descriptive_analysis_cqy.ipynb

{
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   "source": [
    "# start analysis here\n",
    "\n",
    "\n",
    "import os\n",
    "from urllib.request import urlopen\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import time\n",
    "from shapely.geometry import Point\n",
    "from geopy.distance import geodesic\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "# files saved under Data/ActiveTravelCounts\n",
    "dir = 'dataset/'\n",
    "\n",
    "counts_df = pd.read_parquet(os.path.join(dir, 'counts_cleaned.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>UnqID</th>\n",
       "      <th>Zone</th>\n",
       "      <th>datetime</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Time</th>\n",
       "      <th>Day</th>\n",
       "      <th>Round</th>\n",
       "      <th>Dir</th>\n",
       "      <th>Path</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Count</th>\n",
       "      <th>isdry</th>\n",
       "      <th>ispeak</th>\n",
       "      <th>isCarriageway</th>\n",
       "      <th>isPavement</th>\n",
       "      <th>isCycle</th>\n",
       "      <th>isBus</th>\n",
       "      <th>isShared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332714</th>\n",
       "      <td>2022</td>\n",
       "      <td>INNCY183</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-05-03 16:30:00</td>\n",
       "      <td>03/05/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>16:30:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Southbound</td>\n",
       "      <td>Pavement</td>\n",
       "      <td>E-scooters</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84088</th>\n",
       "      <td>2022</td>\n",
       "      <td>CENCY037</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-05-10 20:00:00</td>\n",
       "      <td>10/05/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Westbound</td>\n",
       "      <td>Pavement</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27995</th>\n",
       "      <td>2022</td>\n",
       "      <td>OUTCY013</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-04-28 12:45:00</td>\n",
       "      <td>28/04/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>12:45:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Westbound</td>\n",
       "      <td>Carriageway</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214193</th>\n",
       "      <td>2022</td>\n",
       "      <td>QWPCY087</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-05-09 18:15:00</td>\n",
       "      <td>09/05/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>18:15:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Southbound</td>\n",
       "      <td>Carriageway</td>\n",
       "      <td>E-scooters</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210030</th>\n",
       "      <td>2022</td>\n",
       "      <td>OUTCY114</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-07-19 17:30:00</td>\n",
       "      <td>19/07/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>17:30:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Eastbound</td>\n",
       "      <td>Pavement</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "        Year     UnqID  Zone            datetime        Date Weather  \\\n",
       "332714  2022  INNCY183     1 2022-05-03 16:30:00  03/05/2022     Dry   \n",
       "84088   2022  CENCY037     0 2022-05-10 20:00:00  10/05/2022     Dry   \n",
       "27995   2022  OUTCY013     2 2022-04-28 12:45:00  28/04/2022     Dry   \n",
       "214193  2022  QWPCY087     1 2022-05-09 18:15:00  09/05/2022     Dry   \n",
       "210030  2022  OUTCY114     2 2022-07-19 17:30:00  19/07/2022     Dry   \n",
       "\n",
       "            Time      Day Round         Dir         Path                 Mode  \\\n",
       "332714  16:30:00  Weekday     A  Southbound     Pavement           E-scooters   \n",
       "84088   20:00:00  Weekday     A   Westbound     Pavement  Conventional cycles   \n",
       "27995   12:45:00  Weekday     A   Westbound  Carriageway  Conventional cycles   \n",
       "214193  18:15:00  Weekday     A  Southbound  Carriageway           E-scooters   \n",
       "210030  17:30:00  Weekday     A   Eastbound     Pavement  Conventional cycles   \n",
       "\n",
       "        Count  isdry  ispeak  isCarriageway  isPavement  isCycle  isBus  \\\n",
       "332714      0      1       1              0           1        0      0   \n",
       "84088       0      1       0              0           1        0      0   \n",
       "27995       0      1       0              1           0        0      0   \n",
       "214193      1      1       1              1           0        0      0   \n",
