Point_Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Linking to GEOS 3.5.1, GDAL 2.2.2, proj.4 4.9.2\n"
     ]
    }
   ],
   "source": [
    "library(sp)\n",
    "library(sf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just to generate some random points. Basically, I'm just loading a polygon (of a river) and generating random points inside it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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leXL1Vnalo7thd5HwCiJOygGJKQyoVcly/lcrmQFHkdAOIk7KAYqjLVW9u37Nl2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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “id”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(meuse.riv)\n",
    "meuse.sr = SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)), \"x\")))\n",
    "# this means the same points will be generated each time, comment it\n",
    "# out to see different points\n",
    "set.seed(66)\n",
    "pnts <- spsample(meuse.sr, n = 15, \"regular\")  %>% st_as_sf\n",
    "pnts$id <- seq(nrow(pnts)) %>% as.character\n",
    "pnts %>% plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buffer the points by 500 (meters probably - the units will be whatever the units of the shapefile's projection is)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8aVKllakiSPqrUuXbokOhQAzUCxA6DlgoKCKlaooqMjlyTJukSp0SNmfHf20qOo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\ngg==",
      "text/plain": [
       "Plot with title “id”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pbuff <- pnts %>% st_as_sf %>% st_buffer(500)\n",
    "pbuff %>% plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will union all the buffers together. That operation returns a single multipolygon, so that has to be converted into a set of individual polygons and then coverted back into an sf object. I couldn't figure out an easy way to keep the IDs from the points, so I created a new sequence of IDs for the unioned polygons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P33TJQmoosK6atdPz/NfHVl2xWumQBQOFwVC0CPZWVlffrpp8HBwXUd/M5/uWlI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\nQmCC",
      "text/plain": [
       "Plot with title “id”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters <- pbuff %>% st_union %>% st_cast(\"POLYGON\") %>% st_sf\n",
    "clusters$id <- seq(nrow(clusters)) %>% as.character\n",
    "clusters %>% plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This joins the cluster ids onto the points that fall within each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>id.point</th><th scope=col>id.cluster</th><th scope=col>geometry</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                        </td><td>1                        </td><td>POINT (179052.7 326386.9)</td></tr>\n",
       "\t<tr><td>2                        </td><td>2                        </td><td>POINT (180181.5 328644.5)</td></tr>\n",
       "\t<tr><td>3                      </td><td>2                      </td><td>POINT (180934 329020.7)</td></tr>\n",
       "\t<tr><td>4                      </td><td>2                      </td><td>POINT (180934 329773.2)</td></tr>\n",
       "\t<tr><td>5                        </td><td>5                        </td><td>POINT (179052.7 331278.3)</td></tr>\n",
       "\t<tr><td>6                      </td><td>5                      </td><td>POINT (179429 331654.5)</td></tr>\n",
       "\t<tr><td>7                      </td><td>5                      </td><td>POINT (179429 332030.8)</td></tr>\n",
       "\t<tr><td>8                      </td><td>5                      </td><td>POINT (180181.5 332407)</td></tr>\n",
       "\t<tr><td>9                        </td><td>5                        </td><td>POINT (180557.7 332783.3)</td></tr>\n",
       "\t<tr><td>10                     </td><td>4                      </td><td>POINT (181310.3 333912)</td></tr>\n",
       "\t<tr><td>11                       </td><td>4                        </td><td>POINT (181686.5 334664.6)</td></tr>\n",
       "\t<tr><td>12                       </td><td>4                        </td><td>POINT (181686.5 335040.8)</td></tr>\n",
       "\t<tr><td>13                       </td><td>3                        </td><td>POINT (182062.8 336545.8)</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " id.point & id.cluster & geometry\\\\\n",
       "\\hline\n",
       "\t 1                         & 1                         & POINT (179052.7 326386.9)\\\\\n",
       "\t 2                         & 2                         & POINT (180181.5 328644.5)\\\\\n",
       "\t 3                       & 2                       & POINT (180934 329020.7)\\\\\n",
       "\t 4                       & 2                       & POINT (180934 329773.2)\\\\\n",
       "\t 5                         & 5                         & POINT (179052.7 331278.3)\\\\\n",
       "\t 6                       & 5                       & POINT (179429 331654.5)\\\\\n",
       "\t 7                       & 5                       & POINT (179429 332030.8)\\\\\n",
       "\t 8                       & 5                       & POINT (180181.5 332407)\\\\\n",
       "\t 9                         & 5                         & POINT (180557.7 332783.3)\\\\\n",
       "\t 10                      & 4                       & POINT (181310.3 333912)\\\\\n",
       "\t 11                        & 4                         & POINT (181686.5 334664.6)\\\\\n",
       "\t 12                        & 4                         & POINT (181686.5 335040.8)\\\\\n",
       "\t 13                        & 3                         & POINT (182062.8 336545.8)\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "id.point | id.cluster | geometry | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1                         | 1                         | POINT (179052.7 326386.9) | \n",
       "| 2                         | 2                         | POINT (180181.5 328644.5) | \n",
       "| 3                       | 2                       | POINT (180934 329020.7) | \n",
       "| 4                       | 2                       | POINT (180934 329773.2) | \n",
       "| 5                         | 5                         | POINT (179052.7 331278.3) | \n",
       "| 6                       | 5                       | POINT (179429 331654.5) | \n",
       "| 7                       | 5                       | POINT (179429 332030.8) | \n",
       "| 8                       | 5                       | POINT (180181.5 332407) | \n",
       "| 9                         | 5                         | POINT (180557.7 332783.3) | \n",
       "| 10                      | 4                       | POINT (181310.3 333912) | \n",
       "| 11                        | 4                         | POINT (181686.5 334664.6) | \n",
       "| 12                        | 4                         | POINT (181686.5 335040.8) | \n",
       "| 13                        | 3                         | POINT (182062.8 336545.8) | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   id.point id.cluster geometry                 \n",
       "1  1        1          POINT (179052.7 326386.9)\n",
       "2  2        2          POINT (180181.5 328644.5)\n",
       "3  3        2          POINT (180934 329020.7)  \n",
       "4  4        2          POINT (180934 329773.2)  \n",
       "5  5        5          POINT (179052.7 331278.3)\n",
       "6  6        5          POINT (179429 331654.5)  \n",
       "7  7        5          POINT (179429 332030.8)  \n",
       "8  8        5          POINT (180181.5 332407)  \n",
       "9  9        5          POINT (180557.7 332783.3)\n",
       "10 10       4          POINT (181310.3 333912)  \n",
       "11 11       4          POINT (181686.5 334664.6)\n",
       "12 12       4          POINT (181686.5 335040.8)\n",
       "13 13       3          POINT (182062.8 336545.8)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustered_pnts <- pnts %>% st_join(clusters, join=st_within, suffix = c(\".point\",\".cluster\"))\n",
    "clustered_pnts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the original points colored according to which cluster they fall in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bru3lfX3be5ra4kr19VrzETamaUFgzI9WgABELYQfaUFRxw9IAv5XoKAILlHjsA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\nwg4AIBDCDgAgEMIOACAQwg4AIBD/H7uhUaSzkWEIAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “id.cluster”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustered_pnts %>% dplyr::select(-id.point) %>% plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}