Lab 03: Indexing and Relevance Ranking on Geo-Texts#
In this tutorial, we will experiment on checking/implementing some information retrieval techniques on a geo-text. Most of these techniques like R-tree, TF-IDF, relevance ranking, cosine similarity, etc. have been covered in the lecture. Techniques that you have learnt in Lab 1 and 2 (e.g., geopandas, osm, etc.) will be used/enhanced in this tutoiral. Meanwhile, new techniques such as wordcloud will also be introduced.
A summary of packages you will be using:
Okay, now let’s discuss first in a very general sense what we want to achieve in this tutorial. The goal is to understand the (functional) characteristics of regions in the City of Bristol. To do it, we use the points of interest and their types (e.g., restaurant, school, police office) as a proxy to quantify such a “functional” characteristics. Namely, if a region has a lot of restrants and bars, it might be more likely to be characterized as a food/entertainment region. It is then clearly different from another region where most points of interet are types of, for instance, school and nursery.
More interestingly, we do not only want to qualitatively describe these regions, we also need a more quantitative method so that computers can also understand it and then retrieve information for us more accurately and effectively.
Part 1: Load the local regions (Lower Super Output Areas 2011) for the City of Bristol#
First of all, let’s load the region data about Bristol. There are many ways to delineate the City (e.g., by administrative office or by local citizens’ perception/activity). In this tutorial, we use the data called Lower Super Output Areas 2011. It is created for the collection of census data. “Lower Super Output Areas (LSOAs) are a national geography for collecting, aggregating and reporting statistics. They were designed to improve the reporting of small area statistics and are built up from groups of Output Areas”. You can download the data from the Open Data Bristol portal or find the downloaded file from Blackboard. The code below then loads the data into Python and convert it to a geodataframe.
Several tips:
replace the directory to where your data is downloaded.
../in my code means going up one level in the directorysometimes, we can ignore the parameter
delimiterinpd.read_csvif the delimiter is by default comma. But here, since it is semicolon, we need to indicate it (you can try to see what kind of errors you will encourter if you do not include it)the raw data provide geomery information in a geojson format like
{"coordinates": [[[-2.5705649134167503, 51.408..., to convert it to a format as well-know text, which is supported bygeopandas/shapely, we write a functionparse_geom
import geopandas as gpd
import pandas as pd
import json
from shapely.geometry import shape
def parse_geom(geom_str):
try:
return shape(json.loads(geom_str))
except (TypeError, AttributeError): # Handle NaN and empty strings
return None
bristol_lsoal_df = pd.read_csv('../../LabData/Lab3/lsoa110.csv', delimiter=';')
bristol_lsoal_df["geom"] = bristol_lsoal_df["geo_shape"].apply(parse_geom)
bristol_lsoal_gdf = gpd.GeoDataFrame(bristol_lsoal_df, geometry="geom")
print(bristol_lsoal_gdf.head())
OBJECTID LSOA11 Code LSOA11 Name LSOA11 Local name MSOA11 Code \
0 7 E01014612 Bristol 052B Fortfield East E02003063
1 8 E01014512 Bristol 016E Somerville Road E02003027
2 18 E01014618 Bristol 011C Henleaze North E02003022
3 36 E01014576 Bristol 019D Rose Green E02003030
4 24 E01033370 Bristol 054E Redcliffe South E02006887
Ward code Area square mteres Perimeter (m) MI_PRINX \
0 E05001989 250084.343750 3010.960449 2
1 E05001975 178468.437500 3093.555908 152
2 E05001990 324102.562500 4205.579590 921
3 E05001984 469484.093750 3586.286133 976
4 E05001995 126227.867188 2103.395020 19
geo_shape \
0 {"coordinates": [[[-2.5705649134167503, 51.408...
1 {"coordinates": [[[-2.5836634052406393, 51.476...
2 {"coordinates": [[[-2.610432616527603, 51.4856...
3 {"coordinates": [[[-2.5511455096576006, 51.463...
4 {"coordinates": [[[-2.5911512584061063, 51.445...
geo_point_2d \
0 51.409649589944884,-2.566230812955203
1 51.47614221648566,-2.588736496528701
2 51.489587737185744,-2.606558179420023
3 51.46845752679203,-2.5464321087549955
4 51.44681337159509,-2.5874479497333067
geom
0 POLYGON ((-2.57056 51.40811, -2.57048 51.40829...
1 POLYGON ((-2.58366 51.47624, -2.58344 51.47609...
2 POLYGON ((-2.61043 51.48565, -2.61012 51.48600...
3 POLYGON ((-2.55115 51.46378, -2.55102 51.46396...
4 POLYGON ((-2.59115 51.44567, -2.59098 51.44604...
Alternatively, you can load shapefiles (or geojson, geopackages formated data) using the read_file() function of geopandas, which will directly load the geo-referenced file as a geodataframe. Note that you can download the LSOA_2011 of Bristol in different formats from the Open Data Bristol portal too.
bristol_shp_dir = '../../LabData/Lab3/LSOA_2011_(simplified)/LSOA_2011_(simplified).shp'
bristol_lsoal_gdf2 = gpd.read_file(bristol_shp_dir)
bristol_lsoal_gdf2.head()
| OBJECTID | LSOA11CD | LSOA11NM | LSOA11LN | MSOA11CD | WARDCD | WARDNM | AREA_M2 | PERIMETER_ | Shape__Are | Shape__Len | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | E01014485 | Bristol 023A | Mina Road | E02003034 | E05001972 | Ashley | 274649.437500 | 3500.999023 | 271234.427200 | 3345.873087 | POLYGON ((360647.016 174587.134, 360638.381 17... |
| 1 | 2 | E01033366 | Bristol 023H | St Pauls Grosvenor Road | E02003034 | E05001972 | Ashley | 92561.281250 | 1667.071045 | 91912.916122 | 1590.649592 | POLYGON ((359458.000 173935.000, 359464.000 17... |
| 2 | 3 | E01014486 | Bristol 023B | St Agnes | E02003034 | E05001972 | Ashley | 276066.531250 | 4337.872559 | 276957.510246 | 4153.567496 | POLYGON ((360249.713 174716.608, 360224.497 17... |
| 3 | 4 | E01014489 | Bristol 023D | Lower Montpelier | E02003034 | E05001972 | Ashley | 171743.171875 | 2755.843506 | 171822.423092 | 2655.816875 | POLYGON ((359628.089 174397.518, 359611.263 17... |
| 4 | 5 | E01014488 | Bristol 023C | Upper Montpelier | E02003034 | E05001972 | Ashley | 188000.250000 | 2753.252197 | 188759.246117 | 2636.044692 | POLYGON ((359628.089 174397.518, 359604.571 17... |
Now, even though bristol_lsoal_gdf is created as a geodataframe, we still want to check if the crs is set up already because sometimes we can easily forgot it. Then, .crs can be used here (crs is an attribute of geodataframe):
bristol_lsoal_gdf.crs is None
True
Since there is no crs being set up in this example, we will assign a crs by using:
bristol_lsoal_gdf.crs = 'epsg:4326'
bristol_lsoal_gdf.crs
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
So, how do I know it is epsg:4326?
As usual, we can easily plot the regions:
bristol_lsoal_gdf.plot()
<Axes: >
Part 2: Load in the Points of Interest (POI) from OSM#
Alright, now we have the regions in the City of Bristol. We next want to get the POIs in the City. There are many ways to get the POIs, many of which have been introduced in the lecture (e.g., Yelp and Foursquare). Since we have already used OSM, why not playing with the POIs provided from OSM?
As you may recall there are also many ways to load OSM data into Python. For example, you can readily use pyrosm to get the POIs in Bristol:
from pyrosm import OSM, get_data
bristol = get_data("bristol")
osm = OSM(bristol)
bristol_pois = osm.get_pois()
However, some of you have encourtered issues of using this package. The main reason is that this package is sometimes out of date and its dependencies are not updated so that your system might have conflicts with it. The data extracted by this approach is sometimes also the very raw one without any preprocessing.
The second way, which is recommended in this tutorial, is to directly download the data from https://download.geofabrik.de/, and then load it to Python:
## Option 2 (Recommended): directly download it from the website and load it into python
bristol_pois_file = "../../LabData/Lab3/gis_osm_pois_a_free_1.shp"
bristol_pois = gpd.read_file(bristol_pois_file)
bristol_pois.head()
| osm_id | code | fclass | name | geometry | |
|---|---|---|---|---|---|
| 0 | 3894324 | 2204 | park | Portland Square | POLYGON ((-2.58600 51.46103, -2.58600 51.46110... |
| 1 | 4297151 | 2204 | park | Gainsborough Square | POLYGON ((-2.56426 51.49027, -2.56422 51.49031... |
| 2 | 4309554 | 2008 | town_hall | Bristol City Hall | POLYGON ((-2.60242 51.45242, -2.60241 51.45244... |
| 3 | 4313536 | 2204 | park | None | POLYGON ((-2.57615 51.49791, -2.57609 51.49803... |
| 4 | 4313541 | 2204 | park | Poets Park | POLYGON ((-2.57429 51.49952, -2.57419 51.49972... |
As can be seen, there is a fclass column indicating the place type of the poi. We wil regard it as a token/term of a document, which will be explained in more detail later.
Again, let’s double check the crs of this data:
bristol_pois.crs
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
Let’s also check the number of place types we have in this dataset:
len(set(bristol_pois['fclass']))
104
Don’t forget we can also use .info() to summary the basic info about such a dataset. For example, from the info below, we know that there are 5370 pois in this dataset.
bristol_pois.info() # you can also use list(bristol_pois.columns) to check the list of the columns
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 5370 entries, 0 to 5369
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 osm_id 5370 non-null object
1 code 5370 non-null int64
2 fclass 5370 non-null object
3 name 4567 non-null object
4 geometry 5370 non-null geometry
dtypes: geometry(1), int64(1), object(3)
memory usage: 209.9+ KB
And, we can also simply plot these pois:
bristol_pois.plot()
<Axes: >
Part 3: Use WordCloud to Characterize Regions#
For natural languages like the place types, a popular way to visulize it is to use a technique called WordCloud. It draws a “cloud” of words, where the position and size of the word is determined by its frequency in the document. In Python, we can use the package wordcloud to implement it. To do it, we need a document that contains words/tokens.
However, what is the document in our case study here?
There can be many ways and you are encouraged to be creative in answering this question. One way we will experiment here is to regard the whole City of Bristol as a document. Its words/tokens are those place types whose georeferenced POIs are within the City of Bristol.
Let’s next see how we implement it. We first need to convert the column fclass in geodataframe to a string (document). Then we can use the function WordCloud provided by the package wordcloud to build the cloud map. Note that the example showing below can be modified to other versions. You can check the wordcloud gallaries for some inspirations.
## use word cloud to visualize the frequency of poi types mentioned in Bristol
# first you need to install the package of wordcloud
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
bristol_pois_doc = " ".join(list(bristol_pois['fclass']))
wordcloud = WordCloud(width = 800, height = 800,
background_color ='white',
min_font_size = 10).generate(bristol_pois_doc)
# plot the WordCloud image
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
plt.show()
So, we here regarded the whole City as a document. Can you try to implement it by regarding each LSOA11 region as the document? What other approaches can you think of?
Part 4: Preprocessing the data for GIR#
After exploring some new techniques of visualizing regions using natural language processing techniques, next let’s come back to the implemention of techniques we have learnt in the lecture.
First, we would like to create a dataset where place types of POIs are assigned to each LSOA11 regions, as in later experiment, we will regard the LSOA11 regions as documents and place types will be the words/tokens.
