{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1WqwrHkQU30G"
   },
   "source": [
    "## 1.환경설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "gsty_JknJLEw",
    "outputId": "443c4d4e-cbda-44e4-c18d-d278439db457"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container {width:80% !important;}</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container {width:80% !important;}</style>\"))\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (12,8)  #시각화 figure default 설정\n",
    "mpl.rcParams['font.family'] = 'NanumGothic' #폰트 디폴트 설정\n",
    "mpl.rcParams['font.size'] = 10    #폰트 사이즈 디폴트 설정\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "%config InlineBackend.figure_format='retina' # 그래프 글씨 뚜렷\n",
    "pd.options.display.float_format = '{:.0f}'.format # 넘파이 과학적 표기 제거"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gCQwz0sASSzM"
   },
   "source": [
    "## 2.데이터 로드"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A3sUeWEiTxAr"
   },
   "source": [
    "### 2.1 경로 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e_KwkV6bToCq"
   },
   "source": [
    "### 2.2 로컬 파일 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "MQ1AwSQNSvqf"
   },
   "outputs": [],
   "source": [
    "df_shop = pd.read_csv(\"H:\\내 드라이브\\jeju\\제주도_상권정보.csv\", encoding = \"cp949\")\n",
    "df_facility = pd.read_csv(\"H:\\내 드라이브\\jeju\\제주도_관광편의시설현황_2022.csv\", encoding = \"cp949\")\n",
    "df_card = pd.read_csv(\"H:\\내 드라이브\\jeju\\제주도_유동인구_카드매출액_2018.csv\", encoding = \"cp949\")\n",
    "df_chinese = pd.read_csv(\"H:\\내 드라이브\\jeju\\중국인_업종별_이용금액_2018.csv\", encoding = \"cp949\")\n",
    "df_wifi = pd.read_csv(\"H:\\내 드라이브\\jeju\\외국인유동인구_와이파이이용_2018.csv\", encoding = \"cp949\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gOF9WnBcVEE6"
   },
   "source": [
    "## 3.데이터 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h76Bn2uVYnfp"
   },
   "source": [
    "### 3.1 제주도 유동인구 카드매출액(2018년)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "25SySCy-nDyw"
   },
   "source": [
    "#### 3.1.1 제주도 카드매출 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "cmhVlS8oZp7x"
   },
   "outputs": [],
   "source": [
    "# df_card[\"지역명\"] = \"제주특별자치도\" + \" \" + df_card[\"시도명\"] + \" \" + df_card[\"읍면동명\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "TNbTeORFVHeg"
   },
   "outputs": [],
   "source": [
    "df_card_new = df_card.groupby([\"읍면동명\", \"년월\", \"업종명\"])[[\"이용자수\", \"이용금액\", \"방문인구\"]].agg(\"sum\").reset_index().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "LI0P9QriVHcn",
    "outputId": "5a6fa061-c3c9-43a7-c3a9-7b3fd05f6e05"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>읍면동명</th>\n",
       "      <th>년월</th>\n",
       "      <th>업종명</th>\n",
       "      <th>이용자수</th>\n",
       "      <th>이용금액</th>\n",
       "      <th>방문인구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>건입동</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>과실 및 채소 소매업</td>\n",
       "      <td>65</td>\n",
       "      <td>7802800</td>\n",
       "      <td>4938897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>건입동</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>관광 민예품 및 선물용품 소매업</td>\n",
       "      <td>142</td>\n",
       "      <td>3569450</td>\n",
       "      <td>4938897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>건입동</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>기타 외국식 음식점업</td>\n",
       "      <td>226</td>\n",
       "      <td>7633360</td>\n",
       "      <td>4938897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>건입동</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>기타 주점업</td>\n",
       "      <td>109</td>\n",
       "      <td>22890000</td>\n",
       "      <td>4938897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>건입동</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>기타음식료품위주종합소매업</td>\n",
       "      <td>38</td>\n",
       "      <td>6516550</td>\n",
       "      <td>4938897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  읍면동명          년월                업종명  이용자수      이용금액    방문인구\n",
       "0  건입동  2018-01-01        과실 및 채소 소매업    65   7802800 4938897\n",
       "1  건입동  2018-01-01  관광 민예품 및 선물용품 소매업   142   3569450 4938897\n",
       "2  건입동  2018-01-01        기타 외국식 음식점업   226   7633360 4938897\n",
       "3  건입동  2018-01-01             기타 주점업   109  22890000 4938897\n",
       "4  건입동  2018-01-01      기타음식료품위주종합소매업    38   6516550 4938897"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_card_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "XHSsG5-ga_mV"
   },
   "outputs": [],
   "source": [
    "df_card_new[\"1인당지출\"] = df_card_new[\"이용금액\"] / df_card_new[\"이용자수\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "HNJY0EfsbASP"
   },
   "outputs": [],
   "source": [
    "df_card_new[\"년월\"] = df_card_new[\"년월\"].str[:-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qU2XpQQnmaB9"
   },
   "source": [
    "#### 3.1.2 제주도 지역별 지출 및 방문객 수 분포 및 구분"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "P0XfXXFUbAWI"
   },
   "outputs": [],
   "source": [
    "df_district = df_card_new.groupby(\"읍면동명\").sum().reset_index()\n",
    "df_district[\"1인당지출\"] = df_district[\"이용금액\"] / df_district[\"이용자수\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "WS1rN7gylw21",
    "outputId": "61c74bd9-e085-4a53-fd34-98413c66fa93"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>읍면동명</th>\n",
       "      <th>이용자수</th>\n",
       "      <th>이용금액</th>\n",
       "      <th>방문인구</th>\n",
       "      <th>1인당지출</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>송산동</td>\n",
       "      <td>2723773</td>\n",
       "      <td>100290745684</td>\n",
       "      <td>434374874</td>\n",
       "      <td>36821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   읍면동명     이용자수          이용금액      방문인구  1인당지출\n",
       "15  송산동  2723773  100290745684 434374874  36821"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_district.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rlTsJomGnxlc",
    "outputId": "59cfe3bb-d0b7-4128-c109-e3d4d7ebd14a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38448.68318692885"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11944813957 / 310669"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k8rTCRUijvBW",
    "outputId": "3d82e936-310d-4277-d120-7d6b17f10abb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "평균 1인당지출: 35733.6410177536\n",
      "평균 방문인구: 1807337969.3193033\n"
     ]
    }
   ],
   "source": [
    "## 평균 1인당지출\n",
    "mean_expenditure = np.mean(df_district['1인당지출'])\n",
    "print(\"평균 1인당지출:\", mean_expenditure)\n",
    "\n",
    "## 평균 방문인구\n",
    "mean_tourist = np.mean(df_district['방문인구'])\n",
    "print(\"평균 방문인구:\", mean_tourist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "kTzTPOzzo6f7"
   },
   "outputs": [],
   "source": [
    "## 평균 1인당지출과 평균 방문인구를 기준으로 High/Low 구분\n",
    "def expenditure_segmentation(expenditure, mean_expenditure):\n",
    "    if (expenditure >= mean_expenditure):\n",
    "        return \"높은 관광지출\"\n",
    "    elif (expenditure < mean_expenditure):\n",
    "        return \"낮은 관광지출\"\n",
    "    \n",
    "def tourist_segmentation(tourist, mean_tourist):\n",
    "    if (tourist >= mean_tourist):\n",
    "        return \"높은 방문객수\"\n",
    "    elif (tourist < mean_tourist):\n",
    "        return \"낮은 방문객수\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "SEovoW_tpdSP"
   },
   "outputs": [],
   "source": [
    "## 위에서 정의한 함수르 적용하여 지역 구분 필드 생성\n",
    "df_district[\"관광지출_구분\"] = df_district[\"1인당지출\"].apply(lambda x: expenditure_segmentation(x, mean_expenditure))\n",
    "df_district[\"방문객수_구분\"] = df_district[\"방문인구\"].apply(lambda x: tourist_segmentation(x, mean_tourist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "J7HH5A5Cp8kS",
    "outputId": "97ebbcf8-5102-46b0-e2ea-e19502bdbe09"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>읍면동명</th>\n",
       "      <th>이용자수</th>\n",
       "      <th>이용금액</th>\n",
       "      <th>방문인구</th>\n",
       "      <th>1인당지출</th>\n",
       "      <th>관광지출_구분</th>\n",
       "      <th>방문객수_구분</th>\n",
       "      <th>지역_구분</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>건입동</td>\n",
       "      <td>637606</td>\n",
       "      <td>22837472311</td>\n",
       "      <td>1516734675</td>\n",
       "      <td>35818</td>\n",
