer(\n",
        "    distance=50000, maxError=1000)\n",
        "\n",
        "# 연구지역 경계를 지도에 추가\n",
        "outline = ee.Image().byte().paint(\n",
        "    featureCollection=AOI, color=1, width=3)\n",
        "\n",
        "Map.addLayer(outline, {'palette': 'FF0000'}, \"Study Area\")\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "65881f52-f725-455e-9195-b5f51e78d363",
      "metadata": {
        "id": "65881f52-f725-455e-9195-b5f51e78d363"
      },
      "source": [
        "### 6.2.3. GEE 환경변수 추가\n",
        "\n",
        "이제 환경변수를 추가해 보겠습니다. GEE는 기온, 강수량, 고도, 수목 피복, 지형 등 다양한 환경변수에 대한 광범위한 데이터셋을 제공합니다. 이를 통해 팔색조의 서식지 선호도에 영향을 미치는 다양한 요인들을 종합적으로 분석할 수 있습니다.\n",
        "\n",
        "SDM에서 GEE 환경변수의 선택은 해당 생물종의 서식지 선호도 특성을 반영해야 합니다. 이를 위해서는 팔색조의 이전 문헌 및 연구 고찰이 선행되어야 합니다. 이 글은 주로 튜토리얼에 초점을 맞추었기 때문에, 일부 심도 깊은 내용은 생략되었습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**WorldClim V1 Bioclim**: 월별 기온과 강수량으로부터 파생된 19개 생물기후 변수(bioclimatic variables)들을 제공합니다. 1960년부터 1991년 사이의 평균 기온과 강수량으로 계산되었으며 해상도는 927.67 m입니다."
      ],
      "metadata": {
        "id": "1P-cEMnSD5mW"
      },
      "id": "1P-cEMnSD5mW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f0c88708-b7bc-4662-88c1-af0b54ccca44",
      "metadata": {
        "id": "f0c88708-b7bc-4662-88c1-af0b54ccca44"
      },
      "outputs": [],
      "source": [
        "# WorldClim V1 Bioclim\n",
        "BIO = ee.Image(\"WORLDCLIM/V1/BIO\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NASA SRTM Digital Elevation 30m**: 셔틀 레이더 지형 미션(SRTM: Shuttle Radar Topography Mission)의 디지털 고도 데이터로, 주로 2000년에 데이터를 수집했으며 1 아크초(대략 30m) 해상도로 제공됩니다. 아래 코드는 SRTM 데이터로부터 고도, 경사, 경사방향, 음영기복 레이어를 계산해줍니다."
      ],
      "metadata": {
        "id": "wSg4f3zcEpzW"
      },
      "id": "wSg4f3zcEpzW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70ef5302-d677-47a4-bd6a-cdda20b38280",
      "metadata": {
        "id": "70ef5302-d677-47a4-bd6a-cdda20b38280"
      },
      "outputs": [],
      "source": [
        "# NASA SRTM Digital Elevation 30m\n",
        "Terrain = ee.Algorithms.Terrain(ee.Image(\"USGS/SRTMGL1_003\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m**: Landsat 식생 연속 필드(VCF: Vegetation Continuous Fields) 수목 피복 레이어는 수목이 특정 높이(5m) 이상일 때 수직으로 투영된 식생 면적의 비율을 추정합니다. 이 데이터셋은 2000년, 2005년, 2010년, 2015년을 중심으로 한 네 시기에 대해 제공되며 해상도는 30m입니다. 여기서는 네 시기의 중간값을 택했습니다."
      ],
      "metadata": {
        "id": "55GbRhziEx1L"
      },
      "id": "55GbRhziEx1L"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb14f1e0-1ef5-4e5c-bc94-d27b24723f98",
      "metadata": {
        "id": "eb14f1e0-1ef5-4e5c-bc94-d27b24723f98"
      },
      "outputs": [],
      "source": [
        "# Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m\n",
        "TCC = ee.ImageCollection(\"NASA/MEASURES/GFCC/TC/v3\")\n",
        "MedianTCC = TCC.filterDate('2000-01-01', '2015-12-31')\n",
        "MedianTCC = MedianTCC.select(['tree_canopy_cover'], ['TCC']).median()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`BIO`(생물기후), `Terrain`(지형) 및 `MedianTCC`(수폭피복)를 하나의 다중 밴드 이미지로 결합합니다. `Terrain`에서 `elevation` 밴드를 선택하고, `elevation`이 0보다 큰 지점들로 구성된 마스크를 생성합니다. 즉, 해수면 이하의 지역(예: 바다)를 마스킹하고 분석을 관심 영역으로 제한합니다. 이로써 연구자는 AOI에 대한 다양한 환경적 요인들을 통합적으로 분석할 수 있는 준비를 마치게 됩니다."
      ],
      "metadata": {
        "id": "5ysukAMrE9Kk"
      },
      "id": "5ysukAMrE9Kk"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d97302d8-bb90-4772-8351-220341eeda24",
      "metadata": {
        "id": "d97302d8-bb90-4772-8351-220341eeda24"
      },
      "outputs": [],
      "source": [
        "# 밴드들을 하나의 다중 밴드 이미지로 결합\n",
        "predictors = BIO.addBands(Terrain).addBands(MedianTCC)\n",
        "\n",
        "# 수역 마스크 생성\n",
        "watermask =  Terrain.select('elevation').gt(0)\n",
        "\n",
        "# 해양 픽셀 마스킹 및 관심 영역 클리핑\n",
        "predictors = predictors.updateMask(watermask).clip(AOI)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "상관관계가 높은 예측변수들을 함께 모델에 포함하면 **다중공선성(Multicollinearity)** 문제가 발생할 수 있습니다. 다중공선성은 모델 내에서 독립 변수들 간에 강한 선형 관계가 있을 때 발생하는 현상으로, 이로 인해 모델의 계수(가중치) 추정의 불안정성을 야기합니다. 이러한 불안정성은 모델의 신뢰도를 떨어뜨리며, 새로운 데이터에 대한 예측 또는 해석을 어렵게 만들 수 있습니다.\n",
        "\n",
        "따라서, 다중공선성을 고려하여 예측변수를 선택하는 과정을 진행해 보겠습니다. 먼저 예측변수 이미지에서 임의로 5,000개 지점을 선택하고, 해당 지점에서 다중-밴드 예측 이미지의 예측변수 값을 추출합니다."
      ],
      "metadata": {
        "id": "lP0BYFKiFwhB"
      },
      "id": "lP0BYFKiFwhB"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "19e33c6c-7940-4209-882b-24744c56c088",
      "metadata": {
        "id": "19e33c6c-7940-4209-882b-24744c56c088"
      },
      "outputs": [],
      "source": [
        "# 임의로 5,000개 지점 생성\n",
        "DataCor = predictors.sample(\n",
        "    scale=GrainSize, numPixels=5000, geometries=True)\n",
        "# 예측변수 값 추출\n",
        "PixelVals = predictors.sampleRegions(\n",
        "    collection=DataCor, scale=GrainSize, tileScale=16)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "추출한 지점별 예측변수 값을 DataFrame으로 변환하여 첫번째 행을 확인해 봅니다."
      ],
      "metadata": {
        "id": "aqnc0q6UG7ZX"
      },
      "id": "aqnc0q6UG7ZX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be85eb27-7674-43c3-a136-b32f7f9d306f",
      "metadata": {
        "id": "be85eb27-7674-43c3-a136-b32f7f9d306f"
      },
      "outputs": [],
      "source": [
        "# 예측변수 값 ee를 df로 변환\n",
        "PixelVals_df = geemap.ee_to_df(PixelVals)\n",
        "PixelVals_df.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2e1e6d3c-87dd-463e-a918-9dc51b3b9661",
      "metadata": {
        "id": "2e1e6d3c-87dd-463e-a918-9dc51b3b9661"
      },
      "outputs": [],
      "source": [
        "columns = PixelVals_df.columns\n",
        "print(columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**스피어만(Spearman) 상관계수**를 사용하여 주어진 예측 변수들 간의 상관관계를 계산하고, 이를 시각화한 히트맵을 출력해 봅니다."
      ],
      "metadata": {
        "id": "5LQ0W4nPHZKZ"
      },
      "id": "5LQ0W4nPHZKZ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "52d48707-1f4f-4752-90fa-0a566416717d",
      "metadata": {
        "id": "52d48707-1f4f-4752-90fa-0a566416717d"
      },
      "outputs": [],
      "source": [
        "def plot_correlation_heatmap(dataframe, h_size=10):\n",
        "    # 스피어만 상관계수 계산\n",
        "    correlation_matrix = dataframe.corr(method=\"spearman\")\n",
        "\n",
        "    # 히트맵 그리기\n",
        "    plt.figure(figsize=(h_size, h_size-2))\n",
        "    plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='nearest')\n",
        "\n",
        "    # 히트맵에 수치 표시\n",
        "    for i in range(correlation_matrix.shape[0]):\n",
        "        for j in range(correlation_matrix.shape[1]):\n",
        "            plt.text(j, i, f\"{correlation_matrix.iloc[i, j]:.2f}\",\n",
        "                     ha='center', va='center', color='white', fontsize=8)\n",
        "\n",
        "    columns = dataframe.columns.tolist()\n",
        "    plt.xticks(range(len(columns)), columns, rotation=90)\n",
        "    plt.yticks(range(len(columns)), columns)\n",
        "    plt.title(\"Variables correlation matrix\")\n",
        "    plt.colorbar(label=\"Spearman Correlation\")\n",
        "    plt.savefig('correlation_heatmap_plot.png')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95873b9b-9361-4160-a2d3-3854d8d404cb",
      "metadata": {
        "id": "95873b9b-9361-4160-a2d3-3854d8d404cb"
      },
      "outputs": [],
      "source": [
        "# 변수 상관행렬 히트맵\n",
        "plot_correlation_heatmap(PixelVals_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "스피어만 상관계수는 예측변수 간의 일반적인 연관성을 파악하는 데 유용하지만, 복수의 변수가 서로 어떻게 상호작용하는지, 즉 다중공선성을 직접적으로 평가하지는 않습니다.\n",
        "\n",
        "**분산팽창인자(VIF: Variance Inflation Factor)**는 다중공선성을 평가하고 변수를 선택 하는데 사용되는 통계적 지표입니다. 각 독립변수의 다른 독립변수에 대한 선형관계의 정도를 나타내며, 높은 VIF 값은 다중공선성의 증거일 수 있습니다.\n",
        "\n",
        "일반적으로 VIF 값은 5또는 10보다 크면 해당 변수가 다른 변수들과 강한 상관관계를 가지며 모델의 안정성과 해석력을 저해할 가능성이 높다는 것을 나타냅니다. 여기서는 10보다 작은 값을 기준으로 변수를 선택했습니다."
