DD-graph-v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from math import radians, cos, sin, asin, sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddb_roundabout = gpd.read_file('./data/ddb_roundabout.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddb = pd.DataFrame(ddb_roundabout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>timestamp</th>\n",
       "      <th>xacc</th>\n",
       "      <th>yacc</th>\n",
       "      <th>speed</th>\n",
       "      <th>maxspeed</th>\n",
       "      <th>lanecount</th>\n",
       "      <th>classification</th>\n",
       "      <th>index_right</th>\n",
       "      <th>distances</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-17 01:46:30+00:00</td>\n",
       "      <td>-0.889</td>\n",
       "      <td>0.002</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>Braking</td>\n",
       "      <td>402.0</td>\n",
       "      <td>78.508285</td>\n",
       "      <td>POINT (16595825.835 -4208110.694)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-05 23:53:50+00:00</td>\n",
       "      <td>-0.864</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>Braking</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.548220</td>\n",
       "      <td>POINT (16597539.710 -4206559.210)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-05 23:53:50+00:00</td>\n",
       "      <td>-0.864</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>Braking</td>\n",
       "      <td>903.0</td>\n",
       "      <td>27.548220</td>\n",
       "      <td>POINT (16597539.710 -4206559.210)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-25 19:27:59+00:00</td>\n",
       "      <td>-0.346</td>\n",
       "      <td>0.714</td>\n",
       "      <td>27</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>Steering</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.927812</td>\n",
       "      <td>POINT (16597536.370 -4206536.837)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-25 19:27:59+00:00</td>\n",
       "      <td>-0.346</td>\n",
       "      <td>0.714</td>\n",
       "      <td>27</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>Steering</td>\n",
       "      <td>903.0</td>\n",
       "      <td>4.927812</td>\n",
       "      <td>POINT (16597536.370 -4206536.837)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  timestamp   xacc   yacc  speed  maxspeed  lanecount  \\\n",
       "0 2020-12-17 01:46:30+00:00 -0.889  0.002      7        32          1   \n",
       "1 2020-07-05 23:53:50+00:00 -0.864  0.000      3        48          1   \n",
       "2 2020-07-05 23:53:50+00:00 -0.864  0.000      3        48          1   \n",
       "3 2020-04-25 19:27:59+00:00 -0.346  0.714     27        45          1   \n",
       "4 2020-04-25 19:27:59+00:00 -0.346  0.714     27        45          1   \n",
       "\n",
       "  classification  index_right  distances                           geometry  \n",
       "0        Braking        402.0  78.508285  POINT (16595825.835 -4208110.694)  \n",
       "1        Braking         70.0  27.548220  POINT (16597539.710 -4206559.210)  \n",
       "2        Braking        903.0  27.548220  POINT (16597539.710 -4206559.210)  \n",
       "3       Steering         70.0   4.927812  POINT (16597536.370 -4206536.837)  \n",
       "4       Steering        903.0   4.927812  POINT (16597536.370 -4206536.837)  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddb_roundabout.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "crash_roundabout = gpd.read_file('./data/crash_roundabout.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>CRASH_DATE</th>\n",
       "      <th>CRASH_TIME</th>\n",
       "      <th>SUBURB_LOCATION</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>INTERSECTION</th>\n",
       "      <th>MIDBLOCK</th>\n",
       "      <th>CRASH_DIRECTION</th>\n",
       "      <th>CRASH_SEVERITY</th>\n",
       "      <th>LIGHTING_CONDITION</th>\n",
       "      <th>ROAD_CONDITION</th>\n",
       "      <th>WEATHER_CONDITION</th>\n",
       "      <th>Location</th>\n",
       "      <th>index_right</th>\n",
       "      <th>distances</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14/03/2014</td>\n",
       "      <td>14:45:00</td>\n",
       "      <td>WANNIASSA</td>\n",
       "      <td>149.105038</td>\n",
       "      <td>-35.394988</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>North bound</td>\n",
       "      <td>Property Damage Only</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Good dry surface</td>\n",
       "      <td>Fine</td>\n",
       "      <td>(-35.394987516718835, 149.1050382804053)</td>\n",
       "      <td>124.0</td>\n",
       "      <td>54.232167</td>\n",
       "      <td>POINT (16598296.938 -4217688.770)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25/09/2013</td>\n",