       "      <td>높은 관광지출</td>\n",
       "      <td>낮은 방문객수</td>\n",
       "      <td>높은 관광지출 &amp; 낮은 방문객수</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>구좌읍</td>\n",
       "      <td>801964</td>\n",
       "      <td>24672424016</td>\n",
       "      <td>1946041726</td>\n",
       "      <td>30765</td>\n",
       "      <td>낮은 관광지출</td>\n",
       "      <td>높은 방문객수</td>\n",
       "      <td>낮은 관광지출 &amp; 높은 방문객수</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>남원읍</td>\n",
       "      <td>617808</td>\n",
       "      <td>26116645201</td>\n",
       "      <td>1682710331</td>\n",
       "      <td>42273</td>\n",
       "      <td>높은 관광지출</td>\n",
       "      <td>낮은 방문객수</td>\n",
       "      <td>높은 관광지출 &amp; 낮은 방문객수</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  읍면동명    이용자수         이용금액       방문인구  1인당지출  관광지출_구분  방문객수_구분  \\\n",
       "0  건입동  637606  22837472311 1516734675  35818  높은 관광지출  낮은 방문객수   \n",
       "1  구좌읍  801964  24672424016 1946041726  30765  낮은 관광지출  높은 방문객수   \n",
       "2  남원읍  617808  26116645201 1682710331  42273  높은 관광지출  낮은 방문객수   \n",
       "\n",
       "               지역_구분  \n",
       "0  높은 관광지출 & 낮은 방문객수  \n",
       "1  낮은 관광지출 & 높은 방문객수  \n",
       "2  높은 관광지출 & 낮은 방문객수  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 2개의 구분 필드를 결합하여 \"지역_구분\" 필드로 통합\n",
    "df_district[\"지역_구분\"] = df_district[\"관광지출_구분\"] + \" & \" + df_district[\"방문객수_구분\"]\n",
    "df_district.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "S58nQV_P3FhN"
   },
   "outputs": [],
   "source": [
    "# df_district.to_csv(\"제주도_지역별_연간방문객수_1인당지출.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "bmLzwCU1rCmC",
    "outputId": "0ac6800a-2fce-4fc8-d179-823d7a9828c1"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>방문인구</th>\n",
       "      <th>1인당지출</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역_구분</th>\n",
       "      <th>읍면동명</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">낮은 관광지출 &amp; 낮은 방문객수</th>\n",
       "      <th>대정읍</th>\n",
       "      <td>1402450560</td>\n",
       "      <td>32647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대천동</th>\n",
       "      <td>1130720804</td>\n",
       "      <td>26864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동홍동</th>\n",
       "      <td>1516297911</td>\n",
       "      <td>32851</td>\n",
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       "    <tr>\n",
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       "      <td>1455991387</td>\n",
       "      <td>30550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>삼양동</th>\n",
       "      <td>983827359</td>\n",
       "      <td>25303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서홍동</th>\n",
       "      <td>635625243</td>\n",
       "      <td>22379</td>\n",
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       "    <tr>\n",
       "      <th>외도동</th>\n",
       "      <td>1060109452</td>\n",
       "      <td>26968</td>\n",
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       "    <tr>\n",
       "      <th>우도면</th>\n",
       "      <td>205980081</td>\n",
       "      <td>24304</td>\n",
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       "    <tr>\n",
       "      <th>이호동</th>\n",
       "      <td>586353829</td>\n",
       "      <td>28364</td>\n",
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       "    <tr>\n",
       "      <th>천지동</th>\n",
       "      <td>485510672</td>\n",
       "      <td>35223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>효돈동</th>\n",
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       "      <td>27601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">낮은 관광지출 &amp; 높은 방문객수</th>\n",
       "      <th>구좌읍</th>\n",
       "      <td>1946041726</td>\n",
       "      <td>30765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>노형동</th>\n",
       "      <td>6031387943</td>\n",
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       "    <tr>\n",
       "      <th>성산읍</th>\n",
       "      <td>2226897809</td>\n",
       "      <td>33860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>아라동</th>\n",
       "      <td>3103245604</td>\n",
       "      <td>21638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>안덕면</th>\n",
       "      <td>2314658633</td>\n",
       "      <td>33957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>애월읍</th>\n",
       "      <td>5597200967</td>\n",
       "      <td>34962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>오라동</th>\n",
       "      <td>2630533234</td>\n",
       "      <td>35075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>이도2동</th>\n",
       "      <td>6267381151</td>\n",
       "      <td>29791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>일도2동</th>\n",
       "      <td>2528784477</td>\n",
       "      <td>33756</td>\n",
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       "    <tr>\n",
       "      <th>조천읍</th>\n",
       "      <td>3087423560</td>\n",
       "      <td>32559</td>\n",
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       "    <tr>\n",
       "      <th>화북동</th>\n",
       "      <td>1904236034</td>\n",
       "      <td>27754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">높은 관광지출 &amp; 낮은 방문객수</th>\n",
       "      <th>건입동</th>\n",
       "      <td>1516734675</td>\n",
       "      <td>35818</td>\n",
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       "    <tr>\n",
       "      <th>남원읍</th>\n",
       "      <td>1682710331</td>\n",
       "      <td>42273</td>\n",
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       "    <tr>\n",
       "      <th>도두동</th>\n",
       "      <td>660535341</td>\n",
       "      <td>38354</td>\n",
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       "      <th>봉개동</th>\n",
       "      <td>658321867</td>\n",
       "      <td>38449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>삼도2동</th>\n",
       "      <td>1271362494</td>\n",
       "      <td>42609</td>\n",
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       "    <tr>\n",
       "      <th>송산동</th>\n",
       "      <td>434374874</td>\n",
       "      <td>36821</td>\n",
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       "      <th>영천동</th>\n",
       "      <td>555704091</td>\n",
       "      <td>55658</td>\n",
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       "    <tr>\n",
       "      <th>용담1동</th>\n",
       "      <td>925972239</td>\n",
       "      <td>39207</td>\n",
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       "    <tr>\n",
       "      <th>이도1동</th>\n",
       "      <td>1763670245</td>\n",
       "      <td>41653</td>\n",
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       "    <tr>\n",
       "      <th>일도1동</th>\n",
       "      <td>611658177</td>\n",
       "      <td>46376</td>\n",
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       "    <tr>\n",
       "      <th>정방동</th>\n",
       "      <td>927530860</td>\n",
       "      <td>37971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중문동</th>\n",
       "      <td>1301941336</td>\n",
       "      <td>40258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중앙동</th>\n",
       "      <td>1050344784</td>\n",
       "      <td>37971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>추자면</th>\n",
       "      <td>129501133</td>\n",
       "      <td>40235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>표선면</th>\n",
       "      <td>1490532873</td>\n",
       "      <td>38919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한경면</th>\n",
       "      <td>651516174</td>\n",
       "      <td>39160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">높은 관광지출 &amp; 높은 방문객수</th>\n",
       "      <th>대륜동</th>\n",
       "      <td>2032433025</td>\n",
       "      <td>37854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>연동</th>\n",
       "      <td>6238260113</td>\n",
       "      <td>47008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>예래동</th>\n",
       "      <td>1808471748</td>\n",
       "      <td>55906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>용담2동</th>\n",
       "      <td>2320021949</td>\n",
       "      <td>48561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한림읍</th>\n",