       "210030      0      1       1              0           1        0      0   \n",
       "\n",
       "        isShared  \n",
       "332714         0  \n",
       "84088          0  \n",
       "27995          0  \n",
       "214193         0  \n",
       "210030         0  "
      ]
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     "metadata": {},
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   ],
   "source": [
    "counts_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "db1a3620-5dd8-479b-b437-88198e49d52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles_counts_df = counts_df[counts_df['Mode'] == 'Conventional cycles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cc498257-52ab-45bb-8fa5-a9a91feb0520",
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       "      <th>142940</th>\n",
       "      <td>2022</td>\n",
       "      <td>CENCY071</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-12 13:00:00</td>\n",
       "      <td>12/07/2022</td>\n",
       "      <td>Dry</td>\n",
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       "    <tr>\n",
       "      <th>112229</th>\n",
       "      <td>2022</td>\n",
       "      <td>INNCY059</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-06-07 15:15:00</td>\n",
       "      <td>07/06/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>15:15:00</td>\n",
       "      <td>Weekday</td>\n",
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       "      <td>Westbound</td>\n",
       "      <td>Carriageway</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10062</th>\n",
       "      <td>2022</td>\n",
       "      <td>INNCY005</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-06-28 09:30:00</td>\n",
       "      <td>28/06/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>09:30:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Southbound</td>\n",
       "      <td>Pavement</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54374</th>\n",
       "      <td>2022</td>\n",
       "      <td>OUTCY025</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-04-27 15:30:00</td>\n",
       "      <td>27/04/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>15:30:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Southbound</td>\n",
       "      <td>Pavement</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>351846</th>\n",
       "      <td>2022</td>\n",
       "      <td>INNCY196</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-06-08 15:30:00</td>\n",
       "      <td>08/06/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>15:30:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Northbound</td>\n",
       "      <td>Carriageway</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "        Year     UnqID  Zone            datetime        Date Weather  \\\n",
       "142940  2022  CENCY071     0 2022-07-12 13:00:00  12/07/2022     Dry   \n",
       "112229  2022  INNCY059     1 2022-06-07 15:15:00  07/06/2022     Dry   \n",
       "10062   2022  INNCY005     1 2022-06-28 09:30:00  28/06/2022     Dry   \n",
       "54374   2022  OUTCY025     2 2022-04-27 15:30:00  27/04/2022     Dry   \n",
       "351846  2022  INNCY196     1 2022-06-08 15:30:00  08/06/2022     Dry   \n",
       "\n",
       "            Time      Day Round         Dir         Path                 Mode  \\\n",
       "142940  13:00:00  Weekday     A  Southbound   Cycle lane  Conventional cycles   \n",
       "112229  15:15:00  Weekday     A   Westbound  Carriageway  Conventional cycles   \n",
       "10062   09:30:00  Weekday     A  Southbound     Pavement  Conventional cycles   \n",
       "54374   15:30:00  Weekday     A  Southbound     Pavement  Conventional cycles   \n",
       "351846  15:30:00  Weekday     A  Northbound  Carriageway  Conventional cycles   \n",
       "\n",
       "        Count  isdry  ispeak  isCarriageway  isPavement  isCycle  isBus  \\\n",
       "142940     10      1       0              0           0        1      0   \n",
       "112229      2      1       0              1           0        0      0   \n",
       "10062       0      1       0              0           1        0      0   \n",
       "54374       0      1       0              0           1        0      0   \n",
       "351846      0      1       0              1           0        0      0   \n",
       "\n",
       "        isShared  \n",
       "142940         0  \n",
       "112229         0  \n",
       "10062          0  \n",
       "54374          0  \n",
       "351846         0  "
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     "metadata": {},
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   "source": [
