批量提取不同距离缓冲区内不同栅格值的数量.ipynb

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    "# 导入相关的类库\n",
    "import rioxarray\n",
    "import geopandas as gpd\n",
    "import shapely.geometry\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9dc84f94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取土地利用类型栅格文件\n",
    "file_path = r'D:\\download\\GIS数据\\汕头市gis文件\\汕头市土地利用类型_10m\\汕头市土地利用_Sentinel-2.tif'\n",
    "land_use = rioxarray.open_rasterio(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a307b3bf",
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       "    add_offset:              0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 6729</li><li><span class='xr-has-index'>x</span>: 11005</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-516d0ed8-3713-416e-bbce-1f314c32fc4d' class='xr-array-in' type='checkbox' checked><label for='section-516d0ed8-3713-416e-bbce-1f314c32fc4d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[74052645 values with dtype=uint8]</pre></div></div></li><li class='xr-section-item'><input id='section-e773d67a-e510-407b-9d84-c90b13fb587b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e773d67a-e510-407b-9d84-c90b13fb587b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-93a3b74f-cf7e-4197-b28a-15852f376435' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93a3b74f-cf7e-4197-b28a-15852f376435' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ac24b85-4374-4f5b-bc30-3077094eef9a' class='xr-var-data-in' type='checkbox'><label for='data-7ac24b85-4374-4f5b-bc30-3077094eef9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.226e+05 4.226e+05 ... 5.326e+05</div><input id='attrs-69f72d4a-932e-42f6-ab02-002ed673b2f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69f72d4a-932e-42f6-ab02-002ed673b2f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d06ebad1-725c-4d51-89e6-8d9fb1d0bb62' class='xr-var-data-in' type='checkbox'><label for='data-d06ebad1-725c-4d51-89e6-8d9fb1d0bb62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([422601.855431, 422611.855431, 422621.855431, ..., 532621.855431,\n",
       "       532631.855431, 532641.855431])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.615e+06 2.615e+06 ... 2.548e+06</div><input id='attrs-b1c6c161-fd7f-4344-aaef-add1bb8512cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1c6c161-fd7f-4344-aaef-add1bb8512cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9e4bcab-0b01-4b92-8cdf-56899f19306d' class='xr-var-data-in' type='checkbox'><label for='data-b9e4bcab-0b01-4b92-8cdf-56899f19306d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2615209.502328, 2615199.502328, 2615189.502328, ..., 2547949.502328,\n",
       "       2547939.502328, 2547929.502328])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-d220b95f-513b-4f73-b36a-3459063157e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d220b95f-513b-4f73-b36a-3459063157e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee075957-8c60-463e-8f06-3a0161554c7a' class='xr-var-data-in' type='checkbox'><label for='data-ee075957-8c60-463e-8f06-3a0161554c7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS_1984_UTM_Zone_50N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;World Geodetic System 1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,117],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS_1984_UTM_Zone_50N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>117.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS_1984_UTM_Zone_50N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;World Geodetic System 1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,117],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>422596.8554313086 10.0 0.0 2615214.5023276755 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7dc3b783-3762-405b-ab28-a2deca545cfc' class='xr-section-summary-in' type='checkbox'  ><label for='section-7dc3b783-3762-405b-ab28-a2deca545cfc' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>RepresentationType :</span></dt><dd>THEMATIC</dd><dt><span>STATISTICS_COVARIANCES :</span></dt><dd>9.375565249265343</dd><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>11</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>3.4734564073907</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>1</dd><dt><span>STATISTICS_SKIPFACTORX :</span></dt><dd>1</dd><dt><span>STATISTICS_SKIPFACTORY :</span></dt><dd>1</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>3.0619544819062</dd><dt><span>_FillValue :</span></dt><dd>0.0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (band: 1, y: 6729, x: 11005)>\n",