      ],
      "metadata": {
        "id": "fajzF-2kHpdw"
      },
      "id": "fajzF-2kHpdw"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77bc897a-ec3c-49a8-bf1a-6ab1ba3866d3",
      "metadata": {
        "id": "77bc897a-ec3c-49a8-bf1a-6ab1ba3866d3"
      },
      "outputs": [],
      "source": [
        "def filter_variables_by_vif(dataframe, threshold=10):\n",
        "\n",
        "    original_columns = dataframe.columns.tolist()\n",
        "    remaining_columns = original_columns[:]\n",
        "\n",
        "    while True:\n",
        "        vif_data = dataframe[remaining_columns]\n",
        "        vif_values = [variance_inflation_factor(vif_data.values, i) for i in range(vif_data.shape[1])]\n",
        "\n",
        "        max_vif_index = vif_values.index(max(vif_values))\n",
        "        max_vif = max(vif_values)\n",
        "\n",
        "        if max_vif < threshold:\n",
        "            break\n",
        "\n",
        "        print(f\"Removing '{remaining_columns[max_vif_index]}' with VIF {max_vif:.2f}\")\n",
        "\n",
        "        del remaining_columns[max_vif_index]\n",
        "\n",
        "    filtered_data = dataframe[remaining_columns]\n",
        "    bands = filtered_data.columns.tolist()\n",
        "    print('Bands:', bands)\n",
        "\n",
        "    return filtered_data, bands"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "VIF를 통해 아래 6개 변수가 선택되었습니다."
      ],
      "metadata": {
        "id": "mkTIF4tnH4H7"
      },
      "id": "mkTIF4tnH4H7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0e98ce4b-5983-4d76-8252-9acdd49690c3",
      "metadata": {
        "id": "0e98ce4b-5983-4d76-8252-9acdd49690c3"
      },
      "outputs": [],
      "source": [
        "filtered_PixelVals_df, bands = filter_variables_by_vif(PixelVals_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06be8bea-6f1d-4bb4-879e-482d320cad2a",
      "metadata": {
        "id": "06be8bea-6f1d-4bb4-879e-482d320cad2a"
      },
      "outputs": [],
      "source": [
        "predictors = predictors.select(bands)\n",
        "plot_correlation_heatmap(filtered_PixelVals_df, h_size=6)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이어서 6개 예측변수를 지도화해 보겠습니다. 지도 가시화에 사용가능한 팔레트는 아래 코드로 호출해보실 수 있습니다."
      ],
      "metadata": {
        "id": "JRf0LkKDH_Pd"
      },
      "id": "JRf0LkKDH_Pd"
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "900a9597-2c75-4d25-8946-70941835fcd3",
      "metadata": {
        "id": "900a9597-2c75-4d25-8946-70941835fcd3",
        "outputId": "2759ab1e-80b8-4fa2-8859-23fba05db1aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "                    \n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "                \n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "                    \n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x3320 with 166 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm.plot_colormaps(width=8.0, height=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fa0e48f7-8c5c-4702-b0dc-9a3c09e6553b",
      "metadata": {
        "id": "fa0e48f7-8c5c-4702-b0dc-9a3c09e6553b"
      },
      "outputs": [],
      "source": [
        "cm.plot_colormap('terrain',\n",
        "                 width=8.0, height=0.2, orientation='horizontal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0076356e-9e41-4dfa-9ac9-144f3f114c97",
      "metadata": {
        "id": "0076356e-9e41-4dfa-9ac9-144f3f114c97"
      },
      "outputs": [],
      "source": [
        "# elevation (고도) 레이어\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "vis_params = {'bands':['elevation'],\n",
        "              'min': 0, 'max': 1800, 'palette': cm.palettes.terrain}\n",
        "Map.addLayer(predictors, vis_params, 'elevation')\n",
        "Map.add_colorbar(vis_params, label=\"Elevation (m)\",\n",
        "                 orientation=\"vertical\", layer_name=\"elevation\")\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df49cc91-4960-4b5c-9232-ae463b04ce97",
      "metadata": {
        "id": "df49cc91-4960-4b5c-9232-ae463b04ce97"
      },
      "outputs": [],
      "source": [
        "# bio09 최소값 & 최대값 계산\n",
        "min_val = predictors.select(\"bio09\").multiply(0.1). \\\n",
        "reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n",
        "max_val = predictors.select(\"bio09\").multiply(0.1). \\\n",
        "reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n",
        "\n",
        "# bio09 (가장 건조한 분기의 평균 기온) 레이어\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "vis_params = {'min': math.floor(min_val['bio09']), \\\n",
        "              'max': math.ceil(max_val['bio09']),\n",
        "              'palette': cm.palettes.hot}\n",
        "Map.addLayer(predictors.select(\"bio09\").multiply(0.1),\n",
        "             vis_params, 'bio09')\n",
        "Map.add_colorbar(vis_params,\n",
        "                 label=\"Mean temperature of driest quarter (℃)\",\n",
        "                 orientation=\"vertical\", layer_name=\"bio09\")\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "43116c27-2cf2-4cec-9386-b6a1cb5fb290",
      "metadata": {
        "id": "43116c27-2cf2-4cec-9386-b6a1cb5fb290"
      },
      "outputs": [],
      "source": [
        "# slope (경사) 레이어\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "vis_params = {'bands':['slope'], 'min': 0, 'max': 25, 'palette': cm.palettes.RdYlGn_r}\n",
        "Map.addLayer(predictors, vis_params, 'slope')\n",
        "Map.add_colorbar(vis_params, label=\"Slope\", orientation=\"vertical\", layer_name=\"slope\")\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bfa8c7a1-9ae9-420a-a564-0fc167ef00a2",
      "metadata": {
        "id": "bfa8c7a1-9ae9-420a-a564-0fc167ef00a2"
      },
      "outputs": [],
      "source": [
        "# aspect (경사방향) 레이어\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "vis_params = {'bands':['aspect'], 'min': 0, 'max': 360, 'palette': cm.palettes.rainbow}\n",
        "Map.addLayer(predictors, vis_params, 'aspect')\n",
        "Map.add_colorbar(vis_params, label=\"Aspect\", orientation=\"vertical\", layer_name=\"aspect\")\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "495d2144-e759-47f0-9e7a-546f3ab8fa62",
      "metadata": {
        "id": "495d2144-e759-47f0-9e7a-546f3ab8fa62"
      },
      "outputs": [],
      "source": [
        "# bio14 최소값 & 최대값 계산\n",
        "min_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n",
        "max_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n",
        "\n",
        "# bio14 (가장 건조한 달의 강수량) 레이어\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "vis_params = {'bands':['bio14'], 'min': math.floor(min_val['bio14']), 'max': math.ceil(max_val['bio14']), 'palette': cm.palettes.Blues}\n",
        "Map.addLayer(predictors, vis_params, 'bio14')\n",
        "Map.add_colorbar(vis_params, label=\"Precipitation of driest month (mm)\", orientation=\"vertical\", layer_name=\"bio14\")\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a0348f0-c4ef-4e10-9e0d-b05b02e9ca59",
      "metadata": {
        "id": "6a0348f0-c4ef-4e10-9e0d-b05b02e9ca59"
      },
      "outputs": [],
      "source": [
        "# TCC (수목피복) 레이어\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "vis_params = {'bands':['TCC'], 'min': 0, 'max': 100,\n",
        "              'palette':\n",
        "               ['ffffff', 'afce56', '5f9c00', '0e6a00', '003800']}\n",
        "Map.addLayer(predictors, vis_params, 'TCC')\n",
        "Map.add_colorbar(vis_params, label=\"Tree Canopy Cover (%)\",\n",
        "                 orientation=\"vertical\", layer_name=\"TCC\")\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7c49680b-93c7-43d8-affa-9b91b059ad98",
      "metadata": {
        "id": "7c49680b-93c7-43d8-affa-9b91b059ad98"
      },
      "source": [
        "### 6.2.4. 임의-비출현 데이터 생성\n",
        "\n",
        "종 분포 모델링 과정에서 생물종 입력 데이터 선정은 주로 두 가지 접근방식을 사용합니다:\n",
        "\n",
        "* 출현-배경(Presence- Background) 방법: 특정 종이 관찰된 위치(출현)와 그 종이 관찰되지 않은 다른 위치(배경)을 비교합니다. 여기서 배경 데이터는 반드시 종이 존재하지 않는 지역을 의미하는 것이 아니라, 연구지역의 전체적인 환경적 조건을 반영하도록 설정합니다. 해당 종이 존재할 수 있는 적합한 환경과 덜 적합한 환경을 구분하는데 사용됩니다.\n",
        "\n",
        "* 출현-비출현(Presence-Absence) 방법: 종이 발견된 위치(출현)와 확실히 발견되지 않은 위치(비출현)를 비교합니다. 여기서 비출현 데이터는 해당 종이 존재하지 않는 것을 확인한 구체적인 위치입니다. 연구지역의 전체적인 환경적 조건을 반영하는 것이 아니라, 종이 존재하지 않는 것으로 추정되는 위치를 가리킵니다.\n",
        "실제로는 비출현 데이터의 수집이 어렵기 때문에 임의-비출현(pseudo- absence) 데이터를 사용하는 경우가 많습니다. 가상으로 생성된 비출현 지점이 실제 비출현 지역을 제대로 반영하지 못할 수 있어, 이 방법의 한계와 오류 가능성을 인식하는 것이 중요합니다.\n",