    "cycles_counts_df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab773a3-509f-4fe6-aa26-887c8d4670c3",
   "metadata": {},
   "source": [
    "# Introduction Section: the distribution of active counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d8734d72-df67-42d5-b678-29d941fad0fb",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Site ID</th>\n",
       "      <th>Which folder?</th>\n",
       "      <th>Shared sites</th>\n",
       "      <th>Location description</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Functional area for monitoring</th>\n",
       "      <th>Road type</th>\n",
       "      <th>Is it on the strategic CIO panel?</th>\n",
       "      <th>Easting (UK Grid)</th>\n",
       "      <th>Northing (UK Grid)</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>geometry</th>\n",
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       "      <th>1418</th>\n",
       "      <td>K5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clarence Street jw Wheatfield Way</td>\n",
       "      <td>Kingston upon Thames</td>\n",
       "      <td>Outer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>518271.0000</td>\n",
       "      <td>169347.0000</td>\n",
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       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xfc\\xa1...</td>\n",
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       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>INNCY202</td>\n",
       "      <td>Strategic counts</td>\n",
       "      <td>0</td>\n",
       "      <td>North End Road</td>\n",
       "      <td>Hammersmith &amp; Fulham</td>\n",
       "      <td>Inner</td>\n",
       "      <td>B Road</td>\n",
       "      <td>1</td>\n",
       "      <td>524876.3644</td>\n",
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       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>CSHCY112</td>\n",
       "      <td>Cycleway counts</td>\n",
       "      <td>CENCY014</td>\n",
       "      <td>Victoria Embankment (between Derby Gate and We...</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>Central</td>\n",
       "      <td>A Road</td>\n",
       "      <td>0</td>\n",
       "      <td>530326.9600</td>\n",
       "      <td>179837.8100</td>\n",
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       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\xb8\\x1e\\x85\\xeb-/ A\\xae...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2069</th>\n",
       "      <td>QWPCY163</td>\n",
       "      <td>Cycleway counts</td>\n",
       "      <td>0</td>\n",
       "      <td>Glenesk Road</td>\n",
       "      <td>Greenwich</td>\n",
       "      <td>Outer</td>\n",
       "      <td>Local Street</td>\n",
       "      <td>0</td>\n",
       "      <td>543543.0600</td>\n",
       "      <td>175132.3800</td>\n",
       "      <td>51.456974</td>\n",
       "      <td>0.064667</td>\n",
       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\xecQ\\xb8\\x1en\\x96 A\\xa4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>CGRCY159</td>\n",
       "      <td>Cycleway counts</td>\n",
       "      <td>0</td>\n",
       "      <td>Kensington Park Gardens</td>\n",
       "      <td>Kensington &amp; Chelsea</td>\n",
       "      <td>Inner</td>\n",
       "      <td>Local Street</td>\n",
       "      <td>0</td>\n",
       "      <td>524798.0000</td>\n",
       "      <td>180701.0000</td>\n",
       "      <td>51.511462</td>\n",
       "      <td>-0.203016</td>\n",
       "      <td>b'\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xfc\\x03...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Site ID     Which folder? Shared sites  \\\n",
       "1418        K5                 0            0   \n",
       "861   INNCY202  Strategic counts            0   \n",
       "569   CSHCY112   Cycleway counts     CENCY014   \n",
       "2069  QWPCY163   Cycleway counts            0   \n",
       "401   CGRCY159   Cycleway counts            0   \n",
       "\n",
       "                                   Location description               Borough  \\\n",
       "1418                  Clarence Street jw Wheatfield Way  Kingston upon Thames   \n",
       "861                                      North End Road  Hammersmith & Fulham   \n",
       "569   Victoria Embankment (between Derby Gate and We...           Westminster   \n",
       "2069                                       Glenesk Road             Greenwich   \n",
       "401                             Kensington Park Gardens  Kensington & Chelsea   \n",
       "\n",
       "     Functional area for monitoring     Road type  \\\n",