       "      <td>09:15:00</td>\n",
       "      <td>PARKES</td>\n",
       "      <td>149.140739</td>\n",
       "      <td>-35.290364</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>South bound</td>\n",
       "      <td>Injury</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Good dry surface</td>\n",
       "      <td>Fine</td>\n",
       "      <td>(-35.2903638694626, 149.14073893625283)</td>\n",
       "      <td>598.0</td>\n",
       "      <td>18.107638</td>\n",
       "      <td>POINT (16602271.111 -4203410.781)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21/10/2014</td>\n",
       "      <td>12:50:00</td>\n",
       "      <td>WESTON</td>\n",
       "      <td>149.052234</td>\n",
       "      <td>-35.342376</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>South / West bound</td>\n",
       "      <td>Injury</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Good dry surface</td>\n",
       "      <td>Fine</td>\n",
       "      <td>(-35.342376051736125, 149.05223439721874)</td>\n",
       "      <td>109.0</td>\n",
       "      <td>52.375484</td>\n",
       "      <td>POINT (16592418.835 -4210506.572)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18/12/2014</td>\n",
       "      <td>15:00:00</td>\n",
       "      <td>CURTIN</td>\n",
       "      <td>149.085469</td>\n",
       "      <td>-35.336601</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>North bound</td>\n",
       "      <td>Property Damage Only</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Good dry surface</td>\n",
       "      <td>Fine</td>\n",
       "      <td>(-35.33660063969841, 149.08546898336647)</td>\n",
       "      <td>302.0</td>\n",
       "      <td>37.911366</td>\n",
       "      <td>POINT (16596118.494 -4209718.433)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04/08/2015</td>\n",
       "      <td>07:10:00</td>\n",
       "      <td>CURTIN</td>\n",
       "      <td>149.085534</td>\n",
       "      <td>-35.336096</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>North / East bound</td>\n",
       "      <td>Injury</td>\n",
       "      <td>Daylight</td>\n",
       "      <td>Good dry surface</td>\n",
       "      <td>Fine</td>\n",
       "      <td>(-35.336095898518856, 149.0855345007159)</td>\n",
       "      <td>302.0</td>\n",
       "      <td>1.119271</td>\n",
       "      <td>POINT (16596125.785 -4209649.556)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CRASH_DATE CRASH_TIME SUBURB_LOCATION   LONGITUDE   LATITUDE INTERSECTION  \\\n",
       "0  14/03/2014   14:45:00       WANNIASSA  149.105038 -35.394988          YES   \n",
       "1  25/09/2013   09:15:00          PARKES  149.140739 -35.290364          YES   \n",
       "2  21/10/2014   12:50:00          WESTON  149.052234 -35.342376          YES   \n",
       "3  18/12/2014   15:00:00          CURTIN  149.085469 -35.336601          YES   \n",
       "4  04/08/2015   07:10:00          CURTIN  149.085534 -35.336096          YES   \n",
       "\n",
       "  MIDBLOCK     CRASH_DIRECTION        CRASH_SEVERITY LIGHTING_CONDITION  \\\n",
       "0       NO         North bound  Property Damage Only           Daylight   \n",
       "1       NO         South bound                Injury           Daylight   \n",
       "2       NO  South / West bound                Injury           Daylight   \n",
       "3       NO         North bound  Property Damage Only           Daylight   \n",
       "4       NO  North / East bound                Injury           Daylight   \n",
       "\n",
       "     ROAD_CONDITION WEATHER_CONDITION  \\\n",
       "0  Good dry surface              Fine   \n",
       "1  Good dry surface              Fine   \n",
       "2  Good dry surface              Fine   \n",
       "3  Good dry surface              Fine   \n",
       "4  Good dry surface              Fine   \n",
       "\n",
       "                                    Location  index_right  distances  \\\n",
       "0   (-35.394987516718835, 149.1050382804053)        124.0  54.232167   \n",
       "1    (-35.2903638694626, 149.14073893625283)        598.0  18.107638   \n",
       "2  (-35.342376051736125, 149.05223439721874)        109.0  52.375484   \n",
       "3   (-35.33660063969841, 149.08546898336647)        302.0  37.911366   \n",
       "4   (-35.336095898518856, 149.0855345007159)        302.0   1.119271   \n",
       "\n",
       "                            geometry  \n",
       "0  POINT (16598296.938 -4217688.770)  \n",
       "1  POINT (16602271.111 -4203410.781)  \n",
       "2  POINT (16592418.835 -4210506.572)  \n",
       "3  POINT (16596118.494 -4209718.433)  \n",