To do it, we can use sjoin(), which we have learnt before. Note the meaning of the parameters in the function, e.g., how and op.
bristol_lsoal_pois = gpd.sjoin(bristol_lsoal_gdf, bristol_pois , how="left", op='intersects')
bristol_lsoal_pois.head()
/Users/gy22808/opt/anaconda3/envs/ox/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3493: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.
if await self.run_code(code, result, async_=asy):
| OBJECTID | LSOA11 Code | LSOA11 Name | LSOA11 Local name | MSOA11 Code | Ward code | Area square mteres | Perimeter (m) | MI_PRINX | geo_shape | geo_point_2d | geom | index_right | osm_id | code | fclass | name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | 1172.0 | 211278259 | 2204.0 | park | Briery Leaze Road Open Space |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | 4126.0 | 436088974 | 2205.0 | playground | None |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | 5114.0 | 750955626 | 2253.0 | swimming_pool | None |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | 5113.0 | 750955625 | 2253.0 | swimming_pool | None |
| 1 | 8 | E01014512 | Bristol 016E | Somerville Road | E02003027 | E05001975 | 178468.43750 | 3093.555908 | 152 | {"coordinates": [[[-2.5836634052406393, 51.476... | 51.47614221648566,-2.588736496528701 | POLYGON ((-2.58366 51.47624, -2.58344 51.47609... | 447.0 | 121690642 | 2013.0 | nursing_home | Sycamore Court |
There are many columns from OSM that we might not use. Here we can remove them by using codes like:
drop_cols = ['index_right', 'osm_id', 'code', 'name']
bristol_lsoal_pois_dropped = bristol_lsoal_pois.drop(drop_cols, axis=1)
bristol_lsoal_pois_dropped.head()
| OBJECTID | LSOA11 Code | LSOA11 Name | LSOA11 Local name | MSOA11 Code | Ward code | Area square mteres | Perimeter (m) | MI_PRINX | geo_shape | geo_point_2d | geom | fclass | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | park |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | playground |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | swimming_pool |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | swimming_pool |
| 1 | 8 | E01014512 | Bristol 016E | Somerville Road | E02003027 | E05001975 | 178468.43750 | 3093.555908 | 152 | {"coordinates": [[[-2.5836634052406393, 51.476... | 51.47614221648566,-2.588736496528701 | POLYGON ((-2.58366 51.47624, -2.58344 51.47609... | nursing_home |
There are still many things we need to process before this data can be used for our later study. One thing is that the column fclass might not be recorded as a str (natural language based string) as we expect. To ensure it is, we can use the code:
bristol_lsoal_pois_dropped['fclass'] = bristol_lsoal_pois_dropped['fclass'].astype('str')
bristol_lsoal_pois_dropped.head()
| OBJECTID | LSOA11 Code | LSOA11 Name | LSOA11 Local name | MSOA11 Code | Ward code | Area square mteres | Perimeter (m) | MI_PRINX | geo_shape | geo_point_2d | geom | fclass | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | park |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | playground |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | swimming_pool |
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.34375 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | swimming_pool |
| 1 | 8 | E01014512 | Bristol 016E | Somerville Road | E02003027 | E05001975 | 178468.43750 | 3093.555908 | 152 | {"coordinates": [[[-2.5836634052406393, 51.476... | 51.47614221648566,-2.588736496528701 | POLYGON ((-2.58366 51.47624, -2.58344 51.47609... | nursing_home |
More importantly, we need to group the place types (words/terms) to LSOA11 regions (documents). To do so, we can use the groupby() function provided in pandas. Here is how it looks like:
bristol_lsoal_pois_dropped.groupby('LSOA11 Code')['fclass'].apply(list)
LSOA11 Code
E01014485 [sports_centre, sports_centre, recycling_metal...
E01014486 [sports_centre, pitch, pitch, pitch, cafe, pit...
E01014487 [hairdresser, clothes, sports_shop, furniture_...
E01014488 [park, ruins, pitch, school, school, school, p...
E01014489 [community_centre, fast_food, convenience, caf...
...
E01033364 [sports_shop, convenience, fast_food, convenie...
E01033366 [convenience, playground, library, cafe, park,...
E01033367 [mall, cafe, park, pub, playground, restaurant...
E01033369 [car_dealership, recycling_glass, stationery, ...
E01033370 [convenience, restaurant, kindergarten, school...
Name: fclass, Length: 263, dtype: object
Once we have the grouped dataframe, we want to join it back to the bristol_lsoal_gdf dataframe. Here we can use a general join():
bristol_lsoal_poi_full = bristol_lsoal_gdf.join(bristol_lsoal_pois_dropped.groupby('LSOA11 Code')['fclass'].apply(list), on='LSOA11 Code')
bristol_lsoal_poi_full.head()
| OBJECTID | LSOA11 Code | LSOA11 Name | LSOA11 Local name | MSOA11 Code | Ward code | Area square mteres | Perimeter (m) | MI_PRINX | geo_shape | geo_point_2d | geom | fclass | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7 | E01014612 | Bristol 052B | Fortfield East | E02003063 | E05001989 | 250084.343750 | 3010.960449 | 2 | {"coordinates": [[[-2.5705649134167503, 51.408... | 51.409649589944884,-2.566230812955203 | POLYGON ((-2.57056 51.40811, -2.57048 51.40829... | [park, playground, swimming_pool, swimming_pool] |
| 1 | 8 | E01014512 | Bristol 016E | Somerville Road | E02003027 | E05001975 | 178468.437500 | 3093.555908 | 152 | {"coordinates": [[[-2.5836634052406393, 51.476... | 51.47614221648566,-2.588736496528701 | POLYGON ((-2.58366 51.47624, -2.58344 51.47609... | [nursing_home, hairdresser, computer_shop, fas... |
| 2 | 18 | E01014618 | Bristol 011C | Henleaze North | E02003022 | E05001990 | 324102.562500 | 4205.579590 | 921 | {"coordinates": [[[-2.610432616527603, 51.4856... | 51.489587737185744,-2.606558179420023 | POLYGON ((-2.61043 51.48565, -2.61012 51.48600... | [kindergarten, convenience, hairdresser, fast_... |
| 3 | 36 | E01014576 | Bristol 019D | Rose Green | E02003030 | E05001984 | 469484.093750 | 3586.286133 | 976 | {"coordinates": [[[-2.5511455096576006, 51.463... | 51.46845752679203,-2.5464321087549955 | POLYGON ((-2.55115 51.46378, -2.55102 51.46396... | [sports_shop, beauty_shop, convenience, fast_f... |
| 4 | 24 | E01033370 | Bristol 054E | Redcliffe South | E02006887 | E05001995 | 126227.867188 | 2103.395020 | 19 | {"coordinates": [[[-2.5911512584061063, 51.445... | 51.44681337159509,-2.5874479497333067 | POLYGON ((-2.59115 51.44567, -2.59098 51.44604... | [convenience, restaurant, kindergarten, school... |
We can also save the data as a pickle for future use by applying the following code. pickle is a module in python that convert python object into a byte stream (called “Pickling”), and vice verse (called “unpickling”).
bristol_lsoal_poi_full.to_pickle("./bristol_lsoal_poi_full.pkl")
Part 5: Compute TF-IDF#
After the preprocessing, now let’s apply some techniques in IR on these unstructured geo-text (i.e., poi’s place types for regions). There are many things we can do here. The first thing we want is to find the most representative place types for the region, in analogy to finding the representative words in a document. So, we can use the TF-IDF we discussed in the lecture to do so.
Before computing TF-IDF, we need to have a vocabulary. Here we simply call it a wordSet, which includes all the unique terms used in the document:
wordSet = set()
for index, row in bristol_lsoal_poi_full.iterrows():
wordSet = wordSet.union(set(row['fclass']))
wordSet
{'archaeological',
'arts_centre',
'artwork',
'attraction',
'bakery',
'bank',
'bar',
'beauty_shop',
'beverages',
'bicycle_shop',
'bookshop',
'butcher',
'cafe',
'camp_site',
'car_dealership',
'car_rental',
'car_wash',
'caravan_site',
'castle',
'chemist',
'cinema',
'clinic',
'clothes',
'college',
'comms_tower',
'community_centre',
'computer_shop',
'convenience',
'courthouse',
'dentist',
'department_store',
'doctors',
'doityourself',
'fast_food',
'fire_station',
'florist',
'food_court',
'fountain',
'furniture_shop',
'garden_centre',
'gift_shop',
'golf_course',
'graveyard',
'greengrocer',
'guesthouse',
'hairdresser',
'hospital',
'hostel',
'hotel',
'jeweller',
'kindergarten',
'laundry',
'library',
'mall',
'market_place',
'memorial',
'mobile_phone_shop',
'monument',
'museum',
'nan',
'newsagent',
'nightclub',
'nursing_home',
'observation_tower',
'optician',
'outdoor_shop',
'park',
'pharmacy',
'picnic_site',
'pitch',
'playground',
'police',
'post_office',
'prison',
'pub',
'public_building',
'recycling',
'recycling_glass',
'recycling_metal',
'restaurant',
'ruins',
'school',
'shelter',
'shoe_shop',
'sports_centre',
'sports_shop',
'stadium',
'stationery',
'supermarket',
'swimming_pool',
'theatre',
'theme_park',
'toilet',
'tourist_info',
'tower',
'town_hall',
'toy_shop',
'track',
'travel_agent',
'university',
'veterinary',
'video_shop',
'wastewater_plant',
'water_tower',
'water_works'}
Once we have the vocabulary, next we will see how frequently each word/token in the vacabulary occurred in each of my document:
docDic = {}
for index, row in bristol_lsoal_poi_full.iterrows():
doc = row['fclass']
wordDict = dict.fromkeys(wordSet, 0)
for word in doc:
wordDict[word]+=1
docDic[row['LSOA11 Code']] = wordDict
docDic
{'E01014612': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 0,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 0,
'car_dealership': 0,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 2,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 1,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 0,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 1,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 0,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 0,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01014512': {'cafe': 1,
'university': 0,
'market_place': 0,
'community_centre': 1,
'restaurant': 3,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 3,
'college': 1,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 0,
'car_dealership': 1,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 1,
'department_store': 0,
'jeweller': 0,
'nursing_home': 1,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 3,
'doityourself': 3,
'hostel': 0,
'bakery': 0,
'butcher': 1,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 0,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 1,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 1,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 0,
'car_wash': 1,
'fountain': 0,
'dentist': 1,
'theatre': 1,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 2,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 0,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 1},
'E01014618': {'cafe': 1,
'university': 0,
'market_place': 0,
'community_centre': 1,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 2,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 0,
'car_dealership': 0,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 1,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 1,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 3,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 2,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 2,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 0,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 2},
'E01014576': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 1,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 1,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 0,
'car_dealership': 1,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 1,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 1,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 2,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 1,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 0,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 0,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 1,
'pitch': 2,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01033370': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 1,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 1,
'college': 1,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 0,
'car_dealership': 1,
'furniture_shop': 1,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 1,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 2,
'observation_tower': 0,
'fast_food': 2,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 1,
'park': 1,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 0,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 2,
'pitch': 3,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 1},
'E01014518': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 1,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 1,
'car_dealership': 1,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 1,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 0,
'doityourself': 3,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 0,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 2,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 1,
'car_wash': 1,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 1,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 1,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01014731': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 1,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 1,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 1,
'pharmacy': 0,
'car_dealership': 0,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 1,
'doityourself': 0,
'hostel': 0,
'bakery': 1,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 0,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 0,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 0,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 0,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 1,
'pub': 4,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01033345': {'cafe': 14,
'university': 0,
'market_place': 0,
'community_centre': 1,
'restaurant': 23,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 2,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 2,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 1,
'pharmacy': 1,
'car_dealership': 0,
'furniture_shop': 1,
'water_works': 0,
'beverages': 3,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 4,
'beauty_shop': 2,
'bar': 7,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 1,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 1,
'track': 0,
'castle': 0,
'florist': 1,
'sports_centre': 1,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 1,
'supermarket': 2,
'public_building': 0,
