getting_started.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with MovingPanda\n",
    "\n",
    "MovingPandas provides a trajectory datatype based on GeoPandas\n",
    "\n",
    "https://github.com/anitagraser/movingpandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "import os\n",
    "import pandas as pd\n",
    "from geopandas import GeoDataFrame, read_file\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from datetime import datetime, timedelta\n",
    "from trajectory import Trajectory "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a trajectory from scratch\n",
    "\n",
    "Trajectory objects consist of a trajectory ID and a GeoPandas GeoDataFrame with a DatetimeIndex. The data frame therefore represents the trajectory data as a Pandas time series with associated point locations (and optional further attributes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory 1 (2018-01-01 12:00:00 to 2018-01-01 12:15:00) | Size: 4 | Length: 16.2m\n",
      "Bounds: (0.0, 0.0, 9.0, 9.0)\n",
      "LINESTRING (0 0, 6 0, 6 6, 9 9)\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame([\n",
    "  {'geometry':Point(0,0), 't':datetime(2018,1,1,12,0,0)},\n",
    "  {'geometry':Point(6,0), 't':datetime(2018,1,1,12,6,0)},\n",
    "  {'geometry':Point(6,6), 't':datetime(2018,1,1,12,10,0)},\n",
    "  {'geometry':Point(9,9), 't':datetime(2018,1,1,12,15,0)}\n",
    "]).set_index('t')\n",
    "geo_df = GeoDataFrame(df, crs={'init': '31256'})\n",
    "traj = Trajectory(1, geo_df)\n",
    "\n",
    "print(traj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook environment automatically plots Shapely geometry objects like the LineString returned by to_linestring() or the Point returned by get_position_at():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-0.36 -0.36 9.719999999999999 9.719999999999999\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,9.0)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.1944\" points=\"0.0,0.0 6.0,0.0 6.0,6.0 9.0,9.0\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x7f1701dfedd8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traj.to_linestring()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"5.0 -1.0 2.0 2.0\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,0.0)\"><circle cx=\"6.0\" cy=\"0.0\" r=\"0.06\" stroke=\"#555555\" stroke-width=\"0.02\" fill=\"#66cc99\" opacity=\"0.6\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.point.Point at 0x7f1701fff0f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traj.get_position_at(datetime(2018,1,1,12,6,0), method=\"nearest\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (6 0)\n"
     ]
    }
   ],
   "source": [
    "print(traj.get_position_at(datetime(2018,1,1,12,6,0), method=\"nearest\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting trajectory segments based on time or geometry (i.e. clipping)\n",
    "\n",
    "First, let's extract the trajectory segment for a certain time period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory 1 (2018-01-01 12:06:00 to 2018-01-01 12:10:00) | Size: 2 | Length: 6.0m\n",
      "Bounds: (6.0, 0.0, 6.0, 6.0)\n",
      "LINESTRING (6 0, 6 6)\n"
     ]
    }
   ],
   "source": [
    "print(traj.get_segment_between(datetime(2018,1,1,12,6,0),datetime(2018,1,1,12,12,0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(You can ignore any SettingWithCopyWarnings that may appear here)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's extract the trajectory segment that intersects with a given polygon:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory 1_0 (2018-01-01 12:02:00 to 2018-01-01 12:09:20) | Size: 3 | Length: 9.0m\n",
      "Bounds: (2.0, 0.0, 6.0, 5.0)\n",
      "LINESTRING (2 0, 6 0, 6 5)\n"
     ]
    }
   ],
   "source": [
    "xmin, xmax, ymin, ymax = 2, 8, -10, 5\n",
    "polygon = Polygon([(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)])\n",
    "\n",
    "intersection = traj.clip(polygon)\n",
    "\n",