       "      <td>2343251776</td>\n",
       "      <td>36595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             방문인구  1인당지출\n",
       "지역_구분             읍면동명                  \n",
       "낮은 관광지출 & 낮은 방문객수 대정읍  1402450560  32647\n",
       "                  대천동  1130720804  26864\n",
       "                  동홍동  1516297911  32851\n",
       "                  삼도1동 1455991387  30550\n",
       "                  삼양동   983827359  25303\n",
       "                  서홍동   635625243  22379\n",
       "                  외도동  1060109452  26968\n",
       "                  우도면   205980081  24304\n",
       "                  이호동   586353829  28364\n",
       "                  천지동   485510672  35223\n",
       "                  효돈동   240024140  27601\n",
       "낮은 관광지출 & 높은 방문객수 구좌읍  1946041726  30765\n",
       "                  노형동  6031387943  31720\n",
       "                  성산읍  2226897809  33860\n",
       "                  아라동  3103245604  21638\n",
       "                  안덕면  2314658633  33957\n",
       "                  애월읍  5597200967  34962\n",
       "                  오라동  2630533234  35075\n",
       "                  이도2동 6267381151  29791\n",
       "                  일도2동 2528784477  33756\n",
       "                  조천읍  3087423560  32559\n",
       "                  화북동  1904236034  27754\n",
       "높은 관광지출 & 낮은 방문객수 건입동  1516734675  35818\n",
       "                  남원읍  1682710331  42273\n",
       "                  도두동   660535341  38354\n",
       "                  봉개동   658321867  38449\n",
       "                  삼도2동 1271362494  42609\n",
       "                  송산동   434374874  36821\n",
       "                  영천동   555704091  55658\n",
       "                  용담1동  925972239  39207\n",
       "                  이도1동 1763670245  41653\n",
       "                  일도1동  611658177  46376\n",
       "                  정방동   927530860  37971\n",
       "                  중문동  1301941336  40258\n",
       "                  중앙동  1050344784  37971\n",
       "                  추자면   129501133  40235\n",
       "                  표선면  1490532873  38919\n",
       "                  한경면   651516174  39160\n",
       "높은 관광지출 & 높은 방문객수 대륜동  2032433025  37854\n",
       "                  연동   6238260113  47008\n",
       "                  예래동  1808471748  55906\n",
       "                  용담2동 2320021949  48561\n",
       "                  한림읍  2343251776  36595"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## \"지역_구분\"을 기준으로 그룹화하여 소속된 읍면동명 확인\n",
    "df_district.groupby([\"지역_구분\", \"읍면동명\"])[[\"방문인구\", \"1인당지출\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 525
    },
    "id": "7GtdW14CbAaP",
    "outputId": "ebfbb1dd-db56-41bc-ded3-b217412c185c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JNCOYAgAAZrOjR2yf3Vq7aHtFrbUrk3xmxK5z0wm0Rj4+P9lmuqN3Hj9i14daaxsme91ZYmfW35qSVZm764X99YhdH2ut3TBG+cOr6rkZZ8TbGPcZrKpT0/n/5g4jDn0hnU+WPi83/v/r5kk+V1VfqKo77uz9AABgih2Z5JzW2sbtHPtFkttU1aIkaa2tTXJMkq+ms5bv6iTXJnlzkn9M52fiSX3oz1R+AADAbLb3iO3fj1M78vheowOjqprUp/663ppkfnd7c5LXT8E1d9avuvcebd905pGfiI9X3Sg7+m5rbXsLHI+cku+UqnrIdmq2Z/S0gjs1td8IL01y8IhrvGEHtc/d2YtX1ZIkT0zygiSHjzp8ZpIntNY2d2sfmeQ1SV6cPwZvxyX5y6o6K8kHk5w10YWpAQBgCu2Z5K5Vtb11W7f+7LosnRkItn6w75lV9ex0PnTVkvyutdaq6pQkF0+mGcEUAAAwm60csb3/OLUjj1831Y1U1T8kefCIXW9urf12Aqfet6pGjvIZSvLnrbWf7GIrD2itXbqd/l6bziccd8XyMfavzB+ntVue5E928fo7vR5TVd0/yUtG7Hp/a+38Xbz/6Gs/IMkz0lmXbPTXvimdRaL/b2ttW5jZWtuS5CVV9b/pTH+ydcHowXT+v3hwki1VdWxr7WtT0ScAAEzQmiRnJXnhGMeHW2vXjN7Z/Xl323pSVbUiyX0yyWnCBVMAAMBsdl4686Unyb2r6uDW2k1GTlXV8tw4NFpTVQ8dVTZesDWmqjopNx4ddUmSV07w9MEkS0ft22NXe5kil6fzy+tWPx6j7vVJ3pjkoOzaVPGrk5yR5Ls7c1JV3S3Jf6fz3iXJFemMVNqRd6fzyc6BJP93nNrnJHnUqH1DSf4zyataa2Mu9txa+2JVHZbkqekEZ7cacXh1ku+Pc28AAJhqFyQ5Ygo+yPWMdEZYfWwyFxFMAQAAs9mnkrwpyYJ01ol6W1U9vrW2aWtBVQ10a0YGTw/oPiatql6QTjizdQqMG5I8urW2foKX2JJk7YjXQ0lWTaKlPXdy//Y8v7X2ifGKWmsfTfLRnbjupHUXXf5w/hjmbUrn/b7JJzxH+Xhr7ctVNS/jB1OvTvLIdP6b/iLJJ9MZkTWhKUu6//+9u6rel+T47rWOSfK+7pz9AADQSx9L8qSqemxr7eO7coGq+sskr0ryxu5Uf7tMMAUAAMxarbXfV9Vb8scpKR6R5LtV9X+T/Dqd+dBfkM50E1OqqvZMZxTO40bs3pTOukM7Mw3f11trfzmJVkavz/SjUetDTeScGa/7fv9LkmeP2L0pyWNba9+Zynu11n5cVQ9Lcllr7aeTuM6WJJ/pPgAAoC9aa2dW1ceTfKSq7pXOtH5XJ7lZkhPS+RDWd5KkqvZO8uQkP0iyPskt0plN4KQkpyd5xWT7EUwBAACz3T8muUuSB3Zf3zVjj+L5UTpTx23PXyW5/URuWFVPzB+nsNtqXZJHtta+OJFrTKErduGcm0x3OFNV1fx0psV7dZIDRxxamU4IOC3vd2tNmAQAwGyzOZ0Pb23PielMWf1XSU5JZ1rsK5N8OyPWkUpnJoonJ3ltkoXpBFjnJHlYa+2zU9GkYAoAAJjVWmubq+oh6QQXL0iyaDtlw0nen+QFrbU12zmeqrpPxgmmqupB6Uxf8WejDl2Y5HFTMGf7TmutnVdVr0tybJJl45QPp7N+1P/Z2ftU1VHprI807nCsKfDfrbVHV9X+6aw/deio499J8sTW2qU96AUAAGaF1tqCHRwbSvLv3ceOrnF5bvr7zpQSTAEAALNed8q0l1bVW5M8PJ1RU/uks1bTBUnObK39ejL3qKo/T/K5Ubs3p/OL3ctba+smc/3JaK29JMlLpvk2y9ObUCpJVnSf1ySZP2L/ynRGyL2rtTY81TetqnfnjyPvptNQkoe01n7eg3sBAMCMIpgCAADmjNbaH5K8a5ouf3aS85PcKZ01mj6d5KWttQun6X69tDLJ19P5HbEluXQ7NVels27XwASvuSjJASNeX5GxpxUZ7YIkaa2tr6oXpxP+vSXJm1trqyd4jST5YpKl6XxNW6c8HE6ytrs/6YRfW90uySE7cf3JODCJYAoAgN2OYAoAAGACWmtDVfWMJE9I8u7ZEEhV1UCSY5LcP8kR6QRFy5JsSHJtOgHQ2Uk+31q7746u1Vr7WZJb78S975vkf0fselhr7dyJd7/tvh+pqtNba20Xzj1uO/uGM/aUh6t29h67aLiH9wIAgBlFMAUAAMwpVbUgyZLuy7Wttc1Tde3W2jnpLPy79V4HJDmo+/IP3RFbM0JVPT3JK5PcYgdlxyX5uyQ3VNW/J/nn1toNvehvZ4wMpbph288y/npa49mYzn/Lv9r6/0hr7RETPbmq5qUzleNWL2itvXmSPQEAwJwnmAIAAOaaTyc5vrv9sSSPn+B5bYztHXlfkgd3t7+d5D4TPG94jO1J6wY3H8nEv+6kE/K8JMnDq+r41tpvJ3CfOyT5cpLBJFuS3L+19otdaHlnDST5kym61q2TfDDJF6boegAAwDgEUwAAwFyzeMT20jGrbuoFSf61u33FjgpHGByxvTO/X52cP460mui9JuqluXEodVU64cv/S3JZkquT7Jnk4HRGTD01ye27tYcn+VRV/Vl3yrsdWZHkZiNe7zPpzidm9BpRk3XA+CUAAMBUEUwBAAAkaa1dkuSSHt3rsnRCoilVVfOT/O2IXRcnuVdr7ZpRpVd3Hz+qqn9NclqSp3SPHZnkYUk+NdX9TYVx1ogaV1XdOskvR+6adFMAAMCEDfS7AQAAAKbMrdIZybTVW7YTSt1Ia21LkuflxtMX3n0Kevldks8nOav7PFPW31rd7wYAAGB3ZsQUAADA3DF6baxNEzxvY5Kh/PF3xEmve9UdgfbgcQsBAIDdihFTAADAXNbLD+PNhA/+XZbk2hGv/6aq9pzAea/Njfv/zi7ce/4unAMAAOxmZsIvTgAAAFNp5KihY6vq97npSKKJ+GBr7aU7ca8jq+ry7Npoo4tba/fbhfNu3Exrm6vqrUle1d115yQXVNUHknwmneDqmnTWaDogyf2SPDnJPUdc5ntJPjeR2416/T9VtWqXm09e0Vr74CTOBwAAZgHBFAAAMNeMXFNpIMnNdvE6t97Je1WSg3bxXlP5u9k/JfnTJI/svj44yT92H+M5P8mjWmsTCddGr121Z/exq245iXMBAIBZwlR+AADAXPOWJJeks2bSZKyZQM1bk1yYZEsP7jUhrbWhJI9J8rdJrt6J+/9Tknu01n4/wftclOT9SVbvQpvbs3aKrtMro0eMTXpdLgAA2B1Ua7syowUAAAAzXVUtSPKAJPdNcniS/ZMsTbIhnbWoLkjy7SSfa62t71ObPVVV85O8PsnidMKlf2+t/WwXr3VU9zpJcn5rbdWUNAkAAHOYYAoAAAAAAICeMJUfAAAAAAAAPSGYAgAAAAAAoCcEUwAAAAAAAPSEYAoAAAAAAICeEEwBAAAAAADQE4IpAAAAAAAAekIwBQAAAAAAQE8IpgAAAAAAAOgJwRQAAAAAAAA9IZgCAAAAAACgJwRTAAAAAAAA9IRgCgAAAAAAgJ4QTAEAAAAAANAT/z8mXjAJUyHA8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 506,
       "width": 851
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 제주도 지역별 연간 관광객수와 1인당 지출을 기준으로 산점도 시각화 \n",
    "## 평균 지출액과 평균 방문객수를 기준으로 4개의 영역으로 구분\n",
    "plt.figure(figsize = (14, 8))\n",
    "sns.scatterplot(x = df_district[\"방문인구\"], y = df_district[\"1인당지출\"], hue = df_district[\"지역_구분\"])\n",
    "plt.axhline(y = mean_expenditure, linestyle = \"--\", color = \"#e63946\")\n",
    "plt.axvline(x = mean_tourist, linestyle = \"--\", color = \"#e63946\")\n",
    "plt.xlabel(\"연간 방문객수\", fontsize = 15, fontweight = \"semibold\")\n",
    "plt.ylabel(\"관광객 1인당지출\", fontsize = 15, fontweight = \"semibold\")\n",
    "plt.title(\"<제주도 지역별 연간 방문객수 & 1인당지출>\",fontsize = 20, fontweight = \"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EAmaiT1Mx5W7"
   },
   "source": [
    "#### 3.1.3 분포 지도시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "-itAwbvJ2wOm",
    "outputId": "18727fd7-0e28-48f5-f78d-c6865b653f36"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>이용자수</th>\n",
       "      <th>이용금액</th>\n",
       "      <th>방문인구</th>\n",
       "      <th>1인당지출</th>\n",
       "      <th>관광지출_구분</th>\n",
       "      <th>방문객수_구분</th>\n",
       "      <th>지역_구분</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>제주특별자치도 서귀포시 남원읍</td>\n",
       "      <td>617808</td>\n",
       "      <td>26116645201</td>\n",
       "      <td>1682710331</td>\n",
       "      <td>42273</td>\n",
       "      <td>높은 관광지출</td>\n",
       "      <td>낮은 방문객수</td>\n",
       "      <td>높은 관광지출 &amp; 낮은 방문객수</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>제주특별자치도 서귀포시 대륜동</td>\n",
       "      <td>878040</td>\n",
       "      <td>33236919978</td>\n",
       "      <td>2032433025</td>\n",
       "      <td>37854</td>\n",
       "      <td>높은 관광지출</td>\n",
       "      <td>높은 방문객수</td>\n",