       "4  POINT (16596125.785 -4209649.556)  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crash_roundabout.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_graph(nxgraph):\n",
    "    # Check if the graph has any nodes.\n",
    "    print(\"Graph nodes:\")\n",
    "    if nxgraph.number_of_nodes() > 0:\n",
    "        print(\"The graph has nodes.\")\n",
    "        #count number of nodes\n",
    "        print(\"Number of nodes:\", nxgraph.number_of_nodes())\n",
    "    else:\n",
    "        print(\"The graph does not have any nodes.\")\n",
    "    # Check if the graph has any edges.\n",
    "    print(\"\\nGraph edges:\")\n",
    "    if nxgraph.number_of_edges() > 0:\n",
    "        print(\"The graph has edges.\")\n",
    "        #count number of edges\n",
    "        print(\"Number of edges:\", nxgraph.number_of_edges())\n",
    "    else:\n",
    "        print(\"The graph does not have any edges.\")\n",
    "    # Check if the graph is connected.\n",
    "    print(\"\\nGraph connection:\")\n",
    "    if nx.is_connected(nxgraph):\n",
    "        print(\"The graph is connected.\")\n",
    "    else:\n",
    "        print(\"The graph is disconnected.\")\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph Construction DDB Option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create empty graph\n",
    "G = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum x-acceleration: inf\n",
      "Maximum x-acceleration: -1\n",
      "Minimum y-acceleration: inf\n",
      "Maximum y-acceleration: -1\n"
     ]
    }
   ],
   "source": [
    "# Calculate the minimum and maximum x-acceleration and y-acceleration values\n",
    "max_x, min_x, max_y, min_y = -1, float('inf'), -1, float('inf')\n",
    "print(f\"Minimum x-acceleration: {min_x}\")\n",
    "print(f\"Maximum x-acceleration: {max_x}\")\n",
    "print(f\"Minimum y-acceleration: {min_y}\")\n",
    "print(f\"Maximum y-acceleration: {max_y}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#iterate ddb roundabout dataset for nodes and metric\n",
    "for index, row in ddb_roundabout.iterrows():\n",
    "    # Calculate the minimum and maximum x-acceleration and y-acceleration values\n",
    "    max_x = max(max_x, row['xacc'])\n",
    "    min_x = min(min_x, row['xacc'])\n",
    "    max_y = max(max_y, row['yacc'])\n",
    "    min_y = min(min_y, row['yacc'])\n",
    "\n",
    "    #add nodes to graph\n",
    "    location = row['geometry']\n",
    "    x_acc = row['xacc']\n",
    "    y_acc = row['yacc']\n",
    "\n",
    "    G.add_node(location) \n",
    "    G.nodes[location]['xacc'] = x_acc\n",
    "    G.nodes[location]['yacc'] = y_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x214b941caf0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum x-acceleration: -1.962\n",
      "Maximum x-acceleration: 1.116\n",
      "Minimum y-acceleration: -1.096\n",
      "Maximum y-acceleration: 0.957\n"
     ]
    }
   ],
   "source": [
    "print(f\"Minimum x-acceleration: {min_x}\")\n",
    "print(f\"Maximum x-acceleration: {max_x}\")\n",
    "print(f\"Minimum y-acceleration: {min_y}\")\n",
    "print(f\"Maximum y-acceleration: {max_y}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Similarity Metric\n",
    "def calculate_similarity(node1, node2, max_x, min_x, max_y, min_y):\n",
    "    # Extract feature values from nodes (locations)\n",
    "    x1 = G.nodes[node1]['xacc']\n",
    "    y1 = G.nodes[node1]['yacc']\n",
    "    \n",
    "    x2 = G.nodes[node2]['xacc']\n",
    "    y2 = G.nodes[node2]['yacc']\n",
    "    \n",
    "    # Calculate Euclidean distance between feature vectors\n",
    "    euclidean_distance = np.sqrt((x1 - x2)**2 + (y1 - y2)**2)\n",
    "    \n",
    "    # Normalize the distance to a similarity score (higher values indicate more similarity)\n",
    "    max_possible_distance = np.sqrt((max_x - min_x)**2 + (max_y - min_y)**2)  # Adjust as needed\n",
    "    similarity_score = 1 - (euclidean_distance / max_possible_distance)\n",
    "    \n",
    "    return similarity_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add edges to nodes based on similarity metric\n",
    "#iterate through nodes to create edges based on similarity\n",
    "for node1 in G.nodes():\n",
    "    for node2 in G.nodes():\n",
    "        if node1 != node2:\n",
    "            # Calculate similarity\n",
    "            similarity_score = calculate_similarity(node1, node2, max_x, min_x, max_y, min_y)\n",
    "            \n",
    "            #threshold for considering edges\n",
    "            threshold = 0.7\n",
    "\n",
    "            # Add edge based on similarity\n",
    "            if similarity_score > threshold:\n",
    "                G.add_edge(node1, node2, similarity=similarity_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph nodes:\n",
