├── Pulling Data from OSMnx - Demonstration.ipynb
├── README.md
└── airbnb_rooms.csv


/Pulling Data from OSMnx - Demonstration.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# 1. Intro\n",
  8 |     "This notebook demonstrates the process of gathering local amenity data from OpenStreetMap. Local amenity data of this sort could be used to improve the accuracy of a whole range of ML models. In this notebook, the idea is that that presence of these amenities could help us predict what an AirBnb host might charge.\n",
  9 |     "\n",
 10 |     "In this notebook we cover the following:\n",
 11 |     "\n",
 12 |     "- How to make basic requests from OpenStreetMap using OSMnx\n",
 13 |     "- How to efficiently calculate the number of amenities that fall within a 5 minute walk of each AirBnb (approximately 0.5km distance)."
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "markdown",
 18 |    "metadata": {},
 19 |    "source": [
 20 |     "# 2. Accessing OSM with OSMnx"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 1,
 26 |    "metadata": {},
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "# Import visualisation modules\n",
 30 |     "import matplotlib as mpl \n",
 31 |     "%matplotlib inline \n",
 32 |     "import matplotlib.pyplot as plt \n",
 33 |     "\n",
 34 |     "#Import modules\n",
 35 |     "import osmnx as ox\n",
 36 |     "import pandas as pd\n",
 37 |     "import geopandas as gpd\n",
 38 |     "import numpy as np\n",
 39 |     "\n",
 40 |     "import warnings \n",
 41 |     "warnings.simplefilter(action='ignore')"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "markdown",
 46 |    "metadata": {},
 47 |    "source": [
 48 |     "### Basic OSMnx Query\n",
 49 |     "All queries follow the following dictionary format: {'feature_type' : 'feature'}. A full list of OSMnx features can be found [here](https://wiki.openstreetmap.org/wiki/Map_Features). Note, the quality and accuracy of features may vary. OSMnx returns a geopandas dataframe (basically a pandas dataframe with longitude and latitude geometry built into it). It may take a few minutes to download the data."
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 2,
 55 |    "metadata": {},
 56 |    "outputs": [
 57 |     {
 58 |      "data": {
 59 |       "text/html": [
 60 |        "<div>\n",
 61 |        "<style scoped>\n",
 62 |        "    .dataframe tbody tr th:only-of-type {\n",
 63 |        "        vertical-align: middle;\n",
 64 |        "    }\n",
 65 |        "\n",
 66 |        "    .dataframe tbody tr th {\n",
 67 |        "        vertical-align: top;\n",
 68 |        "    }\n",
 69 |        "\n",
 70 |        "    .dataframe thead th {\n",
 71 |        "        text-align: right;\n",
 72 |        "    }\n",
 73 |        "</style>\n",
 74 |        "<table border=\"1\" class=\"dataframe\">\n",
 75 |        "  <thead>\n",
 76 |        "    <tr style=\"text-align: right;\">\n",
 77 |        "      <th></th>\n",
 78 |        "      <th>unique_id</th>\n",
 79 |        "      <th>osmid</th>\n",
 80 |        "      <th>element_type</th>\n",
 81 |        "      <th>amenity</th>\n",
 82 |        "      <th>name</th>\n",
 83 |        "      <th>geometry</th>\n",
 84 |        "      <th>addr:city</th>\n",
 85 |        "      <th>addr:housename</th>\n",
 86 |        "      <th>addr:housenumber</th>\n",
 87 |        "      <th>addr:postcode</th>\n",
 88 |        "      <th>...</th>\n",
 89 |        "      <th>amenity:disused</th>\n",
 90 |        "      <th>price</th>\n",
 91 |        "      <th>name:fa</th>\n",
 92 |        "      <th>name:ko</th>\n",
 93 |        "      <th>photo</th>\n",
 94 |        "      <th>craft_beer</th>\n",
 95 |        "      <th>payment:contactless</th>\n",