'playground': 0,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 1,
'observation_tower': 0,
'fast_food': 13,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 1,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 1,
'park': 5,
'car_wash': 0,
'fountain': 5,
'dentist': 0,
'theatre': 2,
'fire_station': 0,
'post_office': 0,
'museum': 2,
'ruins': 0,
'library': 1,
'doctors': 0,
'courthouse': 0,
'hairdresser': 9,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 1,
'greengrocer': 0,
'pub': 9,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 1,
'clothes': 2,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01014591': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
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'tower': 0,
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'mobile_phone_shop': 0,
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'doityourself': 0,
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'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
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'memorial': 0,
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'school': 1,
'park': 1,
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'fountain': 0,
'dentist': 0,
'theatre': 0,
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'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
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'recycling': 0,
'recycling_metal': 0,
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'town_hall': 0,
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'pub': 1,
'pitch': 2,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
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'stadium': 0,
'kindergarten': 0},
'E01014590': {'cafe': 2,
'university': 0,
'market_place': 0,
'community_centre': 1,
'restaurant': 4,
'hospital': 0,
'travel_agent': 1,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 1,
'newsagent': 1,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 3,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 1,
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'tower': 0,
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'bank': 2,
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'optician': 2,
'mobile_phone_shop': 0,
'swimming_pool': 0,
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'bar': 1,
'picnic_site': 0,
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'shoe_shop': 0,
'clinic': 1,
'department_store': 0,
'jeweller': 0,
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'toilet': 0,
'track': 0,
'castle': 0,
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'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 1,
'butcher': 1,
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'supermarket': 0,
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'playground': 1,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
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'fast_food': 8,
'outdoor_shop': 0,
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'cinema': 0,
'video_shop': 0,
'laundry': 1,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 1,
'park': 6,
'car_wash': 1,
'fountain': 0,
'dentist': 1,
'theatre': 0,
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'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 2,
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'hairdresser': 5,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 1,
'bicycle_shop': 0,
'town_hall': 0,
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'pub': 3,
'pitch': 1,
'camp_site': 0,
'chemist': 1,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 1},
'E01014614': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 2,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 1,
'car_dealership': 0,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 1,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 1,
'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 0,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 2,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 4,
'park': 1,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 2,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 1,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 0,
'pitch': 1,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01014485': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 2,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 1,
'newsagent': 1,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 1,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 0,
'car_dealership': 0,
'furniture_shop': 1,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 2,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 1,
'public_building': 0,
'playground': 2,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 0,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 2,
'park': 3,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 0,
'police': 0,
'recycling': 0,
'recycling_metal': 1,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 2,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 1},
'E01014558': {'cafe': 8,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 14,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 1,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 1,
'pharmacy': 1,
'car_dealership': 0,
'furniture_shop': 0,
'water_works': 0,
'beverages': 3,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 1,
'archaeological': 0,
'optician': 1,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 5,
'bar': 5,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 2,
'department_store': 0,
'jeweller': 0,
'nursing_home': 1,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 1,
'doityourself': 2,
'hostel': 0,
'bakery': 1,
'butcher': 0,
'tourist_info': 0,
'supermarket': 2,
'public_building': 0,
'playground': 0,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 7,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 1,
'memorial': 0,
'computer_shop': 0,
'bookshop': 1,
'nan': 0,
'school': 1,
'park': 0,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 1,
'courthouse': 0,
'hairdresser': 9,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 1,
'pub': 5,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 3,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01014660': {'cafe': 1,
'university': 0,
'market_place': 0,
'community_centre': 3,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 3,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 1,
'car_dealership': 0,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 2,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 4,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 2,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 2,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 6,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 1,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 0,
'pitch': 2,
'camp_site': 0,
'chemist': 0,
'attraction': 1,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01014704': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 0,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 1,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 0,
'car_dealership': 0,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 0,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 0,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 1,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 0,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 1,
'pitch': 0,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 0},
'E01014685': {'cafe': 0,
'university': 0,
'market_place': 0,
'community_centre': 1,
'restaurant': 0,
'hospital': 0,
'travel_agent': 0,
'arts_centre': 0,
'toy_shop': 0,
'mall': 0,
'monument': 0,
'stationery': 0,
'newsagent': 0,
'food_court': 0,
'graveyard': 0,
'wastewater_plant': 0,
'car_rental': 0,
'artwork': 0,
'convenience': 1,
'college': 0,
'garden_centre': 0,
'shelter': 0,
'gift_shop': 0,
'pharmacy': 0,
'car_dealership': 0,
'furniture_shop': 0,
'water_works': 0,
'beverages': 0,
'water_tower': 0,
'tower': 0,
'sports_shop': 0,
'bank': 0,
'archaeological': 0,
'optician': 0,
'mobile_phone_shop': 0,
'swimming_pool': 0,
'nightclub': 0,
'beauty_shop': 0,
'bar': 0,
'picnic_site': 0,
'caravan_site': 0,
'shoe_shop': 0,
'clinic': 0,
'department_store': 0,
'jeweller': 0,
'nursing_home': 0,
'toilet': 0,
'track': 0,
'castle': 0,
'florist': 0,
'sports_centre': 0,
'doityourself': 0,
'hostel': 0,
'bakery': 0,
'butcher': 0,
'tourist_info': 0,
'supermarket': 0,
'public_building': 0,
'playground': 0,
'golf_course': 0,
'theme_park': 0,
'recycling_glass': 0,
'hotel': 0,
'observation_tower': 0,
'fast_food': 1,
'outdoor_shop': 0,
'comms_tower': 0,
'cinema': 0,
'video_shop': 0,
'laundry': 0,
'memorial': 0,
'computer_shop': 0,
'bookshop': 0,
'nan': 0,
'school': 0,
'park': 0,
'car_wash': 0,
'fountain': 0,
'dentist': 0,
'theatre': 0,
'fire_station': 0,
'post_office': 0,
'museum': 0,
'ruins': 0,
'library': 0,
'doctors': 0,
'courthouse': 0,
'hairdresser': 0,
'police': 0,
'recycling': 0,
'recycling_metal': 0,
'prison': 0,
'veterinary': 0,
'bicycle_shop': 0,
'town_hall': 0,
'greengrocer': 0,
'pub': 0,
'pitch': 3,
'camp_site': 0,
'chemist': 0,
'attraction': 0,
'clothes': 0,
'guesthouse': 0,
'stadium': 0,
'kindergarten': 1}}
docDic here is a dictionary of dictionary. The inner dictionary is the frequency of each word occurred in the document. The outer dictionary is about different documents. We next can convert such a dictionary into a dataframe:
doc2wordMatrix = pd.DataFrame.from_dict(docDic, orient='index').rename_axis('LSOA11 Code').reset_index()
doc2wordMatrix.head()
| LSOA11 Code | cafe | university | market_place | community_centre | restaurant | hospital | travel_agent | arts_centre | toy_shop | ... | greengrocer | pub | pitch | camp_site | chemist | attraction | clothes | guesthouse | stadium | kindergarten | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | E01014612 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | E01014512 | 1 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | E01014618 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 3 | E01014576 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | E01033370 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
5 rows × 106 columns
Note here such a data sometimes is referred as a document matrix, where each row is a document, and each column is a word/token from the vocabulary. The value in the matrix represents the frequency of the word/token in the correspoding document. For example, we can see that place types car_dealership and college both occurred once in the document with LSOA11 code “E01014512” (remember that it is actually a region in our example).
Now, we can start computing the term frequency-inverse document frequency (TF-IDF), which will be used later for us to determine the relevance ranking of documents in relation to a query. The functions are written below. We first compute TF, and then IDF. Multiplying both, we will have TF-IDF. Please read it first and try to think if you can understand it:
## Compute term frequency (TF)
def computeTF(wordDict):
tfDict = {}
bowCount = 0
for word, count in wordDict.items():
if count >0:
bowCount +=1
for word, count in wordDict.items():
tfDict[word] = count/float(bowCount)
return tfDict
We can use the computeTF() function on one example document (region with code ‘E01014612’). From the result, you can see the frequency of “swimming_pool” in this region is 0.666666666, frequency of “park” is 0.3333333, and the frequency of seeing “playground” is also 0.33333333.
computeTF(docDic['E01014612'])
{'cafe': 0.0,
'university': 0.0,
'market_place': 0.0,
'community_centre': 0.0,
'restaurant': 0.0,
'hospital': 0.0,
'travel_agent': 0.0,
'arts_centre': 0.0,
'toy_shop': 0.0,
'mall': 0.0,
'monument': 0.0,
'stationery': 0.0,
'newsagent': 0.0,
'food_court': 0.0,
'graveyard': 0.0,
'wastewater_plant': 0.0,
'car_rental': 0.0,
'artwork': 0.0,
'convenience': 0.0,
'college': 0.0,
'garden_centre': 0.0,
'shelter': 0.0,
'gift_shop': 0.0,
'pharmacy': 0.0,
'car_dealership': 0.0,
'furniture_shop': 0.0,
'water_works': 0.0,
'beverages': 0.0,
'water_tower': 0.0,
'tower': 0.0,
'sports_shop': 0.0,
'bank': 0.0,
'archaeological': 0.0,
'optician': 0.0,
'mobile_phone_shop': 0.0,
'swimming_pool': 0.6666666666666666,
'nightclub': 0.0,
'beauty_shop': 0.0,
'bar': 0.0,
'picnic_site': 0.0,
'caravan_site': 0.0,
'shoe_shop': 0.0,
'clinic': 0.0,
'department_store': 0.0,
'jeweller': 0.0,
'nursing_home': 0.0,
'toilet': 0.0,
'track': 0.0,
'castle': 0.0,
'florist': 0.0,
'sports_centre': 0.0,
'doityourself': 0.0,
'hostel': 0.0,
'bakery': 0.0,
'butcher': 0.0,
'tourist_info': 0.0,
'supermarket': 0.0,
'public_building': 0.0,
'playground': 0.3333333333333333,
'golf_course': 0.0,
'theme_park': 0.0,
'recycling_glass': 0.0,
'hotel': 0.0,
'observation_tower': 0.0,
'fast_food': 0.0,
'outdoor_shop': 0.0,
'comms_tower': 0.0,
'cinema': 0.0,
'video_shop': 0.0,
'laundry': 0.0,
'memorial': 0.0,
'computer_shop': 0.0,
'bookshop': 0.0,
'nan': 0.0,
'school': 0.0,
'park': 0.3333333333333333,
'car_wash': 0.0,
'fountain': 0.0,
'dentist': 0.0,
'theatre': 0.0,
'fire_station': 0.0,
'post_office': 0.0,
'museum': 0.0,
'ruins': 0.0,
'library': 0.0,
'doctors': 0.0,
'courthouse': 0.0,
'hairdresser': 0.0,
'police': 0.0,
'recycling': 0.0,
'recycling_metal': 0.0,
'prison': 0.0,
'veterinary': 0.0,
'bicycle_shop': 0.0,
'town_hall': 0.0,
'greengrocer': 0.0,
'pub': 0.0,
'pitch': 0.0,
'camp_site': 0.0,
'chemist': 0.0,
'attraction': 0.0,
'clothes': 0.0,
'guesthouse': 0.0,
'stadium': 0.0,
'kindergarten': 0.0}
Next, we have the function computeIDF() to compute inverse document frequency. Note the use of math.log10() in the last step is to avoid the situation where the number of documents N is too large making N/float(count) become too large as well. This logarithm is to scale the inversed document frequency to a relatively small value (e.g., a dampen effect).
## Compute inverse document frequency (IDF)
def computeIDF(docDic):
import math
idfDict = {}
N = len(docDic)
idfDict = dict.fromkeys(wordSet, 0)
for doc, wordDic in docDic.items():
for word, count in wordDic.items():
if count > 0:
idfDict[word] += 1
for word, count in idfDict.items(): ## this count is the number of document containing the word
idfDict[word] = math.log10(N / float(count))
return idfDict
With this function, we can then input all the documents (represented using the docDoc variable) to it so as to obtain the inverse document frequency (idfs), which is also represented as a dictionary. The keys of the dictionary are the words/tokens from the vocabulary. The values are the computed IDF.
