communities_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'C:/Users/sol/Documents/01-bikes_bogota/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes=pd.read_csv(path + 'nodes_all.csv')\n",
    "links=pd.read_csv(path + 'links_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes.loc[:,'tuple'] = list(zip(nodes['Latitude'].tolist(),nodes['Longitude'].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Label</th>\n",
       "      <th>income_level</th>\n",
       "      <th>wstrato</th>\n",
       "      <th>pop</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
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      ],
      "text/plain": [
       "    id  Label  income_level   wstrato      pop  Longitude  Latitude  \\\n",
       "0  1.0    1.0           2.0  3.000000  17197.0   -74.1205   4.61808   \n",
       "1  2.0    2.0           1.0  1.860132  24609.0   -74.1327   4.56084   \n",
       "\n",
       "                 tuple  \n",
       "0  (4.61808, -74.1205)  \n",
       "1  (4.56084, -74.1327)  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nodes are the characteristics of the tower\n",
    "nodes.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.000000</td>\n",
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      "text/plain": [
       "   Source  Target     Weight  income_level  wstrato_o      Type\n",
       "0   265.0   455.0  28.228165           1.0   2.000000  Directed\n",
       "1   448.0   455.0  24.726087           2.0   3.000000  Directed\n",
       "2    13.0   455.0  22.728797           1.0   1.963427  Directed\n",
       "3   272.0   455.0  33.571853           1.0   2.000000  Directed\n",
       "4   264.0   455.0  32.409447           2.0   3.030184  Directed"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_source=pd.merge(links,nodes[['id','Latitude','Longitude']],left_on='Source',right_on='id',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weight</th>\n",
       "      <th>income_level</th>\n",
       "      <th>wstrato_o</th>\n",
       "      <th>Type</th>\n",
       "      <th>id</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "      <th>0</th>\n",
       "      <td>265.0</td>\n",
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       "      <td>2.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>448.0</td>\n",
       "      <td>455.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>22.728797</td>\n",
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       "      <th>3</th>\n",
       "      <td>272.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>32.409447</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.030184</td>\n",
       "      <td>Directed</td>\n",
       "      <td>264.0</td>\n",
       "      <td>4.74534</td>\n",
       "      <td>-74.0950</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Source  Target     Weight  income_level  wstrato_o      Type     id  \\\n",
       "0   265.0   455.0  28.228165           1.0   2.000000  Directed  265.0   \n",
       "1   448.0   455.0  24.726087           2.0   3.000000  Directed  448.0   \n",
       "2    13.0   455.0  22.728797           1.0   1.963427  Directed   13.0   \n",
       "3   272.0   455.0  33.571853           1.0   2.000000  Directed  272.0   \n",
       "4   264.0   455.0  32.409447           2.0   3.030184  Directed  264.0   \n",
       "\n",
       "   Latitude  Longitude  \n",
       "0   4.73042   -74.1060  \n",
       "1   4.60370   -74.0725  \n",
       "2   4.55389   -74.0994  \n",
       "3   4.73972   -74.1073  \n",
       "4   4.74534   -74.0950  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_source.rename(columns={'Latitude': 'source_lat', 'Longitude': 'source_lon'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_target=pd.merge(merge_source,nodes[['id','Latitude','Longitude']],left_on='Target',right_on='id',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_target.rename(columns={'Latitude': 'target_lat', 'Longitude': 'target_lon'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "links=merge_target.drop(['id_x','id_y'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph, all trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.DiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_nodes_from(nodes['id'].tolist(),pos=nodes['tuple'],strata=nodes['wstrato'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_weighted_edges_from(zip(links['Source'].tolist(),links['Target'].tolist(),links['Weight'].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos=dict(zip(nodes['id'], nodes['tuple']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "nx.draw(G,pos,node_size=1,figsize=(20,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modularity Maximization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "net=G.to_undirected()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install python-louvain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import community\n",
    "\n",
    "#first compute the best partition\n",
    "partition = community.best_partition(net)\n",
    "\n",
    "#drawing\n",
    "size = float(len(set(partition.values())))\n",
    "count = 0\n",
    "plt.figure(figsize=(15,15))\n",
    "for com in set(partition.values()) :\n",
    "    count = count + 1\n",
    "    list_nodes = [nodes for nodes in partition.keys()\n",
    "                                if partition[nodes] == com]\n",
    "    nx.draw_networkx_nodes(net, pos, list_nodes,\n",
    "                                node_color = np.random.uniform(0.25,0.95,3))\n",
    "\n",
    "nx.draw_networkx_edges(net, pos)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       " 620.0: 0,\n",
       " 621.0: 4,\n",
       " 622.0: 5,\n",
       " 623.0: 0,\n",
       " 624.0: 7,\n",
       " 625.0: 4,\n",
       " 626.0: 4,\n",
       " 627.0: 5,\n",
       " 628.0: 2,\n",
       " 629.0: 4,\n",
       " 630.0: 4,\n",
       " 631.0: 0,\n",
       " 632.0: 2,\n",
       " 633.0: 0,\n",
       " 634.0: 0,\n",
       " 635.0: 0,\n",
       " 636.0: 1,\n",
       " 637.0: 0,\n",
       " 638.0: 5,\n",
       " 639.0: 2,\n",
       " 640.0: 3,\n",
       " 641.0: 2,\n",
       " 642.0: 3,\n",
       " 643.0: 3,\n",
       " 644.0: 2,\n",
       " 645.0: 3,\n",
       " 646.0: 3,\n",
       " 647.0: 3,\n",
       " 648.0: 2,\n",
       " 649.0: 3,\n",
       " 650.0: 1,\n",
       " 651.0: 2,\n",
       " 652.0: 1,\n",
       " 653.0: 2,\n",
       " 654.0: 1}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes['partition'] = nodes['id'].apply(lambda x: partition[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nodes.to_csv('nodes_for_voronoi.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once having the partition each node belongs to, the data can be exported to qgis to get the voronoi diagram for the telephone antennas. In this case, we used Qgis, but any other software or Python package can be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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