EDA_NetworkAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis: Network centrality modeling for mobility networks in each city\n",
    "\n",
    "Create a weighted directional graph and measure how centrality distribution changes with time period and how it compares across cities. Calculate metrics such as:\n",
    "\n",
    "- Network centrality\n",
    "- Degree distribution\n",
    "- Network diameter\n",
    "- Clustering coefficient\n",
    "- Number of Hubs\n",
    "- visualization of network centrality \n",
    "\n",
    "Need to think about three metrics for understanding and categorizing the networks:\n",
    "\n",
    "- Node Importance\n",
    "\n",
    "Node Centrality: weighted in-degree and out-degree centrality measures to understand \n",
    "\n",
    "- Degree centrality: Historically first and conceptually simplest is degree centrality, which is defined as the number of links incident upon a node (i.e., the number of ties that a node has). The degree can be interpreted in terms of the immediate risk of a node for catching whatever is flowing through the network (such as a virus, or some information). In the case of a directed network (where ties have direction), we usually define two separate measures of degree centrality, namely indegree and outdegree. Accordingly, indegree is a count of the number of ties directed to the node and outdegree is the number of ties that the node directs to others. When ties are associated to some positive aspects such as friendship or collaboration, indegree is often interpreted as a form of popularity, and outdegree as gregariousness.\n",
    "\n",
    "<br/>\n",
    "- Closeness centrality: In a connected graph, the normalized closeness centrality (or closeness) of a node is the average length of the shortest path between the node and all other nodes in the graph. Thus the more central a node is, the closer it is to all other nodes.\n",
    "\n",
    "<br/>\n",
    "- Betweenness centrality: Betweenness is a centrality measure of a vertex within a graph (there is also edge betweenness, which is not discussed here). Betweenness centrality quantifies the number of times a node acts as a bridge along the shortest path between two other nodes. It was introduced as a measure for quantifying the control of a human on the communication between other humans in a social network by Linton Freeman[20] In his conception, vertices that have a high probability to occur on a randomly chosen shortest path between two randomly chosen vertices have a high betweenness.\n",
    "\n",
    "<br/>\n",
    "- Eigenvector centrality: Eigenvector centrality (also called eigencentrality) is a measure of the influence of a node in a network. It assigns relative scores to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes. Google's PageRank and the Katz centrality are variants of the eigenvector centrality\n",
    "\n",
    "- Freeman Centralization: The centralization of any network is a measure of how central its most central node is in relation to how central all the other nodes are.[8] Centralization measures then (a) calculate the sum in differences in centrality between the most central node in a network and all other nodes; and (b) divide this quantity by the theoretically largest such sum of differences in any network of the same size.\n",
    "\n",
    "Network Topology\n",
    "\n",
    "see classification section: https://en.wikipedia.org/wiki/Network_topology#Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "#import packages\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/projects/mpi/shared/Data'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set working directory\n",
    "os.chdir('/projects/mpi/shared/Data')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "sf_clean = pd.read_csv('sf_clean.csv')\n",
    "nyc_clean = pd.read_csv('nyc_clean.csv')\n",
    "la_clean = pd.read_csv('la_clean.csv')\n",
    "chic_clean = pd.read_csv('chic_clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Network Graphs for Each City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_network(df,col1,col2,col3):\n",