 96 |        "      <th>old_old_name</th>\n",
 97 |        "      <th>terrace</th>\n",
 98 |        "      <th>ways</th>\n",
 99 |        "    </tr>\n",
100 |        "  </thead>\n",
101 |        "  <tbody>\n",
102 |        "    <tr>\n",
103 |        "      <th>0</th>\n",
104 |        "      <td>node/451153</td>\n",
105 |        "      <td>451153</td>\n",
106 |        "      <td>node</td>\n",
107 |        "      <td>restaurant</td>\n",
108 |        "      <td>Central Restaurant</td>\n",
109 |        "      <td>POINT (-0.19350 51.60203)</td>\n",
110 |        "      <td>NaN</td>\n",
111 |        "      <td>NaN</td>\n",
112 |        "      <td>NaN</td>\n",
113 |        "      <td>NaN</td>\n",
114 |        "      <td>...</td>\n",
115 |        "      <td>NaN</td>\n",
116 |        "      <td>NaN</td>\n",
117 |        "      <td>NaN</td>\n",
118 |        "      <td>NaN</td>\n",
119 |        "      <td>NaN</td>\n",
120 |        "      <td>NaN</td>\n",
121 |        "      <td>NaN</td>\n",
122 |        "      <td>NaN</td>\n",
123 |        "      <td>NaN</td>\n",
124 |        "      <td>NaN</td>\n",
125 |        "    </tr>\n",
126 |        "    <tr>\n",
127 |        "      <th>1</th>\n",
128 |        "      <td>node/26544484</td>\n",
129 |        "      <td>26544484</td>\n",
130 |        "      <td>node</td>\n",
131 |        "      <td>restaurant</td>\n",
132 |        "      <td>Casuarina Tree</td>\n",
133 |        "      <td>POINT (-0.17223 51.39801)</td>\n",
134 |        "      <td>Mitcham</td>\n",
135 |        "      <td>The Crown Inn</td>\n",
136 |        "      <td>407</td>\n",
137 |        "      <td>CR4 4BG</td>\n",
138 |        "      <td>...</td>\n",
139 |        "      <td>NaN</td>\n",
140 |        "      <td>NaN</td>\n",
141 |        "      <td>NaN</td>\n",
142 |        "      <td>NaN</td>\n",
143 |        "      <td>NaN</td>\n",
144 |        "      <td>NaN</td>\n",
145 |        "      <td>NaN</td>\n",
146 |        "      <td>NaN</td>\n",
147 |        "      <td>NaN</td>\n",
148 |        "      <td>NaN</td>\n",
149 |        "    </tr>\n",
150 |        "    <tr>\n",
151 |        "      <th>2</th>\n",
152 |        "      <td>node/26604024</td>\n",
153 |        "      <td>26604024</td>\n",
154 |        "      <td>node</td>\n",
155 |        "      <td>restaurant</td>\n",
156 |        "      <td>Jin Li</td>\n",
157 |        "      <td>POINT (-0.45855 51.52573)</td>\n",
158 |        "      <td>Uxbridge</td>\n",
159 |        "      <td>NaN</td>\n",
160 |        "      <td>91</td>\n",
161 |        "      <td>UB8 3NJ</td>\n",
162 |        "      <td>...</td>\n",
163 |        "      <td>NaN</td>\n",
164 |        "      <td>NaN</td>\n",
165 |        "      <td>NaN</td>\n",
166 |        "      <td>NaN</td>\n",
167 |        "      <td>NaN</td>\n",
168 |        "      <td>NaN</td>\n",
169 |        "      <td>NaN</td>\n",
170 |        "      <td>NaN</td>\n",
171 |        "      <td>NaN</td>\n",
172 |        "      <td>NaN</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>3</th>\n",
176 |        "      <td>node/26845558</td>\n",
177 |        "      <td>26845558</td>\n",
178 |        "      <td>node</td>\n",
179 |        "      <td>restaurant</td>\n",
180 |        "      <td>Old Tree Daiwan Bee</td>\n",
181 |        "      <td>POINT (-0.13256 51.51105)</td>\n",
182 |        "      <td>NaN</td>\n",
183 |        "      <td>NaN</td>\n",
184 |        "      <td>NaN</td>\n",
185 |        "      <td>NaN</td>\n",
186 |        "      <td>...</td>\n",
187 |        "      <td>NaN</td>\n",
188 |        "      <td>NaN</td>\n",
189 |        "      <td>NaN</td>\n",
190 |        "      <td>NaN</td>\n",
191 |        "      <td>NaN</td>\n",
192 |        "      <td>NaN</td>\n",