    "print(intersection[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"1.8 -0.2 4.4 5.4\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,5.0)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.10800000000000001\" points=\"2.0,0.0 6.0,0.0 6.0,5.0\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x7f1701f71208>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection[0].to_linestring()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading trajectory data from GeoPackage\n",
    "\n",
    "The MovingPandas repository contains a demo GeoPackage file that can be loaded as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-06-17 16:46:37--  https://github.com/anitagraser/movingpandas/raw/master/demo/demodata_geolife.gpkg\n",
      "Resolving github.com (github.com)... 192.30.253.113\n",
      "Connecting to github.com (github.com)|192.30.253.113|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://raw.githubusercontent.com/anitagraser/movingpandas/master/demo/demodata_geolife.gpkg [following]\n",
      "--2019-06-17 16:46:37--  https://raw.githubusercontent.com/anitagraser/movingpandas/master/demo/demodata_geolife.gpkg\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 991232 (968K) [application/octet-stream]\n",
      "Saving to: ‘demodata_geolife.gpkg.1’\n",
      "\n",
      "demodata_geolife.gp 100%[===================>] 968.00K  --.-KB/s    in 0.06s   \n",
      "\n",
      "2019-06-17 16:46:37 (14.6 MB/s) - ‘demodata_geolife.gpkg.1’ saved [991232/991232]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "url = 'https://github.com/anitagraser/movingpandas/raw/master/demo/demodata_geolife.gpkg'\n",
    "filename = url.split('/')[-1] \n",
    "\n",
    "response = urllib.request.urlopen(url)\n",
    "content = response.read()\n",
    "with open(filename, 'wb' ) as f:\n",
    "    f.write( content )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "demo\t\t\t LICENSE.txt\t   sandbox\r\n",
      "demodata_geolife.gpkg\t overlay.py\t   tests\r\n",
      "demodata_geolife.gpkg.1  __pycache__\t   trajectory_prediction_evaluator.py\r\n",
      "geometry_utils.py\t README.md\t   trajectory_predictor.py\r\n",
      "getting_started.ipynb\t requirements.txt  trajectory.py\r\n",
      "__init__.py\t\t runtime.txt\t   trajectory_sampler.py\r\n"
     ]
    }
   ],
   "source": [
    "assert(os.path.exists(filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can use GeoPandas' read_file() function to read the GeoPackage and construct the Trajectories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished reading 5908 rows in 0:00:00.311005\n",
      "Trajectory 1 (2008-12-11 04:42:14 to 2008-12-11 05:15:46) | Size: 466 | Length: 6210.1m\n",
      "Bounds: (116.385602, 39.862378, 116.393553, 39.898723)\n",
      "LINESTRING (116.391305 39.898573, 116.391317 39.898617, 116.390928 39.898613, 116.390833 39.898635, \n",
      "Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 897 | Length: 38728.7m\n",
      "Bounds: (116.319212, 39.971703, 116.592616, 40.082514)\n",
      "LINESTRING (116.590957 40.071961, 116.590905 40.072007, 116.590879 40.072027, 116.590915 40.072004, \n",
      "Trajectory 3 (2009-02-04 04:32:53 to 2009-02-04 11:20:12) | Size: 1810 | Length: 12739.2m\n",
      "Bounds: (116.332706, 39.897023, 116.387307, 39.927949)\n",
      "LINESTRING (116.385689 39.899773, 116.385654 39.899651, 116.385548 39.899699, 116.385527 39.899632, \n",
      "Trajectory 4 (2009-03-10 10:36:45 to 2009-03-10 12:01:07) | Size: 1864 | Length: 14357.6m\n",
      "Bounds: (116.327347, 39.896671, 116.389611, 39.927947)\n",
      "LINESTRING (116.388053 39.903418, 116.388042 39.9035, 116.388035 39.903467, 116.388061 39.903541, 11\n",
      "Trajectory 5 (2009-02-25 09:47:03 to 2009-02-25 14:31:24) | Size: 871 | Length: 39289.3m\n",
      "Bounds: (116.294527, 39.89825, 116.38552, 40.052399)\n",
      "LINESTRING (116.385256 39.90027, 116.38525 39.900222, 116.385374 39.900249, 116.385368 39.900264, 11\n",
      "Finished creating 5 trajectories in 0:00:00.945666\n"
     ]
    }
   ],
   "source": [
    "t_start = datetime.now()\n",
