analysis_human_mobility_opendata.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mobility data analysis\n",
    "Notebook inspired from Open humans project https://www.openhumans.org\n",
    "\n",
    "Read mobility files from Open humans\n",
    "https://www.openhumans.org/api/public-data/?source=direct-sharing-138\n",
    "\n",
    "Author: Liubov\n",
    "Thanks to Bastian for inspiration from their notebooks \n",
    "https://exploratory.openhumans.org/notebooks/?source=Moves%20connection\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import json\n",
    "from pprint import pprint\n",
    "\n",
    "\n",
    "'''\n",
    "Functions:\n",
    " to estimate the distribution of stop duration\n",
    " to estimate the distribution of jump length\n",
    "'''\n",
    "\n",
    "def duration_stop_distribution(daily_segments):\n",
    "    \"\"\"\n",
    "    takes a daily segment of Moves \n",
    "    returns sorted distribution of stops duration\n",
    "        Can be misleading for days w/ lots of travel etc. \n",
    "    \"\"\"\n",
    "    places_of_day = []\n",
    "    duration_seq = [] #sequence of durations of stops\n",
    "    for i in daily_segments:\n",
    "        if i['type'] == 'place':\n",
    "            place_location = i['place']['location']\n",
    "            start_time = datetime.strptime(i['startTime'],'%Y%m%dT%H%M%S%z')\n",
    "            end_time = datetime.strptime(i['endTime'],'%Y%m%dT%H%M%S%z')\n",
    "            duration = end_time - start_time\n",
    "            duration_seq.append([duration])\n",
    "    duration_seq.sort() \n",
    "    return duration_seq \n",
    "\n",
    "def len_jumps_distribution(daily_segments):\n",
    "    \"\"\"\n",
    "    takes a daily segment of Moves \n",
    "    returns the distribution of jumps lengths (calculated from lat/long of stops)\n",
    "    can be misleading for days w/ lots of travel etc. \n",
    "    \"\"\"\n",
    "    places_of_traj = [] #returns array of all locations during the day\n",
    "    for i in daily_segments: \n",
    "        if i['type'] == 'place':\n",
    "            place_location = i['place']['location']\n",
    "            places_of_traj.append([place_location])\n",
    "    return places_of_traj\n",
    "\n",
    "\n",
    "def longest_daily_location(daily_segments):\n",
    "    \"\"\"\n",
    "    Function to calculate the longest daily location\n",
    "    \n",
    "    Bastian did analysis of most popular location data in his notebook for Open humans\n",
    "    https://exploratory.openhumans.org/notebooks/?source=Moves%20connection\n",
    "    \n",
    "    takes a daily segment log of Moves and returns the \n",
    "    lat/long for the location where most time was spent. \n",
    "    Can be misleading for days w/ lots of travel etc. \n",
    "    But the most quick/dirty solution for now.\n",
    "    \"\"\"\n",
    "    places_of_day = []\n",
    "    for i in daily_segments:\n",
    "        if i['type'] == 'place':\n",
    "            place_location = i['place']['location']\n",
    "            start_time = datetime.strptime(i['startTime'],'%Y%m%dT%H%M%S%z')\n",
    "            end_time = datetime.strptime(i['endTime'],'%Y%m%dT%H%M%S%z')\n",
    "            duration = end_time - start_time\n",
    "            places_of_day.append([place_location,duration])\n",
    "    places_of_day.sort(key=lambda tup: tup[-1],reverse=True)\n",
    "    return places_of_day[0][0]\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data example\n",
    "Example of data \n",
    "Read mobility files from Open humans\n",
    "\n",
    "https://www.openhumans.org/api/public-data/?source=direct-sharing-138\n",
    "  \n",
    "\n",
    "[{'caloriesIdle': 1679,\n",
    "  'date': '20130515',\n",
    "  'lastUpdate': '20151001T012320Z',\n",
    "  'segments': [{'activities': [{'activity': 'walking',\n",
    "                                'calories': 3,\n",
    "                                'distance': 48.0,\n",
    "                                'duration': 50.0,\n",
    "                                'endTime': '20130515T131851-0700',\n",
    "                                'group': 'walking',\n",
    "                                'manual': False,\n",
    "                                'startTime': '20130515T131801-0700',\n",
    "                                'steps': 96,\n",
    "                                'trackPoints': []}],\n",
    "                'endTime': '20130515T132742-0700',\n",
    "                'lastUpdate': '20151001T012320Z',\n",
    "                'place': {'id': 61807543,\n",
    "                          'location': {'lat': 33.99882, 'lon': -118.43657},\n",
    "                          'name': 'Ramuilar HQ',\n",
    "                          'type': 'home'},\n",