193 |        "      <td>NaN</td>\n",
194 |        "      <td>NaN</td>\n",
195 |        "      <td>NaN</td>\n",
196 |        "      <td>NaN</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>4</th>\n",
200 |        "      <td>node/31098623</td>\n",
201 |        "      <td>31098623</td>\n",
202 |        "      <td>node</td>\n",
203 |        "      <td>restaurant</td>\n",
204 |        "      <td>The Unicorn</td>\n",
205 |        "      <td>POINT (0.20053 51.58686)</td>\n",
206 |        "      <td>Romford</td>\n",
207 |        "      <td>NaN</td>\n",
208 |        "      <td>91</td>\n",
209 |        "      <td>RM2 5EL</td>\n",
210 |        "      <td>...</td>\n",
211 |        "      <td>NaN</td>\n",
212 |        "      <td>NaN</td>\n",
213 |        "      <td>NaN</td>\n",
214 |        "      <td>NaN</td>\n",
215 |        "      <td>NaN</td>\n",
216 |        "      <td>NaN</td>\n",
217 |        "      <td>NaN</td>\n",
218 |        "      <td>NaN</td>\n",
219 |        "      <td>NaN</td>\n",
220 |        "      <td>NaN</td>\n",
221 |        "    </tr>\n",
222 |        "  </tbody>\n",
223 |        "</table>\n",
224 |        "<p>5 rows × 302 columns</p>\n",
225 |        "</div>"
226 |       ],
227 |       "text/plain": [
228 |        "       unique_id     osmid element_type     amenity                 name  \\\n",
229 |        "0    node/451153    451153         node  restaurant   Central Restaurant   \n",
230 |        "1  node/26544484  26544484         node  restaurant       Casuarina Tree   \n",
231 |        "2  node/26604024  26604024         node  restaurant               Jin Li   \n",
232 |        "3  node/26845558  26845558         node  restaurant  Old Tree Daiwan Bee   \n",
233 |        "4  node/31098623  31098623         node  restaurant          The Unicorn   \n",
234 |        "\n",
235 |        "                    geometry addr:city addr:housename addr:housenumber  \\\n",
236 |        "0  POINT (-0.19350 51.60203)       NaN            NaN              NaN   \n",
237 |        "1  POINT (-0.17223 51.39801)   Mitcham  The Crown Inn              407   \n",
238 |        "2  POINT (-0.45855 51.52573)  Uxbridge            NaN               91   \n",
239 |        "3  POINT (-0.13256 51.51105)       NaN            NaN              NaN   \n",
240 |        "4   POINT (0.20053 51.58686)   Romford            NaN               91   \n",
241 |        "\n",
242 |        "  addr:postcode  ... amenity:disused price name:fa name:ko photo craft_beer  \\\n",
243 |        "0           NaN  ...             NaN   NaN     NaN     NaN   NaN        NaN   \n",
244 |        "1       CR4 4BG  ...             NaN   NaN     NaN     NaN   NaN        NaN   \n",
245 |        "2       UB8 3NJ  ...             NaN   NaN     NaN     NaN   NaN        NaN   \n",
246 |        "3           NaN  ...             NaN   NaN     NaN     NaN   NaN        NaN   \n",
247 |        "4       RM2 5EL  ...             NaN   NaN     NaN     NaN   NaN        NaN   \n",
248 |        "\n",
249 |        "  payment:contactless old_old_name terrace ways  \n",
250 |        "0                 NaN          NaN     NaN  NaN  \n",
251 |        "1                 NaN          NaN     NaN  NaN  \n",
252 |        "2                 NaN          NaN     NaN  NaN  \n",
253 |        "3                 NaN          NaN     NaN  NaN  \n",
254 |        "4                 NaN          NaN     NaN  NaN  \n",
255 |        "\n",
256 |        "[5 rows x 302 columns]"
257 |       ]
258 |      },
259 |      "execution_count": 2,
260 |      "metadata": {},
261 |      "output_type": "execute_result"
262 |     }
263 |    ],
264 |    "source": [
265 |     "# Set up query\n",
266 |     "query = {'amenity':'restaurant'}\n",
267 |     "\n",
268 |     "# Run query\n",