      "The graph has nodes.\n",
      "Number of nodes: 1908\n",
      "\n",
      "Graph edges:\n",
      "The graph has edges.\n",
      "Number of edges: 1411244\n",
      "\n",
      "Graph connection:\n",
      "The graph is connected.\n"
     ]
    }
   ],
   "source": [
    "check_graph(G) #threshold 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize Graph    0.7 threshold\n",
    "# Create a layout for the nodes\n",
    "pos = nx.spring_layout(G)\n",
    "\n",
    "# Draw nodes\n",
    "nx.draw_networkx_nodes(G, pos, node_color='skyblue', node_size=50)\n",
    "\n",
    "# Draw edges\n",
    "nx.draw_networkx_edges(G, pos, edge_color='gray')\n",
    "\n",
    "# Label nodes with their location (you can customize labels as needed)\n",
    "#labels = {node: node for node in G.nodes()}\n",
    "#nx.draw_networkx_labels(G, pos, labels, font_size=8)\n",
    "\n",
    "# Set plot options\n",
    "plt.title(\"Graph of Dangerous Driving Behavior at Roundabouts\")\n",
    "plt.axis('off')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph Construction DDB Option 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create empty graph\n",
    "G2 = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum x-acceleration: inf\n",
      "Maximum x-acceleration: -1\n",
      "Minimum y-acceleration: inf\n",
      "Maximum y-acceleration: -1\n"
     ]
    }
   ],
   "source": [
    "# Calculate the minimum and maximum x-acceleration and y-acceleration values\n",
    "max_x, min_x, max_y, min_y = -1, float('inf'), -1, float('inf')\n",
    "print(f\"Minimum x-acceleration: {min_x}\")\n",
    "print(f\"Maximum x-acceleration: {max_x}\")\n",
    "print(f\"Minimum y-acceleration: {min_y}\")\n",
    "print(f\"Maximum y-acceleration: {max_y}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#iterate ddb roundabout dataset for nodes and metric\n",
    "for index, row in ddb_roundabout.iterrows():\n",
    "    # Calculate the minimum and maximum x-acceleration and y-acceleration values\n",
    "    max_x = max(max_x, row['xacc'])\n",
    "    min_x = min(min_x, row['xacc'])\n",
    "    max_y = max(max_y, row['yacc'])\n",
    "    min_y = min(min_y, row['yacc'])\n",
    "\n",
    "    #add nodes to graph\n",
    "    G2.add_node(row['geometry'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculates the distance between two nodes, given their coordinates.\n",
    "#Harvesine formula\n",
    "def calculate_distance(node1, node2, max_x, min_x, max_y, min_y):\n",
    "  # Convert decimal degrees to radians \n",
    "  lon1, lat1, lon2, lat2 = map(radians, [node1.x, node1.y, node2.x, node2.y])\n",
    "\n",
    "  # Calculate the difference in latitude and longitude between the two nodes.\n",
    "  dlon = lon2 - lon1 \n",
    "  dlat = lat2 - lat1\n",
    "\n",
    "  # Calculate the distance between the two nodes using the Haversine formula.\n",
    "  dlon = lon2 - lon1 \n",
    "  dlat = lat2 - lat1 \n",
    "  a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
    "  c = 2 * asin(sqrt(a))\n",
    "  distance = c * 6371 # distance in metres\n",
    "\n",
    "  max_dist = np.sqrt((max_x - min_x)**2 + (max_y - min_y)**2)  # Adjust as needed\n",
    "\n",
    "  similarity = 1 - (distance / max_dist)\n",
    "\n",
    "  return similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#edges\n",
    "threshold = 0.7\n",
    "i, j = 0, 0\n",
    "for node_i in G2.nodes:\n",
    "    for node_j in G2.nodes:\n",
    "        if node_i != node_j:\n",
    "            # Calculate the distance between node i and node j.\n",
    "            similarity = calculate_distance(node_i, node_j, max_x, min_x, max_y, min_y)\n",
    "            if similarity < threshold:\n",
    "                G2.add_edge(node_i, node_j)\n",
    "            j += 1\n",
    "    j = 0\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x214f0afa0b0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph nodes:\n",
      "The graph has nodes.\n",
      "Number of nodes: 1908\n",
      "\n",
      "Graph edges:\n",
      "The graph has edges.\n",
      "Number of edges: 1819277\n",
      "\n",
      "Graph connection:\n",
      "The graph is connected.\n"
     ]
    }
   ],
   "source": [
    "check_graph(G2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the node positions using a force-directed layout.\n",
    "pos = nx.spring_layout(G2)\n",
    "\n",
    "# Draw the graph.\n",
    "nx.draw_networkx_nodes(G2, pos=pos, node_color='skyblue', node_size=50)\n",
    "\n",
    "# Draw edges\n",
    "nx.draw_networkx_edges(G2, pos, edge_color='gray')\n",
    "\n",
    "# Set plot options\n",
    "plt.title(\"Graph of Dangerous Driving Behavior at Roundabouts 2\")\n",
    "plt.axis('off')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}