    "                'startTime': '20130515T131401-0700',\n",
    "                'type': 'place'},\n",
    "               {'activities': [{'activity': 'transport',\n",
    "                                'distance': 3503.0,\n",
    "                                'duration': 534.0,\n",
    "                                'endTime': '20130515T133636-0700',\n",
    "                                'group': 'transport',\n",
    "                                'manual': False,\n",
    "                                'startTime': '20130515T132742-0700',\n",
    "                                'trackPoints': []}],\n",
    "                'endTime': '20130515T133637-0700',\n",
    "                'lastUpdate': '20130515T213146Z',\n",
    "                'startTime': '20130515T132742-0700',\n",
    "                'type': 'move'},\n",
    "               {'activities': [{'activity': 'walking',\n",
    "                                'calories': 5,\n",
    "                                'distance': 92.0,\n",
    "                                'duration': 90.0,\n",
    "                                'endTime': '20130515T134039-0700',\n",
    "                                'group': 'walking',\n",
    "                                'manual': False,\n",
    "                                'startTime': '20130515T133909-0700',\n",
    "                                'steps': 184,\n",
    "                                'trackPoints': []}],\n",
    "                'endTime': '20130515T142848-0700',\n",
    "                'lastUpdate': '20140301T070136Z',\n",
    "                'place': {'foursquareCategoryIds': ['4bf58dd8d48988d14e941735'],\n",
    "                          'foursquareId': '4a6bdc59f964a52020d01fe3',\n",
    "                          'id': 61855165,\n",
    "                          'location': {'lat': 33.99050413968406,\n",
    "                                       'lon': -118.4649780392647},\n",
    "                          'name': 'Gjelina',\n",
    "                          'type': 'foursquare'},"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main questions about the mobility dataset\n",
    "Each trajectory seems random and it is hard to compare one trajectory Tr_1(t) to another Tr_2(t). \n",
    "Primarily we are interested in properties of spatial and temporal properties of Tr_i(t): \n",
    "1. distribution of duration of stops of human trajectories p(t)\n",
    "2. distribution of length of jumps between two stops q(r)\n",
    "\n",
    "These properties (distributions p(t) and q(r)) can help us to see, how different are patterns of their trajectories Tr_i(t) for different people from distributions of duration of stops and lengths of jumps.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x238e12b37b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# main function analyzing moves_data    \n",
    "DATARANGE_START = \"2016-06-01\"\n",
    "DATARANGE_END = \"2018-05-08\"\n",
    "\n",
    "import os\n",
    "import json\n",
    "import requests\n",
    "from datetime import datetime\n",
    "from collections import defaultdict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# sets the axis label sizes for seaborn\n",
    "rc={'font.size': 14, 'axes.labelsize': 14, 'legend.fontsize': 14.0, \n",
    "    'axes.titlesize': 14, 'xtick.labelsize': 14, 'ytick.labelsize': 14}\n",
    "sns.set(rc=rc)\n",
    "\n",
    "with open('moves-storyline-data.json') as f:\n",
    "    moves_data = json.load(f)\n",
    "    \n",
    "with open('moves-storyline-data98933.json') as f: #uploading different datafiles\n",
    "    moves_data2 = json.load(f)\n",
    "\n",
    "with open('moves-storyline-data98972.json') as f:\n",
    "    moves_data3 = json.load(f)   \n",
    "    \n",
    "#for printing this huge file of trajectories:    \n",
    "#pprint(data)\n",
    "    \n",
    "\n",
    "moves_processed_data = defaultdict(list) #empty dict for processed dataframe\n",
    "\n",
    "'''\n",
    "function to create new dataframe from the json trajectory file\n",
    "code adapted from openhumans notebook\n",
    "'''\n",
    "\n",
    "def dataframe_from_json(moves_data):\n",
    "    for datapoint in moves_data:\n",
    "        # we need to have observed segments for that day. If moves wasn't running we ignore the day\n",
    "        if datapoint['segments'] != None:\n",
    "            # did we stay in a place that day and did we walk that day?\n",
    "            has_places = False\n",
    "            walked = False\n",
    "            for i in datapoint['segments']:\n",
    "                if i['type'] == 'place':\n",
    "                        # yes, we were in one place w/o moving around too much, we can keep this day\n",
    "                        has_places = True\n",