269 |     "restaurants_gdf = ox.pois.pois_from_place(\n",
270 |     "            'Greater London, UK',\n",
271 |     "            tags = query,\n",
272 |     "            which_result=1)\n",
273 |     "\n",
274 |     "restaurants_gdf.head(5)"
275 |    ]
276 |   },
277 |   {
278 |    "cell_type": "markdown",
279 |    "metadata": {},
280 |    "source": [
281 |     "## Visualise the Results"
282 |    ]
283 |   },
284 |   {
285 |    "cell_type": "code",
286 |    "execution_count": 3,
287 |    "metadata": {},
288 |    "outputs": [
289 |     {
290 |      "data": {
291 |       "text/plain": [
292 |        "<matplotlib.legend.Legend at 0x7feca2dc45e0>"
293 |       ]
294 |      },
295 |      "execution_count": 3,
296 |      "metadata": {},
297 |      "output_type": "execute_result"
298 |     },
299 |     {
300 |      "data": {
301 |       "image/png": "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\n",
302 |       "text/plain": [
303 |        "<Figure size 1080x720 with 1 Axes>"
304 |       ]
305 |      },
306 |      "metadata": {
307 |       "needs_background": "light"
308 |      },
309 |      "output_type": "display_data"
310 |     }
311 |    ],
312 |    "source": [
313 |     "# Download London's Boundary\n",
314 |     "london_gdf = ox.geocoder.geocode_to_gdf('Greater London, UK')\n",
315 |     "\n",
316 |     "# Set up a plot axis\n",
317 |     "fig, ax = plt.subplots(figsize = (15,10))\n",
318 |     "\n",
319 |     "# Visualise both on the plot\n",
320 |     "london_gdf.plot(ax = ax, alpha = 0.5)\n",
321 |     "restaurants_gdf.plot(ax = ax, markersize = 1, color = 'red', alpha = 0.8, label = 'Restaurant \\nLocations')\n",
322 |     "plt.legend()"
323 |    ]
324 |   },
325 |   {
326 |    "cell_type": "markdown",
327 |    "metadata": {},
328 |    "source": [
329 |     "# Assembling Data"
330 |    ]
331 |   },
332 |   {
333 |    "cell_type": "markdown",
334 |    "metadata": {},
335 |    "source": [
336 |     "OSMNX returns most restaurant properties as a single point (eg. Longitude / Latitude coordinates). However, a few are returned as polygons (a shape). This usually happens when the property is particularly large. Working with polygons is a lot more complicated than working with points, so below, we will work out the centre point of any polygons. We do this using Spapely."
337 |    ]
338 |   },
339 |   {
340 |    "cell_type": "code",
341 |    "execution_count": 4,
342 |    "metadata": {},
343 |    "outputs": [],
344 |    "source": [
345 |     "from shapely.geometry.polygon import Polygon\n",
346 |     "from shapely.geometry.multipolygon import MultiPolygon\n",
347 |     "\n",
348 |     "restaurants_gdf['geometry'] = restaurants_gdf['geometry'].apply(\n",
349 |     "  lambda x: x.centroid if type(x) == Polygon else (\n",
350 |     "  x.centroid if type(x) == MultiPolygon else x)\n",
351 |     ")"
352 |    ]
353 |   },
354 |   {
355 |    "cell_type": "markdown",
356 |    "metadata": {},
357 |    "source": [
358 |     "### Converting Local Projections\n",
359 |     "Before we carry out any calculations we need to convert our point coordinates to a local projection. As you know, the earth is a sphere. A projection is a method of flattening the surface of the earth so we can display it on a map. The problem, however, is that there is no way to flatten the surface of a sphere in a way that all parts are sized proportionately equal.\n",
360 |     "\n",
361 |     "The only way to get around this is to find a projection that makes the specific part of the world that you are interested in proportionately equal. These are called local UTM Coordinate Referencing System (CRS). Fortunately, OSMnx has a method built into it that allows us to find the correct local UTM."