    "df = read_file(filename)\n",
    "df['t'] = pd.to_datetime(df['t'])\n",
    "df = df.set_index('t')\n",
    "print(\"Finished reading {} rows in {}\".format(len(df),datetime.now() - t_start))\n",
    "\n",
    "t_start = datetime.now()\n",
    "trajectories = []\n",
    "for key, values in df.groupby(['trajectory_id']):\n",
    "    trajectory = Trajectory(key, values)\n",
    "    print(trajectory)\n",
    "    trajectories.append(trajectory)\n",
    "print(\"Finished creating {} trajectories in {}\".format(len(trajectories),datetime.now() - t_start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at one of those trajectories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"116.32485644 39.89418044 0.0672451199999955 0.03625712000000192\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,79.824618)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.00134490239999991\" points=\"116.388053,39.903418 116.388042,39.9035 116.388035,39.903467 116.388061,39.903541 116.388084,39.903586 116.388137,39.903734 116.38818,39.903834 116.388184,39.903803 116.388208,39.903817 116.388205,39.903789 116.388192,39.903674 116.388248,39.903757 116.388268,39.903776 116.388286,39.903748 116.388339,39.903825 116.388373,39.903872 116.388395,39.903898 116.388412,39.903925 116.38844,39.903977 116.388448,39.903998 116.388476,39.904092 116.388509,39.904238 116.388551,39.904416 116.388574,39.904486 116.388627,39.904648 116.388646,39.904709 116.38867,39.904773 116.388683,39.90482 116.388707,39.904884 116.388729,39.904942 116.388756,39.905011 116.388775,39.905068 116.388834,39.905185 116.388865,39.90525 116.388875,39.905259 116.38886,39.905219 116.388839,39.905207 116.388852,39.905248 116.388862,39.905288 116.388879,39.905348 116.388882,39.905382 116.388882,39.905411 116.38887,39.905423 116.388861,39.905465 116.38886,39.905475 116.38886,39.905486 116.38887,39.905524 116.388876,39.905549 116.388877,39.905574 116.388874,39.905609 116.388882,39.905661 116.388923,39.905804 116.388908,39.90582 116.388511,39.90702 116.388354,39.906981 116.388152,39.906924 116.388157,39.906947 116.388147,39.906965 116.388937,39.90722 116.389156,39.907059 116.38918,39.907048 116.389306,39.90694 116.38941,39.906849 116.389442,39.906813 116.389476,39.906772 116.38951,39.906731 116.38955,39.906692 116.389566,39.90667 116.389551,39.906598 116.389546,39.906616 116.38955,39.906651 116.38954,39.906694 116.389529,39.906708 116.389532,39.906745 116.389534,39.906781 116.389533,39.906808 116.389541,39.906846 116.389541,39.906865 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116.327587,39.9258 116.327524,39.92581 116.32755,39.925819 116.327534,39.92583 116.327541,39.925857 116.327544,39.92589 116.327565,39.925907 116.3276,39.925928 116.327627,39.925957 116.327619,39.925981 116.32759,39.926026 116.327569,39.926068 116.327518,39.926107 116.327455,39.926147 116.327408,39.926177 116.327349,39.926195 116.327347,39.926224 116.327388,39.926247 116.32745,39.92627 116.327505,39.926298 116.32753,39.926316 116.327563,39.926365 116.327585,39.926409 116.327552,39.926451 116.32753,39.926488 116.327547,39.926549 116.327577,39.926592 116.32763,39.926624 116.327667,39.926647 116.327693,39.926662 116.327733,39.926662 116.327782,39.926678 116.327842,39.92667 116.327897,39.926683 116.327938,39.926725 116.327936,39.926791 116.327896,39.926831 116.327876,39.926878 116.327934,39.926881 116.327998,39.92689 116.32804,39.926894 116.328071,39.926927 116.328138,39.926909 116.32819,39.926871 116.328243,39.926844 116.3283,39.926864 116.328367,39.926861 116.328434,39.92684 116.328493,39.926839 116.32854,39.926849 116.328568,39.926853 116.328583,39.926914 116.328606,39.926957 116.328668,39.926977 116.328729,39.92698 116.328786,39.926985 116.328845,39.927013 116.3289,39.927038 116.328949,39.92702 116.329006,39.927014 116.329072,39.927013 116.329139,39.927009 116.329205,39.926985 116.329266,39.927004 116.329334,39.927016 116.329384,39.927044 116.329451,39.927059 116.329507,39.927052 116.32956,39.927075 116.329587,39.92709 116.329663,39.92709 116.329712,39.927116 