    "                        \n",
    "            # is this day in our date range of interest and has data?\n",
    "            if datapoint['summary'] != None and has_places and datetime.strptime(datapoint['date'],\"%Y%m%d\") > datetime.strptime(DATARANGE_START,\"%Y-%m-%d\"):\n",
    "                moves_processed_data['date'].append(datapoint['date'])\n",
    "                for activity in datapoint['summary']:\n",
    "                    if activity['activity'] == 'walking':\n",
    "                        moves_processed_data['steps'].append(activity['steps'])\n",
    "                        moves_processed_data['distance'].append(activity['distance'])\n",
    "                        walked = True\n",
    "                        \n",
    "                # in case of not walking, step count is zero\n",
    "                if not walked:\n",
    "                    moves_processed_data['steps'].append(0)\n",
    "                    moves_processed_data['distance'].append(0)  \n",
    "                    \n",
    "                # distribution of stops lengths\n",
    "                stops_distrib = duration_stop_distribution(datapoint['segments'])\n",
    "                moves_processed_data['duration'].append(stops_distrib)\n",
    "                location = longest_daily_location(datapoint['segments'])\n",
    "                moves_processed_data['lat'].append(location['lat'])\n",
    "                moves_processed_data['lon'].append(location['lon'])    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #Now that we have all of the data we can convert it into a single pandas dataframe for easier processing and visualization\n",
    "    \n",
    "    moves_dataframe = pd.DataFrame(data={\n",
    "        'date': moves_processed_data['date'],\n",
    "        'steps': moves_processed_data['steps'],\n",
    "        'distance': moves_processed_data['distance'],\n",
    "        'latitude': moves_processed_data['lat'],\n",
    "        'longitude': moves_processed_data['lon']\n",
    "    })   \n",
    "    \n",
    "    return moves_dataframe\n",
    "\n",
    "df1 = dataframe_from_json(moves_data)\n",
    "df2 = dataframe_from_json(moves_data2)\n",
    "df3 = dataframe_from_json(moves_data3)\n",
    "\n",
    "df1.head()\n",
    "\n",
    "#print shape of data\n",
    "array_distance = df1.distance.values \n",
    "type(array_distance)\n",
    "\n",
    "\n",
    "\n",
    "#Now we plot distribution of moves from new dataframe \n",
    "# We compare moves from 1st trajectory and 2nd trajectory\n",
    "\n",
    "sns.distplot(df1['distance'])\n",
    "sns.distplot(df2['distance'])\n",
    "sns.distplot(df3['distance'])\n",
    "# sns.distplot(stops_distrib)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df1.head()\n",
    "header = [\"date\", \"steps\", \"distance\", \"latitude\", \"longitude\"]\n",
    "df1.to_csv('trajectory_oh_1_person_lat_lon.csv', columns = header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22ebe811be0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the distribution of latitude and longitude together to make a comparison\n",
    "'''\n",
    "TODO:\n",
    "to plot latitude and longitude  (on different axes)\n",
    "geolocation as a map\n",
    "\n",
    "'''\n",
    "\n",
    "sns.distplot(df3['longitude'])\n",
    "sns.distplot(df3['latitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d04f02828>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df3['longitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations from data analysis\n",
    "\n",
    "By looking at the variation of latitude and longitude coordinates we can see bimodality of trajectories, \n",
    "which correspond, most probably to \"home\" <-> \"work\". \n",
    "We will explore this below using CTRW models.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
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-118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [44.970341, -93.26981], [44.970341, -93.26981], [44.970341, -93.26981], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345], [38.991153, -77.096001], [33.95853237731105, -118.4369816413345], [33.92070436362096, -118.3977152092923], [33.92070436362096, -118.3977152092923], [33.92070436362096, -118.3977152092923], [33.95853237731105, -118.4369816413345], [33.95853237731105, -118.4369816413345]],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "red", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "noClip": false, "opacity": 1.0, "smoothFactor": 1.0, "stroke": true, "weight": 5}
            ).addTo(map_fa6fc971858d436da04554c09187b171);
        
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x238e1426da0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "We plot location of the most popular places from trajectories.\n",