idfs = computeIDF(docDic)
idfs
{'cafe': 0.428729672797263,
'university': 1.465713239050433,
'market_place': 1.8178957571617955,
'community_centre': 0.5686973997706826,
'restaurant': 0.4905368227754652,
'hospital': 1.189506827111484,
'travel_agent': 1.141202147536929,
'arts_centre': 1.4199557484897578,
'toy_shop': 1.378563063331533,
'mall': 1.4199557484897578,
'monument': 2.1189257528257768,
'stationery': 1.378563063331533,
'newsagent': 0.8401721518729477,
'food_court': 2.1189257528257768,
'graveyard': 0.9885919843307706,
'wastewater_plant': 2.1189257528257768,
'car_rental': 1.306012396182921,
'artwork': 2.419955748489758,
'convenience': 0.258587746254783,
'college': 1.215835765833833,
'garden_centre': 1.720985744153739,
'shelter': 1.189506827111484,
'gift_shop': 1.141202147536929,
'pharmacy': 0.7039524048549587,
'car_dealership': 0.6491037368476137,
'furniture_shop': 0.8636532477224705,
'water_works': 1.378563063331533,
'beverages': 0.8517540244227628,
'water_tower': 2.1189257528257768,
'tower': 1.720985744153739,
'sports_shop': 1.1189257528257768,
'bank': 1.141202147536929,
'archaeological': 1.9428344937700954,
'optician': 0.9885919843307706,
'mobile_phone_shop': 1.141202147536929,
'swimming_pool': 1.058227912472165,
'nightclub': 1.465713239050433,
'beauty_shop': 0.5811066577525026,
'bar': 0.8517540244227628,
'picnic_site': 2.419955748489758,
'caravan_site': 2.419955748489758,
'shoe_shop': 1.465713239050433,
'clinic': 0.6640808928172665,
'department_store': 1.8178957571617955,
'jeweller': 1.2738277128115199,
'nursing_home': 1.2738277128115199,
'toilet': 1.2738277128115199,
'track': 1.306012396182921,
'castle': 2.1189257528257768,
'florist': 0.9575577505908017,
'sports_centre': 0.7765030720035705,
'doityourself': 0.7864872929101714,
'hostel': 1.9428344937700954,
'bakery': 0.7478578905540404,
'butcher': 1.0049824005189398,
'tourist_info': 2.1189257528257768,
'supermarket': 0.5168657614978143,
'public_building': 2.1189257528257768,
'playground': 0.34440878709722716,
'golf_course': 1.3407745024421331,
'theme_park': 2.1189257528257768,
'recycling_glass': 1.9428344937700954,
'hotel': 1.0220157398177203,
'observation_tower': 2.419955748489758,
'fast_food': 0.26461971102469606,
'outdoor_shop': 1.574857708475501,
'comms_tower': 2.1189257528257768,
'cinema': 1.574857708475501,
'video_shop': 1.9428344937700954,
'laundry': 0.9575577505908017,
'memorial': 2.1189257528257768,
'computer_shop': 1.189506827111484,
'bookshop': 1.2738277128115199,
'nan': 2.419955748489758,
'school': 0.19984766044970276,
'park': 0.17198248212795123,
'car_wash': 0.9727977171475386,
'fountain': 2.419955748489758,
'dentist': 0.7297596684612442,
'theatre': 1.378563063331533,
'fire_station': 1.8178957571617955,
'post_office': 1.215835765833833,
'museum': 1.3407745024421331,
'ruins': 1.5168657614978143,
'library': 0.9885919843307706,
'doctors': 0.8288911414632587,
'courthouse': 1.9428344937700954,
'hairdresser': 0.2864168401195404,
'police': 1.574857708475501,
'recycling': 2.419955748489758,
'recycling_metal': 2.419955748489758,
'prison': 1.9428344937700954,
'veterinary': 1.058227912472165,
'bicycle_shop': 1.058227912472165,
'town_hall': 2.1189257528257768,
'greengrocer': 1.1189257528257768,
'pub': 0.2961041075226721,
'pitch': 0.2615932563945082,
'camp_site': 2.419955748489758,
'chemist': 2.1189257528257768,
'attraction': 1.3407745024421331,
'clothes': 0.8401721518729477,
'guesthouse': 1.215835765833833,
'stadium': 1.6418044981061142,
'kindergarten': 0.679593058995514}
By multiplying tf and idf, we can finally have the TF-IDF using the function:
def computeTFIDF(tf, idfs):
tfidf = {}
for word, freq in tf.items():
tfidf[word] = freq*idfs[word]
return tfidf
Here is an example on how we use the function:
tfidfDic = {}
for doc, wordDic in docDic.items():
doc_tfidf = computeTFIDF(wordDic, idfs)
tfidfDic[doc] = doc_tfidf
tfidfDic
{'E01014612': {'cafe': 0.0,
'university': 0.0,
'market_place': 0.0,
'community_centre': 0.0,
'restaurant': 0.0,
'hospital': 0.0,
'travel_agent': 0.0,
'arts_centre': 0.0,
'toy_shop': 0.0,
'mall': 0.0,
'monument': 0.0,
'stationery': 0.0,
'newsagent': 0.0,
'food_court': 0.0,
'graveyard': 0.0,
'wastewater_plant': 0.0,
'car_rental': 0.0,
'artwork': 0.0,
'convenience': 0.0,
'college': 0.0,
'garden_centre': 0.0,
'shelter': 0.0,
'gift_shop': 0.0,
'pharmacy': 0.0,
'car_dealership': 0.0,
'furniture_shop': 0.0,
'water_works': 0.0,
'beverages': 0.0,
'water_tower': 0.0,
'tower': 0.0,
'sports_shop': 0.0,
'bank': 0.0,
'archaeological': 0.0,
'optician': 0.0,
'mobile_phone_shop': 0.0,
'swimming_pool': 2.11645582494433,
'nightclub': 0.0,
'beauty_shop': 0.0,
'bar': 0.0,
'picnic_site': 0.0,
'caravan_site': 0.0,
'shoe_shop': 0.0,
'clinic': 0.0,
'department_store': 0.0,
'jeweller': 0.0,
'nursing_home': 0.0,
'toilet': 0.0,
'track': 0.0,
'castle': 0.0,
'florist': 0.0,
'sports_centre': 0.0,
'doityourself': 0.0,
'hostel': 0.0,
'bakery': 0.0,
'butcher': 0.0,
'tourist_info': 0.0,
'supermarket': 0.0,
'public_building': 0.0,
'playground': 0.34440878709722716,
'golf_course': 0.0,
'theme_park': 0.0,
'recycling_glass': 0.0,
'hotel': 0.0,
'observation_tower': 0.0,
'fast_food': 0.0,
'outdoor_shop': 0.0,
'comms_tower': 0.0,
'cinema': 0.0,
'video_shop': 0.0,
'laundry': 0.0,
'memorial': 0.0,
'computer_shop': 0.0,
'bookshop': 0.0,
'nan': 0.0,
'school': 0.0,
'park': 0.17198248212795123,
'car_wash': 0.0,
'fountain': 0.0,
'dentist': 0.0,
'theatre': 0.0,
'fire_station': 0.0,
'post_office': 0.0,
'museum': 0.0,
'ruins': 0.0,
'library': 0.0,
'doctors': 0.0,
'courthouse': 0.0,
'hairdresser': 0.0,
'police': 0.0,
'recycling': 0.0,
'recycling_metal': 0.0,
'prison': 0.0,
'veterinary': 0.0,
'bicycle_shop': 0.0,
'town_hall': 0.0,
'greengrocer': 0.0,
'pub': 0.0,
'pitch': 0.0,
'camp_site': 0.0,
'chemist': 0.0,
'attraction': 0.0,
'clothes': 0.0,
'guesthouse': 0.0,
'stadium': 0.0,
'kindergarten': 0.0},
'E01014512': {'cafe': 0.428729672797263,
'university': 0.0,
'market_place': 0.0,
'community_centre': 0.5686973997706826,
'restaurant': 1.4716104683263955,
'hospital': 0.0,
'travel_agent': 0.0,
'arts_centre': 0.0,
'toy_shop': 0.0,
'mall': 0.0,
'monument': 0.0,
'stationery': 0.0,
'newsagent': 0.0,
'food_court': 0.0,
'graveyard': 0.0,
'wastewater_plant': 0.0,
'car_rental': 0.0,
'artwork': 0.0,
'convenience': 0.7757632387643489,
'college': 1.215835765833833,
'garden_centre': 0.0,
'shelter': 0.0,
'gift_shop': 0.0,
'pharmacy': 0.0,
'car_dealership': 0.6491037368476137,
'furniture_shop': 0.0,
'water_works': 0.0,
'beverages': 0.0,
'water_tower': 0.0,
'tower': 0.0,
'sports_shop': 0.0,
'bank': 0.0,
'archaeological': 0.0,
'optician': 0.0,
'mobile_phone_shop': 0.0,
'swimming_pool': 0.0,
'nightclub': 0.0,
'beauty_shop': 0.0,
'bar': 0.0,
'picnic_site': 0.0,
'caravan_site': 0.0,
'shoe_shop': 0.0,
'clinic': 0.6640808928172665,
'department_store': 0.0,
'jeweller': 0.0,
'nursing_home': 1.2738277128115199,
'toilet': 0.0,
'track': 0.0,
'castle': 0.0,
'florist': 0.0,
'sports_centre': 2.3295092160107114,
'doityourself': 2.359461878730514,
'hostel': 0.0,
'bakery': 0.0,
'butcher': 1.0049824005189398,
'tourist_info': 0.0,
'supermarket': 0.0,
'public_building': 0.0,
'playground': 0.0,
'golf_course': 0.0,
'theme_park': 0.0,
'recycling_glass': 0.0,
'hotel': 0.0,
'observation_tower': 0.0,
'fast_food': 0.26461971102469606,
'outdoor_shop': 0.0,
'comms_tower': 0.0,
'cinema': 0.0,
'video_shop': 0.0,
'laundry': 0.0,
'memorial': 0.0,
'computer_shop': 1.189506827111484,
'bookshop': 0.0,
'nan': 0.0,
'school': 0.0,
'park': 0.0,
'car_wash': 0.9727977171475386,
'fountain': 0.0,
'dentist': 0.7297596684612442,
'theatre': 1.378563063331533,
'fire_station': 0.0,
'post_office': 0.0,
'museum': 0.0,
'ruins': 0.0,
'library': 0.0,
'doctors': 0.0,
'courthouse': 0.0,
'hairdresser': 0.5728336802390808,
'police': 0.0,
'recycling': 0.0,
'recycling_metal': 0.0,
'prison': 0.0,
'veterinary': 0.0,
'bicycle_shop': 0.0,
'town_hall': 0.0,
'greengrocer': 0.0,
'pub': 0.0,
'pitch': 0.0,
'camp_site': 0.0,
'chemist': 0.0,
'attraction': 0.0,
'clothes': 0.0,
'guesthouse': 0.0,
'stadium': 0.0,
'kindergarten': 0.679593058995514},
'E01014618': {'cafe': 0.428729672797263,
'university': 0.0,
'market_place': 0.0,
'community_centre': 0.5686973997706826,
'restaurant': 0.0,
'hospital': 0.0,
'travel_agent': 0.0,
'arts_centre': 0.0,
'toy_shop': 0.0,
'mall': 0.0,
'monument': 0.0,
'stationery': 0.0,
'newsagent': 0.0,
'food_court': 0.0,
'graveyard': 0.0,
'wastewater_plant': 0.0,
'car_rental': 0.0,
'artwork': 0.0,
'convenience': 0.517175492509566,
'college': 0.0,
'garden_centre': 0.0,
'shelter': 0.0,
'gift_shop': 0.0,
'pharmacy': 0.0,
'car_dealership': 0.0,
'furniture_shop': 0.0,
'water_works': 0.0,
'beverages': 0.0,
'water_tower': 0.0,
'tower': 0.0,
'sports_shop': 0.0,
'bank': 0.0,
'archaeological': 0.0,
'optician': 0.0,
'mobile_phone_shop': 0.0,
'swimming_pool': 0.0,
'nightclub': 0.0,
'beauty_shop': 0.5811066577525026,
'bar': 0.0,
'picnic_site': 0.0,
'caravan_site': 0.0,
'shoe_shop': 0.0,
'clinic': 0.0,
'department_store': 0.0,
'jeweller': 0.0,
'nursing_home': 0.0,
'toilet': 0.0,
'track': 0.0,
'castle': 0.0,
'florist': 0.0,
'sports_centre': 0.0,
'doityourself': 0.0,
'hostel': 0.0,
'bakery': 0.0,
'butcher': 0.0,
'tourist_info': 0.0,
'supermarket': 0.0,
'public_building': 0.0,
'playground': 0.34440878709722716,
'golf_course': 0.0,
'theme_park': 0.0,
'recycling_glass': 0.0,
'hotel': 0.0,
'observation_tower': 0.0,
'fast_food': 0.7938591330740882,
'outdoor_shop': 0.0,
'comms_tower': 0.0,
'cinema': 0.0,
'video_shop': 0.0,
'laundry': 0.0,
'memorial': 0.0,
'computer_shop': 0.0,
'bookshop': 0.0,
'nan': 0.0,
'school': 0.0,
'park': 0.34396496425590245,
'car_wash': 0.0,
'fountain': 0.0,
'dentist': 0.0,
'theatre': 0.0,
'fire_station': 0.0,
'post_office': 0.0,
'museum': 0.0,
'ruins': 0.0,
'library': 0.0,
'doctors': 0.0,
'courthouse': 0.0,
'hairdresser': 0.5728336802390808,
'police': 0.0,
'recycling': 0.0,
'recycling_metal': 0.0,
'prison': 0.0,
'veterinary': 0.0,
'bicycle_shop': 0.0,
'town_hall': 0.0,
'greengrocer': 0.0,
'pub': 0.0,
'pitch': 0.0,
'camp_site': 0.0,
'chemist': 0.0,
'attraction': 0.0,
'clothes': 0.0,
'guesthouse': 0.0,
'stadium': 0.0,
'kindergarten': 1.359186117991028},
'E01014576': {'cafe': 0.0,
'university': 0.0,
'market_place': 0.0,
'community_centre': 0.0,
'restaurant': 0.0,
'hospital': 0.0,
'travel_agent': 0.0,
'arts_centre': 0.0,
'toy_shop': 1.378563063331533,
'mall': 0.0,
'monument': 0.0,
'stationery': 0.0,
'newsagent': 0.0,
'food_court': 0.0,
'graveyard': 0.0,
'wastewater_plant': 0.0,
'car_rental': 0.0,
'artwork': 0.0,
'convenience': 0.258587746254783,
'college': 0.0,
'garden_centre': 0.0,
'shelter': 0.0,
'gift_shop': 0.0,
'pharmacy': 0.0,
'car_dealership': 0.6491037368476137,
'furniture_shop': 0.0,
'water_works': 0.0,
'beverages': 0.0,
'water_tower': 0.0,
'tower': 0.0,
'sports_shop': 1.1189257528257768,
'bank': 0.0,
'archaeological': 0.0,
'optician': 0.0,
'mobile_phone_shop': 0.0,
'swimming_pool': 0.0,
'nightclub': 0.0,
'beauty_shop': 0.5811066577525026,
'bar': 0.0,
'picnic_site': 0.0,
'caravan_site': 0.0,
'shoe_shop': 0.0,
'clinic': 0.0,
'department_store': 0.0,
'jeweller': 0.0,
'nursing_home': 0.0,
'toilet': 0.0,
'track': 0.0,
'castle': 0.0,
'florist': 0.0,
'sports_centre': 0.0,
'doityourself': 0.0,
'hostel': 0.0,
'bakery': 0.0,
'butcher': 0.0,
'tourist_info': 0.0,
'supermarket': 0.0,
'public_building': 0.0,
'playground': 0.6888175741944543,
'golf_course': 0.0,
'theme_park': 0.0,
'recycling_glass': 0.0,
'hotel': 0.0,
'observation_tower': 0.0,
'fast_food': 0.26461971102469606,
'outdoor_shop': 0.0,
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'cinema': 0.0,
'video_shop': 0.0,
'laundry': 0.0,
'memorial': 0.0,
'computer_shop': 0.0,
'bookshop': 0.0,
'nan': 0.0,
'school': 0.0,
'park': 0.0,
'car_wash': 0.0,
'fountain': 0.0,
'dentist': 0.0,
'theatre': 0.0,
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'post_office': 0.0,
'museum': 0.0,
'ruins': 0.0,
'library': 0.0,
'doctors': 0.0,
'courthouse': 0.0,
'hairdresser': 0.0,
'police': 0.0,
'recycling': 0.0,
'recycling_metal': 0.0,
'prison': 0.0,
'veterinary': 0.0,
'bicycle_shop': 0.0,
'town_hall': 0.0,
'greengrocer': 0.0,
'pub': 0.2961041075226721,
'pitch': 0.5231865127890164,
'camp_site': 0.0,
'chemist': 0.0,
'attraction': 0.0,
'clothes': 0.0,
'guesthouse': 0.0,
'stadium': 0.0,
'kindergarten': 0.0},
'E01033370': {'cafe': 0.0,
'university': 0.0,
'market_place': 0.0,
'community_centre': 0.0,
'restaurant': 0.4905368227754652,
'hospital': 0.0,
'travel_agent': 0.0,
'arts_centre': 0.0,
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'travel_agent': 0.0,
'arts_centre': 0.0,
'toy_shop': 0.0,
'mall': 0.0,
'monument': 0.0,
'stationery': 0.0,
'newsagent': 0.0,
'food_court': 0.0,
'graveyard': 0.0,
'wastewater_plant': 0.0,
'car_rental': 0.0,
'artwork': 0.0,
'convenience': 0.258587746254783,
'college': 0.0,
'garden_centre': 0.0,
'shelter': 0.0,
'gift_shop': 0.0,
'pharmacy': 0.0,
'car_dealership': 0.0,
'furniture_shop': 0.0,
'water_works': 0.0,
'beverages': 0.0,
'water_tower': 0.0,
'tower': 0.0,
'sports_shop': 0.0,
'bank': 0.0,
'archaeological': 0.0,
'optician': 0.0,
'mobile_phone_shop': 0.0,
'swimming_pool': 0.0,
'nightclub': 0.0,
'beauty_shop': 0.0,
'bar': 0.0,
'picnic_site': 0.0,
'caravan_site': 0.0,
'shoe_shop': 0.0,
'clinic': 0.0,
'department_store': 0.0,
'jeweller': 0.0,
'nursing_home': 0.0,
'toilet': 0.0,
'track': 0.0,
'castle': 0.0,
'florist': 0.0,
'sports_centre': 0.0,
'doityourself': 0.0,
'hostel': 0.0,
'bakery': 0.0,
'butcher': 0.0,
'tourist_info': 0.0,
'supermarket': 0.0,
'public_building': 0.0,
'playground': 0.0,
'golf_course': 0.0,
'theme_park': 0.0,
'recycling_glass': 0.0,
'hotel': 0.0,
'observation_tower': 0.0,
'fast_food': 0.26461971102469606,
'outdoor_shop': 0.0,
'comms_tower': 0.0,
'cinema': 0.0,
'video_shop': 0.0,
'laundry': 0.0,
'memorial': 0.0,
'computer_shop': 0.0,
'bookshop': 0.0,
'nan': 0.0,
'school': 0.0,
'park': 0.0,
'car_wash': 0.0,
'fountain': 0.0,
'dentist': 0.0,
'theatre': 0.0,
'fire_station': 0.0,
'post_office': 0.0,
'museum': 0.0,
'ruins': 0.0,
'library': 0.0,
'doctors': 0.0,
'courthouse': 0.0,
'hairdresser': 0.0,
'police': 0.0,
'recycling': 0.0,
'recycling_metal': 0.0,
'prison': 0.0,
'veterinary': 0.0,
'bicycle_shop': 0.0,
'town_hall': 0.0,
'greengrocer': 0.0,
'pub': 0.0,
'pitch': 0.7847797691835245,
'camp_site': 0.0,
'chemist': 0.0,
'attraction': 0.0,
'clothes': 0.0,
'guesthouse': 0.0,
'stadium': 0.0,
'kindergarten': 0.679593058995514}}
tfidfDic is a dictionary of dictionary. The inner is a dictionary of words with their tfidf scores, and the outer is the document dictionary.
