bicimad-station-snapshots-analysis.ipynb

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   "source": [
    "# BiciMAD - Station snapshots analysis\n",
    "Copyright © 2017 Javi Ramírez <javi.rmrz@gmail.com> | [@rameerez [tw]](http://twitter.com/rameerez) | [GitHub](http://github.com/rameerez)\n",
    "\n",
    "This code is Open Source, released under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why/what\n",
    "\n",
    "BiciMAD has more than 170 bike stations. What can we do only with each station's usage data?\n",
    "\n",
    "![BiciMAD station by r2hox](img/bicimad-station.jpg)\n",
    "_Photo credit: [r2hox/Flickr](https://www.flickr.com/photos/rh2ox/14927647521)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset description\n",
    "\n",
    "The data provided by BiciMAD is a hourly snapshot of all the stations in the system, and has the following strcture:\n",
    "\n",
    "| Field name     | Description                                                                               |\n",
    "|----------------|-------------------------------------------------------------------------------------------|\n",
    "| ```_id```      | Timestamp of the `stations` snapshot                                                      |\n",
    "| ```stations``` | JSON. Array of all stations in the network. Each element shows the status of one station. |\n",
    "\n",
    "And each of the `station` object has the following structure:\n",
    "\n",
    "| Field name           | Description                                                                                         |\n",
    "|----------------------|-----------------------------------------------------------------------------------------------------|\n",
    "| `id`                 | Station unique ID                                                                                   |\n",
    "| `latitude`           | Station's latitude in format WGS84                                                                  |\n",
    "| `longitude`          | Station's longitude in format WGS84                                                                 |\n",
    "| `name`               | Station name                                                                                        |\n",
    "| `light`              | Occupation level (0=low; 1=med; 2=high)                                                             |\n",
    "| `number`             | Station logic denomination. (Some stations have more than 1 \"part\", `number` might be \"1A\" or \"1B\") |\n",
    "| `activate`           | Is the station active? (0=not active; 1=active)                                                     |\n",
    "| `no_available`       | Station availability (0=available; 1=not available)                                                 |\n",
    "| `total_bases`        | Total number of bike bases within the station                                                       |\n",
    "| `dock_bikes`         | Number of docked bikes in the station                                                               |\n",
    "| `free_bases`         | Number of free bases                                                                                |\n",
    "| `reservations_count` | Number of active reservations                                                                       |\n",
    "\n",
    "These tables are only provided as reference. Please refer to the latest documentation for the BiciMAD API in the [EMT OpenData website](http://opendata.emtmadrid.es).\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global definitions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Hourly stations' snapshot: data describing all stations status each hour\n",
    "STATIONS_SNAPSHOT_DATASET = './data/stations_hour_20170301_20170406.json'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas.io.json import json_normalize\n",
    "from datetime import datetime"
   ]
  },
  {
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   "execution_count": 7,
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    "import matplotlib.pyplot as plt\n",
    "from bokeh.io import output_file, output_notebook, show\n",
    "from bokeh.models import (\n",
    "  GMapPlot, GMapOptions, ColumnDataSource, Circle, DataRange1d, PanTool, WheelZoomTool, BoxSelectTool\n",
    ")\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.models.sources import ColumnDataSource\n",
    "from bokeh.sampledata.sample_geojson import geojson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    "output_notebook()"
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# Watch out! lines=True needed\n",
    "df_raw = pd.read_json(STATIONS_SNAPSHOT_DATASET, lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "\n",
    "To analyze the data, we want tables with this format:\n",
    "\n",
    "| timestamp                  | activate | address             | dock_bikes | free_bases | id | latitude   | longitude  | total_bases | ... |\n",
    "|----------------------------|----------|---------------------|------------|------------|----|------------|------------|-------------|-----|\n",
    "| 2017-03-22T22:49:17.396000 | 1        | Puerta del Sol nº 1 | 2          | 20         | 1  | 40.4168961 | -3.7024255 | 24          | ... |\n",
    "\n",
    "Note that table is incomplete and does not show all the available features.\n",
    "\n",
    "In a nutshell, what we want is a dataframe containing **all** the stations for every single snapshot. Each station (row) will be tagged with its corresponding timestamp from its snapshot.\n",
    "\n",
    "That is, we want instances of each station along time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "frames = []\n",
    "\n",
    "for index, row in df_raw.iterrows():\n",
    "    timestamp= row['_id']\n",
    "    stations = json_normalize(row['stations'])\n",
    "    stations['timestamp'] = timestamp\n",
    "    frames.append(stations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
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   "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>activate</th>\n",
       "      <th>address</th>\n",
       "      <th>dock_bikes</th>\n",
       "      <th>free_bases</th>\n",
       "      <th>id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>light</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>no_available</th>\n",
       "      <th>number</th>\n",
       "      <th>reservations_count</th>\n",