    "Input:\n",
    "latitude and longitude from dataframe moves_dataframe\n",
    "  moves_dataframe = pd.DataFrame(data={\n",
    "        'date': moves_processed_data['date'],\n",
    "        'steps': moves_processed_data['steps'],\n",
    "        'distance': moves_processed_data['distance'],\n",
    "        'latitude': moves_processed_data['lat'],\n",
    "        'longitude': moves_processed_data['lon']\n",
    "    }) \n",
    "\n",
    "Output:\n",
    "Folium map with datapoints\n",
    "'''\n",
    "\n",
    "import folium\n",
    "\n",
    "\n",
    "#first we get trajectory datapoints from the file\n",
    "latitude1 = df1.latitude.values\n",
    "longitude1 = df1.longitude.values\n",
    "latitude2 = df2.latitude.values\n",
    "longitude2 = df2.longitude.values\n",
    "latitude3 = df3.latitude.values\n",
    "longitude3 = df3.longitude.values\n",
    "\n",
    "#then we zip two arrays of lat, lon of datapoints\n",
    "latlon1 = list(zip(latitude1, longitude1))\n",
    "latlon2 = list(zip(latitude2, longitude2))\n",
    "latlon3 = list(zip(latitude3, longitude3))\n",
    "#print(latlon)\n",
    "\n",
    "#We put map to show first location of New York 40.7808 et la longitude de la ville de New York City est -73.9772.\n",
    "\n",
    "mapit = folium.Map( location=[40.7808 , -73.9772], zoom_start=6 )\n",
    "for coord in latlon3:\n",
    "    folium.Marker( location=[ coord[0], coord[1] ], fill_color='#43d9de', radius=8 ).add_to( mapit )\n",
    "mapit\n",
    "\n",
    "\n",
    "#draw connecting line between locations written in latlon file\n",
    "\n",
    "'''\n",
    "TODO:\n",
    "to add information to nodes of the map with added info about cities; \n",
    "plot curved ages multigraph\n",
    "'''\n",
    "\n",
    "SAF=folium.PolyLine(locations=latlon1,weight=5,color = 'red')\n",
    "mapit.add_child(SAF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save trajectories to file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "\n",
    "latitude1 = df1.latitude.values\n",
    "longitude1 = df1.longitude.values\n",
    "\n",
    "#weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
    "#sales = ['10', '8', '19', '12', '25']\n",
    "\n",
    "with open('trajectories.csv', 'w') as csv_file:\n",
    "    csv_writer = csv.writer(csv_file, delimiter=',')\n",
    "    csv_writer.writerow(latitude1)\n",
    "    csv_writer.writerow(longitude1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Continuous time random walk analysis of trajectories\n",
    "### Definition\n",
    "*Continuous time random walk* (CTRW) is a generalization of a random walk where the wandering particle waits for a random time between jumps. \n",
    "It is a stochastic jump process with arbitrary distributions of jump lengths and waiting times.\n",
    "More generally it can be seen to be a special case of a Markov renewal process. \n",
    "\n",
    "Important properties of CTRW are r(t) and d(t) distributions of lengths of jumps and durations correspondingly. \n",
    "\n",
    "For more details look at code and papers here \n",
    "https://github.com/Liyubov/networks_random_walking\n",
    "\n",
    "### Analysis \n",
    "\n",
    "We use here several methods of analysis of trajectories using CTRW framework. \n",
    "\n",
    "1. Standard deviation. \n",
    "Given the input trajectory r(t) =(x(t),y(t)), we first estimate standard deviation for trajectory (STD), which is just the root of variance so var(τ)=(Var(r(t)-r(t-τ))), calculating it in Euclidean space.\n",
    "\n",
    "\n",
    "2. \n",
    "One can also estimate Hurst exponent, which is used as a measure of long-term memory of time series\n",
    "https://en.wikipedia.org/wiki/Hurst_exponent#Estimating_the_exponent.\n",
    "Hurst exponent relates to the autocorrelations of the time series, and the rate at which these decrease as the lag between pairs of values increases. \n",
    "For any trajectory we can write $Var(τ) ∝ τ^{(2H)}$, where $H$ is the Hurst exponent.\n",
    "Hence $(Var(z(t)-z(t-τ))) ∝ τ^{(2H)}$. \n",
    "Then \n",
    "$[log (Var(z(t)-z(t-τ))) / log τ ] / 2 ∝ H$ (gives the Hurst exponent) where we know the term in square brackets on far left is the slope of a log-log plot of tau and a corresponding set of variances.\n",
    "*Range of Hurst exponent.*\n",
    "A value H in the range 0.5–1 indicates a time series with long-term positive autocorrelation, meaning both that a high value in the series will probably be followed by another high value and that the values a long time into the future will also tend to be high. A value in the range 0 – 0.5 indicates a time series with long-term switching between high and low values in adjacent pairs, meaning that a single high value will probably be followed by a low value and that the value after that will tend to be high, with this tendency to switch between high and low values lasting a long time into the future. A value of $H=0.5$ can indicate a completely uncorrelated series.\n",