Again, we can convert the tfidf dictionary to a dataframe by using code:
doc2wordTFIDFMatrix = pd.DataFrame.from_dict(tfidfDic, orient='index').rename_axis('LSOA11 Code').reset_index()
doc2wordTFIDFMatrix.head()
| LSOA11 Code | cafe | university | market_place | community_centre | restaurant | hospital | travel_agent | arts_centre | toy_shop | ... | greengrocer | pub | pitch | camp_site | chemist | attraction | clothes | guesthouse | stadium | kindergarten | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | E01014612 | 0.00000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| 1 | E01014512 | 0.42873 | 0.0 | 0.0 | 0.568697 | 1.471610 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.679593 |
| 2 | E01014618 | 0.42873 | 0.0 | 0.0 | 0.568697 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.359186 |
| 3 | E01014576 | 0.00000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 1.378563 | ... | 0.0 | 0.296104 | 0.523187 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| 4 | E01033370 | 0.00000 | 0.0 | 0.0 | 0.000000 | 0.490537 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.592208 | 0.784780 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.679593 |
5 rows × 106 columns
Different from doc2wordMatrix where the term frequency is recorded, this doc2wordTFIDFMatrix records the TF-IDF values. As we have discussed in the lecture, the TF-IDF can better represent the uniqueness of terms in a document.
So far, we have mannually implemented the TF-IDF computation. In fact, we can also achieve the same using existing packages:
## automatically compute the IF-IDF using packages sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer()
TfidfVectorizer() here is to initialize a Tfidf vector, which we will use in the next steps to fit the TF-IDF scores from the documents. Before that, we also have to prepare a list of string: docList as the input for the next step:
docList = list(bristol_lsoal_poi_full['fclass'].apply(' '.join))
docList
['park playground swimming_pool swimming_pool',
'nursing_home hairdresser computer_shop fast_food restaurant doityourself restaurant sports_centre hairdresser sports_centre doityourself car_dealership restaurant dentist kindergarten community_centre cafe doityourself butcher theatre sports_centre convenience convenience college car_wash convenience clinic',
'kindergarten convenience hairdresser fast_food community_centre playground park park convenience cafe fast_food fast_food hairdresser beauty_shop kindergarten',
'sports_shop beauty_shop convenience fast_food pub car_dealership playground pitch pitch toy_shop playground',
'convenience restaurant kindergarten school pitch park pitch playground pub hotel fast_food fast_food college pitch car_dealership furniture_shop pub hotel',
'car_dealership doityourself car_wash doityourself doityourself convenience beauty_shop fast_food park pharmacy hairdresser pub fast_food',
'sports_centre greengrocer bakery gift_shop pub convenience pub pub pub restaurant',
'restaurant cafe cafe restaurant gift_shop florist restaurant fast_food restaurant beverages hairdresser beverages restaurant restaurant cafe cafe arts_centre attraction bar pub restaurant pub restaurant nightclub bar restaurant restaurant pub pub restaurant pub cafe cinema hotel tourist_info pub park fast_food fast_food beauty_shop bar fast_food supermarket furniture_shop fast_food restaurant bar hairdresser convenience fast_food fast_food fast_food fast_food cafe fast_food restaurant restaurant beauty_shop pub restaurant cafe hairdresser fountain fountain clothes fast_food bar restaurant theatre supermarket pub pharmacy convenience restaurant hairdresser fast_food hairdresser bar theatre hairdresser arts_centre museum hairdresser sports_centre cafe restaurant toilet cafe restaurant community_centre hairdresser cafe restaurant cafe clinic museum school cafe library park fountain park park fountain town_hall park cafe fountain cafe fast_food restaurant bar hairdresser nightclub restaurant clothes nightclub beverages pub restaurant nightclub',
'car_dealership convenience doityourself hairdresser convenience doityourself nursing_home convenience bakery school car_dealership hairdresser park playground school',
'college park park track fast_food sports_centre park school pitch',
'cafe restaurant doityourself pub fast_food fast_food pub clinic sports_shop outdoor_shop convenience fast_food restaurant pub veterinary sports_centre pub convenience playground pub clinic dentist',
'hairdresser convenience pub fast_food car_wash clothes playground bakery butcher convenience restaurant restaurant fast_food hairdresser restaurant hairdresser park playground pub park restaurant convenience supermarket beauty_shop restaurant pub convenience supermarket pharmacy butcher restaurant restaurant fast_food travel_agent supermarket restaurant hairdresser clothes cafe travel_agent pharmacy bakery cafe hairdresser convenience butcher gift_shop mobile_phone_shop fast_food fast_food clothes fast_food restaurant fast_food beauty_shop supermarket toy_shop pub cafe',
'car_wash pitch park mobile_phone_shop bank mobile_phone_shop hairdresser gift_shop travel_agent cafe pub cafe mobile_phone_shop hairdresser bank cafe toilet nightclub convenience outdoor_shop furniture_shop pub cafe fast_food pub fast_food fast_food greengrocer cafe butcher arts_centre fast_food fast_food shoe_shop clothes mobile_phone_shop fast_food beauty_shop fast_food chemist cafe beverages cafe hairdresser mobile_phone_shop playground mobile_phone_shop hairdresser jeweller beauty_shop beauty_shop pub optician pub fast_food pub hairdresser fast_food cafe furniture_shop pharmacy doctors restaurant library clinic hairdresser optician mobile_phone_shop post_office clothes car_dealership supermarket hairdresser convenience sports_centre car_rental',
'playground tower pitch',
'restaurant bookshop restaurant hairdresser park laundry pub kindergarten pub university swimming_pool restaurant school fast_food hairdresser fast_food hairdresser dentist university clinic hotel hotel cafe convenience pub convenience bar',
'community_centre school pitch park convenience pitch park',
'school post_office',
'toilet pitch shelter park playground pitch pitch pitch mall fast_food hairdresser florist bakery hairdresser car_wash community_centre bank bank fast_food gift_shop dentist kindergarten veterinary school kindergarten fast_food newsagent clinic pub park graveyard cafe',
'pitch pitch pub school doityourself convenience beauty_shop beverages fast_food car_dealership optician convenience post_office fast_food beverages restaurant pub bakery fast_food restaurant beverages bakery beauty_shop hairdresser fast_food pharmacy fast_food pub furniture_shop newsagent beverages hairdresser convenience dentist pitch',
'bar bar cafe school sports_centre fast_food school fast_food cafe community_centre cafe convenience furniture_shop cafe clothes optician hairdresser convenience computer_shop fast_food greengrocer restaurant fast_food hairdresser butcher cafe cafe butcher fast_food mobile_phone_shop convenience travel_agent fast_food mobile_phone_shop furniture_shop butcher pharmacy cafe park pitch pitch hairdresser butcher restaurant fast_food convenience restaurant playground park pitch car_dealership park convenience convenience school playground park clothes clinic library sports_centre bicycle_shop',
'doctors fast_food restaurant restaurant hairdresser hairdresser community_centre veterinary pub graveyard',
'pub convenience playground pitch school cafe fast_food',
'pub greengrocer cafe beverages convenience park community_centre car_dealership pharmacy hairdresser car_dealership car_dealership car_dealership pitch',
'hairdresser newsagent hairdresser',
'dentist beauty_shop bank restaurant hairdresser hairdresser convenience fast_food fast_food restaurant hairdresser bakery clinic swimming_pool clinic park doctors',
'university hospital school pitch pharmacy nursing_home university university university cafe park fast_food restaurant hairdresser pitch park playground pitch park hairdresser convenience convenience',
'park fast_food fast_food pub police cafe hairdresser supermarket clinic guesthouse park beauty_shop kindergarten community_centre park park',
'hairdresser cafe convenience optician fast_food pharmacy pharmacy veterinary fast_food convenience hairdresser convenience fast_food fast_food fast_food library school school kindergarten',
'park hairdresser hairdresser convenience convenience fast_food fast_food florist school park',
'convenience playground library cafe park park pitch playground community_centre park',
'pitch sports_centre supermarket mall laundry optician newsagent cafe pub bar doctors furniture_shop convenience cafe cafe hairdresser beauty_shop cafe bar restaurant beauty_shop library furniture_shop furniture_shop restaurant cafe furniture_shop furniture_shop furniture_shop clinic supermarket community_centre furniture_shop restaurant restaurant doityourself furniture_shop furniture_shop police cafe kindergarten shelter cafe cafe school sports_shop fast_food pub pub optician beauty_shop restaurant kindergarten greengrocer hairdresser convenience cafe gift_shop bookshop restaurant fast_food pharmacy restaurant bicycle_shop bar sports_shop clothes toilet shelter',
'playground pitch sports_centre hospital laundry pharmacy hairdresser fast_food hairdresser hairdresser supermarket fast_food convenience doityourself dentist community_centre pitch doctors pitch school pitch',
'park pitch pitch convenience beauty_shop hairdresser park park beauty_shop fast_food cafe park park dentist park playground convenience golf_course library community_centre swimming_pool school',
'park doityourself playground pub convenience pub fast_food park',
'library car_dealership supermarket fast_food fast_food fast_food supermarket clothes hairdresser supermarket pub museum university bank pub school beauty_shop hairdresser pitch restaurant beauty_shop jeweller mobile_phone_shop optician supermarket fast_food travel_agent playground park park hospital school park',
'hairdresser clothes sports_shop furniture_shop cafe clinic gift_shop pitch school kindergarten fast_food restaurant greengrocer mobile_phone_shop park playground shelter park',
'playground pitch school pub car_rental car_dealership convenience beauty_shop hospital fire_station',
'doityourself florist fast_food hairdresser fast_food beverages restaurant fast_food hairdresser restaurant convenience newsagent pub doityourself supermarket bicycle_shop fast_food car_dealership pub cafe furniture_shop hairdresser hairdresser car_dealership pub park bar bookshop clothes doityourself furniture_shop cafe fast_food cafe beauty_shop beauty_shop jeweller clinic',
'park',
'school doctors convenience car_dealership car_rental fast_food supermarket pitch pitch',
'park park school pitch swimming_pool swimming_pool cafe park school hairdresser playground convenience pitch nursing_home pub convenience park',
'pub park',
'pitch graveyard kindergarten beverages pub hairdresser pitch convenience pitch pitch park pitch pitch playground pitch school tower',
'convenience hairdresser playground pitch fast_food beverages hairdresser fast_food beauty_shop doityourself pitch car_wash school pitch park',
'hotel bar convenience park theatre playground park cafe playground school school college hotel bar pub hostel nightclub hotel park bar cafe hairdresser graveyard restaurant restaurant convenience hairdresser',
'clinic nursing_home nursing_home hairdresser hairdresser park fast_food newsagent hairdresser community_centre community_centre hairdresser computer_shop fast_food beverages pitch pitch pitch',
'park playground park convenience museum archaeological car_dealership school track pitch pitch car_rental car_rental bakery fast_food fast_food car_dealership archaeological wastewater_plant recycling_glass pitch pitch pitch pitch police car_dealership park shelter',
'car_dealership pitch pitch pitch swimming_pool car_dealership clinic hairdresser doityourself clinic',
'bakery supermarket playground pub pub park convenience sports_centre fast_food dentist cafe pub pub convenience fast_food restaurant',
'beauty_shop fast_food beauty_shop restaurant hairdresser convenience veterinary dentist',
'playground kindergarten pitch pitch sports_centre community_centre pitch pitch school community_centre school pitch',
'car_dealership pub fast_food hairdresser',
'post_office convenience butcher fast_food travel_agent restaurant furniture_shop convenience laundry cafe butcher school mobile_phone_shop beauty_shop cafe convenience supermarket convenience fast_food pub park playground playground park convenience playground park restaurant pub community_centre park pitch pitch school jeweller convenience',
'nan',
'park park park hospital school doctors community_centre park',
'community_centre doctors swimming_pool water_tower kindergarten pub park',
'school convenience library',
'library pitch pitch school pitch pitch park school',
'camp_site school pitch car_rental supermarket park pitch park hospital park park attraction park supermarket',
'community_centre fast_food convenience cafe restaurant bank convenience butcher convenience convenience cafe supermarket pub restaurant hairdresser fast_food cafe bar cafe convenience fast_food convenience bakery hairdresser cafe restaurant restaurant fast_food pub pitch cafe school doctors fast_food clinic pitch school pub park playground pitch playground park pub pub',