       "      <td>높은 관광지출 &amp; 높은 방문객수</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                지역명    이용자수         이용금액       방문인구  1인당지출  관광지출_구분  방문객수_구분  \\\n",
       "0  제주특별자치도 서귀포시 남원읍  617808  26116645201 1682710331  42273  높은 관광지출  낮은 방문객수   \n",
       "1  제주특별자치도 서귀포시 대륜동  878040  33236919978 2032433025  37854  높은 관광지출  높은 방문객수   \n",
       "\n",
       "               지역_구분  \n",
       "0  높은 관광지출 & 낮은 방문객수  \n",
       "1  높은 관광지출 & 높은 방문객수  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_jeju_map = pd.read_csv(\"H:\\내 드라이브\\jeju\\제주도_지역별_연간방문객수_1인당지출.csv\", encoding = \"utf-8-sig\")\n",
    "df_jeju_map.head(2)\n",
    "# df_jeju_map = df_jeju_map[[\"지역명\", \"지역_구분\"]]\n",
    "# df_jeju_map.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "5a-rukZLx_3b"
   },
   "outputs": [],
   "source": [
    "import folium\n",
    "import requests\n",
    "import json\n",
    "import folium.plugins\n",
    "\n",
    "# 서울 행정구역 json raw파일(githubcontent)\n",
    "# r = requests.get('https://raw.githubusercontent.com/vuski/admdongkor/master/ver20230101/HangJeongDong_ver20230101.geojson')\n",
    "r = requests.get('https://raw.githubusercontent.com/raqoon886/Local_HangJeongDong/master/hangjeongdong_%EC%A0%9C%EC%A3%BC%ED%8A%B9%EB%B3%84%EC%9E%90%EC%B9%98%EB%8F%84.geojson')\n",
    "c = r.content\n",
    "jeju_geo = json.loads(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "UK-MW86Myimd"
   },
   "outputs": [],
   "source": [
    "# jeju_geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 788
    },
    "id": "OvAP3GmDx_1h",
    "outputId": "41d99b18-6d4d-410c-ad55-057aa72e9a0f"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "        \n",
       "    \n",
       "            var choropleth_95934537f43e5be732aa404dcdb3be8c = L.featureGroup(\n",
       "                {}\n",
       "            ).addTo(map_c18a0b802d94772a6d580c9c3f4e0131);\n",
       "        \n",
       "    \n",
       "        function geo_json_7b39687c6386fc248fa97077397de6b7_styler(feature) {\n",
       "            switch(feature.properties.OBJECTID) {\n",
       "                case 3396: case 3397: case 3404: case 3405: case 3410: case 3416: case 3418: case 3420: case 3429: case 3431: \n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#67a9cf&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "                case 3398: case 3399: case 3403: case 3406: case 3407: case 3421: case 3424: case 3435: \n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#fddbc7&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "                case 3400: case 3411: case 3413: case 3417: case 3432: case 3434: \n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#2166ac&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "                case 3401: case 3408: case 3415: \n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#ef8a62&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "                case 3430: case 3436: \n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#b2182b&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "                default:\n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#d1e5f0&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function geo_json_7b39687c6386fc248fa97077397de6b7_onEachFeature(feature, layer) {\n",
       "            layer.on({\n",
       "            });\n",
       "        };\n",
       "        var geo_json_7b39687c6386fc248fa97077397de6b7 = L.geoJson(null, {\n",
       "                onEachFeature: geo_json_7b39687c6386fc248fa97077397de6b7_onEachFeature,\n",
       "            \n",
       "                style: geo_json_7b39687c6386fc248fa97077397de6b7_styler,\n",
       "        });\n",
       "\n",
       "        function geo_json_7b39687c6386fc248fa97077397de6b7_add (data) {\n",
       "            geo_json_7b39687c6386fc248fa97077397de6b7\n",
       "                .addData(data)\n",
       "                .addTo(choropleth_95934537f43e5be732aa404dcdb3be8c);\n",
       "        }\n",
       "            geo_json_7b39687c6386fc248fa97077397de6b7_add({&quot;crs&quot;: {&quot;properties&quot;: {&quot;name&quot;: &quot;urn:ogc:def:crs:OGC:1.3:CRS84&quot;}, &quot;type&quot;: &quot;name&quot;}, &quot;features&quot;: [{&quot;geometry&quot;: {&quot;coordinates&quot;: [[[[126.23127933634665, 33.4047465707345], [126.22993897332888, 33.40412962446276], [126.22589028533956, 33.40408960161301], [126.22500509205072, 33.404219774806876], [126.22487574987228, 33.40424582326579], [126.2233554133589, 33.40497269590151], [126.22323427626428, 33.40503080086705], [126.22243703533582, 33.40548011869599], [126.22209085104105, 33.405681356174604], [126.22201762958439, 33.405787125027516], [126.22198203505886, 33.40588897944633], [126.22180012754559, 33.410023490935686], [126.22297921539035, 33.4116392738847], [126.22306894929268, 33.41170764721054], [126.2231436669505, 33.411749532546736], [126.22323999039423, 33.41179995293035], [126.22638191924455, 33.412573571240856], 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       "\n",
       "        \n",
       "    \n",
       "    var color_map_1b1e779080d769611aee8737f6b5de5f = {};\n",
       "\n",
       "    \n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.color = d3.scale.threshold()\n",
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       "    \n",
       "\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.x = d3.scale.linear()\n",
       "              .domain([21637.730821757454, 55906.07190535761])\n",
       "              .range([0, 400]);\n",
       "\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.legend = L.control({position: &#x27;topright&#x27;});\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.legend.onAdd = function (map) {var div = L.DomUtil.create(&#x27;div&#x27;, &#x27;legend&#x27;); return div};\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.legend.addTo(map_c18a0b802d94772a6d580c9c3f4e0131);\n",
       "\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.xAxis = d3.svg.axis()\n",
       "        .scale(color_map_1b1e779080d769611aee8737f6b5de5f.x)\n",
       "        .orient(&quot;top&quot;)\n",
       "        .tickSize(1)\n",
       "        .tickValues([21637.730821757454, 27349.12100235748, 33060.5111829575, 38771.90136355753, 44483.29154415756, 50194.68172475758, 55906.07190535761]);\n",
       "\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)\n",
       "        .attr(&quot;id&quot;, &#x27;legend&#x27;)\n",
       "        .attr(&quot;width&quot;, 450)\n",
       "        .attr(&quot;height&quot;, 40);\n",
       "\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.g = color_map_1b1e779080d769611aee8737f6b5de5f.svg.append(&quot;g&quot;)\n",
       "        .attr(&quot;class&quot;, &quot;key&quot;)\n",
       "        .attr(&quot;transform&quot;, &quot;translate(25,16)&quot;);\n",
       "\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.g.selectAll(&quot;rect&quot;)\n",
       "        .data(color_map_1b1e779080d769611aee8737f6b5de5f.color.range().map(function(d, i) {\n",
       "          return {\n",
       "            x0: i ? color_map_1b1e779080d769611aee8737f6b5de5f.x(color_map_1b1e779080d769611aee8737f6b5de5f.color.domain()[i - 1]) : color_map_1b1e779080d769611aee8737f6b5de5f.x.range()[0],\n",
       "            x1: i &lt; color_map_1b1e779080d769611aee8737f6b5de5f.color.domain().length ? color_map_1b1e779080d769611aee8737f6b5de5f.x(color_map_1b1e779080d769611aee8737f6b5de5f.color.domain()[i]) : color_map_1b1e779080d769611aee8737f6b5de5f.x.range()[1],\n",
       "            z: d\n",
       "          };\n",
       "        }))\n",
       "      .enter().append(&quot;rect&quot;)\n",
       "        .attr(&quot;height&quot;, 10)\n",
       "        .attr(&quot;x&quot;, function(d) { return d.x0; })\n",
       "        .attr(&quot;width&quot;, function(d) { return d.x1 - d.x0; })\n",
       "        .style(&quot;fill&quot;, function(d) { return d.z; });\n",
       "\n",
       "    color_map_1b1e779080d769611aee8737f6b5de5f.g.call(color_map_1b1e779080d769611aee8737f6b5de5f.xAxis).append(&quot;text&quot;)\n",
       "        .attr(&quot;class&quot;, &quot;caption&quot;)\n",
       "        .attr(&quot;y&quot;, 21)\n",
       "        .text(&#x27;1인당지출&#x27;);\n",
       "    \n",