        "\n",
        "두 방법의 선택은 데이터 가용성, 연구 목적, 모델의 정확성과 신뢰성, 시간과 자원 등에 따라 달라질 수 있습니다. 여기서는 GBIF를 통해 수집한 출현 데이터와 가상으로 생성된 비출현 데이터를 사용하여 \"출현-비출현(Presence-Absence) 방법\"으로 모델링 과정을 진행해 보겠습니다.\n",
        "\n",
        "임의 비출현 데이터는 \"환경적 특성 분석 접근법(environmental profiling approach)\"으로 생성하겠습니다. 세부적인 방법은 다음과 같습니다:\n",
        "\n",
        "* k-means 클러스터링을 사용한 환경 분류: 유클리디안 거리를 기반으로 k-means 클러스터링 알고리즘을 사용하여 연구지역 내의 픽셀들을 두 개의 클러스터로 나눕니다. 하나의 클러스터는 무작위로 선택된 100개의 출현 위치(presence locations)와 유사한 환경적 특성을 가지는 영역이며, 다른 하나는 그와 다른 특성을 가진 영역입니다.\n",
        "\n",
        "* 유사하지 않은 클러스터 내에서 가상 비출현 데이터 생성: 첫 번째 단계에서 식별된 두 번째 클러스터(출현 데이터와 다른 환경 특성을 가진 클러스터) 내에서 임의로 가상 비출현 지점(pseudo-absences)을 생성합니다. 가상 부재 지점은 해당 종이 없을 것으로 예상되는 위치를 나타냅니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "072b61f7-12f6-465b-8f53-09ffb32fb37e",
      "metadata": {
        "id": "072b61f7-12f6-465b-8f53-09ffb32fb37e"
      },
      "outputs": [],
      "source": [
        "# 100개의 출현 위치 무작위로 선택\n",
        "PixelVals = predictors.sampleRegions(\n",
        "    collection=Data.randomColumn().sort('random').limit(100),\n",
        "    properties=[],\n",
        "    tileScale=16,\n",
        "    scale=GrainSize\n",
        ")\n",
        "\n",
        "# k-means 클러스터링\n",
        "clusterer = ee.Clusterer.wekaKMeans(\n",
        "    nClusters=2,\n",
        "    distanceFunction=\"Euclidean\"\n",
        ").train(PixelVals)\n",
        "\n",
        "Clresult = predictors.cluster(clusterer)\n",
        "\n",
        "# 출현 위치와 유사한 클러스터 ID\n",
        "clustID = Clresult.sampleRegions(\n",
        "    collection=Data.randomColumn().sort('random').limit(200),\n",
        "    properties=[],\n",
        "    tileScale=16,\n",
        "    scale=GrainSize\n",
        ")\n",
        "# 유사하지 않은 클러스터에서 임의-비출현 영역 정의\n",
        "clustID = ee.FeatureCollection(clustID).reduceColumns(ee.Reducer.mode(),['cluster'])\n",
        "clustID = ee.Number(clustID.get('mode')).subtract(1).abs()\n",
        "cl_mask = Clresult.select(['cluster']).eq(clustID)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9171b1cc-86a6-4074-9f77-13bb1bd8da36",
      "metadata": {
        "id": "9171b1cc-86a6-4074-9f77-13bb1bd8da36"
      },
      "outputs": [],
      "source": [
        "# 출현 위치 마스크\n",
        "presence_mask = Data.reduceToImage(\n",
        "    properties=['random'],\n",
        "    reducer=ee.Reducer.first()\n",
        ").reproject('EPSG:4326', None,\n",
        "            ee.Number(GrainSize)).mask().neq(1).selfMask()\n",
        "\n",
        "# 임의-비출현 영역에서 출현 위치 마스킹 및 AOI 클리핑\n",
        "AreaForPA = presence_mask.updateMask(cl_mask).clip(AOI)\n",
        "\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "Map.addLayer(AreaForPA, {'palette': 'black'},'AreaForPA')\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bcaa33ab-7895-40b8-a3c4-7545f189deb1",
      "metadata": {
        "id": "bcaa33ab-7895-40b8-a3c4-7545f189deb1"
      },
      "source": [
        "### 6.2.5. 모델 적합 및 예측\n",
        "\n",
        "이제 데이터를 훈련 데이터(training data)와 시험 데이터(test data)로 나눌 것입니다. 훈련 데이터는 모델에 학습하면서 최적의 매개변수를 찾고, 시험 데이터는 앞서 훈련한 모델을 평가할 것입니다. 이때 생각해 볼 부분으로 공간자기상관성이라는 개념이 있습니다.\n",
        "\n",
        "**공간자기상관성(spatial autocorrelation)**은 종 분포 모델링에서 필수적인 요소로, Tobler의 법칙과 연관되어 있습니다. 이는 '모든 것은 서로 관련되어 있지만 가까운 것들이 먼 것들보다 더 밀접하게 관련되어 있다'는 개념입니다. 공간자기상관성은 종의 위치와 환경변수 간의 중요한 관계를 나타냅니다. 예를 들어, 특정 환경변수가 해당 종에 대해 의미가 없다면, 그 변수는 거리에 따라 달라지는 관계가 없다고 볼 수 있습니다.\n",
        "\n",
        "그러나 훈련 데이터와 시험 데이터 사이에 공간자기상관성이 존재하면, 두 데이터 세트 간의 독립성이 손상될 수 있습니다. 이는 모델의 일반화 능력 평가에 있어 중대한 영향을 미칩니다.\n",
        "\n",
        "이 문제를 해결하기 위한 방법 중 하나로, 데이터를 훈련 및 시험 데이터 세트로 분할하는 **공간 블록 교차 검증(Spatial block cross-validation)** 기법이 있습니다.\n",
        "\n",
        "<p align=\"center\"><img src=\"https://github.com/osgeokr/GEE-PAM-Book/blob/main/img/spatial_block.png?raw=1\" width = \"500px\"></img></p>\n",
        "\n",
        "이 기법은 데이터를 여러 개의 블록으로 분할하여 각 블록을 독립적으로 훈련 및 시험 데이터 세트로 사용함으로써 공간 자기상관성의 영향을 줄일 수 있습니다. 이를 통해 데이터 세트 간의 독립성을 높이고, 모델의 일반화 능력을 더 정확하게 평가할 수 있게 합니다.구체적인 절차는 다음과 같습니다:\n",
        "\n",
        "* 공간 블록의 생성: 전체 데이터 세트를 같은 크기의 공간 블록(예: 50x50 km)으로 나눕니다.\n",
        "\n",
        "* 훈련 및 검증 세트의 할당: 각 공간적 블록은 무작위로 훈련 세트(70%)와 시험 세트(30%)로 할당됩니다. 이는 모델이 특정 지역의 데이터에만 과도하게 적합되는 것을 방지하고, 보다 일반화된 결과를 얻기 위함입니다.\n",
        "\n",
        "* 반복적인 교차 검증: 전체 과정은 n회(예: 10회) 반복됩니다. 각 반복에서 블록은 다시 무작위로 훈련 및 시험 세트로 분할되며, 이는 모델의 안정성과 신뢰성을 향상시키기 위한 것입니다.\n",
        "\n",
        "* 임의-비출현 데이터의 생성: 각 반복마다, 모델의 성능을 평가하기 위해 가상 부재 데이터(pseudo-absences)가 무작위로 생성됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a8702356-893d-4a42-ae50-ed212518517e",
      "metadata": {
        "id": "a8702356-893d-4a42-ae50-ed212518517e"
      },
      "outputs": [],
      "source": [
        "# 공간 블록 교차 검증을 위한 격자 생성\n",
        "def makeGrid(geometry, scale):\n",
        "    # 경도 & 위도 도 단위 이미지 생성\n",
        "    lonLat = ee.Image.pixelLonLat()\n",
        "    # 경도 & 위도 이미지 정수화\n",
        "    lonGrid = lonLat.select('longitude').multiply(100000).toInt()\n",
        "    latGrid = lonLat.select('latitude').multiply(100000).toInt()\n",
        "\n",
        "    return lonGrid.multiply(latGrid).reduceToVectors(\n",
        "        # geometry 경계 포함 격자 생성\n",
        "        geometry = geometry.buffer(distance=20000, maxError=1000),\n",
        "        scale = scale,\n",
        "        geometryType = 'polygon'\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2993be59-1179-4f45-9578-12aed2250fde",
      "metadata": {
        "id": "2993be59-1179-4f45-9578-12aed2250fde"
      },
      "outputs": [],
      "source": [
        "Scale = 50000\n",
        "grid = makeGrid(AOI, Scale)\n",
        "Grid = watermask.reduceRegions(\n",
        "    collection=grid,\n",
        "    reducer=ee.Reducer.mean()).filter(ee.Filter.neq('mean', None))\n",
        "\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "Map.addLayer(Grid, {}, \"Grid for spatial block cross validation\")\n",
        "Map.addLayer(outline, {'palette': 'FF0000'}, \"Study Area\")\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이제 모델을 적합(fit)할 수 있습니다. 모델을 적합하는 것은 데이터의 패턴을 파악하여 모델의 매개변수(가중치와 편향)를 조정하는 과정을 의미합니다. 이렇게 하면 모델은 새로운 데이터를 입력 받았을 때 더 정확한 예측을 수행할 수 있게 됩니다. 일단, 모델을 적합하는 SDM() 함수를 정의했습니다.\n",
        "\n",
        "알고리즘은 랜덤 포레스트(random forest)를 사용해 보겠습니다.\n",
        "* numberOfTrees: 생성할 결정 트리의 수\n",
        "* variablesPerSplit: 분할당 변수의 수. 기본값: 변수 수의 제곱근 사용\n",
        "* minLeafPopulation: 훈련 셋에 최소한 이 개수의 지점이 포함된 노드만 생성\n",
        "* bagFraction: 트리당 백에 대한 입력 비율\n",
        "* maxNodes: 각 트리의 최대 리프 노드 수. 기본값: 제한 없음\n",
        "* seed: 무작위화 시드"
      ],
      "metadata": {
        "id": "1hE3zG8MTM-D"
      },
      "id": "1hE3zG8MTM-D"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f5700e59-3068-4859-ab54-ccfa6f525255",
      "metadata": {
        "id": "f5700e59-3068-4859-ab54-ccfa6f525255"
      },
      "outputs": [],
      "source": [
        "def SDM(x):\n",
        "    Seed = ee.Number(x)\n",
        "\n",
        "    # 훈련 및 검증을 위한 무작위 블록 분할\n",
        "    GRID = ee.FeatureCollection(Grid).randomColumn(seed=Seed).sort('random')\n",
        "    TrainingGrid = GRID.filter(ee.Filter.lt('random', split)) # 훈련용 격자\n",
        "    TestingGrid = GRID.filter(ee.Filter.gte('random', split)) # 시험용 격자\n",
        "\n",
        "    # 출현 지점\n",
        "    PresencePoints = ee.FeatureCollection(Data)\n",
        "    PresencePoints = PresencePoints.map(lambda feature: feature.set('PresAbs', 1))\n",
        "    TrPresencePoints = PresencePoints.filter(ee.Filter.bounds(TrainingGrid)) # 훈련용 출현 지점\n",
        "    TePresencePoints = PresencePoints.filter(ee.Filter.bounds(TestingGrid)) # 시험용 출현 지점\n",
        "\n",
        "    # 임의-비출현 지점\n",
        "    TrPseudoAbsPoints = AreaForPA.sample(region=TrainingGrid,\n",
        "                                         scale=GrainSize,\n",
        "                                         numPixels=TrPresencePoints.size().add(300),\n",
        "                                         seed=Seed,\n",
        "                                         geometries=True)\n",
        "    # 훈련용 출현 지점과 동일한 수의 임의-비출현 지점\n",
        "    TrPseudoAbsPoints = TrPseudoAbsPoints.randomColumn().sort('random').limit(ee.Number(TrPresencePoints.size()))\n",
        "    TrPseudoAbsPoints = TrPseudoAbsPoints.map(lambda feature: feature.set('PresAbs', 0))\n",