362 |    ]
363 |   },
364 |   {
365 |    "cell_type": "code",
366 |    "execution_count": 5,
367 |    "metadata": {},
368 |    "outputs": [
369 |     {
370 |      "data": {
371 |       "text/plain": [
372 |        "<Projected CRS: +proj=utm +zone=30 +ellps=WGS84 +datum=WGS84 +unit ...>\n",
373 |        "Name: unknown\n",
374 |        "Axis Info [cartesian]:\n",
375 |        "- E[east]: Easting (metre)\n",
376 |        "- N[north]: Northing (metre)\n",
377 |        "Area of Use:\n",
378 |        "- undefined\n",
379 |        "Coordinate Operation:\n",
380 |        "- name: UTM zone 30N\n",
381 |        "- method: Transverse Mercator\n",
382 |        "Datum: World Geodetic System 1984\n",
383 |        "- Ellipsoid: WGS 84\n",
384 |        "- Prime Meridian: Greenwich"
385 |       ]
386 |      },
387 |      "execution_count": 5,
388 |      "metadata": {},
389 |      "output_type": "execute_result"
390 |     }
391 |    ],
392 |    "source": [
393 |     "def get_local_crs(y,x):\n",
394 |     "    x = ox.utils_geo.bbox_from_point((y, x), dist = 500, project_utm = True, return_crs = True)\n",
395 |     "    return x[-1]\n",
396 |     "\n",
397 |     "lon_latitude = 51.509865\n",
398 |     "lon_longitude = -0.118092\n",
399 |     "local_utm_crs = get_local_crs(lon_latitude, lon_longitude)\n",
400 |     "\n",
401 |     "local_utm_crs"
402 |    ]
403 |   },
404 |   {
405 |    "cell_type": "markdown",
406 |    "metadata": {},
407 |    "source": [
408 |     "Next we import our AirBnb room data and convert it into a Geographic dataframe."
409 |    ]
410 |   },
411 |   {
412 |    "cell_type": "code",
413 |    "execution_count": 6,
414 |    "metadata": {},
415 |    "outputs": [],
416 |    "source": [
417 |     "# Create a Geographic data of our \n",
418 |     "air_df = pd.read_csv('airbnb_rooms.csv')\n",
419 |     "\n",
420 |     "# Note below: \"crs = 4326\" is our way of telling geopandas that the initial projection uses the standard\n",
421 |     "# longitude latitude coordinates. You can't manipulate the CRS if you haven't set one initially.\n",
422 |     "\n",
423 |     "air_gdf = gpd.GeoDataFrame(air_df, geometry = gpd.points_from_xy(air_df.longitude, air_df.latitude), crs = 4326)\n",
424 |     "air_gdf = air_gdf.to_crs(local_utm_crs)\n",
425 |     "\n",
426 |     "#Convert amenities into local projection (amenities already had an initial CRS set when we downloaded it via OSMnx)\n",
427 |     "restaurants_gdf = restaurants_gdf.to_crs(local_utm_crs)"
428 |    ]
429 |   },
430 |   {
431 |    "cell_type": "markdown",
432 |    "metadata": {},
433 |    "source": [
434 |     "### Calculating Distance Using a KDTree\n",
435 |     "So next, we need to iterate through each AirBnb property and work out how many restaurants there are within a 10 minute walk (approximately 1km). \n",
436 |     "I do this using a K-D Tree. Explaining how K-D Trees work is outside the scope of this article, but in short, they're a super efficient way of searching through our 80,000 AirBnb rooms and 6,000 restaurants and figuring out which ones are close to which. First we set up the tree of all restaurant points."