       "            var marker_cluster_bb693854ba030698464bd39f30086284 = L.markerClusterGroup(\n",
       "                {}\n",
       "            );\n",
       "            map_c18a0b802d94772a6d580c9c3f4e0131.addLayer(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_f9f2a91159ba279b36ea093ac71f6d9a = L.marker(\n",
       "                [33.250877195134, 126.561556489379],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_eaa09983f56634494cc73efc3a7122fc = L.marker(\n",
       "                [33.248178993731, 126.559014683003],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_bbc03943431bb095ec072b14d38d322f = L.marker(\n",
       "                [33.412363326374, 126.264859998915],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_b608cdbae929d840f915e187480e4c9e = L.marker(\n",
       "                [33.4859779473805, 126.474887246863],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_6866d3638cd63ba4dfba1c602a29a0ac = L.marker(\n",
       "                [33.5113388449026, 126.517955691732],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_e349fe97f22d6f7d6c3ae79071439ef0 = L.marker(\n",
       "                [33.483334760175, 126.47989353095],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_4daa77837c2306aa3fbe73480cbf91a6 = L.marker(\n",
       "                [33.2501268524523, 126.559047095576],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_f772988450ba0d38a02dfc2da59a3c8c = L.marker(\n",
       "                [33.1665206924152, 126.274125717719],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_660b8a84773136377acc29f2341dd7ec = L.marker(\n",
       "                [33.5167365372118, 126.526797219834],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_ea6f819d97039959e43209bc14bcef58 = L.marker(\n",
       "                [33.5163881846419, 126.559265353291],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_537d4199c5648eba7460f601ec3bb77e = L.marker(\n",
       "                [33.4261143188778, 126.403305492273],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_33b35d0f524f1d1e769654a091b9e466 = L.marker(\n",
       "                [33.4890196429357, 126.476630813603],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_0c2ae73288a80980943bc2bd3c0b4737 = L.marker(\n",
       "                [33.5193396294864, 126.56732151746],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_8d43ba85e827e1a001ccdcde3f23c36c = L.marker(\n",
       "                [33.5373807203395, 126.638488980005],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_ab0ee40642a3d1a528a0d995dc8da309 = L.marker(\n",
       "                [33.4887525676203, 126.539817323759],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_726b1b8f583c30120219438b5f851004 = L.marker(\n",
       "                [33.5198240855272, 126.57101628156],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_433fa65b6ac1bad683b0de5479095793 = L.marker(\n",
       "                [33.5036970167776, 126.516740749881],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_522fc798ecd8565b1474b9f0d29155c0 = L.marker(\n",
       "                [33.2582900582595, 126.229163288622],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_badff05dd3343f2c4b7eaccb4ed2f8bf = L.marker(\n",
       "                [33.3460661071272, 126.179394698211],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_c287a7ced27a111edd06f3d92763a4a2 = L.marker(\n",
       "                [33.4832940002137, 126.480980914106],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_42d97f85842423e73c08714c4ca151d6 = L.marker(\n",
       "                [33.2593592175678, 126.559035038357],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_50687fd0b310a98145da26df51f177ec = L.marker(\n",
       "                [33.4146171180761, 126.9060689496],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_4cc4c9997354d77eb460024ceb3001c9 = L.marker(\n",
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       "            var marker_09de11972a48d58f01ecce8f55aa7423 = L.marker(\n",
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       "    \n",
       "            var marker_cc8dd94e2319d77a74b44b64d1ea8da0 = L.marker(\n",
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       "        \n",
       "    \n",
       "            var marker_ae9fa2415b160e08ac275071187df7e2 = L.marker(\n",
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       "    \n",
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       "            var marker_ef4a0a593ab6f5552c4e80af9b1ce5c2 = L.marker(\n",
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       "    \n",
       "            var marker_776d3e7831970e659359f2af9b295b5a = L.marker(\n",
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       "    \n",
       "            var marker_35428c111d2db6eb7adbaf4281e95248 = L.marker(\n",
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       "    \n",
       "            var marker_f924b0e2ebb376895e2c27374a53a04d = L.marker(\n",
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       "            var marker_ad0786674119064a519d7ec0af8829ff = L.marker(\n",
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       "        \n",
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       "        \n",
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       "    \n",
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       "            var marker_bce062aaa091f3f75dd975a97ac18fca = L.marker(\n",
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       "    \n",
       "            var marker_3e4a6c427d2c640ad69f8a9d76042a2d = L.marker(\n",
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       "        \n",
       "    \n",
       "            var marker_0779996e3fccff87964084bdee5f0939 = L.marker(\n",
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       "            var marker_32db68c55ff949811c90e96d23a8b432 = L.marker(\n",
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       "            var marker_078b3d9755551eb1d0ec0846ebddbd72 = L.marker(\n",
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       "            var marker_dd572534542a83535adefa8a0e50b471 = L.marker(\n",
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       "            var marker_2b27360b1d8627ba50f9ddc4886c2f57 = L.marker(\n",
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       "    \n",
       "            var marker_65898713b0a3819474ab8864b40bb442 = L.marker(\n",
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       "            var marker_958190e4f4dc7ec43b28360ac53229bb = L.marker(\n",
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       "            var marker_a21cdd7c8d53f517b20c215314839915 = L.marker(\n",
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       "            var marker_d23b52376b8bcef3df04ae803a333682 = L.marker(\n",
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       "    \n",
       "            var marker_50eb1fc5d55b601b82a1248192eb9769 = L.marker(\n",
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       "    \n",
       "            var marker_0b9c52c840eba757e314d56cba723bf3 = L.marker(\n",
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       "            var marker_2845cdb2dfaf51348bfc4139ed4b1cb0 = L.marker(\n",
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       "    \n",
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       "    \n",
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       "            var marker_95016de0350a8c53bac8c035bfdad086 = L.marker(\n",
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       "        \n",
       "    \n",
       "            var marker_140c4548b218932bd8b6ecc71c00ca32 = L.marker(\n",
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       "            var marker_b531ce5fc9823fb9a1facad0512ca215 = L.marker(\n",
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       "        \n",
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       "            var marker_d3326bf7b6c271326b8c7e4863cad4e1 = L.marker(\n",
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       "            var marker_92682113c1aa59ab60ced81058f2cdb8 = L.marker(\n",
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       "            var marker_b15c21aa9b0417238966330696224284 = L.marker(\n",