116.32975,39.927152 116.329808,39.927164 116.329836,39.927201 116.329848,39.927219 116.329902,39.927223 116.32995,39.927213 116.330026,39.927228 116.330098,39.927207 116.330156,39.927192 116.330209,39.927188 116.330244,39.927193 116.330302,39.927176 116.33035,39.927162 116.330346,39.927212 116.330356,39.927273 116.330396,39.927296 116.330436,39.927336 116.330483,39.927337 116.330512,39.927339 116.330562,39.92733 116.330611,39.927344 116.330646,39.927329 116.330661,39.927327 116.330709,39.927353 116.330775,39.927364 116.330819,39.927405 116.330865,39.927437 116.330923,39.927448 116.330979,39.927462 116.331039,39.927491 116.331093,39.927524 116.331138,39.927481 116.331182,39.927421 116.331223,39.927385 116.33126,39.927352 116.331307,39.927318 116.331354,39.927274 116.331395,39.927238 116.331439,39.927211 116.331499,39.9272 116.331559,39.927175 116.331615,39.927132 116.331671,39.927108 116.331722,39.927119 116.331742,39.927147 116.331794,39.927124 116.331852,39.927119 116.331878,39.927154 116.331916,39.927131 116.33198,39.927129 116.332029,39.927166 116.332078,39.927189 116.332119,39.927212 116.332173,39.927174 116.332226,39.927145 116.332294,39.927124 116.332352,39.927118 116.332414,39.927134 116.332457,39.927169 116.332532,39.927139 116.332598,39.927137 116.332662,39.927139 116.332715,39.927136 116.332765,39.927138 116.332838,39.927143 116.332897,39.92714 116.332949,39.927096 116.332994,39.927051 116.333026,39.927014 116.333075,39.926976 116.333095,39.926961 116.333136,39.926946 116.333202,39.926927 116.33325,39.926897 116.333307,39.926855 116.333363,39.92682 116.333362,39.926857 116.333422,39.92686 116.333431,39.926939 116.333427,39.926992 116.333476,39.927017 116.333499,39.927021 116.333551,39.927012 116.333577,39.927065 116.333603,39.927064 116.333646,39.927067 116.333713,39.927064 116.333778,39.927065 116.333843,39.92708 116.333905,39.927097 116.333955,39.927107 116.333997,39.927059 116.334048,39.927014 116.334108,39.926992 116.334151,39.926988 116.33421,39.926978 116.334265,39.926986 116.334301,39.927016 116.334362,39.927022 116.334422,39.927028 116.334488,39.927036 116.334551,39.927047 116.33462,39.927069 116.334672,39.927091 116.334734,39.927081 116.33478,39.927051 116.33484,39.927039 116.334892,39.92705 116.334945,39.927039 116.334992,39.927069 116.335068,39.92707 116.335118,39.927048 116.335193,39.927051 116.335235,39.927048 116.335264,39.927008 116.335313,39.926979 116.335372,39.926948 116.335445,39.926943 116.335501,39.926927 116.335544,39.926956 116.335588,39.926985 116.335638,39.927001 116.335695,39.927013 116.335758,39.927018 116.335817,39.927012 116.335863,39.926966 116.33592,39.926952 116.335959,39.926931 116.336029,39.926931 116.33609,39.926914 116.33615,39.926905 116.33621,39.926898 116.336258,39.926925 116.336328,39.926932 116.336372,39.926939 116.336428,39.926961 116.336491,39.926943 116.336502,39.926898 116.336513,39.926855 116.33652,39.926804 116.336525,39.926761 116.336514,39.926706 116.336496,39.926663 116.336502,39.926601 116.336521,39.926558 116.336545,39.926506 116.336584,39.926468 116.336576,39.926423 116.336517,39.9264 116.336838,39.926116 116.336694,39.925924 116.336652,39.925951 116.336439,39.926041 116.336958,39.925927 116.337001,39.92593 116.337113,39.925897 116.337158,39.925876 116.337238,39.925835 116.336986,39.925929 116.336897,39.925958 116.336866,39.925964 116.336834,39.926003 116.336872,39.926032 116.336912,39.92605 116.336916,39.926079 116.336922,39.926109 116.336933,39.926153 116.336953,39.926192 116.336971,39.926228 116.336973,39.926262 116.337001,39.926271 116.337037,39.926263 116.337096,39.926274 116.337174,39.92626 116.337224,39.926249 116.337244,39.926291 116.33726,39.92634 116.337292,39.926386 116.337332,39.926426 116.337371,39.926461 116.337409,39.926497\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x7f172885ff28>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trajectories[3].to_linestring()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding intersections with a Shapely polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3 intersections\n"