437 |    ]
438 |   },
439 |   {
440 |    "cell_type": "code",
441 |    "execution_count": 30,
442 |    "metadata": {},
443 |    "outputs": [],
444 |    "source": [
445 |     "import time\n",
446 |     "from scipy import spatial\n",
447 |     "from scipy.spatial import KDTree\n",
448 |     "\n",
449 |     "# Turn long/lats into an array for Scipy\n",
450 |     "Lon = restaurants_gdf.geometry.apply(lambda x: x.x).values\n",
451 |     "Lat = restaurants_gdf.geometry.apply(lambda x: x.y).values\n",
452 |     "coords = list(zip(Lat,Lon))\n",
453 |     "\n",
454 |     "tree = spatial.KDTree(coords) # Create a KDTree of all tube stations"
455 |    ]
456 |   },
457 |   {
458 |    "cell_type": "markdown",
459 |    "metadata": {},
460 |    "source": [
461 |     "Then we create a function which we will run on each of our AirBnb rooms. The function will query the tree, and find the 500 closest restaurants along with calculating their distances from the AirBnb property. We use a figure of 500 in the hope that no property has more than 500 restaurants close to it."
462 |    ]
463 |   },
464 |   {
465 |    "cell_type": "code",
466 |    "execution_count": 31,
467 |    "metadata": {},
468 |    "outputs": [],
469 |    "source": [
470 |     "def find_points_closeby(lat_lon, k = 500, max_distance = 1000 ):\n",
471 |     "  '''\n",
472 |     "  Queries a pre-existing kd tree and returns the number of points within x distance\n",
473 |     "  of long/lat point.\n",
474 |     "  lat_lon:        A longitude and latitude pairings in the (y, x) tuple form.\n",
475 |     "  k:              The maximum number of closest points to query\n",
476 |     "  max_distance:   The maximum distance (in meters)\n",
477 |     "  '''\n",
478 |     "  \n",
479 |     "  results = tree.query((lat_lon), k = k, distance_upper_bound= max_distance)\n",
480 |     "  zipped_results = list(zip(results[0], results[1]))\n",
481 |     "  zipped_results = [i for i in zipped_results if i[0] != np.inf]\n",
482 |     "  \n",
483 |     "  return len(zipped_results)"
484 |    ]
485 |   },
486 |   {
487 |    "cell_type": "markdown",
488 |    "metadata": {},
489 |    "source": [
490 |     "And finally, we set up a timer and apply the function to each AirBnb row"
491 |    ]
492 |   },
493 |   {
494 |    "cell_type": "code",
495 |    "execution_count": 32,
496 |    "metadata": {},
497 |    "outputs": [
498 |     {
499 |      "name": "stdout",
500 |      "output_type": "stream",
501 |      "text": [
502 |       "Completed in 95.89 seconds\n"
503 |      ]
504 |     }
505 |    ],
506 |    "source": [
507 |     "# Set up a timer \n",
508 |     "import time \n",
509 |     "t0 = time.time()\n",
510 |     "\n",
511 |     "#Apply the function\n",
512 |     "air_gdf['restaurants'] = air_gdf.apply(lambda row: find_points_closeby(\n",
513 |     "  (row.geometry.y, row.geometry.x)) , axis = 1)\n",
514 |     "\n",
515 |     "# Report the time\n",
516 |     "time_passed = round(time.time() - t0, 2)\n",
517 |     "print (\"Completed in %s seconds\" % (time_passed))"
518 |    ]
519 |   },
520 |   {
521 |    "cell_type": "markdown",
522 |    "metadata": {},
523 |    "source": [
524 |     "You now know how many restaurants there are within a 10-minute walk of each AirBnb property. You could repeat this process for bars, shops, subway stations, tourist hotspots, public parks, and whatever else you think may influence the price of an AirBnb property"
525 |    ]
526 |   },
527 |   {
528 |    "cell_type": "code",
529 |    "execution_count": 36,
530 |    "metadata": {},
531 |    "outputs": [
532 |     {
533 |      "data": {
534 |       "text/html": [
535 |        "<div>\n",
536 |        "<style scoped>\n",
537 |        "    .dataframe tbody tr th:only-of-type {\n",