'playground pitch pub convenience fast_food hairdresser fast_food fast_food convenience cafe restaurant fast_food pharmacy hairdresser bicycle_shop convenience convenience fast_food hairdresser beauty_shop restaurant community_centre hairdresser park butcher dentist mobile_phone_shop clothes convenience hairdresser convenience butcher travel_agent restaurant florist laundry fast_food travel_agent hairdresser convenience car_wash clinic fast_food convenience pub greengrocer fast_food fast_food hairdresser butcher convenience convenience hairdresser park park clinic',
'pitch community_centre convenience fast_food fast_food pub park park',
'park bakery hairdresser toy_shop cafe school school newsagent pub',
'park school convenience community_centre cafe optician newsagent fast_food hairdresser beverages travel_agent doctors doctors mall fast_food hairdresser bicycle_shop bar convenience florist beauty_shop bakery beauty_shop beauty_shop',
'sports_shop convenience fast_food convenience fast_food car_dealership car_dealership fast_food hairdresser restaurant school doctors pub restaurant fast_food park park playground park community_centre park beverages bakery park bar bar',
'pitch pitch pitch pitch park playground pub park cafe park pharmacy fast_food fast_food doctors cafe water_works car_wash car_dealership car_wash supermarket',
'pub cafe toilet playground pitch pitch park pitch school community_centre playground pitch shelter',
'community_centre supermarket market_place furniture_shop fast_food supermarket pharmacy cafe department_store doctors furniture_shop bicycle_shop hairdresser school community_centre school supermarket laundry car_wash playground fast_food convenience veterinary hairdresser school park park attraction',
'pitch pitch school fast_food swimming_pool pitch pitch hotel pitch pitch kindergarten pitch pitch pitch sports_centre pitch pitch pitch cafe school car_dealership pitch pitch pitch cafe doityourself doityourself sports_centre car_dealership pitch ruins water_works pitch pitch pitch pitch playground park pitch cafe',
'park nursing_home hairdresser community_centre pub convenience car_dealership school pitch park park doityourself convenience dentist doityourself doityourself fast_food fast_food',
'beauty_shop bicycle_shop beverages restaurant supermarket restaurant fast_food dentist beauty_shop hairdresser hairdresser swimming_pool pitch school dentist playground college',
'furniture_shop hotel shoe_shop pub hairdresser fast_food restaurant pub clothes convenience bar restaurant fast_food laundry restaurant beauty_shop restaurant restaurant restaurant furniture_shop restaurant',
'school computer_shop hairdresser pub beauty_shop car_dealership fast_food convenience convenience hairdresser hairdresser car_dealership car_dealership pub pub',
'supermarket furniture_shop school',
'park shelter playground park pitch park park pitch pitch park doityourself pub doityourself school park',
'park school pitch kindergarten swimming_pool swimming_pool park',
'car_wash convenience pharmacy supermarket dentist park doctors community_centre',
'pub school fast_food cafe veterinary library',
'attraction school school sports_centre playground park',
'fast_food clinic convenience beauty_shop fast_food butcher hairdresser fast_food fast_food convenience pub pitch pitch pub pitch graveyard park school playground',
'pub hairdresser fast_food park convenience graveyard school',
'pub kindergarten community_centre car_dealership park graveyard',
'convenience beverages hairdresser fast_food beauty_shop hairdresser fast_food supermarket supermarket fast_food greengrocer park pitch school school school pitch community_centre park playground community_centre',
'hairdresser supermarket pitch pitch swimming_pool pitch pitch pitch school school school',
'track track golf_course pitch track school track playground park community_centre park playground car_dealership furniture_shop clothes furniture_shop sports_shop clothes fast_food',
'car_wash doctors pitch playground school nursing_home convenience doityourself convenience hairdresser car_dealership',
'pitch school school pharmacy doctors supermarket community_centre doityourself',
'park school playground playground pitch playground park',
'pharmacy restaurant fast_food hairdresser hairdresser restaurant clothes clothes bakery pharmacy hairdresser mobile_phone_shop clothes pub fast_food laundry convenience florist restaurant restaurant pub furniture_shop optician hairdresser clothes clinic bar restaurant cafe convenience restaurant convenience greengrocer fast_food hairdresser fast_food bar fast_food pub hairdresser computer_shop supermarket convenience fast_food beverages convenience restaurant dentist cafe cafe bar pitch supermarket pub swimming_pool park playground',
'community_centre hairdresser pub fast_food pub bakery fast_food pub restaurant restaurant convenience',
'park pitch pub pitch pitch school',
'hairdresser beauty_shop hairdresser fast_food furniture_shop beauty_shop jeweller library florist pub hairdresser beauty_shop supermarket clothes',
'playground pitch school',
'supermarket pharmacy beverages hairdresser convenience bakery cafe video_shop convenience fast_food beauty_shop fast_food fast_food clothes hairdresser car_dealership cafe florist butcher hairdresser fast_food fast_food optician bakery convenience newsagent post_office hairdresser fast_food beauty_shop fast_food hairdresser hairdresser school',
'park swimming_pool swimming_pool school kindergarten park sports_centre school school pitch pitch school school veterinary',
'school golf_course park',
'hairdresser pub golf_course convenience pitch school bar kindergarten water_works playground park',
'garden_centre doityourself pitch pitch community_centre convenience school pitch car_dealership sports_centre',
'restaurant bicycle_shop restaurant convenience cafe hairdresser cafe supermarket clinic fast_food beauty_shop hairdresser bicycle_shop nightclub pub school dentist school pub bakery beauty_shop restaurant sports_centre kindergarten convenience pub kindergarten convenience hairdresser beauty_shop',
'park pitch school pitch furniture_shop playground park car_wash pitch pitch pitch convenience pitch',
'park park hospital pitch park pitch playground track graveyard guesthouse hotel supermarket graveyard furniture_shop',
'car_dealership graveyard beauty_shop fast_food hairdresser hairdresser fast_food hairdresser recycling toy_shop restaurant beauty_shop hairdresser supermarket convenience restaurant toy_shop fast_food toilet fast_food community_centre bar car_dealership cafe library playground cafe park pitch pitch pitch',
'hairdresser school bank kindergarten pub car_wash',
'park graveyard veterinary pub dentist car_wash car_dealership cafe hairdresser hairdresser newsagent restaurant supermarket dentist convenience',
'pharmacy butcher dentist guesthouse kindergarten fast_food hairdresser supermarket school convenience pub hairdresser convenience',
'playground park pitch pub pub',
'school newsagent beauty_shop beauty_shop hairdresser convenience beauty_shop outdoor_shop laundry fast_food restaurant butcher hairdresser fast_food hairdresser cafe laundry beauty_shop hairdresser fast_food greengrocer hairdresser convenience bakery convenience shelter post_office restaurant fast_food fast_food hairdresser fast_food convenience supermarket graveyard pub beverages pub beauty_shop hairdresser beauty_shop bicycle_shop pub restaurant car_dealership castle car_dealership car_dealership car_dealership car_dealership',
'park library fast_food school supermarket pub convenience school school school',
'school hotel college memorial tower park pub restaurant supermarket restaurant park hotel fast_food bank hairdresser pub restaurant bakery fast_food laundry fast_food cafe hairdresser convenience pub fast_food pub cafe supermarket fast_food cafe pub pub courthouse kindergarten cafe newsagent clinic fast_food bar cafe fast_food hotel restaurant pub hotel hotel park ruins fire_station',
'park pitch pitch school pitch toilet playground park school park playground pitch community_centre park swimming_pool restaurant convenience car_rental swimming_pool museum pitch',
'pitch school',
'school park fast_food fast_food convenience police',
'pitch pitch pitch park pitch playground playground pitch pitch fast_food convenience fast_food fast_food school hairdresser hairdresser hairdresser hairdresser hairdresser cafe doityourself supermarket doityourself beauty_shop doityourself mobile_phone_shop fast_food fast_food pub fast_food hairdresser car_dealership car_dealership doityourself school',
'pitch school pitch pitch pitch pitch shelter school pitch',
'pitch stadium hairdresser fast_food convenience',
'pitch school track pitch beverages pitch fast_food pitch pitch pitch',
'market_place car_dealership cafe cafe car_dealership car_dealership cafe hotel newsagent hairdresser car_rental ruins park playground pitch stationery park graveyard pub playground pitch school cafe car_dealership doityourself doityourself bar doityourself car_rental car_dealership restaurant fast_food hairdresser fast_food',
'computer_shop beauty_shop sports_shop hairdresser convenience',
'hairdresser hairdresser doityourself guesthouse guesthouse guesthouse',
'sports_centre pitch pitch pitch cafe pitch school laundry hairdresser playground convenience butcher bicycle_shop park restaurant stationery fast_food hairdresser convenience car_wash convenience community_centre park community_centre clothes clinic computer_shop pub playground park hairdresser hairdresser cafe fast_food convenience convenience fast_food dentist convenience travel_agent convenience pub travel_agent restaurant restaurant restaurant hairdresser supermarket restaurant pitch park pitch playground hairdresser restaurant restaurant convenience pitch park convenience pub laundry post_office fast_food fast_food fast_food butcher newsagent restaurant furniture_shop school playground furniture_shop school',
'clinic pub playground playground pub restaurant school clinic dentist furniture_shop fast_food playground pub',
'car_wash fast_food sports_shop doityourself car_dealership car_dealership car_dealership fast_food beverages newsagent hairdresser mobile_phone_shop park bakery supermarket pub car_dealership doityourself doityourself car_dealership car_dealership',
'pitch school pub pub veterinary restaurant kindergarten restaurant butcher theatre beauty_shop hairdresser sports_centre restaurant florist greengrocer car_dealership furniture_shop gift_shop pub newsagent cafe shoe_shop gift_shop beauty_shop hairdresser cafe bicycle_shop furniture_shop hairdresser florist toy_shop bar fast_food fast_food bakery pub convenience restaurant optician cafe doityourself cafe fast_food fast_food restaurant cafe newsagent cafe laundry laundry bicycle_shop gift_shop hairdresser cafe college stadium prison bank kindergarten pub car_wash',
'fire_station car_rental park car_dealership school pitch pitch park kindergarten convenience fast_food playground kindergarten clinic park clinic park convenience fast_food beauty_shop florist convenience fast_food hairdresser supermarket',
'fast_food convenience hairdresser florist fast_food convenience hairdresser beverages car_dealership pub butcher pub fast_food cafe fast_food laundry',
'school market_place veterinary car_dealership restaurant computer_shop bakery shelter shelter cafe bar cafe cafe nightclub restaurant clinic cafe pub nightclub car_wash hotel car_rental doityourself doityourself sports_centre park pub nightclub pub cafe restaurant pub fast_food clothes pub cafe dentist pub hairdresser dentist pub stationery convenience fast_food bicycle_shop bakery pub hairdresser fast_food travel_agent nightclub bakery pub florist clothes theatre pub pitch park clothes',
'doctors hospital swimming_pool water_works hairdresser fast_food clothes school pitch park playground pitch track',
'park pitch park park',
'pitch school convenience fast_food pharmacy fast_food hairdresser fast_food pub pub college stadium park playground convenience',
'pub pitch school kindergarten doctors pitch park pitch supermarket playground',
'park pitch pitch public_building park park dentist',
'park',
'convenience pitch pitch school pitch pitch pitch pitch pitch nightclub fast_food supermarket pitch clinic pitch pitch pitch playground school convenience',
'hairdresser guesthouse fast_food hairdresser convenience pub guesthouse car_dealership fast_food car_dealership hairdresser fast_food supermarket beauty_shop hairdresser hairdresser fast_food restaurant hairdresser hairdresser restaurant hairdresser restaurant beauty_shop beauty_shop fast_food hairdresser convenience restaurant convenience pub hairdresser cafe sports_shop beauty_shop sports_shop pitch hairdresser laundry fast_food fast_food pitch pitch school',
'convenience bakery fast_food park playground park convenience pitch greengrocer pitch newsagent playground playground playground park artwork shelter pitch',
'nursing_home toilet university museum pitch park nursing_home pitch pitch pitch recycling_glass pitch pitch town_hall beauty_shop bicycle_shop beverages',
'kindergarten hairdresser kindergarten dentist restaurant pub pub kindergarten supermarket bar hairdresser convenience bakery hairdresser stationery beauty_shop fast_food laundry pharmacy laundry hairdresser pitch pitch pitch graveyard park library playground',
'kindergarten playground cafe pitch pitch school pub school',