       "[74052645 values with dtype=uint8]\n",
       "Coordinates:\n",
       "  * band         (band) int32 1\n",
       "  * x            (x) float64 4.226e+05 4.226e+05 ... 5.326e+05 5.326e+05\n",
       "  * y            (y) float64 2.615e+06 2.615e+06 ... 2.548e+06 2.548e+06\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    RepresentationType:      THEMATIC\n",
       "    STATISTICS_COVARIANCES:  9.375565249265343\n",
       "    STATISTICS_MAXIMUM:      11\n",
       "    STATISTICS_MEAN:         3.4734564073907\n",
       "    STATISTICS_MINIMUM:      1\n",
       "    STATISTICS_SKIPFACTORX:  1\n",
       "    STATISTICS_SKIPFACTORY:  1\n",
       "    STATISTICS_STDDEV:       3.0619544819062\n",
       "    _FillValue:              0.0\n",
       "    scale_factor:            1.0\n",
       "    add_offset:              0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看土地利用栅格数据\n",
    "land_use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "97462b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x25595cef160>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 土地利用类型栅格可视化\n",
    "land_use.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7a61227",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读入采样点矢量数据\n",
    "points = gpd.read_file(\"采样点\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d850d15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 采样点可视化\n",
    "points.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf5f6d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将采样点SHP文件的坐标系转换为土地利用类型栅格文件的地理坐标系\n",
    "points_converted = points.to_crs(land_use.rio.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a9773dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: PROJCS[\"WGS_1984_UTM_Zone_50N\",GEOGCS[\"WGS 84\",DAT ...>\n",
       "Name: WGS_1984_UTM_Zone_50N\n",
       "Axis Info [cartesian]:\n",
       "- [east]: Easting (metre)\n",
       "- [north]: Northing (metre)\n",
       "Area of Use:\n",
       "- undefined\n",
       "Coordinate Operation:\n",
       "- name: UTM zone 50N\n",
       "- method: Transverse Mercator\n",
       "Datum: World Geodetic System 1984\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看坐标系\n",
    "points_converted.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4733a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立缓冲区\n",
    "points_converted[\"buffer_1\"] = points_converted.apply(lambda r: r[\"geometry\"].buffer(500), axis=1) # 500m\n",
    "points_converted[\"buffer_2\"] = points_converted.apply(lambda r: r[\"geometry\"].buffer(1000), axis=1) # 1000m\n",
    "points_converted[\"buffer_3\"] = points_converted.apply(lambda r: r[\"geometry\"].buffer(2000), axis=1) # 2000m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf3769b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      POLYGON ((458106.3507007839 2589628.526419907,...\n",
       "1      POLYGON ((460552.1899543617 2588330.058934925,...\n",
       "2      POLYGON ((460500.4367036904 2585409.948763818,...\n",
       "3      POLYGON ((461609.6775125783 2585255.535851272,...\n",
       "4      POLYGON ((462155.6817973576 2586853.719617908,...\n",
       "                             ...                        \n",
       "137    POLYGON ((504193.988260004 2592594.947332415, ...\n",
       "138    POLYGON ((509944.3225133607 2593860.995968501,...\n",
       "139    POLYGON ((511380.4674203126 2596178.999431872,...\n",
       "140    POLYGON ((513662.3927654709 2593253.694174197,...\n",
       "141    POLYGON ((510958.5115225642 2588897.225873868,...\n",
       "Name: buffer_1, Length: 142, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看缓冲区\n",