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       "        \n",
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       "            var marker_4729fa4cd63037227be015467f6e676c = L.marker(\n",
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       "            var marker_16b2a1d2ff596e4eff06e005caf51c13 = L.marker(\n",
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       "    \n",
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       "            var marker_f1f7a2a66844ebe05a0db339260812ea = L.marker(\n",
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       "    \n",
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       "    \n",
       "            var marker_529da131a2971098da4a5608cffb2d43 = L.marker(\n",
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       "            var marker_e03d5ce8f467ecd033aeb5dd9dedde4e = L.marker(\n",
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       "            var marker_aa552d77192e5720c73c891e2ebe51b0 = L.marker(\n",
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       "            var marker_d2e160a732b1461288b56d09559c56f2 = L.marker(\n",
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       "        \n",
       "    \n",
       "            var marker_25aa7781ffe5af6be8d034f445c0755b = L.marker(\n",
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       "    \n",
       "            var marker_4c9b2b058a0b56ae738fcdff5020f48a = L.marker(\n",
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       "        \n",
       "    \n",
       "            var marker_8619da3b3af75e55abdc55da1af37927 = L.marker(\n",
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       "    \n",
       "            var marker_69a97806a96068adde6a2c07ed06ce60 = L.marker(\n",
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       "            var marker_fe952b673c1ca368e23d27469ebc8093 = L.marker(\n",
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       "            var marker_13d14778d6b108b4b2773eb8284d7213 = L.marker(\n",
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       "            var marker_5a67ca9412b7413d0996fd9e90a472cd = L.marker(\n",
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       "    \n",
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       "    \n",
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       "            var marker_4395acd4c70bb89394348fcb955e6d3d = L.marker(\n",
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       "            var marker_2652b61144693c00e871b35a3dedd5d8 = L.marker(\n",
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       "            var marker_60e489b857993652c66b0bce83aee576 = L.marker(\n",
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       "    \n",
       "            var marker_96cdb19d539e8790363ec290e6cc1af6 = L.marker(\n",
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       "            var marker_2127fa8252118da4cd91a8c23e5addcb = L.marker(\n",
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       "            var marker_5dbb46d616597f8216a9defd2e6ff275 = L.marker(\n",
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       "            var marker_a23508b0ac5cbeadcda566d9298dbd94 = L.marker(\n",
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       "            var marker_211e6fb270e0017b427eb168761ba6b1 = L.marker(\n",
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       "    \n",
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       "            var marker_66e9b880e6e81f05bff51d7c20cb3959 = L.marker(\n",
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       "    \n",
       "            var marker_d2b72356bac275361d698f99635c2d5b = L.marker(\n",
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       "    \n",
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       "        \n",
       "    \n",
       "            var marker_8fb2d5a511f7aea8be271d35a800189e = L.marker(\n",
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       "            var marker_2767bc78efad33c2bb51fa8f85439a62 = L.marker(\n",
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       "        \n",
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       "        \n",
       "    \n",
       "            var marker_128e31be65ade221bb596abdc062fa67 = L.marker(\n",
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       "            var marker_eaff61608c9f24200803e07fb2c18ddd = L.marker(\n",
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       "        \n",
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       "                {}\n",
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       "    \n",
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       "    \n",
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       "    \n",
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       "            var marker_cf0c3f1e3cc13f10c8c32d4faf962885 = L.marker(\n",
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       "            var marker_2e6fbd0f2ab864dadcd14ce41afc7081 = L.marker(\n",
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       "    \n",
       "            var marker_c45cdf8e1736d0bb286765016f2c4cd2 = L.marker(\n",
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       "            var marker_9b1c801eb3ff3eddf756c1814607c3d0 = L.marker(\n",
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       "    \n",
       "            var marker_aa0682e6cbfeb58835e4722d99c2fd49 = L.marker(\n",
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       "            var marker_a3be6aa0b93de3efd5415b9990c405b6 = L.marker(\n",
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       "            var marker_19294fab924e8e3928c775a59aab6179 = L.marker(\n",
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       "            var marker_aecd7476325aca7c26ff60f39e2a3870 = L.marker(\n",
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       "    \n",
       "            var marker_14d98a91fe9a03bf81a8834278310743 = L.marker(\n",
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       "    \n",
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       "            var marker_94c96123396e618d3542a59a50b67c70 = L.marker(\n",
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       "    \n",
       "            var marker_055b2d1708c8938ad86f6e80417d8fe3 = L.marker(\n",
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       "            var marker_8b8ffab66d3c4334469a921fcd460e20 = L.marker(\n",
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       "    \n",
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       "            var marker_726e0aa299c570f2850929c867a608b6 = L.marker(\n",
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       "            var marker_cb588d32f1e04eb51f3012d7c2b78213 = L.marker(\n",
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       "    \n",
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       "    \n",
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       "    \n",
       "            var marker_dc25f1ce77a4a43069003030c27cef5d = L.marker(\n",
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       "    \n",
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       "            var marker_98550f3db1fcfd9d680aa43f6a301151 = L.marker(\n",
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       "            var marker_c185a9de45cfbac548ec04d8e021dd5a = L.marker(\n",
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       "    \n",
       "            var marker_f1481bc40095441a9a24150d6ab04a6b = L.marker(\n",
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       "    \n",
       "            var marker_0482ca6de53730a302a44ed0db08dc3d = L.marker(\n",
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       "            var marker_20d14bf56614314e18091e364dd0c79b = L.marker(\n",
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       "            var marker_d5c2d1895db4ccc53fdc69bac806a952 = L.marker(\n",
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       "            var marker_7495c1ec38a4d6b0577c4536ca9e4c3a = L.marker(\n",