       "1418                          Outer             0   \n",
       "861                           Inner        B Road   \n",
       "569                         Central        A Road   \n",
       "2069                          Outer  Local Street   \n",
       "401                           Inner  Local Street   \n",
       "\n",
       "      Is it on the strategic CIO panel?  Easting (UK Grid)  \\\n",
       "1418                                  0        518271.0000   \n",
       "861                                   1        524876.3644   \n",
       "569                                   0        530326.9600   \n",
       "2069                                  0        543543.0600   \n",
       "401                                   0        524798.0000   \n",
       "\n",
       "      Northing (UK Grid)   Latitude  Longitude  \\\n",
       "1418         169347.0000  51.410820  -0.300820   \n",
       "861          177885.3501  51.486138  -0.202888   \n",
       "569          179837.8100  51.502451  -0.123720   \n",
       "2069         175132.3800  51.456974   0.064667   \n",
       "401          180701.0000  51.511462  -0.203016   \n",
       "\n",
       "                                               geometry  \n",
       "1418  b'\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xfc\\xa1...  \n",
       "861   b'\\x01\\x01\\x00\\x00\\x00\\x05\\xa3\\x92\\xba\\x98\\x04...  \n",
       "569   b'\\x01\\x01\\x00\\x00\\x00\\xb8\\x1e\\x85\\xeb-/ A\\xae...  \n",
       "2069  b'\\x01\\x01\\x00\\x00\\x00\\xecQ\\xb8\\x1en\\x96 A\\xa4...  \n",
       "401   b'\\x01\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xfc\\x03...  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the location of active counts\n",
    "dir = 'dataset/'\n",
    "\n",
    "travel_counts_df = pd.read_parquet(os.path.join(dir, 'count_locations.geoparquet'))\n",
    "travel_counts_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "06146ba3-2a54-4430-9e5b-8ffba99238e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The DataFrame has 2297 rows.\n"
     ]
    }
   ],
   "source": [
    "# the number of active counts\n",
    "print(f\"The DataFrame has {len(travel_counts_df)} rows.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "554b1983-54e1-4de4-9de8-c9988934df13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function cache_data in module __main__:\n",
      "\n",
      "cache_data(src: str, dest: str) -> str\n",
      "    Downloads and caches a remote file locally.\n",
      "    \n",
      "    The function sits between the 'read' step of a pandas or geopandas\n",
      "    data frame and downloading the file from a remote location. The idea\n",
      "    is that it will save it locally so that you don't need to remember to\n",
      "    do so yourself. Subsequent re-reads of the file will return instantly\n",
      "    rather than downloading the entire file for a second or n-th itme.\n",
      "    \n",
      "    We've built in some basic logic around looking at the extension of the \n",
      "    destination file and converting it accordingly *once* it is downloaded.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    src : str\n",
      "        The remote *source* for the file, any valid URL should work.\n",
      "    dest : str\n",
      "        The *destination* location to save the downloaded file.\n",
      "        \n",
      "    Returns\n",
      "    -------\n",
      "    str\n",
      "        A string representing the local location of the file.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# caching data\n",
    "import os\n",
    "from requests import get\n",
    "from urllib.parse import urlparse\n",
    "\n",
    "def cache_data(src:str, dest:str) -> str:\n",
    "    \"\"\"Downloads and caches a remote file locally.\n",
    "    \n",
    "    The function sits between the 'read' step of a pandas or geopandas\n",
    "    data frame and downloading the file from a remote location. The idea\n",
    "    is that it will save it locally so that you don't need to remember to\n",
    "    do so yourself. Subsequent re-reads of the file will return instantly\n",
    "    rather than downloading the entire file for a second or n-th itme.\n",
    "\n",
    "    We've built in some basic logic around looking at the extension of the \n",
    "    destination file and converting it accordingly *once* it is downloaded.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    src : str\n",
    "        The remote *source* for the file, any valid URL should work.\n",
    "    dest : str\n",
    "        The *destination* location to save the downloaded file.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    str\n",