    "points_converted[\"buffer_1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dc288c43",
   "metadata": {},
   "outputs": [],
   "source": [
    "points_copy = points_converted.copy() # 生成采样点矢量的副本，便于可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0335c657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w4GdbOT+pRytFrLzlrOHdWZkQg80G3aKqDoo6c1h3PvrDJNJyizljaDcvRajaKk30Xja5XyfmrbeXKpjoMoPQvWedhL9FqLAa7jp9YI3jXEc/uo6KVL6trlGv4xP1Tt7bDmUXsT+rkAmJnQj0bzsNJproveyiMfH06RRKTlE5MwZVzhoUGxbIPy8cXutxF4+Jp6isgv1ZRfxuckIrRKqUqsuW1FwuenkFZVYbMwZ15o1rx3s7JCdN9G3A2ISG34n5+Qm/m5LYAtGo1nKii6ROXuIblqVkUGa1N8Mu2ZVBhdXm0eCo0gorR3NL6BUTil8LzQPZdr5bKNWBpKQXMOPpJQx/+PtWn2RctYzTBnclIsh+73zByB4eJfnjhWWcNmcpJz+1hGvfWON2fERz0Dv6NiAjv5QXfkwmwOLHrbMGEBUS4NyWfCyfl5bsoXtUMLefOoAgf0sdZ1LtxZsr9nMgqwiA//t+d6tOMq5axoCuESy79xTScks4qVuER8csTc7gYLb9/8FPuzNIPV5Mr9jQZo9NE30b8NDnW/lm61EAisutVdrmb31/AzuP2vvRR4YE8MeT+3klRtW8+nUOc7t8wvHCMh74bAtHc0u498yTmNBXx0s0t+Rj+fz1s634W4R//mo4CXE1/x0aKjo0sEGjX0f1iiEkwEJxuZXEuDC6RrZMiWlN9G2Aa82avJKqtTAKyyp/LvJSnQxVyWozPLtoN1sO53L91L5MHRDXqPNcPSmB8OAAsgpKOWNoN95euZ9B3SKdPWdeW76Xr7fYP/zvm7+FH++e0VxvQTn88+sdrN5nn43rqe928eJvR7d6DL07hfL9ndPZlJrDtP6dW6ynjib6NuD+swfz18+2EmDx467TqnalfOLXI3jiu110jwzmuqn68NXbFmw+wnM/2id0WbP/OOsfPK1Rv5x+fsLFY+IxxnDWs8uc39o+mD2RiX07ERpY+asZHKDNdS0hzOUahwZ67xr3ig1tkeYaV5ro24ChPaL49KYpbrdN7h/H5/0bd9eomp/VZqosG5r28Ky0wsYulxIXWw/nMrFvJ66fmkhecTlH80q4dabWrWkJfzt/CJEhAfj7SY0bLF+jiV6pBjgvqQfbjuSx5XAuN0zr2+SH48EBFmZP78t/ftpLYlyYc4RzcICF+84eXGN/YwzbjuQREexPn05Nb1P2FXszCrjlvQ0UlFbw2IXDPWpS6xIRzL9+XftYFV8iLdWdpynGjh1r1q5d6+0wlGo1haUVhARY6u1H/X/f7+L5H1MQgVeuHKPlDhz+/PEmPl5nH2E+rGckC26d5uWIWp+IrDPGjHW3TfvRK9UGhAX5ezRY5itHiWpj4FtHTy2ArIJSHvp8Kw99vrVD1qR3rf3TJSKY77YdZeWeLC9G1LbU23QjIr2At4BugA2Ya4x5tto+M4DPgX2OVfONMY84tt0O3AAI8Kox5plmil2pDufs4d15YbH9jv7MYZV3848u2M5nG+1VTAtKK5hzyUgvRegdt8zsT1iQPwUlFaTlFvOHt9cB8MRFw7l0XG/nfqv2ZvHIl9uJDQvk6d8k1SgO56s8aaOvAP5kjFkvIhHAOhFZaIzZXm2/ZcaYc11XiMgw7El+PFAGfCsiXxljkpsjeKXas9V7s/j3ot3Ex4Ty8PlDCQ+q+uuYnlfCsz8kE+Rvn/g7KiSAP50+kDOHdSM8yL9Kv2/XKeoKSjpeN9wgf4tzjMms/1viXL96b3aVRP/wF9ucPZxe+WkPD58/tFXj9JZ6E70xJg1Icyzni8gOoCdQPdG7MxhYZYwpAhCRn4ALgScbHbGqU3p+CTe9s55Dx4u4/+zBNUZcLth8hG+2HOWMYd2qlDb+ZV82jy7YTkxYIE9dPKLFBm6oSnd9tInDOcVANolxYTVmhfrrZ1v5fvsxAMqsVv7xq+GICMN6RtU4171nnkReSQUC3HPmSa0Qfdt19aQEHv5yG8H+Fn4ztmrd/kiXUeeuI9B9XYN63YhIAjAKWO1m8yQR2QQcAe42xmwDtgL/FJFOQDFwNqBPWVvQO6sOOuvUP/Ll9iqJfn9mIbe+vwFj4KstaQzpHkn/LuGAfXTuiTudl5e07J1OSnoBO9LymDGoMxHBHeeXrboAi7hdPqEhd+kDukbw0R8mNV9w7dg1kxM4Z0R3gvz9avz/mnNJEi8v2UNsWCA3zug4o8w9TvQiEg7MA+4wxuRV27we6GOMKRCRs4HPgAHGmB0i8gSwECgANmFvCnJ3/tnAbIDevXu720V5oLtLm2P19sficiuunaxKyq3OZdc7ncjglut1u/1IHhe8uJxyq2Foj0gW3Dq1XVZvTM8vYf2BHMYnxhIb1rgJn5+/fDTP/5hMr9hQrp6UUGP7A+cM5qHPtxHk78ddpw2qeQJVq7hw93M0xMeE1ln+21d51L1SRAKABcB3xpg5Huy/HxhrjMmstv4xINUY81Jdx2v3ysaz2Qzv/XKQQ9lFXD05gZ7RIVW2v7g4hS83HeGsYd25bVZ/Z5I9lF3ES0tSiAkN5NaZAwhpoZGCb686wIOfbXX+vOmh04kKbV939dmFZZw65yeyC8voHhXMortOJiyo5ofjKz/t4cUfUxjaM5K5V48lsgN/e1Etr67ulZ70uhHgdWBHbUleRLoBx4wxRkTGY++2meXY1sUYky4ivYFfA/r9sgX5+QlXTuxT6/abT+lfoy0Y7MOw//XrES0ZGmAv5frS4hTScku4YGSPdpfkwT569UQXxrTcElLSC0jqFV1ln+IyK09+uxObgVV7s/li45E6/10a61B2ESnpBUzq10lLJbQxmw7l8NR3u+gcEcTD5w316v91T76jTwGuAraIyEbHuvuB3gDGmFeAi4EbRaQCe1v8Zabyq8I8Rxt9OXCzMeZ4M8av2pluUcEsvnsGGfmlxMeE1H9AGzSmTwz9u4STkl7AiPgoBnePrLFPoL8f3SKDOZJbAkDvFqhlsv1IHr966WfKKmxMSIzlg9kT22UzmK+655PNzvIW3aOCvfqQ3JNeN8ux94Gva58XgBdq2dbxhqipOgUHWOos4rQ3o4B/frWDAIsffzt/CN2j2tYHQliQP1/fNo0DWYUkxoW5nWDC4ie8d8NEPl53iOE9o5g+sLObMzXNij2ZlFXYZzRavS+b4nJrlWJoyrssLgPg/Fto5ihP6f+KDqiswsbm1Bz6dwlvUO3s1vL3L7fz0+4MwF5VcM6lI70bkBuB/n4M6Fr35BIJcWH8+YzG38WtO5DNbe9vxGYMz10+inHVppw8bUhXXlicQk5ROeeM6K5Jvo2Zc2kS/164m84RQcz28jwS+j+jg7HZDJfOXcmGgznEhgXy1W1T29wds+vdj8XLd0Le9PyPKY5+9vDcD8m8ff2EKtv7dApj2T2ncCSnhIFdw70RoqrDSd0i+c9Vbp+NtjpN9B3MkdxiNhzMAey9R1btzeLCUfEA5BaX87+f9xESYOGayQlee7j38PlDCQ3yJ8AiHrVrllttLNp+jJiwQCb60ExMfVyat/p0ct/UFREcwKBu7e+Btmpdmug7mO5RIST1imbToRxiQgOqJMa/fraVLzfZ66VkF5Vx31k1y+S2hl6xoTx/+SiP979//hZn5cKnf5PExWPiWyq0VnXf2YPpFRuKMbRIjx3VcWii72AsfsJHf5jIxoM5DOwaQYzLYJ8jjmYC+3KJN8Kr07YjuXy6/jDjE2M53aU87y/7syuX92X5TKIPDrDw+2l9vR2G8gFaprgDCvK3MKFvpypJHuBPpw8kPiaEAV3CufmUtjU8vKisgsvnruK15fuY/fY6Nhys7KV7zaQERCAs0MIl1WqbKKX0jl65mNwvjuX3zvR2GG4VlFRUmTj9SE4JoxyVMq6bmsgFI3sQFGCpUQFSKaV39Kqd6BIZzB2nDiAmNIAzh3Zj1uAuABzIKuRgViFfbUlj9d4s2uKMaUp5m04l6GO2H8nj1WX2+UdvmtHP7WAeX/HUdzt5cfEe/AROzNn97GUja5RmVqojaFKtG9W+3PDWWmff607hgfx2QvvrrXG8sIz5Gw6T0CmUWYO71rrfh2sOAZVJHhzVMTXRK1WFJnofU1hW2Y5dWNo+Zxq6/s01rHf09X/5t6M5llfCi0v2MKxHJM9fMdrZDj99YGfmrz8MgEWErpFBXDpOH8YqVZ0meh/z9MVJ/HvRbhLjwriiHd7NA+xyTIACsPlwLq8u3UuFzbB4Vwafbzzs/Jby5EUjOD+pB10igunbOYxAi59HE2wr1dFooq/H4p3pLEvO5Lyk7ozqHePtcOp16pCunDqk9uaO9uCOUwfy5Hc76RkdwqXj4pm3LpX0/FKg6sQq/hY/Zgzq4q0wlWo39GFsHXak5XH2c8swBkICLKy8b2abLALWXIwxLNicxtHcEn4zNt6r77WswkaARRARko/l8/4vhxgeH8mvRvbUUrxKuaEPYxvpaF6Jc+q94nIrucXlPp3oP16byj3zNgOwZHc6cy4ZyYuLUwgL8ufWmf1btTpioH9lb6EBXSN46LwhrfbaSvkaTfR1mNY/jgtG9mDp7gwuHdebPp3CvB1Sk63dn83/VuxneM8oZk/rW6VNOzm9sm08+VgB987bzJJd9nLBNpvhvrO9U/tGKdU0mujr4G/x49nLPC+u1daVW21c98Ya8koq+GpzGr1iQjlnRHfn9t9O6MP3249xLK+Eu04byAeO7osAOUXl3ghZKdUMNNF3IFabodQxIxHY68e4SogLY8ndMzDGPvfsgK4R/O2LrYQH+XPLzJrzzCql2gdN9A1QXGYlOMCv3T4MDA6w8PRvkvjP0j0M6xHF+SN71NhHRDjx9sb0iWHBrToTpFLtnSZ6D/1jwXZeW76PQV0jeH/2RGLD2udD2fOSenBeUs0Er5TyXb5bCKUZ5ZeU89ryfQDsOpbP11vSvByRUkp5ThO9B0ID/UmMs/e4EYHB3eueFFoppdoSbbrxgMVP+HD2RBZsTmN4fBRj+sS63a+4zEpIoHfmWW1J89enMnfpXpLio3nkV0MJ8ve996iUL9NE76EukcFcNzXR7bYT3RaXJWdy2pCuvHLlGCw+UnOlqKyCe+dtptxq2Hk0nwl9Y/n1aN+Yqk+pjkKbbhpgf2YhLy5O4Zd92VXWr91/nGXJmQAs3H6MrYdzvRFei7D4CcEBlXfwOoOTUu2P/tZ6qKTcysWvrCCzoAw/gS9vncrQHlEAJMaFERJgobjcSkSQP71iQ70cbfMJ8rfw39+N4+2VBxgRH8Vp7bxgmlIdUb2JXkR6AW8B3QAbMNcY82y1fWYAnwP7HKvmG2MecWy7E/g9YIAtwLXGmJJmir9Z7UjL48HPtmLxEx6/aITzASzA8aIyMgvKAPtEF3szCp2JvltUMJ/ePJnlyZnMGNSl3Xa9rM24hFjGJbh/LqGUavs8abqpAP5kjBkMTARuFhF3FaaWGWNGOv6cSPI9gduAscaYYYAFuKyZYm+U3KJyFmw+wsGsohrbHvt6B2sPHGf1vmye/m5XlW3do0K4ZlIfRGB8YqxzztITTuoWye+n9aV/l/AWjV8ppRqq3jt6Y0wakOZYzheRHUBPYHsDXiNERMqBUOBII2NtsnKrjQtf+pm9mYWEBFj49o5pVQqVRQYHOJcjgmtemuumJpJTVEZESADl1trLOxtj2Ho4j5BAiyZ+pZTXNaiNXkQSgFHAajebJ4nIJuyJ/G5jzDZjzGEReRo4CBQD3xtjvm9izI12NLeEvZmFgL3s8MZDOVUS/cPnDyU2LBB/i3DHrIE1jv/LvC2s3JsFQKDFUmvp3Gd/SOaZRckAPH/5KB2JqpTyKo973YhIODAPuMMYk1dt83qgjzEmCXge+MxxTAxwAZAI9ADCROTKWs4/W0TWisjajIyMBr8RT8THhDBjUGcAeseGMm1A5yrbO0cE8eivhvG384YSFRpQ4/gya2VBsNIKa62v882Wo87lb7cdrXU/pZRqDR4lehEJwJ7k3zXGzK++3RiTZ4wpcCx/DQSISBxwKrDPGJNhjCkH5gOT3b2GMWauMWasMWZs586d3e3SZCLC/343jiV3z2DhXdMb/ND0kQuGMrlfJ04f0pXbZw2odb/zkro7Xg/OHtbd7T7GGF5btpc7PtjAugPHGxSHUko1RL1TCYq9VOObQLYx5o5a9ukGHDPGGBEZD3wC9AHGA/8FxmFvunkDWGuMeb6u12wrUwk2xY60PAL9hV4xYVVmSwL4eksab67Yz2pHf/zo0ADWPHAqARYd1qCUapymTiU4BbgK2CIiGx3r7gd6AxhjXgEuBm4UkQrsCf0yY/8EWS0in2Bv2qkANgBzm/BeWkWJY9rArpHB9e9ci7zicm54ay0VNsMzl47k9KHdANh9LJ+b3l1fZd+iMitWmyFAKwsopVqAJ71ulgN1juc3xrwAvFDLtr8Bf2tUdF6wP7OQi19ZSWZBKddOSeBv5w1t1HleX76PvBL7xB6v/LTHmejziqvO1JQYF8btswZUGX2qlFLNSdsKqvlqSxqZBaUAvLliP1Zb7U1bNpth/cHjHMkprrFtSI9It8tj+sRww7REEuPCuPPUgSy+ewa/GtWzGd+BUkpVpSUQqhnTJwYRMMY+IrSu4mT3ztvMx+tSCbT48d4NExjrMnr01pkDSOgURlmFjQtGVXavFBEeOGcID5zjvmumUko1N0301Uzs24kvb5nK3sxCTq02+rW6ExOQlFltLNqRXiXRW/xE79SVUm2CNt24MaxnFOcn9SA0sO7PwbOH27tOBlr8OG1I3R8KSinlLXpH3wRPXDSCKyb0pmtkMD2iQ5p0rsW70rn3k82EB/vzypVjGNhVZ7FSSjUPvaNvAj8/YVTvGKJCArjytdUMfehbnvshuVHnevq7XaTnl7I3o5BXftrTzJEqpToyTfTN4OstaSxPyaSwzMqchbvJLSqvsc+Gg8f5y7zNfLz2kNtz9IqprGHf24fq2bcl+zILeePnfezJKPB2KEq1Km26aQauiblzRFCNeWOLy6xc/fov5JdW8MGaQ/SIDmFK/7gq+zxx8QiG9YwkPMif307s0ypxdyTZhWWc/8Jy8ksqCAu0sPjPM+gS0fgBcUq1J5rom8GEvp149eqxbDx0nF+Pjq9R8qC0wkpBWYXz56zCshrniAoJ4JaZtdfPUY33xs/7eGZRMvmOAWyFZVb2ZRRqolcdhib6ZnLakK61TrMXHRrI384dwus/72Nsn1jOGKrT8bWWknIr//hqBxUuA9+m9O/EqN4xXoxKqdalib6V/G5KIr+bkujtMDqcAIsfceFBHM2zz175whWjOGd4d+y1+pTqGDTRK59m8RPe+f14PvjlEEN7RmqSVx2SJnrl8/p3ieCv52rJCdVxafdKpZTycZroVYeQnlfCYTdVRpXqCDTRK5/37dY0Jj3+I1Me/5F3Vh3wdjhKtTpN9MrnfbLusHNegdpGJivlyzTR1+KjtYc485ml3Dd/M+VWm7fDUU1w8qDKyeZPHtgyE88r1ZZ12F432YVl/POrHRSUlnPPmSfRr3O4c1t+STn3z99Chc2w82g+k/rFcX5S5eQhJeVWliVnktAplAFaZbLNu2piH4b2iKS03MbEvrH1H6CUj+mwif6ZRbuZtz4VgJyicj78wyTntgCLH8EBFgpK7UPmI4KqXqbZb69j6e4MLH7CB7MnMs4x4UjysXzunbcZm4HHLxrOSd3sUwjmFpfz9y+2kVFQyt2nDyKpV3SdsZWUW/l4XSohARYuHNWzzlmulGdG60hY1YF12ERvXKaCrT4rbHCAhdevGcu7qw8yunc0M1y++ttshp9TMgGw2gwr92Q5E/2T3+1i/cEcAJ74Zif/u3Y8AHOX7mH+hsMAHMsr4fs7T64ztoc+38pHa+0fQkdyirltVvPVwElJL+C/P++jT2wov5/WVz9ElOoAOmyiv+PUAeSVlFNQUsFfzjqpxvYJfTsxoW+nGuv9/ITLxvXi3dUHiQoJ4Ozh3ZzbokMCKpdDA53LFj8/t8u12XU03+1yc5j99lr2ZhQCEB7sz28naKVMpXxdh030ncKDePayUY069h+/GsYN0/oSFxFEuEuzzl/PGUJUSAA2A7fO7O9cP3t6X7ILS0nPK+XO0wbWe/6bT+nPXR9tIsjfj+unVa2Ps/VwLo9/s5Po0AAeuWAYsWGBtZzFvRyXWvk5burmK6V8jxhTveHC+8aOHWvWrl3r7TC8ymozCPZvEK7Oe345Ww7nAnD91EQebODQ/m+3pvHUd7vo0ymMOZckVfnmoZRqv0RknTFmrLttHfaOvq2z+Ak5RWX8d/k+QoP8uXZKAkH+FgIslYnf39Lw9vUzh3XnzGHdmzNUpVQb12ET/dbDubz3y0GS4qO4ZGyvKhUN0/NKeGnJHsKCLNx8Sn9CA+2XqbjMyup9WQzqFkH3qKZNBu6Jv8zbwrfbjgKQV2zvBvrkxUn8e+FuokMDuPmU/vWcQSmlOmiiL6uwcfV/fyG7sIz3Vtun/5t5UuVkIH+Zv4Ufd6YDUGE13Hf2YGw2w6VzV7I5NZewQAtf3TaNhLgwj19z7f5s3l19kFG9o7lqYh+PSuWm5VbWZjmaa6+n3r9LOC/+drTHr9va5i7dw8drU5kxqDP3nTW4RtOTUqr11dsFRER6ichiEdkhIttE5HY3+8wQkVwR2ej485Bj/SCXdRtFJE9E7miB99Eg5VYbecWVDyIzC6pO7ZftMtXf8SL7cmZhKZtT7W3jhY47e0+VlFu59n9r+HTDYR76fBtLdmV4dNy9Z55Er9gQTuoWwU2n9PP49bzlYFYRj329k+T0Al5dto9VDbhG3rBo+zHu+mgj3249WmPbhoPHuejlFVz/xhrSHZOWKNVeeXJHXwH8yRizXkQigHUistAYs73afsuMMee6rjDG7AJGAoiIBTgMfNr0sJsmLMifh88fyis/7SEpPprzRvSosv3Bcwfz4GfbCA/y51bHPK6dw4OYNiCOZcmZxIUHMr0BQ+nLrTaKy63On/NKPOvtMrl/HMvumenx6zRUaYWVbUfyGNg1okrvocYKDvTD30+c0/ZFBgfUc0TLMsYwd+leFu9K56LR8fxmbC/ntkPZRcx+ey02A59uOMwPd51MX5fR0Q9+vpWth/MAeHFxCn+/YFirx69Uc6n3t9sYkwakOZbzRWQH0BOonujrMwvYY4xpE+UDr5zYhysnuu9DPqZPLF/fPq3KOhHhjWvHs/NoHn06hTUoMUYEB/DYhcN5bfleRvaK5qw28DC03GrjopdXsPVwHt0ig/n69mkN7qpZXZeIYF69eiyfbjjMjEGdGdYzqpmibZz1B4/zr292ArBqbzaT+8fRM9r+bCW/pIIT08gaA3klFVWOPfFcBiC0GT4ElfKmBv0PFpEEYBSw2s3mSSKyCTgC3G2M2VZt+2XA+40Jsq2w+AlDezQueV0yrheXjOtV/461+H7bUd5aeYDRfWK4Y9aARrd9r9yTxevL99E1Msh5x3o0r4Q1+7M5Y2i3eo62Kym38vg3OzmYXcTNp/RnTJ/K8gKnnNSFU07q0qjYml/lNRJx/QkGd4/g1pn9WbA5jTOGdiMpvuq/69MXJ/HsD8lEhwZwiz70Vu2cx/3oRSQc+An4pzFmfrVtkYDNGFMgImcDzxpjBrhsD8T+ATDUGHOslvPPBmYD9O7de8yBA23ixr9NKCitYPSjCymrsFfRnHvVGE73MCm7KrfaGPPoQufda9fIII7llRIXHsg3t0+nc0SQR+d5bdle/vHVDgB6Rofw819arnmpKYwxvL58H4t3pfPrUfFcNCbe2yEp1WKa3I9eRAKAecC71ZM8gDEmz2X5axF5SUTijDGZjtVnAetrS/KO4+YCc8E+YMqTuNw5XljGij1ZjO4T3SpdIFtD9Q/j+i5OhdXG+oM59IwJcTZV2M+Ds/0c4NZTBpDYOYwh3SOJaUCzjc0lHlsbHHB3gojw+2l9+f20vt4ORSmvqjfRi70f4OvADmPMnFr26QYcM8YYERmPvTePa5eLy2mFZpviMivnPr+cwznFRAb7s+iuk+kSGdzSL9viIoIDeO6ykby54gBjE2I4bXDXOve/7YMNfL3lKIH+fnz8h0nOapmB/n48e9ko/vPTHob0iOSScb0I9Pfjl33ZzFm4i57Rofz9gqH1Pn+4amIC+7OKOJhV1KwF15RSLcOTO/opwFXAFhHZ6Fh3P9AbwBjzCnAxcKOIVADFwGXGcRsqIqHAacAfmjf0mvZnFTrnBc0rqWDL4VxmNSLRP7NoN//7eT/jEmJ54YpRBAdYmhRXSnqBfTq7fp0Y06dx9dA9HdFqjOG7bfYvTmUVNhbvSq9SFvm0IV05bUjVD4q7PtpI6vFiIJvEuFBumVl38g4JtPDYhcMb/B6UUt7hSa+b5VR9juVunxeAF2rZVgTULAPZAgZ0CWdcQgxr9h+nX+cwxic2PKlm5JfyzKJkABbtOMbC7cc4L6lHPUfVLr+knIteXkFucTkWP+G7O6bTv0t4/Qc2kohw3ojufLbxCMEBfpxaz90/2Ovvu1tWSvkGn+o35m/x44PZkziQVUh8TCiB/g1PWuFB/kSFBJDrGFDVI7pp7fyZBWXOc1lthv2ZhU1K9MYYDmQV0T06mCB/99805lwykuumJtItKpguEfV/o3n+8lE8/2My8TGhXDM5odGxKaXaJq1e6caOtDw+3XCYcQmxNZo5GsoYw33zt/Dh2kNM7R/Hq1ePrbMp6FB2ESv2ZDK5Xxy9YkNrnOu6N9aweFcGvWJD+OymKXQK96ynTFuQnlfC7mMFjE2IaXJzmFKqqrp63WiibyVWm6l3NqfswjJmPLWYvJIKIoP9WfLnU6oMYjqcU8yUx390/vzvS5O4cFT76DJ4IKuQc55bTkFpBaN6RzPvj5O1Do5SzaiuRO+zDbLGGLak5pKSXuB2+66j+by4OIUtjvo11ZVV2JxzxjYHT6bs25NR4OzjnldSwZ6MqrF3jQiib2d7IbWQAAujerXNeVBzi8qdff5PWLkny3k9NxzMIauwzN2hLWbOwt2MfnQhN7+3vkZsSvk6n030zyxK5rwXlnPqnJ/4ctORKtuyC8u46OUVPPXdLi56ZQWpx4uqbN96OJcJjy0i6e/f89bK/a0Wc1J8NOMd88+OT4glKT66ynZ/ix+f3jSFF64YxXd3THdbPTMlvYANB4/X6Ht/wvu/HOSCF5bzxLc7sdma/9vci4tTGPno90x+/AeSj1VOgzhtYGfnt5Mp/TsRF94yE558sekID3+xjW1HKj/A03KLee6HZLILy/hqcxqLd6W3yGsr1Vb51MNYV64VCb/ddrRKz5kjOcXOu8uyChsHs4qIj6lsD/9gzUGOO6bZ+89Pe7l6UkKTYrHaDEuTM4gOCWBU79rvwgP9/fjwDxPJKCilc3iQ21LGUSEBnDvCfS+gr7ekcdO76wG4cUY/7j2z6ly46Xkl3P/pFoyBTam5TOrbqUHF2Twxd+lejLE/hP5kfSr3nTUYsI+gXXz3DPZnFjK0R6RHZZo98cu+bO74YAN+fsLs6X156HN75Y3PNh5m1X2zCA6wEB7kT3iQv/PfvJsPjK1QqiF89o7+vCR7n3MROGd41f7nQ7pHOhP/rJO6MK5aN0zXO+mkXk0vzPXwF9u49n9ruPClFXy6IbXOfUWELhHBjUqEC7dXDjz+blvN0rv+Fnt1yROCGtErqT6jekdXLldrWooKCSCpVzT+zdiF89kfdnMkt4TU48V8uOaQc31ucTml5fYmmojgAN67YQLXTUlk7lVjqowrUKoj8Nk7+ltmDuDUIV0JCbDQp1PVJg4/P+H5y0fx70uS3Cadi8fEExcRRGZ+aZP60J+wPCWzcjk5q8UeoJ49vDufbzyMzcD5buKODQvk5d+OcfYAasw4g/q89NvRLNicRnx0CJP7x9W7v9VmeGfVAY7llfC7yQkNHsncOzaUnx2DsEf3jqZXTCjrDh7nhmmJRIVWlkkeER/NiGpNYUp1FNrrphXMXbqHx77eSaC/H2/8bpxHCbCxUo8XUVhqZVC3iBZ7jbos2HyEP3+8mbAgC69dM