538 |        "        vertical-align: middle;\n",
539 |        "    }\n",
540 |        "\n",
541 |        "    .dataframe tbody tr th {\n",
542 |        "        vertical-align: top;\n",
543 |        "    }\n",
544 |        "\n",
545 |        "    .dataframe thead th {\n",
546 |        "        text-align: right;\n",
547 |        "    }\n",
548 |        "</style>\n",
549 |        "<table border=\"1\" class=\"dataframe\">\n",
550 |        "  <thead>\n",
551 |        "    <tr style=\"text-align: right;\">\n",
552 |        "      <th></th>\n",
553 |        "      <th>id</th>\n",
554 |        "      <th>restaurants</th>\n",
555 |        "    </tr>\n",
556 |        "  </thead>\n",
557 |        "  <tbody>\n",
558 |        "    <tr>\n",
559 |        "      <th>0</th>\n",
560 |        "      <td>13913</td>\n",
561 |        "      <td>39</td>\n",
562 |        "    </tr>\n",
563 |        "    <tr>\n",
564 |        "      <th>1</th>\n",
565 |        "      <td>15400</td>\n",
566 |        "      <td>108</td>\n",
567 |        "    </tr>\n",
568 |        "    <tr>\n",
569 |        "      <th>2</th>\n",
570 |        "      <td>17402</td>\n",
571 |        "      <td>335</td>\n",
572 |        "    </tr>\n",
573 |        "    <tr>\n",
574 |        "      <th>3</th>\n",
575 |        "      <td>25023</td>\n",
576 |        "      <td>1</td>\n",
577 |        "    </tr>\n",
578 |        "    <tr>\n",
579 |        "      <th>4</th>\n",
580 |        "      <td>25123</td>\n",
581 |        "      <td>6</td>\n",
582 |        "    </tr>\n",
583 |        "  </tbody>\n",
584 |        "</table>\n",
585 |        "</div>"
586 |       ],
587 |       "text/plain": [
588 |        "      id  restaurants\n",
589 |        "0  13913           39\n",
590 |        "1  15400          108\n",
591 |        "2  17402          335\n",
592 |        "3  25023            1\n",
593 |        "4  25123            6"
594 |       ]
595 |      },
596 |      "execution_count": 36,
597 |      "metadata": {},
598 |      "output_type": "execute_result"
599 |     }
600 |    ],
601 |    "source": [
602 |     "air_gdf[['id','restaurants']].head(5)"
603 |    ]
604 |   }
605 |  ],
606 |  "metadata": {
607 |   "kernelspec": {
608 |    "display_name": "Python 3",
609 |    "language": "python",
610 |    "name": "python3"
611 |   },
612 |   "language_info": {
613 |    "codemirror_mode": {
614 |     "name": "ipython",
615 |     "version": 3
616 |    },
617 |    "file_extension": ".py",
618 |    "mimetype": "text/x-python",
619 |    "name": "python",
620 |    "nbconvert_exporter": "python",
621 |    "pygments_lexer": "ipython3",
622 |    "version": "3.8.3"
623 |   }
624 |  },
625 |  "nbformat": 4,
626 |  "nbformat_minor": 4
627 | }
628 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Accessing-OpenStreetMap-Data
 2 | A Jupyter project that demonstrates how to access local data from OpenStreetMap to improve your ML models. Demonstrates the use of K-D Trees to turn data into features.
 3 | 
 4 | 
 5 | This Jupyter Project demonstrates how to gather local amenity data from OpenStreetMap. Local amenity data of this sort could be used to improve the accuracy of a whole range of ML models. If, for example, you wanted to predict the price a AirBnb host might charge in parts of London, you might want to know something about the local amenities that exist within the area.
 6 | 
 7 | In this notebook we cover the following:
 8 | 
 9 | - How to make basic requests from OpenStreetMap using OSMnx
10 | - Converting geographic projections.
11 | - Using K-D Trees to to efficiently calculate the number of amenities that fall within a 5 minute walk of each AirBnb (approximately 0.5km distance).
12 | 


--------------------------------------------------------------------------------