     ]
    }
   ],
   "source": [
    "xmin, xmax, ymin, ymax = 116.3685035,116.3702945,39.904675,39.907728\n",
    "polygon = Polygon([(xmin,ymin), (xmin,ymax), (xmax,ymax), (xmax,ymin), (xmin,ymin)])\n",
    "\n",
    "intersections = []\n",
    "for key, values in df.groupby(['trajectory_id']):\n",
    "    traj = Trajectory(key, values)\n",
    "    for intersection in traj.clip(polygon):\n",
    "        intersections.append(intersection)\n",
    "print(\"Found {} intersections\".format(len(intersections)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"116.3684349616 39.9056864616 0.0017445767999930695 0.0018505367999992473\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,79.81322345999999)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"3.701073599998494e-05\" points=\"116.3685035,39.90746846 116.368565,39.907466 116.368638,39.907458 116.368709,39.907448 116.368782,39.90744 116.368847,39.907441 116.368912,39.907439 116.368984,39.907434 116.36905,39.907433 116.36912,39.907437 116.369232,39.907444 116.36933,39.907447 116.369404,39.907456 116.369476,39.907442 116.369546,39.907458 116.369606,39.90745 116.369685,39.907456 116.369793,39.907459 116.369878,39.907451 116.36995,39.907447 116.370006,39.907463 116.370057,39.907439 116.370078,39.907371 116.370093,39.907325 116.370109,39.907236 116.370111,39.907187 116.37011,39.907137 116.370105,39.907081 116.370093,39.907023 116.370074,39.906962 116.370037,39.906899 116.370028,39.906829 116.370019,39.906756 116.370014,39.906681 116.370004,39.9066 116.370002,39.906524 116.369998,39.906441 116.369995,39.906369 116.369994,39.9063 116.369994,39.906235 116.369982,39.906176 116.369971,39.906121 116.369955,39.906048 116.369954,39.905985 116.369926,39.905924 116.369842,39.905898 116.369749,39.905864 116.369646,39.905847 116.369586,39.905843 116.36952,39.90584 116.369445,39.905836 116.369363,39.90583 116.369278,39.905823 116.36919,39.905815 116.3691,39.905813 116.36901,39.905819 116.36892,39.905825 116.368838,39.905822 116.368772,39.905818 116.368714,39.905815 116.368626,39.905801 116.368566,39.90578 116.368579,39.905775 116.36859,39.905772 116.368562,39.905755 116.368536,39.905777 116.368536,39.905777 116.368539,39.905772 116.3685035,39.90578557352941\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x7f1701eb1b70>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersections[2].to_linestring()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting trajectories\n",
    "\n",
    "Gaps are quite common in trajectories. For example, GPS tracks may contain gaps if moving objects enter tunnels where GPS reception is lost. In other use cases, moving objects may leave the observation area for longer time before returning and continuing their recorded track.\n",
    "\n",
    "Depending on the use case, we therefore might want to split trajectories at observation gaps that exceed a certain minimum duration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 897 | Length: 38728.7m\n",
      "Bounds: (116.319212, 39.971703, 116.592616, 40.082514)\n",
      "LINESTRING (116.590957 40.071961, 116.590905 40.072007, 116.590879 40.072027, 116.590915 40.072004, \n"
     ]
    }
   ],
   "source": [
    "print(trajectories[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory 2_0 (2009-06-29 07:02:25 to 2009-06-29 07:17:05) | Size: 152 | Length: 2554.1m\n",
      "Bounds: (116.586342, 40.07196, 116.592616, 40.082514)\n",
      "LINESTRING (116.590957 40.071961, 116.590905 40.072007, 116.590879 40.072027, 116.590915 40.072004, \n",
      "Trajectory 2_1 (2009-06-29 07:29:35 to 2009-06-29 08:20:15) | Size: 619 | Length: 33733.3m\n",
      "Bounds: (116.319212, 39.971703, 116.587085, 40.079854)\n",
      "LINESTRING (116.587031 40.07951, 116.587041 40.079514, 116.587032 40.079524, 116.586998 40.079528, 1\n",
      "Trajectory 2_2 (2009-06-29 10:57:17 to 2009-06-29 11:13:12) | Size: 126 | Length: 2326.4m\n",
      "Bounds: (116.319598, 39.999772, 116.327485, 40.007696)\n",