    "\n",
    "Basically, the idea of Hurst exponent is to characterize trajectory in terms of self-repetition. If Hurst exponent is between $[0,0.5]$, then it indicates that trajectory has switches between different regimes. \n",
    "While if Hurst exponent  $[0.5, 1]$, then it means that there are less switches between long and short jumps and trajectory has long-term positive autocorrelation. If Hurst exponent is 0.5, then it indicates completely uncorrelated series (although it depends on scales). \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating STD of first trajectory \n",
      "std of trajectory :  75.8244193362\n",
      "hurtst exponent\n",
      "0.0316731771675\n"
     ]
    }
   ],
   "source": [
    "\n",
    "'''\n",
    "We adapt the function to estimate:\n",
    "1.  STD of trajectories;\n",
    "2.  Hurst exponent for trajectories.\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "print(\"Calculating STD of the first trajectory \")  \n",
    "\n",
    "#load trajectory in 2D \n",
    "data1 = np.asarray(latlon1)\n",
    "print(\"std of trajectory : \", np.std(data1)) \n",
    "\n",
    "\n",
    "lags = range(2,100)\n",
    "def hurst_exponen_chan(p):\n",
    "    '''\n",
    "    given time-series p(t), where t is time \n",
    "    '''    \n",
    "    variancetau = []; tau = []\n",
    "\n",
    "    for lag in lags: \n",
    "\n",
    "        #  Write the different lags into a vector to compute a set of tau or lags\n",
    "        tau.append(lag)\n",
    "\n",
    "        # Compute the log returns on all days, then compute the variance on the difference in log returns\n",
    "        # call this pp or the price difference\n",
    "        pp = np.subtract(p[lag:], p[:-lag])\n",
    "        variancetau.append(np.var(pp))\n",
    "\n",
    "    # we now have a set of tau or lags and a corresponding set of variances.\n",
    "    #print tau\n",
    "    #print variancetau\n",
    "\n",
    "    # plot the log of those variance against the log of tau and get the slope\n",
    "    m = np.polyfit(np.log10(tau),np.log10(variancetau),1)\n",
    "\n",
    "    hurst = m[0] / 2\n",
    "\n",
    "    return hurst\n",
    "\n",
    "print('hurtst exponent')\n",
    "print(hurst_exponen_chan(data1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation of scaling exponent of trajectory \n",
    "\n",
    "Given trajectory displacements r(t) = t^(-b-1),  we estimate b constant from the trajectory. \n",
    "Then  we fit this distribution of travelling distances to a power law. \n",
    "\n",
    "If parameter b<2, then this indicates that trajectories are reminiscent of scale-free random walks known as Le´vy flights. If parameter b>2, this indicates that trajectory generating process is related to normal spreading.\n",
    "\n",
    "For this we are using Python package for  analysis of diffusion data\n",
    "https://diffusion-analysis-md-simulations.readthedocs.io/en/latest/diffusion_analysis.html\n",
    "\n",
    "    *diffusion_analysis.fit_anomalous_diffusion_data(time_data_array, MSD_data_array, degrees_of_freedom=2)*\n",
    "    \n",
    "function should fit anomalous diffusion data to Equation 1 in [Kneller2011], and return appropriate diffusion parameters:    MSD=ND_αt^α.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating beta constant of first trajectory \n",
      "std of trajectory :  75.8244193362\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "We adapt the function to estimate:\n",
    "1.  STD of trajectories;\n",
    "2.  beta scaling exponent \n",
    "'''\n",
    "\n",
    "import numpy as np \n",
    "#to prepare for diffusion analysis, load my github-hosted diffusion module:\n",
    "import sys; sys.path.append('/sansom/sc2/bioc1009/github_projects/diffusion_utility/diffusion_analysis_MD_simulations')\n",
    "#import diffusion_analysis\n",
    "\n",
    "\n",
    "print(\"Calculating beta scaling exponent of first trajectory \")  \n",
    "\n",
    "#load trajectory in 2D \n",
    "data1 = np.asarray(latlon1)\n",
    "print(\"std of trajectory : \", np.std(data1)) \n",
    "\n",
    "#diffusion_analysis.fit_anomalous_diffusion_data(time_data_array, MSD_data_array)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convex hull analysis of trajectories \n",
    "\n",
    "Given trajectory in 2D (in general in N dimensions) we can characterize some of its properties by the applying convex hull algorithms. \n",
    "\n",
    "### Example\n",
    "\n",
    "Given random trajectory in N-dimensions \n",
    "<img src=\"convex.png\" alt=\"Drawing\" style=\"width: 200px;\"/>\n",