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       "        \n",
       "    \n",
       "            var marker_180c9c3bbfe82b68d1d2365c791527c9 = L.marker(\n",
       "                [33.3018201601756, 126.320294234453],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_4655631f121ed40186e29bacfcf05c72 = L.marker(\n",
       "                [33.3146150305378, 126.273725348477],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_679cb68f8ef5eddfc9fc64161a75f811 = L.marker(\n",
       "                [33.2502707778176, 126.424473886322],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_57726e5106a0d5184c3118bd2f069ac5 = L.marker(\n",
       "                [33.4889806867804, 126.489650888473],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_bce006ad6fd67dc00631b8f02b28222a = L.marker(\n",
       "                [33.5120376274377, 126.531967659622],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_fa9a7e2ff7b0f50dd442167115796a58 = L.marker(\n",
       "                [33.2868264513273, 126.32826756373],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_3842b7d1c8facade9fc68c2b8a8f2010 = L.marker(\n",
       "                [33.5064230370162, 126.523807963858],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_8d811a4638878c52fe359be0efd713b5 = L.marker(\n",
       "                [33.4901849920385, 126.48933945947],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_d0471e973d006921d01eaf3db322a1e4 = L.marker(\n",
       "                [33.2519011969992, 126.424358779455],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_151c98a160aad0effaa33f3b55dcbd73 = L.marker(\n",
       "                [33.2640714402118, 126.61427215467],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_4b75f883a24dea2ce64771925670014d = L.marker(\n",
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       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_bea84d6327629a98c67fda2bbfe3eece = L.marker(\n",
       "                [33.4862580492588, 126.533188241842],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_c37bd0376bfd961c50c0bd132b51ecdb = L.marker(\n",
       "                [33.4900093023909, 126.534771281288],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_823bd543f6169c78295d14b17617350b = L.marker(\n",
       "                [33.4878708561841, 126.478523114224],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_a003ecdf83cfaa00d299ec848bbe03fb = L.marker(\n",
       "                [33.4874945890683, 126.432424851901],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "    \n",
       "            var marker_190a9b232e65adfadad47bd472cc1c37 = L.marker(\n",
       "                [33.5552002191202, 126.79455670203],\n",
       "                {}\n",
       "            ).addTo(marker_cluster_bb693854ba030698464bd39f30086284);\n",
       "        \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x18572a3ad30>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = folium.Map(\n",
    "    location=[33.361417, 126.529417],\n",
    "    width=750, height=500,\n",
    "    zoom_start=10, \n",
    "    tiles='cartodbpositron',\n",
    "    min_zoom=10,\n",
    "    max_zoom=10, \n",
    "    zoom_control = False\n",
    ")\n",
    "\n",
    "folium.GeoJson(\n",
    "    jeju_geo,\n",
    "    name='지역명'\n",
    ").add_to(m)\n",
    "\n",
    "folium.Choropleth(\n",
    "            geo_data= jeju_geo,\n",
    "            data=df_jeju_map,\n",
    "            columns = [\"지역명\", \"1인당지출\"],\n",
    "            fill_color='RdBu_r',\n",
    "            line_color=\"Black\", \n",
    "            fill_opacity=0.8,\n",
    "            line_opacity=1.0,\n",
    "            key_on='properties.adm_nm',\n",
    "            legend_name='1인당지출').add_to(m) \n",
    "\n",
    "locations = list(zip(df_shop[\"위도\"], df_shop[\"경도\"]))[:20000]\n",
    "\n",
    "\n",
    "cluster = folium.plugins.MarkerCluster(locations=locations)\n",
    "m.add_child(cluster)\n",
    "\n",
    "# for i in range(len(locations[:10000])):\n",
    "#     folium.CircleMarker(location=locations[i],radius=1).add_to(m)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 788
    },
    "id": "kq8F-m__CuSL",
    "outputId": "377488fe-e141-4f9d-b3a7-649518e3aefc"
   },
   "outputs": [
    {
     "data": {
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       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
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       "    \n",
       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
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       "                    position: relative;\n",
       "                    width: 750.0px;\n",
       "                    height: 500.0px;\n",
       "                    left: 0.0%;\n",
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       "            &lt;/style&gt;\n",
       "        \n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js&quot;&gt;&lt;/script&gt;\n",
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       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_cdce8660bc3dbce3bde52bf4a7ac0020&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;    \n",
       "    \n",
       "            var map_cdce8660bc3dbce3bde52bf4a7ac0020 = L.map(\n",
       "                &quot;map_cdce8660bc3dbce3bde52bf4a7ac0020&quot;,\n",
       "                {\n",
       "                    center: [33.361417, 126.529417],\n",
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       "\n",
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       "                &quot;https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;\\u0026copy; \\u003ca href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors \\u0026copy; \\u003ca href=\\&quot;http://cartodb.com/attributions\\&quot;\\u003eCartoDB\\u003c/a\\u003e, CartoDB \\u003ca href =\\&quot;http://cartodb.com/attributions\\&quot;\\u003eattributions\\u003c/a\\u003e&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 10, &quot;maxZoom&quot;: 10, &quot;minZoom&quot;: 10, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_cdce8660bc3dbce3bde52bf4a7ac0020);\n",
       "        \n",
       "    \n",
       "\n",
       "        function geo_json_ba148c88319979dd2f3732aa6f1cefce_onEachFeature(feature, layer) {\n",
       "            layer.on({\n",
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       "            geo_json_ba148c88319979dd2f3732aa6f1cefce\n",
       "                .addData(data)\n",
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       "\n",
       "        \n",
       "    \n",
       "            var choropleth_8aff55704cd368c16afbf80c534d753f = L.featureGroup(\n",
       "                {}\n",
       "            ).addTo(map_cdce8660bc3dbce3bde52bf4a7ac0020);\n",
       "        \n",
       "    \n",
       "        function geo_json_5c6f400c0481964d262462e39d45c7c6_styler(feature) {\n",
       "            switch(feature.properties.OBJECTID) {\n",
       "                case 3394: case 3397: case 3402: case 3408: case 3413: case 3414: case 3422: case 3423: \n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#d1e5f0&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "                case 3395: case 3404: case 3415: case 3416: \n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#b2182b&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "                case 3396: case 3403: case 3405: case 3406: case 3409: case 3410: case 3420: case 3421: case 3424: case 3431: case 3433: case 3435: case 3436: \n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#67a9cf&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "                default:\n",
       "                    return {&quot;color&quot;: &quot;Black&quot;, &quot;fillColor&quot;: &quot;#2166ac&quot;, &quot;fillOpacity&quot;: 0.8, &quot;opacity&quot;: 1.0, &quot;weight&quot;: 1};\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function geo_json_5c6f400c0481964d262462e39d45c7c6_onEachFeature(feature, layer) {\n",
       "            layer.on({\n",
       "            });\n",
       "        };\n",
       "        var geo_json_5c6f400c0481964d262462e39d45c7c6 = L.geoJson(null, {\n",
       "                onEachFeature: geo_json_5c6f400c0481964d262462e39d45c7c6_onEachFeature,\n",
       "            \n",