        "\n",
        "    TePseudoAbsPoints = AreaForPA.sample(region=TestingGrid,\n",
        "                                         scale=GrainSize,\n",
        "                                         numPixels=TePresencePoints.size().add(100),\n",
        "                                         seed=Seed,\n",
        "                                         geometries=True)\n",
        "    # 검증용 출현 지점과 동일한 수의 임의-비출현 지점\n",
        "    TePseudoAbsPoints = TePseudoAbsPoints.randomColumn().sort('random').limit(ee.Number(TePresencePoints.size()))\n",
        "    TePseudoAbsPoints = TePseudoAbsPoints.map(lambda feature: feature.set('PresAbs', 0))\n",
        "\n",
        "    # 훈련 및 임의-비출현 지점 병합\n",
        "    trainingPartition = TrPresencePoints.merge(TrPseudoAbsPoints)\n",
        "    testingPartition = TePresencePoints.merge(TePseudoAbsPoints)\n",
        "\n",
        "    # 훈련용 지점에서 예측 변수 이미지의 공변량 값 추출\n",
        "    trainPixelVals = predictors.sampleRegions(collection=trainingPartition,\n",
        "                                              properties=['PresAbs'],\n",
        "                                              scale=GrainSize,\n",
        "                                              tileScale=16,\n",
        "                                              geometries=True)\n",
        "\n",
        "    # 랜덤 포레스트 분류기(Random Forest classifier)\n",
        "    Classifier = ee.Classifier.smileRandomForest(\n",
        "        numberOfTrees=500, # 생성할 결정 트리의 수\n",
        "        variablesPerSplit=None, # 분할당 변수의 수. 기본값: 변수 수의 제곱근 사용\n",
        "        minLeafPopulation=10, # 훈련 셋에 최소한 이 개수의 지점이 포함된 노드만 생성\n",
        "        bagFraction=0.5, # 트리당 백에 대한 입력 비율\n",
        "        maxNodes=None, # 각 트리의 최대 리프 노드 수. 기본값: 제한 없음\n",
        "        seed=Seed # 무작위화 시드\n",
        "    )\n",
        "    # 출현 확률(Presence probability)\n",
        "    ClassifierPr = Classifier.setOutputMode('PROBABILITY').train(trainPixelVals, 'PresAbs', bands)\n",
        "    ClassifiedImgPr = predictors.select(bands).classify(ClassifierPr)\n",
        "\n",
        "    # 이진 출현/비출현 지도(Binary presence/absence map)\n",
        "    ClassifierBin = Classifier.setOutputMode('CLASSIFICATION').train(trainPixelVals, 'PresAbs', bands)\n",
        "    ClassifiedImgBin = predictors.select(bands).classify(ClassifierBin)\n",
        "\n",
        "    return [ClassifiedImgPr, ClassifiedImgBin, trainingPartition, testingPartition], ClassifierPr"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "공간 블록은 모델 훈련의 경우 70%, 모델 시험의 경우 30%로 각각 분할됩니다. 임의-비출현 데이터는 반복할 때마다 각 훈련 및 시험 세트 내에서 무작위로 생성됩니다. 결과적으로 매 실행 때마다 모델 훈련 및 시험을 위한 서로 다른 출현 및 임의-비출현 세트를 갖게 됩니다."
      ],
      "metadata": {
        "id": "e6dr7hIvTVvJ"
      },
      "id": "e6dr7hIvTVvJ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eda5880a-c4cb-48ee-a939-779b1a2fcc03",
      "metadata": {
        "id": "eda5880a-c4cb-48ee-a939-779b1a2fcc03"
      },
      "outputs": [],
      "source": [
        "split = 0.7\n",
        "numiter = 10\n",
        "runif = lambda length: [random.randint(1, 1000) \\\n",
        "                        for _ in range(length)] # 시드\n",
        "items = runif(numiter)\n",
        "# items = [287, 288, 553, 226, 151, 255, 902, 267, 419, 538]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c893658b-5f2e-4c0e-949d-d11f1878f0b0",
      "metadata": {
        "id": "c893658b-5f2e-4c0e-949d-d11f1878f0b0"
      },
      "outputs": [],
      "source": [
        "results_list = [] # SDM 결과 리스트 초기화\n",
        "importances_list = [] # 변수 중요도 리스트 초기화\n",
        "\n",
        "for item in runif(numiter):\n",
        "    result, trained = SDM(item)\n",
        "    # SDM 결과 리스트 누적\n",
        "    results_list.extend(result)\n",
        "\n",
        "    # 변수 중요도 리스트 누적\n",
        "    importance = ee.Dictionary(trained.explain()).get('importance')\n",
        "    importances_list.extend(importance.getInfo().items())\n",
        "\n",
        "# SDM 결과 누적\n",
        "results = ee.List(results_list).flatten()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이제 팔색조의 서식지 적합성 지도와 잠재 분포 지도를 가시화할 수 있습니다."
      ],
      "metadata": {
        "id": "C_Tp0E-rTkCL"
      },
      "id": "C_Tp0E-rTkCL"
    },
    {
      "cell_type": "code",
      "source": [
        "cm.plot_colormap('viridis_r',\n",
        "                 width=8.0, height=0.2, orientation='horizontal')"
      ],
      "metadata": {
        "id": "lhMSKNDV9Xp2"
      },
      "id": "lhMSKNDV9Xp2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e22e4868-edc1-4650-a1b3-8a8b596a4d5e",
      "metadata": {
        "id": "e22e4868-edc1-4650-a1b3-8a8b596a4d5e"
      },
      "outputs": [],
      "source": [
        "# 서식지 적합성 지도(Habitat suitability map)\n",
        "images = ee.List.sequence(\n",
        "    0, ee.Number(numiter).multiply(4).subtract(1), 4).\\\n",
        "    map(lambda x: results.get(x))\n",
        "ModelAverage = ee.ImageCollection.fromImages(images).mean()\n",
        "\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "vis_params = {\n",
        "    'min': 0,\n",
        "    'max': 1,\n",
        "    'palette': cm.palettes.viridis_r}\n",
        "Map.addLayer(ModelAverage, vis_params, 'Habitat suitability')\n",
        "Map.add_colorbar(vis_params, label=\"Habitat suitability\",\n",
        "                 orientation=\"horizontal\",\n",
        "                 layer_name=\"Habitat suitability\")\n",
        "Map.addLayer(Data, {'color':'red'}, 'Presence')\n",
        "Map.centerObject(AOI, 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e07f4f6f-5e48-453f-bf4e-be1952a596ce",
      "metadata": {
        "id": "e07f4f6f-5e48-453f-bf4e-be1952a596ce"
      },
      "outputs": [],
      "source": [
        "# 잠재 분포 지도(Potential distribution map)\n",
        "images2 = ee.List.sequence(1, ee.Number(numiter).multiply(4).\\\n",
        "                           subtract(1), 4).map(lambda x: results.get(x))\n",
        "DistributionMap = ee.ImageCollection.fromImages(images2).mode()\n",
        "\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "\n",
        "vis_params = {\n",
        "    'min': 0,\n",
        "    'max': 1,\n",
        "    'palette': ['white', 'green']}\n",
        "Map.addLayer(DistributionMap, vis_params, 'Potential distribution')\n",
        "Map.addLayer(Data, {'color':'red'}, 'Presence')\n",
        "Map.add_colorbar(vis_params, label=\"Potential distribution\",\n",
        "                 discrete=True, orientation=\"horizontal\",\n",
        "                 layer_name=\"Potential distribution\")\n",
        "Map.centerObject(Data.geometry(), 6)\n",
        "Map"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ba3f8e98-6596-43b7-88cd-bf61daca8cd4",
      "metadata": {
        "id": "ba3f8e98-6596-43b7-88cd-bf61daca8cd4"
      },
      "source": [
        "### 6.2.6. 정확도 평가 및 변수 중요도\n",
        "\n",
        "Random Forest(ee.Classifier.smileRandomForest)는 앙상블 학습 방법 중 하나로, 여러 개의 의사 결정 트리(decision tree)를 구성하여 예측을 수행하는 방법입니다. 각 의사 결정 트리는 데이터의 서로 다른 부분 집합을 기반으로 독립적으로 학습하며, 그 결과를 종합하여 더 정확하고 안정적인 예측을 가능하게 합니다.\n",
        "\n",
        "변수 중요도(variable importance)란 Random Forest 모델 내에서 어떤 변수가 예측에 영향을 많이 미치는지를 평가하는 지표입니다. 앞서 정의된 SDM() 함수에서 ClassifierPr 변수는 확률 모드로 훈련된 Random Forest 분류기입니다. 이 변수를 추가 리턴해서 변수 중요도를 계산해 보겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ef7fd8f-cdf4-408b-afe8-263649f25a34",
      "metadata": {
        "id": "1ef7fd8f-cdf4-408b-afe8-263649f25a34"
      },
      "outputs": [],
      "source": [
        "# 각 변수 중요도 값을 리스트로 추출\n",
        "variables = [item[0] for item in importances_list]\n",
        "importances = [item[1] for item in importances_list]\n",
        "\n",
        "# 변수 중요도 평균 계산\n",
        "average_importances = {}\n",
        "for variable in set(variables):\n",
        "    indices = [i for i, var in enumerate(variables) if var == variable]\n",
        "    average_importance = np.mean([importances[i] for i in indices])\n",
        "    average_importances[variable] = average_importance\n",
        "\n",
        "# 평균 변수 중요도 출력\n",
        "for variable, avg_importance in average_importances.items():\n",
        "    print(f\"{variable}: {avg_importance}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "평균 변수 중요도를 시각화하여, 변수 중요도가 높은 순으로 정렬된 막대 그래프를 생성합니다. 각 막대 위에 해당 변수의 평균 중요도 값을 표시하여 변수 간의 상대적 중요도를 시각적으로 비교할 수 있습니다."