    "we draw the polygon around it, and estimate its volume, which allows us to characterize the trajectory qualitatively .\n",
    "\n",
    "\n",
    "General information about convex hull\n",
    "https://en.wikipedia.org/wiki/Convex_hull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data [[  32.71696    -117.13974   ]\n",
      " [  33.99882    -118.43657   ]\n",
      " [  33.99882    -118.43657   ]\n",
      " ..., \n",
      " [  33.92070436 -118.39771521]\n",
      " [  33.95853238 -118.43698164]\n",
      " [  33.95853238 -118.43698164]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume is 1.33\n",
      "Area is 10.47\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "#from RDP import rdp #import rdp\n",
    "import networkx as nx\n",
    "from scipy.spatial import ConvexHull\n",
    "\n",
    "\n",
    "N = 10 # number of points  \n",
    "M = 5 # dimensions\n",
    "\n",
    "\"\"\"\n",
    "LOADING input data:\n",
    "# entries:  public_i pub_tag_i pub_time_i  \n",
    "# public#i (Name and title encoded into number -> separate table) \n",
    "# tag of publication: there are finite number of tags (some of them are not corresponding to one field)\n",
    "# time index is better to make in linear scale -> 1:365*y, where y is number of years)\n",
    "\"\"\"\n",
    "\n",
    "#data points should be data from trajectories of researchers\n",
    "data = np.random.rand(N,M) #generate N datapoints in M dimensions\n",
    "#data = np.random.rand(30, 2)   # 30 random points in 2-D\n",
    "\n",
    "#load trajectory from Openhumans file\n",
    "data1 = np.asarray(latlon1) #give array, not the list\n",
    "data2 = np.asarray(latlon2) #get data from the second participant\n",
    "\n",
    "#print(data)\n",
    "                       \n",
    "\"\"\"\n",
    "function ALGORITHM convext hull\n",
    "\"\"\"\n",
    "         \n",
    "\n",
    "def convex_hull(data):\n",
    "              \n",
    "    hull = ConvexHull(data)\n",
    "    print('data', data)\n",
    "\n",
    "    plt.plot(data[:,0], data[:,1], 'o')\n",
    "    for simplex in hull.simplices:\n",
    "        plt.plot(data[simplex, 0], data[simplex, 1], 'k-')\n",
    "    \n",
    "    \n",
    "    plt.plot(data[hull.vertices,0], data[hull.vertices,1], 'r--', lw=2)\n",
    "    plt.plot(data[hull.vertices[0],0], data[hull.vertices[0],1], 'ro')\n",
    "    plt.show()   \n",
    "\n",
    "\n",
    "    \"\"\"\n",
    "    estimate the volume of the convex hull for trajectory in time frame\n",
    "    \"\"\" \n",
    "\n",
    "\n",
    "    points = np.array([[-1,-1, 1], [1, 1, 0], [-1, 1, 0], [1,-1, -1]])\n",
    "    hull = ConvexHull(points)\n",
    "    print(\"Volume is %2.2f\" % hull.volume) # N-d object\n",
    "    print(\"Area is %2.2f\" % hull.area) # (N-1)-d object\n",
    "\n",
    "    \n",
    "'''\n",
    "Now we apply convex hull to different data\n",
    "'''\n",
    "convex_hull(data1)\n",
    "#convex_hull(data2)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convex hull sliding window analysis of trajectories\n",
    "We also analyze trajectory using sliding window analysis, where we measure the volume of convex hull V(t,t+delta t) for trajectory X(t) during the period of (delat t). \n",
    "To read more about convex hull algorithm applied to particle trajectories \n",
    "https://arxiv.org/pdf/1708.06517.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steps  1424\n",
      "making sliding window analysis...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "#from RDP import rdp #import rdp\n",
    "import networkx as nx\n",
    "from scipy.spatial import ConvexHull\n",
    "\n",
    "\n",
    "N = 10 # number of points  \n",
    "M = 5 # dimensions\n",
    "\n",
    "\"\"\"\n",
    "LOADING input data:\n",
    "# entries:  public_i pub_tag_i pub_time_i  \n",
    "# public#i (Name and title encoded into number -> separate table) \n",
    "# tag of publication: there are finite number of tags (some of them are not corresponding to one field)\n",
    "# time index is better to make in linear scale -> 1:365*y, where y is number of years)\n",
    "\"\"\"\n",
    "\n",
    "#data points should be data from trajectories of researchers\n",
    "#data = np.random.rand(N,M) #generate N datapoints in M dimensions\n",
    "#data = np.random.rand(30, 2)   # 30 random points in 2-D\n",
    "\n",
    "#load trajectory from Openhumans file\n",
    "data1 = np.asarray(latlon1) #give array, not the list\n",
    "#data2 = np.asarray(latlon2) #get data from the second participant\n",
    "\n",
    "#print(data)\n",
    "                       \n",
    "\"\"\"\n",
    "function ALGORITHM convext hull\n",
    "\"\"\"\n",
    "size_window = 100 #sliding window should not be too small, otherwise it is not convex\n",