46R9dw9/3f5Ph5ZYK94PT4hlo/+OKlBr1dUVsFbKw9gEeGqSX2026bqsHRycC+bPb0f547oQUiApd7iYTvS8nh2UTLdooK558xBVeqie8L1WYM3vLh4D8XlVorLrfzv5308e9ko57asglJKK2xVBqEdyamcLvGwy7KnQgP9+ePJbX/2LaW8SRN9K/F0hO2dH25k51F7b5XYsMAaRcPS80t4bdk+okIC+P20xCqjY8utNr7depTQQAszT+ritp1/97F8nvpuFzGhATxwzhCiQpp3FqjB3SLYkWYvZjq4e6Rz/eJd6cx+ay3lVsND5w7huqmJAFw7NZE1+7M5llfKg+cObtZYlFJ2mujbmHJrZR/vCmvN/t53f7yZpbvtc86WVdi487SBzm2PLtjOWyvtdfwfOHswN0yvWZ73nk82s/FQDgDRoYHcf7Y9uR7NLeG1ZXvpFB7E9VMTG1U+AuCfFw5ndJ8YIoL9q0zROG9dKuVWezPhh2sOORN9z+gQPr9laqNeSynlGU30bcxTv0niqW930S0qmOvd1FHPyC91Lqe7LIN96rwTNhw6jjuuz2Rc+9Hf/sEGVu/Ltq83hpsbOatSSKDF7RSN0wd0ZsHmNACmDWi5ZxRKqZo00bcxo3vH8P7sibVu/+s5g3nws61EhQZwY7W26eumJHLPJ5sJ9Pfjygnu58N9/KIRPPntTmJCA7llZmUyzyio/NDIqPYB0hwuGdeL/l3DKSytYEo/TfRKtSbtdeNj8kvK8ffzIySwYb1PluxK5+EvttEpPIjnLh9VZWYqdwpKK8gqKKV3bGiNZwGHsouYtz6VoT2imlwUTinlGe1104FEBDfu4eqMQV1Y8mfPJvVOSc/nopdXkltczpUTe/OPX1VOQmKzGS5/dZVjIhN467rxzT76VinVMD47Mla1nK+3HHXW2H//l0NV2vpLK2xVukzuzyps9fiUUlVpom+HjDE8+e1OTpvzEy/8mOxcn1lQymNf7+DZRcmUlFtb7PUn9evEiUoKU/rHVSk3HBJo4U+nDyI4wI8xfWK4IKlni8WhlPKMttG3Q+sPHufXL61w/rzwzukM6BrB799cw6Id9sqMfzy5H38566TaTtFku4/lsz+zkOkDO+toVKXaAG2j9zHhQZX/bH6C88FrRkFljffMgob3nDHGsDwlE5uB6QPi6iysNrBrBAO7eqfMglKqYTTRt0MDu0bw3OWjWLT9GOeO6O4se/DA2YN54NMthAX5N6of/IuLU3j6+90A3HnqQG4/dUA9Ryil2gNN9O3U+Uk9alSoHJ8Yy8K7Tm70OVftzXZZzuJ2NNEr5Qv0Yaxyunx8b/z9BIufcPmE3t4ORynVTPSOXjmdM6I7k/t1wkCVScmVUu2bJnofZLOZKl0eG6KuMsonJkzXDwGl2hdtuvEhJeVWrnxtNf0f+JoHPt1S6wThxhjeXrmfP769jh93HnO7T3UHs4qY9uSPjH50If/6ekdzhq2UamH1JnoR6SUii0Vkh4hsE5Hb3ewzQ0RyRWSj489DLtuiReQTEdnpOEfDphBSHluyK8PZPfLd1Qc5lO1+Io81+4/z4Ofb+HbbUf749nqOF5a53c/Vl5uPcCzP3mXzteX7qoyGVUq1bZ403VQAfzLGrBeRCGCdiCw0xmyvtt8yY8y5bo5/FvjWGHOxiAQC3p0CyYf17RyGxU+w2gzRoQF0CnffxFJUVuFcLrfZqtTAr43rlICjekU3umlIKdX66k30xpg0IM2xnC8iO4CeQPVEX4OIRALTgd85ji8D6r99VI0ysGsEH86eyKq9WZw1vDthQe7/eacP6Mzs6X35OSWTKyb09mhC7in94/j0psnsySjkjKFakVKp9qRBJRBEJAFYCgwzxuS5rJ8BzANSgSPA3caYbSIyEpiL/UMhCVgH3G6MqbPSlZZAUEqphqmrBILHD2NFJBx7Mr/DNck7rAf6GGOSgOeBzxzr/YHRwMvGmFFAIfCXWs4/W0TWisjajIwMT8NSSilVD48SvYgEYE/y7xpj5lffbozJM8YUOJa/BgJEJA77HX6qMWa1Y9dPsCf+Gowxc40xY40xYzt31vrlSinVXDzpdSPA68AOY8ycWvbp5tgPERnvOG+WMeYocEhEBjl2nYUHbftKKaWajye9bqYAVwFbRGSjY939QG8AY8wrwMXAjSJSARQDl5nKxv9bgXcdPW72Atc2X/hKKaXq40mvm+VAnX3pjDEvAC/Usm0j4PYBgVJKqZanI2NVrQpLK3hr5X4+33hYB0gp1Y5prRtVq3s+2cxXW9IAyCwo4/qpiV6OSCnVGHpHr2q182hlL9pdR6v3qFVKtRea6FWtbps1gOAAP7pEBHHN5ARvh6OUaiRtulG1umBkT84Z3h0/Ea1to1Q7pole1cnfol/6lGrv9LdYKaV8nCZ6pZTycZrolVLKx2miV0opH6eJXimlfJwmeqWU8nGa6JVSysdpoldKKR/XoDljW4uIZAAHvB1HK4kDMr0dRBul18Y9vS6168jXpo8xxu30fG0y0XckIrK2tgl9Ozq9Nu7pdamdXhv3tOlGKaV8nCZ6pZTycZrovW+utwNow/TauKfXpXZ6bdzQNnqllPJxekevlFI+ThN9E4mIRUQ2iMiCauvvFhEjInEu6+4TkRQR2SUiZ7isHyMiWxzbnhMRcawPEpEPHetXi0iCyzHXiEiy4881rfBWG8TddRGRWx3vfZuIPOmyvsNcF6h5bURkpIisEpGNIrJWRMa77Nthro2I7He8p40istaxLlZEFjpiXigiMS77d5hr02TGGP3ThD/AXcB7wAKXdb2A77CPBYhzrBsCbAKCgERgD2BxbPsFmAQI8A1wlmP9TcArjuXLgA8dy7HAXsffMY7lGG9fi7quC3AKsAgIcvzcpSNel1quzfcu7+1sYElHvDbA/hO/Ly7rngT+4lj+C/BER7w2Tf2jd/RNICLxwDnAa9U2/Ru4B3B9AHIB8IExptQYsw9IAcaLSHcg0hiz0tj/170F/MrlmDcdy58Asxx3J2cAC40x2caY48BC4Mxmf4ONVMt1uRF43BhTCmCMSXes7zDXBWq9NgaIdCxHAUccyx3q2tTC9f28SdX32dGvjcc00TfNM9gTuu3EChE5HzhsjNlUbd+ewCGXn1Md63o6lquvr3KMMaYCyAU61XGutuIZql0XYCAwzfGV+ScRGedY35GuC7i/NncAT4nIIeBp4D7H+o52bQzwvYisE5HZjnVdjTFpAI6/uzjWd7Rr0ySa6BtJRM4F0o0x61zWhQIPAA+5O8TNOlPH+sYe41XurouDP/avxROBPwMfOe6mOsR1gTqvzY3AncaYXsCdwOsnDnFzGp+8Ng5TjDGjgbOAm0Vkeh37drRr0ySa6BtvCnC+iOwHPgBmAm9jby/c5FgfD6wXkW7Y7xJ6uRwfj/0reqpjufp6XI8REX/sX+uz6zhXW1DjuojIO9hjnm/sfsF+RxtHx7kuUPu1uQaY79jnY+DEw9iOdG0wxhxx/J0OfIr9OhxzNMfg+PtEk1+HujZN5u2HBL7wB5iBy8NYl/X7qXwYO5SqD4/2UvnwaA32O90TD4/Odqy/maoPjz5yLMcC+7DfIcc4lmO9fR3qui7AH4FHHMsDsX9Vlo54Xdxcmx3ADMfyLGBdR/s/A4QBES7LK7C3kz9F1YexT3a0a9Ms19fbAfjCHzxI9I6fH8DeO2AXjp4AjvVjga2ObS9QOZAtGPsdXgr2ngR9XY65zrE+BbjW29egvusCBALvON7nemBmR70ubq7NVGCdI3GtBsZ0tGsD9HW8/03ANuABx/pOwA9AsuPvWJdjOsS1aY4/OjJWKaV8nLbRK6WUj9NEr5RSPk4TvVJK+ThN9Eop5eM00SullI/TRK+UUj5OE71SSvk4TfRKKeXj/h/alQ8aFwfFOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 500m缓冲区的可视化\n",
    "points_copy[\"geometry\"] = points_converted[\"buffer_1\"]\n",