'pitch pub beauty_shop cafe graveyard convenience pub kindergarten',
'hairdresser hairdresser convenience community_centre school',
'park public_building park park library convenience public_building park community_centre park hairdresser hairdresser community_centre fast_food supermarket community_centre restaurant fast_food cafe hairdresser cafe pharmacy hairdresser beverages cafe pharmacy convenience fast_food fast_food bakery fast_food pub convenience clinic supermarket park',
'hospital supermarket school playground pitch school pitch pitch park pitch',
'pitch pitch pitch pitch pitch community_centre school pitch',
'library convenience car_dealership hairdresser hairdresser fast_food cafe convenience convenience park park convenience veterinary veterinary car_dealership',
'school hairdresser park pitch kindergarten school',
'pitch playground park pitch school playground school',
'school graveyard pitch pitch pitch doctors clinic dentist convenience fast_food fast_food bakery convenience convenience convenience pharmacy convenience fast_food park fast_food cafe hairdresser hairdresser cafe convenience playground community_centre supermarket pitch school pitch',
'pitch park playground car_dealership school pub bakery pub',
'library hairdresser fast_food cafe dentist pitch pitch playground beauty_shop restaurant',
'school track community_centre pub',
'playground dentist post_office cafe cafe veterinary beverages bakery school graveyard convenience beauty_shop pub doityourself pub convenience hairdresser bank pharmacy fast_food optician butcher cafe florist hairdresser supermarket hairdresser hairdresser convenience fast_food cafe convenience convenience fast_food kindergarten hairdresser hairdresser playground park',
'mall cafe park pub playground restaurant clothes hairdresser clothes pub hairdresser restaurant gift_shop restaurant convenience restaurant restaurant museum attraction fast_food furniture_shop fast_food post_office cafe pub fast_food restaurant restaurant bakery cafe laundry park',
'pitch school pitch pitch pitch playground pitch school school school pub pub pub park library',
'pitch pitch school pitch pitch water_works park playground park pub community_centre school',
'doityourself hairdresser cafe bar beverages beauty_shop hairdresser bookshop restaurant restaurant restaurant computer_shop pharmacy cafe butcher fast_food supermarket doityourself stadium restaurant bakery fast_food bakery fast_food beauty_shop restaurant greengrocer convenience pub restaurant hairdresser clinic kindergarten florist optician cafe cafe hairdresser newsagent bank',
'toy_shop supermarket beauty_shop clothes toy_shop hairdresser supermarket cafe fast_food gift_shop pub beverages bookshop post_office gift_shop beverages bakery hairdresser greengrocer gift_shop mobile_phone_shop kindergarten bar bicycle_shop florist hairdresser beauty_shop cafe greengrocer restaurant pub cafe supermarket pub gift_shop cafe convenience pub bakery newsagent restaurant restaurant restaurant cafe library',
'playground park sports_centre park pitch park playground community_centre pub newsagent fast_food fast_food pub bar',
'clinic pitch park clinic playground pitch park park park',
'school community_centre community_centre pitch pitch pitch school kindergarten park park park park park playground playground playground playground playground park kindergarten clinic clinic',
'park golf_course school community_centre park playground park',
'sports_centre dentist ruins school guesthouse pub sports_shop pub pub convenience cafe college school clinic',
'park cafe restaurant optician pharmacy fast_food school supermarket cafe laundry park bar hairdresser graveyard hairdresser newsagent fast_food cafe shoe_shop florist furniture_shop cafe hairdresser fast_food',
'car_wash pitch doctors doctors pub florist school hospital pitch pitch water_works veterinary community_centre restaurant beauty_shop convenience dentist doityourself supermarket',
'golf_course hairdresser pitch pitch playground park pitch pitch pitch pitch pitch pitch pitch sports_centre pitch pitch pitch pitch pitch',
'park pitch pitch shelter toilet pitch pitch pitch pitch pitch pitch pitch toilet pitch pitch pitch pitch pitch pitch pitch pitch pitch pitch swimming_pool ruins swimming_pool swimming_pool swimming_pool doctors nursing_home swimming_pool swimming_pool college cafe pitch park water_tower water_works park',
'doctors restaurant florist jeweller laundry jeweller pub clinic theatre pitch pitch pitch school attraction shelter shelter pitch toilet playground attraction park shelter school',
'car_wash car_wash doityourself doctors park hairdresser beauty_shop hairdresser car_wash hairdresser butcher bank cafe cafe clothes hairdresser mobile_phone_shop greengrocer bakery cafe hairdresser cafe convenience restaurant fast_food optician newsagent clothes arts_centre pub hotel graveyard doctors clinic supermarket guesthouse dentist fast_food hairdresser beverages post_office restaurant bank pharmacy hairdresser pub fast_food toy_shop beauty_shop cafe fast_food hairdresser hairdresser pub community_centre dentist fast_food sports_centre pitch pitch pitch pub park playground school',
'park park park school sports_centre school pitch sports_centre pitch pitch school swimming_pool pitch convenience veterinary furniture_shop school pitch pitch pitch pitch pitch doctors pub',
'kindergarten bar school park fast_food fast_food fast_food fast_food supermarket fast_food convenience garden_centre supermarket supermarket bakery cafe sports_shop pharmacy cinema doityourself car_dealership doityourself fast_food park car_rental comms_tower car_dealership police doityourself playground doityourself doityourself doityourself doityourself playground park hairdresser',
'school school pub supermarket beverages fast_food car_dealership hairdresser fast_food kindergarten',
'jeweller hairdresser pitch graveyard restaurant restaurant hairdresser cafe pharmacy cafe hospital doityourself fast_food hairdresser beauty_shop pub restaurant convenience clinic hairdresser restaurant restaurant mobile_phone_shop bookshop fast_food dentist hotel cafe park bookshop pub graveyard restaurant cafe hairdresser restaurant car_dealership supermarket restaurant pub shoe_shop laundry optician florist library jeweller pub bookshop cafe jeweller beverages gift_shop clothes computer_shop travel_agent bakery florist cafe restaurant jeweller butcher restaurant pharmacy clothes cafe beauty_shop fast_food clothes pub jeweller restaurant clothes jeweller cafe bar clothes gift_shop clothes restaurant clothes clothes hotel clothes furniture_shop hairdresser pub dentist jeweller restaurant cafe beauty_shop hairdresser clothes restaurant clothes beauty_shop pub restaurant clothes clothes jeweller laundry furniture_shop pub',
'library park playground kindergarten community_centre track beauty_shop pitch school',
'convenience school pitch park toilet graveyard school pitch',
'furniture_shop pub hospital school school nursing_home nursing_home nursing_home nursing_home pitch pitch pitch pitch pitch pitch pitch playground park school park school',
'fast_food fast_food pub cafe convenience hairdresser florist school pub beauty_shop car_dealership park clothes playground convenience car_dealership pub sports_shop car_wash computer_shop fast_food clinic',
'park school',
'school convenience park hairdresser pitch',
'park pitch graveyard doctors park cafe convenience playground sports_centre pitch',
'guesthouse dentist stationery hairdresser hairdresser cafe optician hairdresser pharmacy optician cafe supermarket greengrocer hairdresser laundry bakery beauty_shop butcher gift_shop restaurant convenience garden_centre fast_food post_office hairdresser fast_food hairdresser doityourself hairdresser hairdresser bicycle_shop school beverages bar pitch cafe community_centre pitch school pitch pitch school restaurant hairdresser playground park beauty_shop pub',
'swimming_pool archaeological swimming_pool park',
'pitch track playground pitch park pub park',
'car_dealership park furniture_shop pitch school graveyard kindergarten restaurant pub beauty_shop florist cafe hairdresser pitch pitch park community_centre hairdresser convenience playground beauty_shop pub bicycle_shop laundry cafe mobile_phone_shop hairdresser cafe computer_shop hairdresser hairdresser pub convenience beauty_shop fast_food dentist supermarket fast_food',
'pub hairdresser restaurant pub dentist pub hotel restaurant park',
'furniture_shop fast_food florist hairdresser bakery fast_food convenience clothes school park pub computer_shop restaurant police community_centre park bar hairdresser hairdresser cafe restaurant kindergarten park playground restaurant mall hotel bar newsagent pub community_centre cafe pub theatre pitch doityourself arts_centre bar supermarket furniture_shop furniture_shop cafe school hotel pub department_store car_rental',
'convenience furniture_shop fast_food supermarket beverages pub bookshop restaurant beauty_shop bicycle_shop pitch school',
'playground pitch park pub beauty_shop hotel clinic pub restaurant sports_centre pub bar restaurant ruins park gift_shop bookshop beauty_shop restaurant restaurant restaurant restaurant attraction pub nightclub attraction restaurant restaurant pub restaurant cafe restaurant jeweller clothes cafe restaurant restaurant fast_food restaurant bicycle_shop clothes pub fast_food cafe bicycle_shop arts_centre bank pub restaurant nightclub bakery beverages fast_food butcher museum attraction attraction attraction hostel cafe convenience cafe pub hotel restaurant bar college pub dentist park cafe doctors dentist college restaurant pub pub pub pub hotel kindergarten pub restaurant restaurant pub pub theatre doctors restaurant pub bar hotel fast_food hotel convenience restaurant convenience fast_food convenience restaurant fast_food hairdresser restaurant hairdresser bakery hairdresser restaurant fast_food restaurant newsagent restaurant nightclub restaurant cafe florist hairdresser restaurant fast_food cafe convenience nightclub fast_food fast_food bar fast_food restaurant hairdresser restaurant fast_food hairdresser fast_food fast_food optician fast_food restaurant bar cafe restaurant pub restaurant bar bicycle_shop hairdresser hairdresser cafe pub clothes bar pub restaurant pub bar gift_shop gift_shop cafe restaurant dentist beauty_shop clinic restaurant restaurant restaurant bar restaurant mobile_phone_shop bar guesthouse cafe cafe bookshop bar bookshop jeweller mall bookshop bar cafe food_court market_place travel_agent mall hotel pub bar restaurant memorial hotel restaurant travel_agent pub fast_food restaurant park bank clothes cafe clothes clothes clothes clothes gift_shop restaurant pub fast_food clothes ruins travel_agent convenience bank toy_shop ruins pitch cafe bookshop mall mall clothes mall cafe beauty_shop mobile_phone_shop hairdresser bicycle_shop clinic restaurant clothes toy_shop fast_food beauty_shop cafe fast_food cafe supermarket fast_food nightclub jeweller cinema hairdresser mall jeweller gift_shop fast_food bakery shoe_shop gift_shop shoe_shop shoe_shop travel_agent beauty_shop mall cafe hairdresser jeweller cafe school outdoor_shop sports_centre bicycle_shop restaurant bookshop bar bar cafe hostel bar cafe arts_centre bar cafe cafe courthouse monument cafe bar pub restaurant restaurant bar restaurant cafe bar clothes hotel bakery cafe hairdresser clothes cafe hotel hairdresser restaurant pub pub pub restaurant clothes pub hairdresser shoe_shop hairdresser cafe video_shop cinema clothes beverages hairdresser bar cafe hairdresser pub fast_food travel_agent cafe pub clothes clothes shoe_shop clothes cafe clothes clothes bank restaurant optician mobile_phone_shop mall beauty_shop gift_shop pharmacy doctors fast_food mobile_phone_shop clothes mobile_phone_shop convenience community_centre cafe bakery outdoor_shop bank mobile_phone_shop cafe pharmacy clothes restaurant travel_agent shoe_shop restaurant bank fast_food mobile_phone_shop cafe mobile_phone_shop clothes mobile_phone_shop cafe bank video_shop bank clothes observation_tower observation_tower hairdresser fast_food pub fast_food fast_food hotel clothes hostel',
'doityourself doityourself supermarket beauty_shop restaurant beauty_shop computer_shop cafe restaurant optician car_dealership convenience doityourself pub',
'bicycle_shop attraction restaurant park bicycle_shop sports_centre cafe park cafe museum cafe tourist_info attraction sports_centre attraction sports_centre museum museum pub pub restaurant convenience caravan_site arts_centre arts_centre pub toilet museum attraction',
'pub school',
'park fast_food hairdresser cafe park post_office convenience convenience park park',
'hairdresser convenience hotel beauty_shop pub kindergarten',
'school park laundry hotel hairdresser pub pub doctors supermarket sports_shop hairdresser fast_food pharmacy bakery fast_food guesthouse fast_food library hotel playground park cafe cafe fast_food community_centre playground park fire_station clinic fast_food fast_food track fire_station tower shelter',
'convenience pitch pitch guesthouse pitch hairdresser restaurant playground graveyard graveyard park park',
'pharmacy car_dealership park pub clothes fast_food furniture_shop supermarket sports_shop park car_dealership supermarket clothes doityourself school',