    "size_window2 = 200 #we study how changing the sliding window changes the convex hull\n",
    "\n",
    "steps = np.size(data1) \n",
    "print('steps ', steps)\n",
    "\n",
    "                       \n",
    "def convex_hull_window(data):\n",
    "              \n",
    "    hull = ConvexHull(data)\n",
    "\n",
    "#    plt.plot(data[:,0], data[:,1], 'o')\n",
    "#    for simplex in hull.simplices:\n",
    "#        plt.plot(data[simplex, 0], data[simplex, 1], 'k-')   \n",
    "#    plt.plot(data[hull.vertices,0], data[hull.vertices,1], 'r--', lw=2)\n",
    "#    plt.plot(data[hull.vertices[0],0], data[hull.vertices[0],1], 'ro')\n",
    "#    plt.show()   \n",
    "\n",
    "\n",
    "    #points = np.array([[-1,-1, 1], [1, 1, 0], [-1, 1, 0], [1,-1, -1]])\n",
    "    #hull = ConvexHull(data)\n",
    "    #print(hull.volume) # Volume of N-d object \"Volume is %2.2f\" % \n",
    "    #print(\"Area is %2.2f\" % hull.area) # (N-1)-d object\n",
    "    return hull.volume\n",
    "\n",
    "\n",
    "def convex_hull_sliding_window(data, steps, size_window):\n",
    "    '''\n",
    "    data - trajectory\n",
    "    steps - number of time steps in trajectory \n",
    "    size_window - time size of the sliding window\n",
    "    '''\n",
    "\n",
    "    volume_array = np.zeros(steps)\n",
    "    \n",
    "    for itime in range(0, int((steps-size_window)/2)): #steps-1):\n",
    "        #print(itime)\n",
    "        time_max = (itime +size_window)%(steps+1) #calculate maximum time for sliding window\n",
    "        data_i = data[itime: time_max] #cut data_i  from origianal data by cutting trajectory \n",
    "        volume_array[itime] = convex_hull_window(data_i)#apply convex_hull function\n",
    "        #make exception for non-convex areas\n",
    "    return volume_array\n",
    "\n",
    "'''\n",
    "Apply sliding window analysis to example trajectory\n",
    "'''\n",
    "\n",
    "\n",
    "print('making sliding window analysis...')\n",
    "\n",
    "conv_array1 = convex_hull_sliding_window(np.asarray(data1), steps, size_window)\n",
    "\n",
    "conv_array1 = convex_hull_sliding_window(np.asarray(data1), steps, size_window2)\n",
    "                                         \n",
    "conv_array2 = convex_hull_sliding_window(np.asarray(data2), steps, size_window)\n",
    "\n",
    "\n",
    "#np.savetxt('convex_array.out', conv_array)#, delimiter=',') \n",
    "plt.plot(conv_array1)\n",
    "plt.plot(conv_array2)\n",
    "plt.title(\"Convex hull volume\")\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"Convex volume\")\n",
    "plt.legend(['1st trajectory', '2nd trajectory']) #plt.legend([\"Time window size\"])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recurrence network analysis of trajectories\n",
    "\n",
    "(under construction). \n",
    "Given the trajectory (in 2D or 3D) we can calculate its recurrence matrix.\n",
    "\n",
    "<img src=\"recurrence.JPG\" alt=\"Drawing\" style=\"width: 200px;\"/>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# code adapted from recurrence netwokr analysis\n",
    "\n",
    "#!/usr/bin/python\n",
    "# coding: UTF-8\n",
    "from __future__ import division, print_function\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "\n",
    "def rec_plot(s, eps=0.10, steps=10):\n",
    "    d = pdist(s[:,None])\n",
    "    d = np.floor(d/eps)\n",
    "    d[d>steps] = steps\n",
    "    Z = squareform(d) #distance\n",
    "    return Z\n",
    "\n",
    "def moving_average(s, r=5):\n",
    "    return np.convolve(s, np.ones((r,))/r, mode='valid')\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Generate singal\n",
    "    N = 200\n",
    "    eps = 0.1\n",
    "    steps = 10\n",
    "\n",
    "    ru = np.random.uniform(low=-1, high=1, size=N)\n",
    "    ru_filtered = moving_average(ru)\n",
    "\n",
    "    # Plot unifrom dist filtered with moving average\n",
    "\n",
    "    plt.title(\"Normal\")\n",
    "    plt.subplot(221)\n",
    "    plt.plot(ru_filtered)\n",
    "    plt.title(\"Unitary\")\n",
    "    plt.subplot(223)\n",
    "    plt.imshow(rec_plot(ru_filtered, eps=eps, steps=steps))\n",
    "\n",
    "    # Plot normal dist filtered with moving average\n",
    "    rn = np.random.normal(size=N)\n",
    "    rn_filtered = moving_average(rn)\n",
    "\n",
    "    plt.subplot(222)\n",
    "    plt.plot(rn_filtered)\n",
    "    plt.title(\"Normal\")\n",
    "    plt.subplot(224)\n",
    "    plt.imshow(rec_plot(rn_filtered, eps=eps, steps=steps))\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Scaling laws\n",
    "Now we analyze scaling laws of human travels, inspired by work [The scaling laws of human travel] by D. Brockmann, L. Hufnagel & T. Geisel.\n",
    "\n",
    "\n",
    "### Hypothesis\n",