       "      <th>total_bases</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Puerta del Sol n� 1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>40.4168961</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.7024255</td>\n",
       "      <td>Puerta del Sol A</td>\n",
       "      <td>0</td>\n",
       "      <td>1a</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2017-03-22T22:49:17.396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Puerta del Sol n� 1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>40.4170009</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.7024207</td>\n",
       "      <td>Puerta del Sol B</td>\n",
       "      <td>0</td>\n",
       "      <td>1b</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2017-03-22T22:49:17.396000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   activate              address  dock_bikes  free_bases  id    latitude  \\\n",
       "0         1  Puerta del Sol n� 1           2          20   1  40.4168961   \n",
       "1         1  Puerta del Sol n� 1           3          20   2  40.4170009   \n",
       "\n",
       "   light   longitude              name  no_available number  \\\n",
       "0      0  -3.7024255  Puerta del Sol A             0     1a   \n",
       "1      0  -3.7024207  Puerta del Sol B             0     1b   \n",
       "\n",
       "   reservations_count  total_bases                   timestamp  \n",
       "0                   0           24  2017-03-22T22:49:17.396000  \n",
       "1                   0           24  2017-03-22T22:49:17.396000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat(frames)\n",
    "df[:2] # showing only two rows to keep it short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-f62f06b01f54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstation1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m174\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mstation2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m101\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mstation3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "station1 = df.loc[df['id'] == 174]\n",
    "station2 = df.loc[df['id'] == 101]\n",
    "station3 = df.loc[df['id'] == 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay! Now we can see how a single station has performed along time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def datetime(x):\n",
    "    return np.array(x, dtype=np.datetime64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "p1 = figure(x_axis_type=\"datetime\", title=\"Station free bases\")\n",
    "p1.grid.grid_line_alpha=0.3\n",
    "p1.xaxis.axis_label = 'Date'\n",
    "p1.yaxis.axis_label = '# of free bases'\n",
    "\n",
    "p1.line(datetime(station1['timestamp'].values), station1['free_bases'].values, color='#A6CEE3', legend=station1['name'].iloc[0])\n",
    "p1.legend.location = \"top_left\"\n",
    "\n",
    "aapl = np.array(station1['free_bases'].values)\n",
    "aapl_dates = np.array(station1['timestamp'].values, dtype=np.datetime64)\n",
    "\n",
    "window_size = 30\n",
    "window = np.ones(window_size)/float(window_size)\n",
    "aapl_avg = np.convolve(aapl, window, 'same')\n",
    "\n",
    "p2 = figure(x_axis_type=\"datetime\", title=\"Station free bases (average)\")\n",
    "p2.grid.grid_line_alpha = 0\n",
    "p2.xaxis.axis_label = 'Date'\n",
    "p2.yaxis.axis_label = '# of free bases'\n",
    "p2.ygrid.band_fill_color = \"olive\"\n",
    "p2.ygrid.band_fill_alpha = 0.1\n",
    "\n",
    "p2.circle(aapl_dates, aapl, size=4, legend='close',\n",
    "          color='darkgrey', alpha=0.2)\n",
    "\n",
    "p2.line(aapl_dates, aapl_avg, legend='avg', color='navy')\n",
    "p2.legend.location = \"top_left\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
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       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"805e0009-410c-47d7-bb60-7edaa66f06f2\">Loading BokehJS ...</span>\n",
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       "     \"<div style='background-color: #fdd'>\\n\"+\n",
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       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
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       "\n",
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       "\n",
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       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
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       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
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       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
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       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
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       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
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       "    function(Bokeh) {\n",
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       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
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       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
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       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
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       "\n",
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       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
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       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
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       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
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       "       \"<div style='background-color: #fdd'>\\n\"+\n",
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       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
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       "       \"<code>\\n\"+\n",
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       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
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    }
   ],
   "source": [
    "output_notebook()\n",
    "output_file(\"stocks.html\", title=\"stocks.py example\")\n",
    "show(gridplot([[p1,p2]], plot_width=400, plot_height=400))  # open a browser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ⚠️ Work in progress! ⚠️\n",
    "\n",
    "## To-do:\n",
    "\n",
    "- Compare different stations along time"
   ]
  }
 ],
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