    "    '''function to create network x graphs for each df of city movements and returns centrality\n",
    "    measures and the graph object'''\n",
    "    mob_graph = nx.MultiDiGraph() #initialize graph\n",
    "    node_list = list(set(list(df[col1])) & set(list(df[col2]))) #create list of nodes\n",
    "    mob_graph.add_nodes_from(node_list) #add nodes to teh graph\n",
    "    \n",
    "    weighted_edge_list = [] #initialize empty edge list\n",
    "    for i, j, k in zip(df[col1],df[col2],df[col3]):\n",
    "        weighted_edge_list.append((i,j,k))\n",
    "    \n",
    "    mob_graph.add_weighted_edges_from(weighted_edge_list)\n",
    "    \n",
    "    in_deg = nx.in_degree_centrality(mob_graph)\n",
    "    out_deg = nx.out_degree_centrality(mob_graph)\n",
    "    centrality = nx.degree_centrality(mob_graph)\n",
    "    \n",
    "    centrality_df = pd.DataFrame.from_dict(centrality,orient='index',columns=['centrality'])\n",
    "    indeg_df = pd.DataFrame.from_dict(in_deg,orient='index',columns=['in_degree'])\n",
    "    outdeg_df = pd.DataFrame.from_dict(out_deg,orient='index',columns=['out_degree'])\n",
    "    \n",
    "    return mob_graph, indeg_df, outdeg_df, centrality_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#generate graphs for each city\n",
    "nyc_graph, nyc_in_deg, nyc_out_deg, nyc_centrality = create_network(nyc_clean,'origin_ct','dest_ct','od_counts')\n",
    "chic_graph, chic_in_deg, chic_out_deg, chic_centrality = create_network(chic_clean,'origin_ct','dest_ct','od_counts')\n",
    "sf_graph, sf_in_deg, sf_out_deg, sf_centrality = create_network(sf_clean,'origin_ct','dest_ct','od_counts')\n",
    "la_graph, la_in_deg, la_out_deg, la_centrality = create_network(la_clean,'origin_ct','dest_ct','od_counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the Centrality Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution plots of distance travelled \n",
    "fig = plt.figure(figsize=(15,12))\n",
    "ax1 = fig.add_subplot(221)\n",
    "\n",
    "ax1.set_ylabel(\"Frequency\")\n",
    "ax1.set_xlabel(\"In-Degree Centrality\")\n",
    "ax1.set_title(\"NYC Mobility Network Degree Distribution\")\n",
    "ax1.hist(nyc_in_deg['in_degree'],bins=50,color='green')\n",
    "ax1.text(x=0.5,y=650,s=\"Mean in-degree centrality = {}\".format(round(nyc_in_deg['in_degree'].mean(),2)))\n",
    "ax1.text(x=0.5,y=620,s=\"Number of Network Nodes = {}\".format(nyc_in_deg.shape[0]))\n",
    "\n",
    "ax2 = fig.add_subplot(222)\n",
    "\n",
    "ax2.set_ylabel(\"Frequency\")\n",
    "ax2.set_xlabel(\"In-Degree Centrality\")\n",
    "ax2.set_title(\"LA Mobility Network Degree Distribution\")\n",
    "ax2.hist(la_in_deg['in_degree'],bins=50,color='red')\n",
    "ax2.text(x=0.6,y=400,s=\"Mean in-degree centrality = {}\".format(round(la_in_deg['in_degree'].mean(),2)))\n",
    "ax2.text(x=0.6,y=380,s=\"Number of Network Nodes = {}\".format(la_in_deg.shape[0]))\n",
    "\n",
    "ax3 = fig.add_subplot(223)\n",
    "\n",
    "ax3.set_ylabel(\"Frequency\")\n",
    "ax3.set_xlabel(\"In-Degree Centrality\")\n",
    "ax3.set_title(\"SF Mobility Network Degree Distribution\")\n",
    "ax3.hist(sf_in_deg['in_degree'],bins=50,color='yellow')\n",
    "ax3.text(x=0.8,y=110,s=\"Mean in-degree centrality = {}\".format(round(sf_in_deg['in_degree'].mean(),2)))\n",
    "ax3.text(x=0.8,y=100,s=\"Number of Network Nodes = {}\".format(sf_in_deg.shape[0]))\n",
    "\n",
    "ax4 = fig.add_subplot(224)\n",
    "\n",
    "ax4.set_ylabel(\"Frequency\")\n",
    "ax4.set_xlabel(\"In-Degree Centrality\")\n",
    "ax4.set_title(\"Chicago Mobility Network Degree Distribution\")\n",
    "ax4.hist(chic_in_deg['in_degree'],bins=50,color='blue')\n",
    "ax4.text(x=0.6,y=200,s=\"Mean in-degree centrality = {}\".format(round(chic_in_deg['in_degree'].mean(),2)))\n",
    "ax4.text(x=0.6,y=190,s=\"Number of Network Nodes = {}\".format(chic_in_deg.shape[0]))\n",
    "\n",
    "plt.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deeplearn",
   "language": "python",
   "name": "deeplearn"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}