    "        A string representing the local location of the file.\n",
    "    \"\"\"\n",
    "    \n",
    "    url = urlparse(src) # We assume that this is some kind of valid URL \n",
    "    fn  = os.path.split(url.path)[-1] # Extract the filename\n",
    "    dfn = os.path.join(dest,fn) # Destination filename\n",
    "    \n",
    "    if not os.path.isfile(dfn):\n",
    "        \n",
    "        print(f\"{dfn} not found, downloading!\")\n",
    "\n",
    "        path = os.path.split(dest)\n",
    "        \n",
    "        if len(path) >= 1 and path[0] != '':\n",
    "            os.makedirs(os.path.join(*path), exist_ok=True)\n",
    "            \n",
    "        with open(dfn, \"wb\") as file:\n",
    "            response = get(src)\n",
    "            file.write(response.content)\n",
    "            \n",
    "        print(\"\\tDone downloading...\")\n",
    "\n",
    "    else:\n",
    "        print(f\"Found {dfn} locally!\")\n",
    "\n",
    "    return dfn\n",
    "\n",
    "help(cache_data) # <- This should show the docstring you've written"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0d973fb2-f6d2-44b3-8d26-35b296875650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found data/geo/Boroughs.gpkg locally!\n",
      "Found data/geo/Water.gpkg locally!\n",
      "Found data/geo/Greenspace.gpkg locally!\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# load Borough data\n",
    "ddir  = os.path.join('data','geo') # destination directory\n",
    "spath = 'https://github.com/jreades/fsds/blob/master/data/src/' # source path\n",
    "\n",
    "boros = gpd.read_file( cache_data(spath+'Boroughs.gpkg?raw=true', ddir) )\n",
    "water = gpd.read_file( cache_data(spath+'Water.gpkg?raw=true', ddir) )\n",
    "green = gpd.read_file( cache_data(spath+'Greenspace.gpkg?raw=true', ddir) )\n",
    "\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c3deb60f-b689-45c2-bdba-8762702b87eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:27700\n",
      "EPSG:27700\n",
      "EPSG:27700\n"
     ]
    }
   ],
   "source": [
    "# check crs\n",
    "for g in [boros,water,green]:\n",
    "    print(g.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "22498991-061a-4545-9891-43243d563f43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The distance between the two points is 4.38 kilometers.\n",
      "The length of the line segment is 72.99 units.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mapping \n",
    "\n",
    "# create a GeoDataFrame with the active counts (point data)\n",
    "points_gdf = gpd.GeoDataFrame(geometry=gpd.points_from_xy(travel_counts_df['Easting (UK Grid)'], travel_counts_df['Northing (UK Grid)']))\n",
    "# map london borough \n",
    "ax=boros.plot(edgecolor='black', facecolor='white', linewidth=0.5, figsize=(8,6),legend=True)\n",
    "# map the active counts\n",
    "points_gdf.plot(marker='o', color='royalblue', markersize=2, ax=ax,label='Point Data')\n",
    "\n",
    "# The line ax = plt.gca() is used to get the current Axes instance in Matplotlib. \n",
    "# The gca stands for \"get current axes.\" It retrieves the Axes (the subplot, the area where you can plot your data) that is currently being used or created in the current figure.\n",
    "# ax = ax simply assigns the existing Axes instance to the variable ax. \n",
    "\n",
    "# increase the elements of map\n",
    "\n",
    "# Title\n",
    "plt.title('The Distribution of Active Counts in London,2022 (N=2297)')\n",
    "\n",
    "# scaler\n",
    "# calculate the geographic distance\n",
    "latitude1, longitude1 = travel_counts_df.iloc[0]['Latitude'], travel_counts_df.iloc[0]['Longitude']\n",
    "latitude2, longitude2 = travel_counts_df.iloc[1]['Latitude'], travel_counts_df.iloc[1]['Longitude']\n",
    "\n",
    "distance_km = geodesic((latitude1, longitude1), (latitude2, longitude2)).kilometers\n",
    "\n",
    "print(f\"The distance between the two points is {distance_km:.2f} kilometers.\")\n",
    "\n",
    "# calculate the map distance in the two points\n",
    "line_segment = Line2D([latitude1, longitude1], [latitude2, longitude2], linestyle='-', marker='o', markersize=8,)\n",
    "line_data = line_segment.get_xydata()\n",
    "# calculate the x-cooridnate and x-cooridnate difference between two points \n",
    "# calculate the Euclidean distance between two points\n",
    "scale_value = ((line_data[1, 0] - line_data[0, 0])**2 + (line_data[1, 1] - line_data[0, 1])**2)**0.5\n",
    "print(f\"The length of the line segment is {scale_value:.2f} units.\")\n",
    "\n",
    "scale_bar_length_km = distance_km/scale_value  # calculate the scale\n",
    "scale_bar_location = (0.5, 0.009)  # set the location of scale bar\n",