      "LINESTRING (116.319697 40.007513, 116.319706 40.007586, 116.319636 40.007687, 116.319598 40.007696, \n"
     ]
    }
   ],
   "source": [
    "split = trajectories[1].split_by_observation_gap(timedelta(minutes=5))\n",
    "for traj in split:\n",
    "    print(traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"116.58591984 40.07153784 0.007118320000003564 0.011398320000004958\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,80.154474)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.00022796640000009914\" points=\"116.590957,40.071961 116.590905,40.072007 116.590879,40.072027 116.590915,40.072004 116.590961,40.071983 116.591012,40.07196 116.590986,40.071984 116.590975,40.071993 116.590973,40.071995 116.590971,40.071996 116.590971,40.071996 116.59097,40.071999 116.590868,40.072105 116.590686,40.072249 116.590569,40.072501 116.590522,40.072815 116.590499,40.073112 116.590477,40.073414 116.590427,40.073659 116.590385,40.073836 116.590376,40.073902 116.590377,40.073909 116.590377,40.073909 116.590375,40.073914 116.59037,40.073947 116.590368,40.073956 116.590368,40.073956 116.590367,40.073957 116.590367,40.073957 116.590367,40.073957 116.590399,40.073989 116.590572,40.074046 116.590906,40.074084 116.591239,40.074103 116.591372,40.074108 116.59149,40.074121 116.591789,40.074157 116.592173,40.074131 116.592448,40.074038 116.592604,40.074115 116.592598,40.074178 116.592598,40.074178 116.592599,40.074182 116.592608,40.074192 116.592608,40.074192 116.592616,40.074186 116.592605,40.074195 116.59259,40.074191 116.59259,40.074191 116.592596,40.074191 116.592576,40.074202 116.592589,40.074187 116.592585,40.074195 116.592584,40.074198 116.592585,40.074198 116.592584,40.074198 116.592584,40.074198 116.592584,40.074198 116.592584,40.074198 116.592583,40.074198 116.592583,40.074198 116.592583,40.074197 116.592541,40.0743 116.592493,40.074551 116.592464,40.074882 116.592436,40.075256 116.592394,40.075638 116.592349,40.076016 116.592276,40.07639 116.592178,40.076753 116.591957,40.077053 116.591573,40.077249 116.591097,40.077314 116.590599,40.077221 116.590097,40.077144 116.589675,40.077105 116.589421,40.077166 116.589408,40.077356 116.589378,40.07763 116.589395,40.077941 116.5894,40.078235 116.589394,40.078551 116.589374,40.078893 116.589331,40.079223 116.589263,40.079594 116.589186,40.079972 116.58914,40.080333 116.589089,40.08067 116.588988,40.08103 116.588907,40.081342 116.588916,40.081568 116.589112,40.08171 116.589403,40.081833 116.589764,40.081866 116.590149,40.081798 116.590462,40.08189 116.590546,40.082094 116.590358,40.082369 116.590084,40.082511 116.58969,40.082514 116.589324,40.082396 116.588987,40.082324 116.588637,40.082307 116.588289,40.082325 116.587905,40.08233 116.587495,40.082266 116.587199,40.082251 116.587,40.082267 116.586968,40.082282 116.586949,40.082285 116.586933,40.082287 116.586918,40.082289 116.586902,40.082291 116.5869,40.082292 116.5869,40.082292 116.586901,40.082293 116.58689,40.082299 116.586793,40.08228 116.586615,40.082235 116.586492,40.082144 116.586342,40.081982 116.58637,40.081711 116.586551,40.081469 116.586793,40.08123 116.58685,40.08096 116.586913,40.080769 116.587197,40.080762 116.587594,40.0808 116.587904,40.080827 116.588008,40.080865 116.588021,40.080874 116.58802,40.080874 116.588025,40.080877 116.588025,40.080877 116.588026,40.080874 116.588048,40.080838 116.588079,40.080753 116.588085,40.080725 116.588079,40.080665 116.588098,40.080655 116.588099,40.080627 116.588112,40.080616 116.588111,40.08061 116.588108,40.080608 116.588106,40.080608 116.588104,40.080607 116.588104,40.080607 116.588104,40.080607 116.588102,40.080607 116.586902,40.079608 116.586888,40.079364 116.586843,40.07929\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x7f1701dfecf8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split[0].to_linestring()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalizing trajectories\n",
    "\n",