    "points_copy.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a1070b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1000m缓冲区的可视化\n",
    "points_copy[\"geometry\"] = points_converted[\"buffer_2\"]\n",
    "points_copy.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b798aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2000m缓冲区的可视化\n",
    "points_copy[\"geometry\"] = points_converted[\"buffer_3\"]\n",
    "points_copy.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "341b294a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;group&#x27; (group: 9)&gt;\n",
       "array([ 8455609, 31980595,  8848276,     8807,  3317112, 18361618,\n",
       "         289241,        1,  2791386])\n",
       "Coordinates:\n",
       "    spatial_ref  int32 0\n",
       "  * group        (group) uint64 0 1 2 4 5 7 8 10 11</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'group'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>group</span>: 9</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b1a35d69-0ef8-4982-b734-c7dab4049052' class='xr-array-in' type='checkbox' checked><label for='section-b1a35d69-0ef8-4982-b734-c7dab4049052' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>8455609 31980595 8848276 8807 3317112 18361618 289241 1 2791386</span></div><div class='xr-array-data'><pre>array([ 8455609, 31980595,  8848276,     8807,  3317112, 18361618,\n",
       "         289241,        1,  2791386])</pre></div></div></li><li class='xr-section-item'><input id='section-eddba4a5-6866-47f5-a233-fc6413a65b84' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eddba4a5-6866-47f5-a233-fc6413a65b84' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-1c74458f-f0ca-4d76-8986-aaadfdb9eb6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c74458f-f0ca-4d76-8986-aaadfdb9eb6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ac52d7b-edea-4e90-a2da-8808c90903f7' class='xr-var-data-in' type='checkbox'><label for='data-6ac52d7b-edea-4e90-a2da-8808c90903f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS_1984_UTM_Zone_50N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;World Geodetic System 1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,117],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS_1984_UTM_Zone_50N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>117.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS_1984_UTM_Zone_50N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;World Geodetic System 1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,117],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>422596.8554313086 10.0 0.0 2615214.5023276755 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>group</span></div><div class='xr-var-dims'>(group)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>0 1 2 4 5 7 8 10 11</div><input id='attrs-d37513a1-964d-4cb1-8267-0cf97a202da7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d37513a1-964d-4cb1-8267-0cf97a202da7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1aa58d05-3632-4c6e-8821-04743194a173' class='xr-var-data-in' type='checkbox'><label for='data-1aa58d05-3632-4c6e-8821-04743194a173' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  4,  5,  7,  8, 10, 11], dtype=uint64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cad95f4d-5b53-46a4-920c-40bc393aa30b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cad95f4d-5b53-46a4-920c-40bc393aa30b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'group' (group: 9)>\n",
       "array([ 8455609, 31980595,  8848276,     8807,  3317112, 18361618,\n",
       "         289241,        1,  2791386])\n",
       "Coordinates:\n",
       "    spatial_ref  int32 0\n",
       "  * group        (group) uint64 0 1 2 4 5 7 8 10 11"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算土地利用栅格不同栅格值得数量\n",
    "land_use.groupby(land_use).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2061f230",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用采样点500m缓冲区裁剪栅格\n",
    "buffer = land_use.rio.clip(points_converted[\"buffer_1\"], drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17993fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2559620af70>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 裁剪栅格可视化\n",