    "\n",
    "ax.set_axis_off()\n",
    "ax.annotate(f'Scale: 1 unit = {scale_bar_length_km:.2f} km', xy=scale_bar_location, xycoords='axes fraction', ha='center', fontsize=8)\n",
    "\n",
    "# mapping \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e16b2eb3-af54-4e03-b5fb-60446965f338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The DataFrame has 552896 rows.\n"
     ]
    }
   ],
   "source": [
    "# Descriptive Analysis\n",
    "# firstly check the total number of the dependent variable\n",
    "\n",
    "number_of_rows_df = len(cycles_counts_df)\n",
    "print(f\"The DataFrame has {number_of_rows_df} rows.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "58e4dce2-3cd5-41d7-b241-ec1d9b9ac9ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximun value of count are 562\n",
      "The minmun value of count are 0\n",
      "The mean and standard deviation of count are 3.066+12.268\n"
     ]
    }
   ],
   "source": [
    "# the max and min of the dependent variable\n",
    "max= cycles_counts_df['Count'].max()\n",
    "min= cycles_counts_df['Count'].min()\n",
    "print(f\"The maximun value of count are {max}\")\n",
    "print(f\"The minmun value of count are {min}\")\n",
    "\n",
    "# the mean and standard deviation of the dependent variable\n",
    "cycles_counts_df_mean = cycles_counts_df['Count'].mean().round(3)\n",
    "cycles_counts_df_std =cycles_counts_df['Count'].std()\n",
    "cycles_counts_df_std_3 = round(cycles_counts_df_std,3)\n",
    "\n",
    "print(f\"The mean and standard deviation of count are {cycles_counts_df_mean}+{cycles_counts_df_std_3}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6c74e86a-8ffc-4848-95c7-7702c3314dd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "percentile_values25 = cycles_counts_df['Count'].quantile(25/ 100)\n",
    "percentile_values50 = cycles_counts_df['Count'].quantile(50/ 100)\n",
    "percentile_values75 = cycles_counts_df['Count'].quantile(75/ 100)\n",
    "print(percentile_values25)\n",
    "print(percentile_values50)\n",
    "print(percentile_values75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0195a618-c939-4f30-9bf7-3cc88fd90d6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "562\n",
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check distribution: result:skewed distribution\n",
    "max= cycles_counts_df['Count'].max()\n",
    "min= cycles_counts_df['Count'].min()\n",
    "print(max)\n",
    "print(min)\n",
    "\n",
    "hist_min=int(min)-1\n",
    "hist_max=int(max)+2\n",
    "x_range=[hist_min,hist_max]\n",
    "y_range = [0,0.11]\n",
    "n_cols = hist_max - hist_min\n",
    "\n",
    "plt.figure(1)\n",
    "plt.ylim(y_range)\n",
    "cycles_counts_df['Count'].hist(range=x_range, bins=n_cols, density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b50ef5ca-c15f-44aa-8587-d6984799b164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the outliter \n",
    "plt.boxplot(cycles_counts_df['Count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "28ef38c4-e528-48f4-af6a-4548b2f080e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>UnqID</th>\n",
       "      <th>Zone</th>\n",
       "      <th>datetime</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Time</th>\n",
       "      <th>Day</th>\n",
       "      <th>Round</th>\n",
       "      <th>Dir</th>\n",
       "      <th>Path</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Count</th>\n",
       "      <th>isdry</th>\n",
       "      <th>ispeak</th>\n",
       "      <th>isCarriageway</th>\n",
       "      <th>isPavement</th>\n",
       "      <th>isCycle</th>\n",
       "      <th>isBus</th>\n",
       "      <th>isShared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75415</th>\n",
       "      <td>2022</td>\n",
       "      <td>CSHCY063</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-19 11:45:00</td>\n",
       "      <td>19/07/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>11:45:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Northbound</td>\n",
       "      <td>Cycle lane</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241276</th>\n",
       "      <td>2022</td>\n",
       "      <td>INNCY132</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-20 21:00:00</td>\n",
       "      <td>20/07/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>21:00:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Northbound</td>\n",
       "      <td>Carriageway</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217707</th>\n",
       "      <td>2022</td>\n",
       "      <td>QWPCY090</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-06-13 16:45:00</td>\n",