      ],
      "metadata": {
        "id": "Rd31euVAUJuK"
      },
      "id": "Rd31euVAUJuK"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4371fd5e-7b53-424a-9263-13dce4022a8d",
      "metadata": {
        "id": "4371fd5e-7b53-424a-9263-13dce4022a8d"
      },
      "outputs": [],
      "source": [
        "# 변수 중요도가 높은 순으로 정렬\n",
        "sorted_importances = sorted(average_importances.items(),\n",
        "                            key=lambda x: x[1], reverse=False)\n",
        "variables = [item[0] for item in sorted_importances]\n",
        "avg_importances = [item[1] for item in sorted_importances]\n",
        "\n",
        "# 그래프 크기 조절\n",
        "plt.figure(figsize=(8, 4))  # 원하는 크기로 조절\n",
        "\n",
        "# 막대그래프로 평균 중요도 출력\n",
        "plt.barh(variables, avg_importances)\n",
        "plt.xlabel('Importance')\n",
        "plt.ylabel('Variables')\n",
        "plt.title('Average Variable Importance')\n",
        "\n",
        "# 막대 위에 수치 표시\n",
        "for i, v in enumerate(avg_importances):\n",
        "    plt.text(v + 0.02, i, f\"{v:.2f}\", va='center')\n",
        "\n",
        "# x축 범위 조정\n",
        "plt.xlim(0, max(avg_importances) + 5)  # 원하는 범위로 조절\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('variable_importance.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p align=\"center\"><img src=\"https://github.com/osgeokr/GEE-PAM-Book/blob/main/img/accuracy_assessment.png?raw=1\" width = \"800px\"></img></p>\n",
        "\n",
        "검증 데이터셋을 사용하여 각 실행에 대해 AUC-ROC와 AUC-PR를 계산합니다. 그런 다음 n번의 반복에 대해 평균 AUC-ROC 및 AUC-PR를 계산합니다.\n",
        "\n",
        "* AUC-ROC는 \"민감도(Sensitivity, = Recall) 대 1-특이도 (1-Specificity)\" 그래프 아래의 면적으로, 임계값을 변화시켰을 때의 민감도와 특이도의 관계를 나타냅니다. 특이도는 모든 관측된 비출현에 기반합니다. 따라서 AUC-ROC는 혼동 행렬의 모든 사분면을 포함합니다.\n",
        "\n",
        "* AUC-PR은 \"정밀도(Precision) 대 재현율(Recall = Sensitivity)\" 그래프 아래의 면적으로, 임계값을 변화시켰을 때의 정밀도와 재현율의 관계를 나타냅니다. 정밀도는 모든 예측된 출현에 기반합니다. 따라서, AUC-PR은 진음성(TN)의 수를 포함하지 않습니다.\n",
        "\n",
        "각 실행에서 모델 검증에 충분한 지점 수가 있는지 확인하는 것이 중요합니다. 최종 지점 수는 공간 블록의 무작위 분할에 따라 달라지기 때문에 모델 검증에 충분한 출현 및 임의-비출현 지점이 있는지 확인해야 합니다.\n",
        "\n",
        "멸종위기종이나 희귀종과 같은 경우, 출현 데이터가 부족하여 검증용 데이터셋 또한 부족할 수 있습니다. 이는 전문가들의 경험과 지식을 활용한 추가 데이터 수집 또는 관련 다른 데이터를 활용하는 대안이 필요합니다."
      ],
      "metadata": {
        "id": "Xve623mlUSaJ"
      },
      "id": "Xve623mlUSaJ"
    },
    {
      "cell_type": "code",
      "source": [
        "def print_pres_abs_sizes(TestingDatasets, numiter):\n",
        "    # 출현 및 임의-비출현 좌표가 충분한 지 체크 및 출력\n",
        "    def get_pres_abs_size(x):\n",
        "        fc = ee.FeatureCollection(TestingDatasets.get(x))\n",
        "        presence_size = fc.filter(ee.Filter.eq('PresAbs', 1)).size()\n",
        "        pseudo_absence_size = fc.filter(ee.Filter.eq('PresAbs', 0)).size()\n",
        "        return ee.List([presence_size, pseudo_absence_size])\n",
        "\n",
        "    sizes_info = ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(get_pres_abs_size).getInfo()\n",
        "\n",
        "    for i, sizes in enumerate(sizes_info):\n",
        "        presence_size = sizes[0]\n",
        "        pseudo_absence_size = sizes[1]\n",
        "        print(f'Iteration {i + 1}: Presence Size = {presence_size}, Pseudo-absence Size = {pseudo_absence_size}')"
      ],
      "metadata": {
        "id": "u3iJRyIy-Xaz"
      },
      "id": "u3iJRyIy-Xaz",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "46164b61-3179-4982-9373-c01bde89b2c8",
      "metadata": {
        "id": "46164b61-3179-4982-9373-c01bde89b2c8"
      },
      "outputs": [],
      "source": [
        "# 검증용 데이터셋 추출\n",
        "TestingDatasets = (ee.List.sequence(3, ee.Number(numiter).multiply(4).subtract(1), 4).map(lambda x: results.get(x)))\n",
        "\n",
        "print_pres_abs_sizes(TestingDatasets, numiter)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def getAcc(HSM, TData, GrainSize):\n",
        "    Pr_Prob_Vals = HSM.sampleRegions(collection=TData, properties=['PresAbs'], scale=GrainSize, tileScale=16)\n",
        "    seq = ee.List.sequence(start=0, end=1, count=25) # Divide 0 to 1 into 25 intervals\n",
        "    def calculate_metrics(cutoff):\n",
        "        # Each element of the seq list is passed as cutoff(threshold value)\n",
        "\n",
        "        # Observed present = TP + FN\n",
        "        Pres = Pr_Prob_Vals.filterMetadata('PresAbs', 'equals', 1)\n",
        "\n",
        "        # TP (True Positive)\n",
        "        TP = ee.Number(Pres.filterMetadata('classification', 'greater_than', cutoff).size())\n",
        "\n",
        "        # TPR (True Positive Rate) = Recall = Sensitivity = TP / (TP + FN) = TP / Observed present\n",
        "        TPR = TP.divide(Pres.size())\n",
        "\n",
        "        # Observed absent = FP + TN\n",
        "        Abs = Pr_Prob_Vals.filterMetadata('PresAbs', 'equals', 0)\n",
        "\n",
        "        # FN (False Negative)\n",
        "        FN = ee.Number(Pres.filterMetadata('classification', 'less_than', cutoff).size())\n",
        "\n",
        "        # TNR (True Negative Rate) = Specificity = TN  / (FP + TN) = TN / Observed absent\n",
        "        TN = ee.Number(Abs.filterMetadata('classification', 'less_than', cutoff).size())\n",
        "        TNR = TN.divide(Abs.size())\n",
        "\n",
        "        # FP (False Positive)\n",
        "        FP = ee.Number(Abs.filterMetadata('classification', 'greater_than', cutoff).size())\n",
        "\n",
        "        # FPR (False Positive Rate) = FP / (FP + TN) = FP / Observed absent\n",
        "        FPR = FP.divide(Abs.size())\n",
        "\n",
        "        # Precision = TP / (TP + FP) = TP / Predicted present\n",
        "        Precision = TP.divide(TP.add(FP))\n",
        "\n",
        "        # SUMSS = SUM of Sensitivity and Specificity\n",
        "        SUMSS = TPR.add(TNR)\n",
        "\n",
        "        return ee.Feature(\n",
        "            None,\n",
        "            {\n",
        "                'cutoff': cutoff,\n",
        "                'TP': TP,\n",
        "                'TN': TN,\n",
        "                'FP': FP,\n",
        "                'FN': FN,\n",
        "                'TPR': TPR,\n",
        "                'TNR': TNR,\n",
        "                'FPR': FPR,\n",
        "                'Precision': Precision,\n",
        "                'SUMSS': SUMSS\n",
        "            }\n",
        "        )\n",
        "\n",
        "    return ee.FeatureCollection(seq.map(calculate_metrics))"
      ],
      "metadata": {
        "id": "A5H22XyB_TfI"
      },
      "id": "A5H22XyB_TfI",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_and_print_auc_metrics(images, TestingDatasets, GrainSize, numiter):\n",
        "    # AUC-ROC와 AUC-PR 계산\n",
        "    def calculate_auc_metrics(x):\n",
        "        HSM = ee.Image(images.get(x))\n",
        "        TData = ee.FeatureCollection(TestingDatasets.get(x))\n",
        "        Acc = getAcc(HSM, TData, GrainSize)\n",
        "\n",
        "        # AUC-ROC 계산\n",
        "        X = ee.Array(Acc.aggregate_array('FPR'))\n",
        "        Y = ee.Array(Acc.aggregate_array('TPR'))\n",
        "        X1 = X.slice(0,1).subtract(X.slice(0,0,-1))\n",
        "        Y1 = Y.slice(0,1).add(Y.slice(0,0,-1))\n",
        "        auc_roc = X1.multiply(Y1).multiply(0.5).reduce('sum',[0]).abs().toList().get(0)\n",
        "\n",
        "        # AUC-PR 계산\n",
        "        X = ee.Array(Acc.aggregate_array('TPR'))\n",
        "        Y = ee.Array(Acc.aggregate_array('Precision'))\n",
        "        X1 = X.slice(0,1).subtract(X.slice(0,0,-1))\n",
        "        Y1 = Y.slice(0,1).add(Y.slice(0,0,-1))\n",
        "        auc_pr = X1.multiply(Y1).multiply(0.5).reduce('sum',[0]).abs().toList().get(0)\n",
        "\n",
        "        return (auc_roc, auc_pr)\n",
        "\n",
        "    auc_metrics = ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(calculate_auc_metrics).getInfo()\n",
        "\n",
        "    # 각 반복마다 AUC-ROC와 AUC-PR 출력\n",
        "    df = pd.DataFrame(auc_metrics, columns=['AUC-ROC', 'AUC-PR'])\n",
        "    df.index = [f'Iteration {i + 1}' for i in range(len(df))]\n",
        "    df.to_csv('auc_metrics.csv', index_label='Iteration')\n",
        "    print(df)\n",
        "\n",
        "    # AUC-ROC와 AUC-PR의 평균과 표준편차 계산\n",
        "    mean_auc_roc, std_auc_roc = df['AUC-ROC'].mean(), df['AUC-ROC'].std()\n",
        "    mean_auc_pr, std_auc_pr = df['AUC-PR'].mean(), df['AUC-PR'].std()\n",
        "    print(f'Mean AUC-ROC = {mean_auc_roc:.4f} ± {std_auc_roc:.4f}')\n",
        "    print(f'Mean AUC-PR = {mean_auc_pr:.4f} ± {std_auc_pr:.4f}')"
      ],
      "metadata": {
        "id": "uUQ8oH90_KVK"
      },
      "id": "uUQ8oH90_KVK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# AUC-ROC와 AUC-PR 계산\n",
        "calculate_and_print_auc_metrics(images, TestingDatasets, GrainSize, numiter)"
      ],
      "metadata": {
        "id": "RutnJ2Py_FtJ"
      },
      "id": "RutnJ2Py_FtJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_and_print_ss_metrics(images, TestingDatasets, GrainSize, numiter):\n",
        "    # 민감도(Sensitivity)와 특이도(Specificity) 계산\n",
        "    def calculate_ss_metrics(x):\n",
        "        HSM = ee.Image(images.get(x))\n",
        "        TData = ee.FeatureCollection(TestingDatasets.get(x))\n",
        "        Acc = getAcc(HSM, TData, GrainSize)\n",
        "\n",
        "        # 민감도(Sensitivity) 계산\n",
        "        sensitivity = Acc.sort('SUMSS', False).first().get('TPR')\n",
        "\n",
        "        # 특이도(Specificity) 계산\n",
        "        specificity = Acc.sort('SUMSS', False).first().get('TNR')\n",
        "\n",
        "        return (sensitivity, specificity)\n",
        "\n",
        "    ss_metrics = ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(calculate_ss_metrics).getInfo()\n",