    "To reduce the size of trajectory objects, we can generalize them, for example, using the Douglas-Peucker algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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116.326362,40.000248 116.323486,40.000369 116.325127,40.000571 116.326878,40.00087 116.327222,40.001014 116.32746,40.000522\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x7f1701eb1978>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trajectories[1].to_linestring()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try different tolerance settings and observe the results in line geometry and therefore also length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"116.3117482 39.9673292 0.28994760000000497 0.11821359999999714\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,80.05287200000001)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.005798952000000099\" points=\"116.590957,40.071961 116.582176,40.074804 116.56637,40.035079 116.472209,39.978068 116.322487,39.994491 116.324874,39.999788 116.32746,40.000522\" opacity=\"0.8\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.linestring.LineString at 0x7f1701eb1a90>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generalized_traj = trajectories[1].generalize(mode='douglas-peucker', tolerance=0.01)\n",
    "generalized_traj.to_linestring()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original length: 38728.7215399247\n",
      "Generalized length: 29395.614300556055\n"
     ]
    }
   ],
   "source": [
    "print('Original length: %s'%(trajectories[1].get_length()))\n",
    "print('Generalized length: %s'%(generalized_traj.get_length()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th>trajectory_id</th>\n",
       "      <th>tracker</th>\n",
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       "      <th>dist_to_prev</th>\n",
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       "      <th>2009-06-29 07:02:25</th>\n",
       "      <td>1556</td>\n",
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       "      <td>2009-06-29 07:34:00</td>\n",
       "      <td>1</td>\n",
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       "      <th>2009-06-29 07:38:20</th>\n",
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       "                       id  sequence  trajectory_id  tracker  \\\n",
       "t                                                             \n",
       "2009-06-29 07:02:25  1556      1090              2        0   \n",
       "2009-06-29 07:34:00  1762      1296              2        0   \n",
       "2009-06-29 07:38:20  1814      1348              2        0   \n",
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       "2009-06-29 08:13:40  2248      1782              2        0   \n",
       "2009-06-29 11:03:32  2402      1936              2        0   \n",
       "2009-06-29 11:13:12  2452      1986              2        0   \n",
       "\n",
       "                                         geometry  \\\n",
       "t                                                   \n",
       "2009-06-29 07:02:25  POINT (116.590957 40.071961)   \n",
       "2009-06-29 07:34:00  POINT (116.582176 40.074804)   \n",
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       "2009-06-29 11:13:12   POINT (116.32746 40.000522)   \n",
       "\n",
       "                                          prev_pt  dist_to_prev  \\\n",
       "t                                                                 \n",
       "2009-06-29 07:02:25                           NaN      0.000000   \n",
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       "\n",
       "                                      t  gap  \n",
       "t                                             \n",
       "2009-06-29 07:02:25 2009-06-29 07:02:25    0  \n",
       "2009-06-29 07:34:00 2009-06-29 07:34:00    1  \n",
       "2009-06-29 07:38:20 2009-06-29 07:38:20    1  \n",
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       "2009-06-29 11:13:12 2009-06-29 11:13:12    2  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generalized_traj.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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