    "For the long trajectory which we have the distribution of travelling distances decays as a power law, indicating that trajectories of peopla are reminiscent of scale-free random walks known as Le´vy flight.\n",
    "\n",
    "### Estimation of scaling exponent of trajectory \n",
    "\n",
    "Given trajectory displacements $r(t) = t^{(-b-1)}$,  we estimate b constant from the trajectory. \n",
    "Then  we fit this distribution of travelling distances to a power law. \n",
    "\n",
    "If parameter b<2, then this indicates that trajectories are reminiscent of scale-free random walks known as Le´vy flights. If parameter b>2, this indicates that trajectory generating process is related to normal spreading.\n",
    "\n",
    "For this we are using Python package for  analysis of diffusion data\n",
    "https://diffusion-analysis-md-simulations.readthedocs.io/en/latest/diffusion_analysis.html\n",
    "\n",
    "    *diffusion_analysis.fit_anomalous_diffusion_data(time_data_array, MSD_data_array, degrees_of_freedom=2)*\n",
    "    \n",
    "function should fit anomalous diffusion data to Equation 1 in [Kneller2011], and return appropriate diffusion parameters:    $MSD=ND_αt^α$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating beta constant of first trajectory \n",
      "std of trajectory :  75.8244193362\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "We adapt the function to estimate:\n",
    "1.  STD of trajectories;\n",
    "2.  beta scaling exponent \n",
    "'''\n",
    "\n",
    "import numpy as np \n",
    "#to prepare for diffusion analysis, load my github-hosted diffusion module:\n",
    "import sys; sys.path.append('/sansom/sc2/bioc1009/github_projects/diffusion_utility/diffusion_analysis_MD_simulations')\n",
    "#import diffusion_analysis\n",
    "\n",
    "\n",
    "print(\"Calculating beta scaling exponent of first trajectory \")  \n",
    "\n",
    "#load trajectory in 2D \n",
    "data1 = np.asarray(latlon1)\n",
    "print(\"std of trajectory : \", np.std(data1)) \n",
    "\n",
    "#diffusion_analysis.fit_anomalous_diffusion_data(time_data_array, MSD_data_array)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          t          x         y\n",
      "0  0.000000 -117.13974  32.71696\n",
      "1  1.001406 -118.43657  33.99882\n",
      "2  2.002813 -118.43657  33.99882\n",
      "3  3.004219 -118.43657  33.99882\n",
      "4  4.005626 -118.43657  33.99882\n",
      "         msds       tau    msds_std\n",
      "0    0.000000  0.000000    0.000000\n",
      "1  183.466353  1.001406  535.248332\n",
      "2  200.418657  2.002813  683.228374\n",
      "3  164.631306  3.004219  501.115540\n",
      "4  135.656714  4.005626  495.768392\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x238ec75ef60>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def compute_msd(trajectory, t_step, coords=['x', 'y']):\n",
    "\n",
    "    tau = trajectory['t'].copy()\n",
    "    shifts = np.floor(tau / t_step).astype(np.int)\n",
    "    msds = np.zeros(shifts.size)\n",
    "    msds_std = np.zeros(shifts.size)\n",
    "\n",
    "    for i, shift in enumerate(shifts):\n",
    "        diffs = trajectory[coords] - trajectory[coords].shift(-shift)\n",
    "        sqdist = np.square(diffs).sum(axis=1)\n",
    "        msds[i] = sqdist.mean()\n",
    "        msds_std[i] = sqdist.std()\n",
    "\n",
    "    msds = pd.DataFrame({'msds': msds, 'tau': tau, 'msds_std': msds_std})\n",
    "    return msds\n",
    "\n",
    "# load dataframe of trajectory\n",
    "df1.head()\n",
    "traj = df1\n",
    "dt = len(df1.index)\n",
    "df1.head()\n",
    "\n",
    "# Parameters\n",
    "N = 5000\n",
    "max_time = 100\n",
    "dt = max_time / N\n",
    "\n",
    "t = np.linspace(0,  len(df1.index),  len(df1.index))\n",
    "latitude1 = df1.latitude.values # y\n",
    "longitude1 = df1.longitude.values #x\n",
    "\n",
    "\n",
    "#xy = np.cumsum(np.random.choice([-1, 0, 1], size=(N, 2)), axis=0)\n",
    "traj = pd.DataFrame({'t': t, 'x': longitude1, 'y': latitude1})\n",
    "print(traj.head())\n",
    "\n",
    "\n",
    "\n",
    "# Compute MSD\n",
    "msd = compute_msd(traj, t_step=dt, coords=['x', 'y'])\n",
    "print(msd.head())\n",
    "\n",
    "# Plot MSD\n",
    "ax = msd.plot(x=\"tau\", y=\"msds\", logx=True, logy=True, legend=False)\n",
    "ax.fill_between(msd['tau'], msd['msds'] - msd['msds_std'], msd['msds'] + msd['msds_std'], alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# TODO: \n",
    " 1. to plot days on x-axis\n",
    " 2. extract seasonality from data  \n",
    " 3. make statistics about visits of cities; bimodality: where people are traveling and where people stay \n",
    " 4. can we identify places for conferences from this data? \n",
    " 5. extract max radius for which people can travel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}