        "\n",
        "    # 각 반복마다 민감도와 특이도 출력\n",
        "    df = pd.DataFrame(ss_metrics, columns=['Sensitivity', 'Specificity'])\n",
        "    df.index = [f'Iteration {i + 1}' for i in range(len(df))]\n",
        "    df.to_csv('ss_metrics.csv', index_label='Iteration')\n",
        "    print(df)\n",
        "\n",
        "    # 민감도와 특이도의 평균과 표준편차 계산\n",
        "    mean_sensitivity, std_sensitivity = df['Sensitivity'].mean(), df['Sensitivity'].std()\n",
        "    mean_specificity, std_specificity = df['Specificity'].mean(), df['Specificity'].std()\n",
        "    print(f'Mean Sensitivity = {mean_sensitivity:.4f} ± {std_sensitivity:.4f}')\n",
        "    print(f'Mean Specificity = {mean_specificity:.4f} ± {std_specificity:.4f}')"
      ],
      "metadata": {
        "id": "grdz7Ld7_25V"
      },
      "id": "grdz7Ld7_25V",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c8e5f26f-311a-4ea1-97f8-9c4d5e13b003",
      "metadata": {
        "id": "c8e5f26f-311a-4ea1-97f8-9c4d5e13b003"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "\n",
        "# 민감도(sensitivity)와 특이도(specificity) 계산\n",
        "# 민감도(sensitivity): 출현 예측의 정확성\n",
        "# 특이도(specificity): 비출현 예측의 정확성\n",
        "calculate_and_print_ss_metrics(images, TestingDatasets, GrainSize, numiter)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_roc_pr_curves(images, TestingDatasets, GrainSize, numiter):\n",
        "    # Plot ROC and PR curves\n",
        "    def get_roc_pr_values(x):\n",
        "        HSM = ee.Image(images.get(x))\n",
        "        TData = ee.FeatureCollection(TestingDatasets.get(x))\n",
        "        Acc = getAcc(HSM, TData, GrainSize)\n",
        "\n",
        "        # Get ROC and PR values\n",
        "        varFPR = Acc.aggregate_array('FPR')\n",
        "        varTPR = Acc.aggregate_array('TPR')\n",
        "        varPrecision = Acc.aggregate_array('Precision')\n",
        "\n",
        "        return (varFPR, varTPR, varPrecision)\n",
        "\n",
        "    # Retrieve ROC and PR values for each iteration and model\n",
        "    roc_pr_values = np.array(ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(get_roc_pr_values).getInfo())\n",
        "\n",
        "    all_model_data = []\n",
        "    for model_data in roc_pr_values:\n",
        "        # Transpose data to match the desired format\n",
        "        transposed_data = np.transpose(model_data)\n",
        "        all_model_data.append(transposed_data)\n",
        "\n",
        "    # Calculate mean and standard deviation across models\n",
        "    mean_data = np.mean(all_model_data, axis=0)\n",
        "    std_data = np.std(all_model_data, axis=0)\n",
        "\n",
        "    # Create DataFrames for mean and standard deviation data\n",
        "    df_mean = pd.DataFrame(mean_data, columns=['FPR', 'TPR', 'Precision'])\n",
        "    df_std = pd.DataFrame(std_data, columns=['FPR', 'TPR', 'Precision'])\n",
        "\n",
        "    # Set font size and style\n",
        "    plt.rcParams.update({'font.size': 14})\n",
        "\n",
        "    # Create a figure with two subplots\n",
        "    plt.figure(figsize=(16, 6))\n",
        "\n",
        "    # Plot mean ROC Curve\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(df_mean['FPR'], df_mean['TPR'], color='blue', lw=2, label='Mean ROC Curve')\n",
        "    plt.fill_between(df_mean['FPR'], df_mean['TPR'] - df_std['TPR'], df_mean['TPR'] + df_std['TPR'], color='gray', alpha=0.2)\n",
        "    plt.plot([0, 1], [0, 1], linestyle='--', color='gray')\n",
        "    plt.xlim([0, 1])\n",
        "    plt.ylim([0, 1])\n",
        "    plt.xlabel('False positive rate (1 − specificity)')\n",
        "    plt.ylabel('Sensitivity')\n",
        "    plt.title('Receiver Operating Characteristic (ROC) curve')\n",
        "    plt.grid(True)\n",
        "    plt.legend(loc='lower right')\n",
        "\n",
        "    # Plot mean PR Curve\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(df_mean['TPR'], df_mean['Precision'], color='blue', lw=2, label='Mean PR Curve')\n",
        "    plt.fill_between(df_mean['TPR'], df_mean['Precision'] - df_std['Precision'], df_mean['Precision'] + df_std['Precision'], color='gray', alpha=0.2)\n",
        "    plt.xlim([0, 1])\n",
        "    plt.ylim([0, 1])\n",
        "    plt.xlabel('Recall (sensitivity)')\n",
        "    plt.ylabel('Precision')\n",
        "    plt.title('Precision-Recall (PR) curve')\n",
        "    plt.grid(True)\n",
        "    plt.legend(loc='lower right')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('roc_pr_curves_plot.png')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "CjrE4b42_8YD"
      },
      "id": "CjrE4b42_8YD",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c8826ce5-c0ff-4c8c-ab16-9d801f2c8615",
      "metadata": {
        "id": "c8826ce5-c0ff-4c8c-ab16-9d801f2c8615"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "\n",
        "# ROC and PR 곡선 플롯\n",
        "plot_roc_pr_curves(images, TestingDatasets, GrainSize, numiter)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_DistributionMap2(images, TestingDatasets, GrainSize, numiter, ModelAverage):\n",
        "    # Potential Distribution Map using the optimal threshold\n",
        "    def get_metrics(x):\n",
        "        HSM = ee.Image(images.get(x))\n",
        "        TData = ee.FeatureCollection(TestingDatasets.get(x))\n",
        "        Acc = getAcc(HSM, TData, GrainSize)\n",
        "        return Acc.sort('SUMSS', False).first()\n",
        "\n",
        "    metrics = ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(get_metrics)\n",
        "    MeanThresh = ee.Number(ee.FeatureCollection(metrics).aggregate_array(\"cutoff\").reduce(ee.Reducer.mean()))\n",
        "    print('Mean threshold:', MeanThresh.getInfo())\n",
        "\n",
        "    DistributionMap2 = ModelAverage.gte(MeanThresh)\n",
        "\n",
        "    return DistributionMap2"
      ],
      "metadata": {
        "id": "t7VQwZ_Jmc03"
      },
      "id": "t7VQwZ_Jmc03",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e80acf43-ef5b-4587-8155-01f5cb9bb352",
      "metadata": {
        "id": "e80acf43-ef5b-4587-8155-01f5cb9bb352"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "\n",
        "# Potential Distribution Map using the optimal threshold\n",
        "DistributionMap2 = create_DistributionMap2(images, TestingDatasets, GrainSize, numiter, ModelAverage)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "547904dd-cb76-407f-9120-b6e369b47047",
      "metadata": {
        "id": "547904dd-cb76-407f-9120-b6e369b47047"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "\n",
        "# Custom Binary Distribution Mapping Based on Optimal Threshold\n",
        "vis_params = {\n",
        "    'min': 0,\n",
        "    'max': 1,\n",
        "    'palette': ['white', 'green']}\n",
        "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "Map.add_layer(Vworld_Base)\n",
        "Map.addLayer(DistributionMap2, vis_params, 'Potential distribution2')\n",
        "Map.addLayer(Data, {'color':'red'}, 'Presence')\n",
        "Map.add_colorbar(vis_params, label=\"Potential distribution2\", discrete=True, orientation=\"horizontal\", layer_name=\"Potential distribution2\")\n",
        "Map.centerObject(Data.geometry(), 7)\n",
        "Map"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d7e09ef-4851-4da6-ad75-fef371db2e48",
      "metadata": {
        "id": "3d7e09ef-4851-4da6-ad75-fef371db2e48"
      },
      "source": [
        "### 10. Export Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "81b61d0d-943d-4f7d-9b5a-8900e7aef4e6",
      "metadata": {
        "id": "81b61d0d-943d-4f7d-9b5a-8900e7aef4e6"
      },
      "outputs": [],
      "source": [
        "# Save the Habitat Suitability Map as a GeoTIFF\n",
        "geemap.ee_export_image_to_drive(ModelAverage,\n",
        "                                description='habitat_suitability',\n",
        "                                folder='export',\n",
        "                                region=AOI,\n",
        "                                scale=100)\n",
        "\n",
        "# Save the Potential Distribution Map as a GeoTIFF\n",
        "geemap.ee_export_image_to_drive(DistributionMap,\n",
        "                                description='distribution_map',\n",
        "                                folder='export',\n",
        "                                region=AOI,\n",
        "                                scale=100)\n",
        "\n",
        "# Save the Potential Distribution Map using the optimal threshold as a GeoTIFF\n",
        "geemap.ee_export_image_to_drive(DistributionMap2,\n",
        "                                description='distribution_map2',\n",
        "                                folder='export',\n",
        "                                region=AOI,\n",
        "                                scale=100)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J9kSydU6q0Eq"
      },
      "id": "J9kSydU6q0Eq",
      "execution_count": null,
      "outputs": []
    }
  ],
  "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": "3.12.1"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "34897ce609d84bd8bb49f2dbfdb444a0": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletMapModel",
          "model_module_version": "^0.18",
          "state": {
            "_dom_classes": [],
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletMapModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletMapView",
            "bottom": 3364750,
            "bounce_at_zoom_limits": true,
            "box_zoom": true,
            "center": [
              33.516,
              126.712
            ],
            "close_popup_on_click": true,
            "controls": [
              "IPY_MODEL_f7043b1d9c3c48fc8b87b44712cd55ce",
              "IPY_MODEL_482cf3f9b1b24be5969e3e9d7ba13359",