    "buffer.plot(figsize=(30,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d813718",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义获取缓冲区栅格数量的函数\n",
    "def land_usage(g):\n",
    "    buffer = land_use.rio.clip([g], drop=True) # 裁剪栅格\n",
    "    # buffer = buffer.where(buffer != 0, drop=True)\n",
    "    counts = buffer.groupby(buffer).count().drop(\"spatial_ref\").squeeze().to_dict()\n",
    "\n",
    "    return {\n",
    "        str(int(k)): v\n",
    "        for k, v in zip(counts[\"coords\"][\"group\"][\"data\"], counts[\"data\"])\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ee54314",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 对500m缓冲区矢量运用land_usage函数，计算每个缓冲区内不同栅格值数量\n",
    "landuse_count = points_converted[\"buffer_1\"].apply(land_usage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "02deb6d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      {'0': 2158, '1': 4024, '5': 1735, '7': 1947, '...\n",
       "1      {'0': 2170, '1': 3946, '2': 398, '5': 788, '7'...\n",
       "2             {'0': 2166, '1': 6993, '5': 475, '7': 366}\n",
       "3      {'0': 2156, '1': 493, '5': 1825, '7': 5152, '8...\n",
       "4      {'0': 2156, '1': 1724, '5': 116, '7': 5775, '8...\n",
       "                             ...                        \n",
       "137    {'0': 2157, '1': 1467, '2': 2838, '7': 3531, '...\n",
       "138    {'0': 2155, '1': 513, '2': 2269, '7': 4951, '8...\n",
       "139          {'0': 2165, '1': 2949, '2': 4025, '7': 861}\n",
       "140         {'0': 2154, '2': 2501, '7': 4907, '11': 438}\n",
       "141    {'0': 2154, '1': 29, '2': 53, '7': 7431, '8': ...\n",
       "Name: buffer_1, Length: 142, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看计算结果\n",
    "landuse_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d03a470",
   "metadata": {},
   "outputs": [
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       "          0       1       5       7     11       2      8   4\n",
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      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将计算结果转为pandas.Series类型\n",
    "landuse_count.apply(pd.Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "478033c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将不同土地利用类型栅格计数以列的形式加到缓冲点上  \n",
    "counts_1 = points_converted.join(points_converted[\"buffer_1\"].apply(land_usage).apply(pd.Series)) # 计算500m缓冲区的栅格数量\n",
    "counts_2 = points_converted.join(points_converted[\"buffer_2\"].apply(land_usage).apply(pd.Series)) # 计算1000m缓冲区的栅格数量\n",
    "counts_3 = points_converted.join(points_converted[\"buffer_3\"].apply(land_usage).apply(pd.Series)) # 计算2000m缓冲区的栅格数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52730660",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "139  POLYGON ((512880.4674203126 2596178.999431872,...  2165.0  2949.0   \n",
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       "..      ...     ...    ...     ...    ...  ..  \n",
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       "\n",
       "[142 rows x 15 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看500m缓冲区计算结果\n",
    "counts_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49cbe323",
   "metadata": {},
   "outputs": [
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       "\n",
       "[142 rows x 15 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看1000m缓冲区计算结果\n",
    "counts_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6090181d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 填充缺失值为0\n",
    "counts_3.fillna(0, inplace=True)\n",
    "counts_2.fillna(0, inplace=True)\n",
    "counts_1.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8930e204",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出csv文件\n",
    "counts_1.to_csv(\"土地利用类型栅格计数_500m.csv\")\n",
    "counts_2.to_csv(\"土地利用类型栅格计数_1000m.csv\")\n",
    "counts_3.to_csv(\"土地利用类型栅格计数_2000m.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "197c2acb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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