       "      <td>13/06/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>16:45:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Southbound</td>\n",
       "      <td>Carriageway</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33641</th>\n",
       "      <td>2022</td>\n",
       "      <td>CENCY015</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-06 16:15:00</td>\n",
       "      <td>06/07/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>16:15:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Southbound</td>\n",
       "      <td>Bus lane</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233047</th>\n",
       "      <td>2022</td>\n",
       "      <td>CENCY121</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-06-08 11:45:00</td>\n",
       "      <td>08/06/2022</td>\n",
       "      <td>Dry</td>\n",
       "      <td>11:45:00</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>A</td>\n",
       "      <td>Northbound</td>\n",
       "      <td>Cycle lane</td>\n",
       "      <td>Conventional cycles</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year     UnqID  Zone            datetime        Date Weather  \\\n",
       "75415   2022  CSHCY063     1 2022-07-19 11:45:00  19/07/2022     Dry   \n",
       "241276  2022  INNCY132     1 2022-07-20 21:00:00  20/07/2022     Dry   \n",
       "217707  2022  QWPCY090     1 2022-06-13 16:45:00  13/06/2022     Dry   \n",
       "33641   2022  CENCY015     0 2022-07-06 16:15:00  06/07/2022     Dry   \n",
       "233047  2022  CENCY121     0 2022-06-08 11:45:00  08/06/2022     Dry   \n",
       "\n",
       "            Time      Day Round         Dir         Path                 Mode  \\\n",
       "75415   11:45:00  Weekday     A  Northbound   Cycle lane  Conventional cycles   \n",
       "241276  21:00:00  Weekday     A  Northbound  Carriageway  Conventional cycles   \n",
       "217707  16:45:00  Weekday     A  Southbound  Carriageway  Conventional cycles   \n",
       "33641   16:15:00  Weekday     A  Southbound     Bus lane  Conventional cycles   \n",
       "233047  11:45:00  Weekday     A  Northbound   Cycle lane  Conventional cycles   \n",
       "\n",
       "        Count  isdry  ispeak  isCarriageway  isPavement  isCycle  isBus  \\\n",
       "75415       8      1       0              0           0        1      0   \n",
       "241276     22      1       0              1           0        0      0   \n",
       "217707      8      1       1              1           0        0      0   \n",
       "33641      85      1       1              0           0        0      1   \n",
       "233047      8      1       0              0           0        1      0   \n",
       "\n",
       "        isShared  \n",
       "75415          0  \n",
       "241276         0  \n",
       "217707         0  \n",
       "33641          0  \n",
       "233047         0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1=cycles_counts_df['Count'].quantile(0.25)\n",
    "q3=cycles_counts_df['Count'].quantile(0.75)\n",
    "iqr = q3 - q1\n",
    "\n",
    "lower_bound = q1 - 1.5 * iqr\n",
    "upper_bound = q3 + 1.5 * iqr\n",
    "\n",
    "outliers = cycles_counts_df[(cycles_counts_df['Count'] < lower_bound) | (cycles_counts_df['Count'] > upper_bound)]\n",
    "outliers.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0533b612-257d-491c-a123-7b7ec59bd836",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5285/3130011033.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cycles_counts_df['Count_log'] = np.log1p(cycles_counts_df['Count'])\n"
     ]
    }
   ],
   "source": [
    "# normal distribution: log change\n",
    "import numpy as np\n",
    "cycles_counts_df['Count_log'] = np.log1p(cycles_counts_df['Count'])cycles_counts_df['Count']."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3d4172f8-978a-4f9d-923f-6acfa531acc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.333279628139691\n",
      "0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 0.9)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution: result:skewed distribution\n",
    "max_normal= cycles_counts_df['Count_log'].max()\n",
    "min_normal= cycles_counts_df['Count_log'].min()\n",
    "print(max_normal)\n",
    "print(min_normal)\n",
    "\n",
    "hist_min_normal=int(min_normal)-1\n",
    "hist_max_normal=int(max_normal)+2\n",
    "x_range_normal=[hist_min_normal,hist_max_normal]\n",
    "y_range_normal = [0,0.9]\n",
    "n_cols_normal = hist_max_normal - hist_min_normal\n",
    "\n",
    "plt.figure(2)\n",
    "cycles_counts_df['Count_log'].hist(range=x_range_normal,bins=n_cols_normal, density=True)\n",
    "plt.ylim(y_range_normal)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}