              "IPY_MODEL_d50a36965010473fbc33c5c08789c754",
              "IPY_MODEL_23ce7bfe2c03476ab233ab6fe0a343c5",
              "IPY_MODEL_274cccbd5bfe4d7c9526a1771443c1d7",
              "IPY_MODEL_41d7b5ae6d544795852288fc127f83f8",
              "IPY_MODEL_293d02f5ba4e4be8bcdc1e850ecb713d",
              "IPY_MODEL_2620bb91f7fe4ea18efbe57d7e00335a"
            ],
            "crs": {
              "name": "EPSG3857",
              "custom": false
            },
            "default_style": "IPY_MODEL_6f98c46298d441e3b40c4c658e07c754",
            "double_click_zoom": true,
            "dragging": true,
            "dragging_style": "IPY_MODEL_664ebb5831dd462888a81526d3e60b08",
            "east": 126.7291831970215,
            "fullscreen": false,
            "inertia": true,
            "inertia_deceleration": 3000,
            "inertia_max_speed": 1500,
            "interpolation": "bilinear",
            "keyboard": true,
            "keyboard_pan_offset": 80,
            "keyboard_zoom_offset": 1,
            "layers": [
              "IPY_MODEL_e544af921e314f00a46d2fb9416c9994",
              "IPY_MODEL_e5a821b29c2d4fe09c7b02ac68464d24",
              "IPY_MODEL_26f1e97296364f039ac78eba85b1a193",
              "IPY_MODEL_151742d6122148b29c5acc0851149a6e",
              "IPY_MODEL_06a4e51b79a74269906950774b7675a6"
            ],
            "layout": "IPY_MODEL_46c69ce2a9e6450da9fbd39d6689f3b2",
            "left": 7146508,
            "max_zoom": 24,
            "min_zoom": null,
            "modisdate": "2024-02-03",
            "north": 33.52315036278108,
            "options": [
              "bounce_at_zoom_limits",
              "box_zoom",
              "center",
              "close_popup_on_click",
              "double_click_zoom",
              "dragging",
              "fullscreen",
              "inertia",
              "inertia_deceleration",
              "inertia_max_speed",
              "interpolation",
              "keyboard",
              "keyboard_pan_offset",
              "keyboard_zoom_offset",
              "max_zoom",
              "min_zoom",
              "prefer_canvas",
              "scroll_wheel_zoom",
              "tap",
              "tap_tolerance",
              "touch_zoom",
              "world_copy_jump",
              "zoom",
              "zoom_animation_threshold",
              "zoom_delta",
              "zoom_snap"
            ],
            "panes": {},
            "prefer_canvas": false,
            "right": 7147308,
            "scroll_wheel_zoom": true,
            "south": 33.508838409451315,
            "style": "IPY_MODEL_0c36e741c438483bb18a48b3f4cb445e",
            "tap": true,
            "tap_tolerance": 15,
            "top": 3364350,
            "touch_zoom": true,
            "west": 126.69485092163087,
            "window_url": "https://duwmgkk6pjv-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229",
            "world_copy_jump": false,
            "zoom": 15,
            "zoom_animation_threshold": 4,
            "zoom_delta": 1,
            "zoom_snap": 1
          }
        },
        "f7043b1d9c3c48fc8b87b44712cd55ce": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletWidgetControlModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletWidgetControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletWidgetControlView",
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "options": [
              "position",
              "transparent_bg"
            ],
            "position": "topleft",
            "transparent_bg": false,
            "widget": "IPY_MODEL_55fd7be0f4ea413881d317a91ea2bf37"
          }
        },
        "482cf3f9b1b24be5969e3e9d7ba13359": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletZoomControlModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletZoomControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletZoomControlView",
            "options": [
              "position",
              "zoom_in_text",
              "zoom_in_title",
              "zoom_out_text",
              "zoom_out_title"
            ],
            "position": "topleft",
            "zoom_in_text": "+",
            "zoom_in_title": "Zoom in",
            "zoom_out_text": "-",
            "zoom_out_title": "Zoom out"
          }
        },
        "d50a36965010473fbc33c5c08789c754": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletFullScreenControlModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletFullScreenControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletFullScreenControlView",
            "options": [
              "position"
            ],
            "position": "topleft"
          }
        },
        "23ce7bfe2c03476ab233ab6fe0a343c5": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletDrawControlModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletDrawControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletDrawControlView",
            "circle": {},
            "circlemarker": {},
            "data": [],
            "edit": true,
            "marker": {
              "shapeOptions": {
                "color": "#3388ff"
              }
            },
            "options": [
              "position"
            ],
            "polygon": {
              "shapeOptions": {}
            },
            "polyline": {
              "shapeOptions": {}
            },
            "position": "topleft",
            "rectangle": {
              "shapeOptions": {
                "color": "#3388ff"
              }
            },
            "remove": true
          }
        },
        "274cccbd5bfe4d7c9526a1771443c1d7": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletScaleControlModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletScaleControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletScaleControlView",
            "imperial": true,
            "max_width": 100,
            "metric": true,
            "options": [
              "imperial",
              "max_width",
              "metric",
              "position",
              "update_when_idle"
            ],
            "position": "bottomleft",
            "update_when_idle": false
          }
        },
        "41d7b5ae6d544795852288fc127f83f8": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletMeasureControlModel",
          "model_module_version": "^0.18",
          "state": {
            "_custom_units": {},
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletMeasureControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletMeasureControlView",
            "active_color": "orange",
            "capture_z_index": 10000,
            "completed_color": "#C8F2BE",
            "options": [
              "active_color",
              "capture_z_index",
              "completed_color",
              "popup_options",
              "position",
              "primary_area_unit",
              "primary_length_unit",
              "secondary_area_unit",
              "secondary_length_unit"
            ],
            "popup_options": {
              "className": "leaflet-measure-resultpopup",
              "autoPanPadding": [
                10,
                10
              ]
            },
            "position": "bottomleft",
            "primary_area_unit": "acres",
            "primary_length_unit": "kilometers",
            "secondary_area_unit": null,
            "secondary_length_unit": null
          }
        },
        "293d02f5ba4e4be8bcdc1e850ecb713d": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletWidgetControlModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletWidgetControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletWidgetControlView",
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "options": [
              "position",
              "transparent_bg"
            ],
            "position": "topright",
            "transparent_bg": false,
            "widget": "IPY_MODEL_07c496a120fc416e8c1e1bb30ebb86cb"
          }
        },
        "2620bb91f7fe4ea18efbe57d7e00335a": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletAttributionControlModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletAttributionControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletAttributionControlView",
            "options": [
              "position",
              "prefix"
            ],
            "position": "bottomright",
            "prefix": "ipyleaflet"
          }
        },
        "6f98c46298d441e3b40c4c658e07c754": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletMapStyleModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletMapStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "cursor": "grab"
          }
        },
        "664ebb5831dd462888a81526d3e60b08": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletMapStyleModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletMapStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "cursor": "move"
          }
        },
        "e544af921e314f00a46d2fb9416c9994": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletTileLayerModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletTileLayerModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletTileLayerView",
            "attribution": "&copy; <a href=\"https://www.openstreetmap.org/copyright\">OpenStreetMap</a> contributors",
            "base": true,
            "bottom": true,
            "bounds": null,
            "detect_retina": false,
            "loading": false,
            "max_native_zoom": null,
            "max_zoom": 19,
            "min_native_zoom": null,
            "min_zoom": 1,
            "name": "OpenStreetMap.Mapnik",
            "no_wrap": false,
            "opacity": 1,
            "options": [
              "attribution",
              "bounds",
              "detect_retina",
              "max_native_zoom",
              "max_zoom",