       "                style: geo_json_5c6f400c0481964d262462e39d45c7c6_styler,\n",
       "        });\n",
       "\n",
       "        function geo_json_5c6f400c0481964d262462e39d45c7c6_add (data) {\n",
       "            geo_json_5c6f400c0481964d262462e39d45c7c6\n",
       "                .addData(data)\n",
       "                .addTo(choropleth_8aff55704cd368c16afbf80c534d753f);\n",
       "        }\n",
       "            geo_json_5c6f400c0481964d262462e39d45c7c6_add({&quot;crs&quot;: {&quot;properties&quot;: {&quot;name&quot;: &quot;urn:ogc:def:crs:OGC:1.3:CRS84&quot;}, &quot;type&quot;: &quot;name&quot;}, &quot;features&quot;: [{&quot;geometry&quot;: {&quot;coordinates&quot;: [[[[126.23127933634665, 33.4047465707345], [126.22993897332888, 33.40412962446276], [126.22589028533956, 33.40408960161301], [126.22500509205072, 33.404219774806876], [126.22487574987228, 33.40424582326579], [126.2233554133589, 33.40497269590151], [126.22323427626428, 33.40503080086705], [126.22243703533582, 33.40548011869599], [126.22209085104105, 33.405681356174604], [126.22201762958439, 33.405787125027516], [126.22198203505886, 33.40588897944633], [126.22180012754559, 33.410023490935686], [126.22297921539035, 33.4116392738847], [126.22306894929268, 33.41170764721054], [126.2231436669505, 33.411749532546736], [126.22323999039423, 33.41179995293035], [126.22638191924455, 33.412573571240856], 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       "\n",
       "        \n",
       "    \n",
       "    var color_map_774c352e2ddd7fc7554a84c73df0e1b5 = {};\n",
       "\n",
       "    \n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.color = d3.scale.threshold()\n",
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       "    \n",
       "\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.x = d3.scale.linear()\n",
       "              .domain([129501133.077, 6267381150.667])\n",
       "              .range([0, 400]);\n",
       "\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.legend = L.control({position: &#x27;topright&#x27;});\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.legend.onAdd = function (map) {var div = L.DomUtil.create(&#x27;div&#x27;, &#x27;legend&#x27;); return div};\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.legend.addTo(map_cdce8660bc3dbce3bde52bf4a7ac0020);\n",
       "\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.xAxis = d3.svg.axis()\n",
       "        .scale(color_map_774c352e2ddd7fc7554a84c73df0e1b5.x)\n",
       "        .orient(&quot;top&quot;)\n",
       "        .tickSize(1)\n",
       "        .tickValues([129501133.077, 1152481136.0086668, 2175461138.9403334, 3198441141.872, 4221421144.803667, 5244401147.735333, 6267381150.667]);\n",
       "\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.svg = d3.select(&quot;.legend.leaflet-control&quot;).append(&quot;svg&quot;)\n",
       "        .attr(&quot;id&quot;, &#x27;legend&#x27;)\n",
       "        .attr(&quot;width&quot;, 450)\n",
       "        .attr(&quot;height&quot;, 40);\n",
       "\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.g = color_map_774c352e2ddd7fc7554a84c73df0e1b5.svg.append(&quot;g&quot;)\n",
       "        .attr(&quot;class&quot;, &quot;key&quot;)\n",
       "        .attr(&quot;transform&quot;, &quot;translate(25,16)&quot;);\n",
       "\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.g.selectAll(&quot;rect&quot;)\n",
       "        .data(color_map_774c352e2ddd7fc7554a84c73df0e1b5.color.range().map(function(d, i) {\n",
       "          return {\n",
       "            x0: i ? color_map_774c352e2ddd7fc7554a84c73df0e1b5.x(color_map_774c352e2ddd7fc7554a84c73df0e1b5.color.domain()[i - 1]) : color_map_774c352e2ddd7fc7554a84c73df0e1b5.x.range()[0],\n",
       "            x1: i &lt; color_map_774c352e2ddd7fc7554a84c73df0e1b5.color.domain().length ? color_map_774c352e2ddd7fc7554a84c73df0e1b5.x(color_map_774c352e2ddd7fc7554a84c73df0e1b5.color.domain()[i]) : color_map_774c352e2ddd7fc7554a84c73df0e1b5.x.range()[1],\n",
       "            z: d\n",
       "          };\n",
       "        }))\n",
       "      .enter().append(&quot;rect&quot;)\n",
       "        .attr(&quot;height&quot;, 10)\n",
       "        .attr(&quot;x&quot;, function(d) { return d.x0; })\n",
       "        .attr(&quot;width&quot;, function(d) { return d.x1 - d.x0; })\n",
       "        .style(&quot;fill&quot;, function(d) { return d.z; });\n",
       "\n",
       "    color_map_774c352e2ddd7fc7554a84c73df0e1b5.g.call(color_map_774c352e2ddd7fc7554a84c73df0e1b5.xAxis).append(&quot;text&quot;)\n",
       "        .attr(&quot;class&quot;, &quot;caption&quot;)\n",
       "        .attr(&quot;y&quot;, 21)\n",
       "        .text(&#x27;연간방문객수&#x27;);\n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1856b2a31f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = folium.Map(\n",
    "    location=[33.361417, 126.529417],\n",
    "    width=750, height=500,\n",
    "    zoom_start=10, \n",
    "    tiles='cartodbpositron',\n",
    "    min_zoom=10,\n",
    "    max_zoom=10, \n",
    "    zoom_control = False\n",
    ")\n",
    "\n",
    "folium.GeoJson(\n",
    "    jeju_geo,\n",
    "    name='지역명'\n",
    ").add_to(m)\n",
    "\n",
    "folium.Choropleth(\n",
    "            geo_data= jeju_geo,\n",
    "            name = \"지역별 연간방문객수\",\n",
    "            data=df_jeju_map,\n",
    "            columns = [\"지역명\", \"방문인구\"],\n",
    "            fill_color='RdBu_r', \n",
    "            line_color=\"Black\", \n",
    "            fill_opacity=0.8,\n",
    "            line_opacity=1.0,\n",
    "            key_on='properties.adm_nm',\n",
    "            legend_name='연간방문객수').add_to(m)\n",
    "m "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "OCeq3etBx_zd"
   },
   "outputs": [],
   "source": [
    "# from folium.features import DivIcon\n",
    "# import folium.plugins\n",
    "\n",
    "# m = folium.plugins.DualMap(\n",
    "#     location=[33.361417, 126.529417],\n",
    "#     zoom_start=10, \n",
    "#     tiles='cartodbpositron',\n",
    "#     min_zoom=10,\n",
    "#     max_zoom=10, \n",
    "#     zoom_control = False\n",
    "# )\n",
    "\n",
    "# folium.GeoJson(\n",
    "#     jeju_geo,\n",
    "#     name='지역명'\n",
    "# ).add_to(m)\n",
    "\n",
    "# # 왼쪽 지도 - 지역별 방문객 1인당 지출\"\n",
    "# choropleth1 = folium.Choropleth(\n",
    "#             geo_data= jeju_geo,\n",
    "#             name = \"지역별 방문객 1인당 지출\",\n",
    "#             data=df_jeju_map,\n",
    "#             columns = [\"지역명\", \"1인당지출\"],\n",
    "#             fill_color='RdBu', \n",
    "#             fill_opacity=0.8,\n",
    "#             line_opacity=0.2,\n",
    "#             key_on='properties.adm_nm',\n",
    "#             legend_name='1인당지출').add_to(m.m1) \n",
    "\n",
    "# # 오른쪽 지도 - 지역별 연간방문객수\n",
    "\n",
    "# choropleth2 = folium.Choropleth(\n",
    "#             geo_data= jeju_geo,\n",
    "#             name = \"지역별 연간방문객수\",\n",
    "#             data=df_jeju_map,\n",
    "#             columns = [\"지역명\", \"방문인구\"],\n",
    "#             fill_color='RdBu', \n",
    "#             fill_opacity=0.8,\n",
    "#             line_opacity=0.2,\n",
    "#             key_on='properties.adm_nm',\n",
    "#             legend_name='연간방문객수').add_to(m.m2) \n",
    "\n",
    "# m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OUClzAjsLVPR"
   },
   "source": [
    "### 3.2 제주도 상권 / 관광시설 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "Njtl6gI3T8sL"
   },
   "outputs": [],
   "source": [
    "# df_facility.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "TykB83_EHFWE"
   },
   "outputs": [],
   "source": [
    "# shop_list = ['숙박', '음식','스포츠', '관광/여가/오락']\n",
    "# df_shop[df_shop[\"상권업종대분류명\"].isin(shop_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "mZU2agtbHJ_r"
   },
   "outputs": [],
   "source": [
    "# df_shop = df_shop.groupby([\"상권업종대분류명\", \"상권업종중분류명\", \"상권업종소분류명\"]).mean()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "FV-PHzSNJOc8"
   },
   "outputs": [],
   "source": [
    "# print(df_shop[\"상권업종중분류명\"].unique())\n",
    "# print()\n",
    "# print(df_shop[\"상권업종소분류명\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MmoM53WyJk-X"
   },
   "outputs": [],
   "source": []
  }
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