              "min_native_zoom",
              "min_zoom",
              "no_wrap",
              "tile_size",
              "tms",
              "zoom_offset"
            ],
            "pane": "",
            "popup": null,
            "popup_max_height": null,
            "popup_max_width": 300,
            "popup_min_width": 50,
            "show_loading": false,
            "subitems": [],
            "tile_size": 256,
            "tms": false,
            "url": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
            "visible": true,
            "zoom_offset": 0
          }
        },
        "e5a821b29c2d4fe09c7b02ac68464d24": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletTileLayerModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletTileLayerModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletTileLayerView",
            "attribution": "Vworld",
            "base": false,
            "bottom": true,
            "bounds": null,
            "detect_retina": false,
            "loading": false,
            "max_native_zoom": null,
            "max_zoom": 18,
            "min_native_zoom": null,
            "min_zoom": 0,
            "name": "Vworld Base",
            "no_wrap": false,
            "opacity": 1,
            "options": [
              "attribution",
              "bounds",
              "detect_retina",
              "max_native_zoom",
              "max_zoom",
              "min_native_zoom",
              "min_zoom",
              "no_wrap",
              "tile_size",
              "tms",
              "zoom_offset"
            ],
            "pane": "",
            "popup": null,
            "popup_max_height": null,
            "popup_max_width": 300,
            "popup_min_width": 50,
            "show_loading": false,
            "subitems": [],
            "tile_size": 256,
            "tms": false,
            "url": "https://xdworld.vworld.kr/2d/Base/service/{z}/{x}/{y}.png",
            "visible": true,
            "zoom_offset": 0
          }
        },
        "26f1e97296364f039ac78eba85b1a193": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletTileLayerModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletTileLayerModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletTileLayerView",
            "attribution": "Google Earth Engine",
            "base": false,
            "bottom": true,
            "bounds": null,
            "detect_retina": false,
            "loading": false,
            "max_native_zoom": null,
            "max_zoom": 24,
            "min_native_zoom": null,
            "min_zoom": 0,
            "name": "Random Raster",
            "no_wrap": false,
            "opacity": 1,
            "options": [
              "attribution",
              "bounds",
              "detect_retina",
              "max_native_zoom",
              "max_zoom",
              "min_native_zoom",
              "min_zoom",
              "no_wrap",
              "tile_size",
              "tms",
              "zoom_offset"
            ],
            "pane": "",
            "popup": null,
            "popup_max_height": null,
            "popup_max_width": 300,
            "popup_min_width": 50,
            "show_loading": false,
            "subitems": [],
            "tile_size": 256,
            "tms": false,
            "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/470074cb26268596f91e594d8e05b884-c1494829133fde35f6e9ebc99f4c06d2/tiles/{z}/{x}/{y}",
            "visible": true,
            "zoom_offset": 0
          }
        },
        "151742d6122148b29c5acc0851149a6e": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletTileLayerModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletTileLayerModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletTileLayerView",
            "attribution": "Google Earth Engine",
            "base": false,
            "bottom": true,
            "bounds": null,
            "detect_retina": false,
            "loading": false,
            "max_native_zoom": null,
            "max_zoom": 24,
            "min_native_zoom": null,
            "min_zoom": 0,
            "name": "Original data",
            "no_wrap": false,
            "opacity": 1,
            "options": [
              "attribution",
              "bounds",
              "detect_retina",
              "max_native_zoom",
              "max_zoom",
              "min_native_zoom",
              "min_zoom",
              "no_wrap",
              "tile_size",
              "tms",
              "zoom_offset"
            ],
            "pane": "",
            "popup": null,
            "popup_max_height": null,
            "popup_max_width": 300,
            "popup_min_width": 50,
            "show_loading": false,
            "subitems": [],
            "tile_size": 256,
            "tms": false,
            "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/b8c6626abab342d31a0a20518f122c13-3f5273c4ec06c4820b97baa20146f788/tiles/{z}/{x}/{y}",
            "visible": true,
            "zoom_offset": 0
          }
        },
        "06a4e51b79a74269906950774b7675a6": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletTileLayerModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletTileLayerModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletTileLayerView",
            "attribution": "Google Earth Engine",
            "base": false,
            "bottom": true,
            "bounds": null,
            "detect_retina": false,
            "loading": false,
            "max_native_zoom": null,
            "max_zoom": 24,
            "min_native_zoom": null,
            "min_zoom": 0,
            "name": "Final data",
            "no_wrap": false,
            "opacity": 1,
            "options": [
              "attribution",
              "bounds",
              "detect_retina",
              "max_native_zoom",
              "max_zoom",
              "min_native_zoom",
              "min_zoom",
              "no_wrap",
              "tile_size",
              "tms",
              "zoom_offset"
            ],
            "pane": "",
            "popup": null,
            "popup_max_height": null,
            "popup_max_width": 300,
            "popup_min_width": 50,
            "show_loading": false,
            "subitems": [],
            "tile_size": 256,
            "tms": false,
            "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-ed80a3716c158b2a4aab97248c1f6115/tiles/{z}/{x}/{y}",
            "visible": true,
            "zoom_offset": 0
          }
        },
        "46c69ce2a9e6450da9fbd39d6689f3b2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": "400px",
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "800px"
          }
        },
        "0c36e741c438483bb18a48b3f4cb445e": {
          "model_module": "jupyter-leaflet",
          "model_name": "LeafletMapStyleModel",
          "model_module_version": "^0.18",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletMapStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "cursor": "grab"
          }
        },
        "55fd7be0f4ea413881d317a91ea2bf37": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "geemap-colab"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_97c315b820b047ac819efdaaf86d0af0"
            ],
            "layout": "IPY_MODEL_f6d89b4f82a54a8c8553297a76a0fd71"
          }
        },
        "07c496a120fc416e8c1e1bb30ebb86cb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "geemap-colab"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d731805205734333a95f475096060ea0"
            ],
            "layout": "IPY_MODEL_27d6e359081c4e84b07a5fc422172cbc"
          }
        },
        "97c315b820b047ac819efdaaf86d0af0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ToggleButtonModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ToggleButtonModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ToggleButtonView",
            "button_style": "",
            "description": "",
            "description_tooltip": null,
            "disabled": false,
            "icon": "globe",
            "layout": "IPY_MODEL_5493ed6833594b529b1dd229b70f6d09",
            "style": "IPY_MODEL_44085fd87c3143a0b2b700848f765beb",
            "tooltip": "Search location/data",
            "value": false
          }
        },
        "f6d89b4f82a54a8c8553297a76a0fd71": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d731805205734333a95f475096060ea0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ToggleButtonModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ToggleButtonModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ToggleButtonView",
            "button_style": "",
            "description": "",
            "description_tooltip": null,
            "disabled": false,
            "icon": "wrench",
            "layout": "IPY_MODEL_3648cfa1df90494699dab548a73f612a",
            "style": "IPY_MODEL_8ed7d07f5c7b4cd983d4690cfb96534c",
            "tooltip": "Toolbar",
            "value": false
          }
        },
        "27d6e359081c4e84b07a5fc422172cbc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5493ed6833594b529b1dd229b70f6d09": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": "28px",
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": "0px 0px 0px 4px",
            "right": null,
            "top": null,
            "visibility": null,
            "width": "28px"
          }
        },
        "44085fd87c3143a0b2b700848f765beb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3648cfa1df90494699dab548a73f612a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": "28px",
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": "0px 0px 0px 4px",
            "right": null,
            "top": null,
            "visibility": null,
            "width": "28px"
          }
        },
        "8ed7d07f5c7b4cd983d4690cfb96534c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}