'university clinic clinic hotel veterinary clinic pub kindergarten swimming_pool attraction doityourself clinic theatre kindergarten water_works dentist clinic supermarket cinema butcher cafe restaurant garden_centre bank mobile_phone_shop pharmacy supermarket mall clinic fast_food laundry optician gift_shop florist fast_food newsagent mobile_phone_shop cafe cafe',
'playground school',
'park picnic_site pitch pitch playground',
'arts_centre pub hairdresser hairdresser hairdresser clinic hairdresser pub clinic hotel pub food_court fast_food stationery bookshop park hairdresser convenience florist arts_centre hotel fast_food cafe pub cafe hairdresser doityourself school florist restaurant sports_shop bicycle_shop restaurant pub cafe clothes jeweller hostel cafe guesthouse hospital restaurant cafe gift_shop clothes bar beauty_shop cafe cafe beauty_shop beverages clothes travel_agent cafe fast_food nightclub outdoor_shop clothes bar bar supermarket clothes clothes restaurant restaurant bookshop stationery restaurant restaurant restaurant bar nightclub arts_centre cafe nightclub car_wash convenience theatre museum bar fast_food bar optician department_store clothes restaurant bar cafe hairdresser bar restaurant hairdresser cafe pub restaurant fast_food bar clothes hairdresser hairdresser hairdresser restaurant beauty_shop restaurant nightclub cafe hairdresser cafe bakery clothes fast_food cafe clothes bar bar dentist clothes cafe restaurant optician newsagent mobile_phone_shop bookshop cafe cafe bar pub restaurant fast_food cafe gift_shop hairdresser fast_food clothes stationery cafe bar post_office cafe clothes restaurant restaurant nightclub bank restaurant hairdresser bar stationery clothes bar cafe optician hairdresser bank supermarket mobile_phone_shop outdoor_shop clothes nightclub pharmacy hostel fast_food fast_food cafe fast_food tower restaurant museum museum sports_centre school fast_food university nightclub restaurant bank cafe bicycle_shop university beauty_shop cafe theatre university park tower wastewater_plant hospital cafe bar fast_food university arts_centre college school bank park car_rental bicycle_shop police pub hotel park hostel pub pub pub hospital pub clinic hospital hospital',
'car_dealership school pitch pitch pitch pitch pitch hairdresser supermarket doityourself',
'pitch pitch pitch pitch pitch graveyard school golf_course park',
'pitch fast_food community_centre car_dealership pitch pitch pitch pitch pitch pitch school pitch park pitch track college pitch stadium pitch pub doityourself newsagent cafe park playground pitch university university pitch park theme_park playground kindergarten pitch park garden_centre park water_works greengrocer cafe pub beauty_shop pub stationery cafe bakery computer_shop sports_centre pitch restaurant restaurant bakery fast_food school pitch school guesthouse pub convenience',
'convenience laundry school nursing_home park park clinic dentist',
'school track playground golf_course school park',
'hairdresser fast_food car_dealership park school pharmacy convenience school car_dealership car_dealership car_dealership car_dealership',
'hospital optician convenience park guesthouse pub guesthouse guesthouse outdoor_shop',
'school school school',
'school pitch pitch park pitch supermarket hairdresser hairdresser supermarket restaurant sports_centre fast_food fast_food fast_food beauty_shop veterinary dentist pub swimming_pool swimming_pool park swimming_pool swimming_pool',
'car_dealership hairdresser pub cafe hairdresser hairdresser fast_food playground car_wash car_dealership car_dealership car_dealership car_dealership school laundry bar hairdresser pub',
'park castle graveyard cafe toilet school playground pitch playground pitch playground museum pub sports_centre community_centre school pub hotel supermarket veterinary pitch school school park',
'monument playground park graveyard car_dealership clinic convenience cafe pitch butcher fast_food cafe pitch school butcher butcher supermarket beverages pitch restaurant clothes hairdresser travel_agent convenience fast_food',
'school park fast_food park',
'pub convenience hairdresser supermarket hairdresser cafe cafe hairdresser restaurant restaurant clinic butcher jeweller hairdresser fast_food clinic restaurant cafe clothes restaurant fast_food fast_food cafe beauty_shop hairdresser pub computer_shop supermarket convenience shelter hairdresser beauty_shop cafe school prison pitch pitch sports_centre pitch park hairdresser fast_food cafe sports_centre cafe supermarket doityourself restaurant restaurant mobile_phone_shop cafe hairdresser hairdresser cafe fast_food furniture_shop furniture_shop cafe hairdresser hairdresser cafe sports_shop restaurant beauty_shop beverages veterinary restaurant fast_food fast_food restaurant convenience clothes hairdresser dentist restaurant',
'park pitch pitch pitch sports_centre pitch pitch supermarket',
'hospital sports_centre pitch cinema playground restaurant hotel park track school sports_centre pitch park pitch restaurant fast_food fast_food pub',
'doctors cafe playground',
'school pub hairdresser park restaurant hairdresser hairdresser beauty_shop fast_food hairdresser',
'pitch pitch pitch sports_centre fast_food newsagent convenience community_centre hairdresser hairdresser school dentist fast_food car_wash restaurant dentist post_office doctors',
'convenience fast_food comms_tower pitch pitch pitch pitch school playground community_centre park attraction',
'golf_course fast_food supermarket fast_food doityourself fast_food newsagent doityourself dentist fast_food fast_food fast_food school playground pitch hospital supermarket',
'pub nursing_home park community_centre clinic fast_food fast_food sports_centre clinic hairdresser convenience pharmacy cafe restaurant bakery hairdresser fast_food travel_agent convenience kindergarten hairdresser cafe',
'school fast_food doityourself doityourself community_centre school pub school pitch pitch cafe playground hairdresser prison pitch pitch sports_centre pitch park',
'pitch school school pitch pitch pitch pitch playground school',
'park convenience fast_food school park park park playground school',
'college kindergarten clinic pub hairdresser furniture_shop clinic beauty_shop optician car_dealership community_centre pitch sports_centre dentist dentist beauty_shop cafe supermarket mall pub hairdresser furniture_shop clinic clothes restaurant cafe convenience pub',
'hairdresser cafe fast_food fast_food clinic',
'dentist clothes fast_food pub fast_food fast_food fast_food fast_food cafe fast_food hairdresser convenience convenience convenience car_dealership fast_food sports_shop park car_wash',
'doctors clinic pub pub clinic doityourself computer_shop beauty_shop hairdresser pub hairdresser supermarket cafe cafe computer_shop gift_shop toy_shop hairdresser convenience supermarket sports_centre bank beauty_shop greengrocer florist hairdresser restaurant jeweller supermarket bar bank cafe fast_food optician bank bakery doityourself bank hairdresser optician bank dentist pharmacy doctors shoe_shop beauty_shop newsagent pub fast_food restaurant laundry convenience clinic restaurant cafe hairdresser fast_food beauty_shop hairdresser',
'clinic doctors car_dealership pitch kindergarten museum school pitch pitch pitch school pitch pitch shelter',
'park swimming_pool college park',
'park clinic hairdresser fast_food fast_food fast_food convenience hairdresser golf_course park',
'community_centre park convenience fast_food convenience park park park convenience fast_food',
'community_centre school doctors pub school',
'newsagent fast_food convenience park park',
'park water_works convenience beauty_shop bar park hairdresser supermarket restaurant fast_food fast_food newsagent park kindergarten pub park car_dealership park',
'pharmacy arts_centre laundry convenience hairdresser beverages fast_food cafe arts_centre clothes hairdresser hairdresser newsagent cafe fast_food cafe toy_shop cafe cafe pitch park pub car_dealership restaurant school community_centre playground school guesthouse pub',
'car_dealership recycling_glass stationery playground pub playground school convenience clothes playground park park doctors pharmacy park playground',
'park playground pitch butcher hairdresser fast_food fast_food convenience pharmacy school hairdresser convenience',
'beauty_shop restaurant newsagent car_wash cafe hairdresser fast_food',
'kindergarten restaurant kindergarten hairdresser park park shelter kindergarten guesthouse hairdresser beauty_shop clothes dentist sports_shop cafe dentist restaurant veterinary pharmacy butcher travel_agent dentist bookshop hairdresser bookshop supermarket cinema restaurant school hotel',
'sports_centre school cafe university university library kindergarten university arts_centre school hotel clinic restaurant fast_food pub cafe cafe bakery pharmacy hairdresser restaurant sports_centre university university university pub doctors hairdresser hairdresser stationery pub hairdresser supermarket pub kindergarten cafe bar furniture_shop clinic sports_shop clinic pub',
'pitch doityourself doityourself school pharmacy school park beverages hairdresser fast_food fast_food hairdresser',
'clothes mobile_phone_shop convenience mobile_phone_shop convenience cafe bookshop clothes newsagent restaurant restaurant mobile_phone_shop cafe beauty_shop mobile_phone_shop clothes jeweller mobile_phone_shop travel_agent bank gift_shop sports_shop bank optician toy_shop mobile_phone_shop video_shop bank fast_food mobile_phone_shop clothes beauty_shop clothes hairdresser fast_food fast_food beauty_shop hairdresser clothes fast_food fast_food cafe shoe_shop clothes department_store outdoor_shop shoe_shop bank cafe fast_food fast_food beauty_shop furniture_shop hairdresser cafe fast_food bakery cafe gift_shop fast_food shoe_shop fast_food beauty_shop theme_park cafe shoe_shop mobile_phone_shop clothes restaurant clothes department_store clothes clothes clothes clothes clothes clothes clothes clothes hairdresser convenience mall fast_food park clothes cafe cafe fast_food bakery hotel cafe restaurant clothes clothes outdoor_shop clothes beauty_shop mobile_phone_shop beauty_shop clothes hairdresser fast_food mobile_phone_shop bicycle_shop beauty_shop hairdresser fast_food outdoor_shop hairdresser bookshop clinic cafe clothes hairdresser furniture_shop mobile_phone_shop mall park park courthouse hospital convenience fast_food fast_food playground cinema',
'restaurant cafe pub clinic supermarket hotel school college school pub school cafe pub bar restaurant restaurant hotel attraction cafe museum hairdresser bookshop pub bicycle_shop hairdresser pub fast_food cafe clothes library convenience school bank pub cafe museum cafe nightclub cafe school pitch playground park park toilet attraction university hotel jeweller clothes fast_food furniture_shop park bar restaurant hairdresser clinic restaurant fast_food fast_food travel_agent fast_food restaurant fast_food clothes hairdresser sports_centre school theatre',
'pitch pitch sports_centre park playground pub playground school graveyard doctors shelter',
'travel_agent fast_food hairdresser convenience community_centre park restaurant convenience restaurant fast_food restaurant convenience clothes convenience pharmacy cafe restaurant convenience fast_food fast_food restaurant bicycle_shop cafe restaurant hairdresser convenience bar arts_centre',
'pharmacy kindergarten clinic dentist kindergarten playground',
'playground car_dealership pitch pitch school kindergarten pitch pub community_centre playground park pitch restaurant convenience pub',
'park fast_food fast_food hairdresser park kindergarten dentist pub doctors fast_food supermarket hairdresser newsagent supermarket convenience cafe beverages optician hairdresser supermarket hairdresser fast_food bakery fast_food fast_food library community_centre pitch car_rental beauty_shop car_dealership',
'clinic pub convenience hairdresser veterinary school pitch playground',
'park ruins pitch school school school playground playground convenience',
'school playground park',
'pitch pitch stadium park swimming_pool stadium school pitch pharmacy doctors pitch pitch beauty_shop hairdresser kindergarten park golf_course park',
'library fast_food bakery post_office pharmacy supermarket supermarket',
'park',
'convenience dentist clothes pub cafe greengrocer clothes shoe_shop hairdresser greengrocer cafe bookshop restaurant cafe gift_shop cafe furniture_shop beverages jeweller jeweller cafe jeweller clothes mall florist bar gift_shop restaurant hairdresser beauty_shop pitch pitch clinic pitch pitch kindergarten clinic swimming_pool school school',
'school community_centre water_works car_wash supermarket golf_course pitch pitch restaurant beauty_shop park nursing_home restaurant pub nursing_home nursing_home',
'convenience car_wash doctors community_centre hairdresser clinic restaurant hairdresser fast_food bakery fast_food optician bank travel_agent convenience pub fast_food beauty_shop stationery pharmacy fast_food cafe cafe pub chemist bank fast_food convenience hairdresser pub restaurant restaurant restaurant park dentist hairdresser fast_food newsagent fast_food fast_food laundry veterinary hairdresser kindergarten butcher playground park bar optician doctors school park park pitch park park',
'park school pitch school convenience school school clinic veterinary florist fast_food convenience hairdresser fast_food pharmacy hairdresser',
'sports_centre sports_centre recycling_metal park community_centre convenience stationery supermarket furniture_shop community_centre newsagent school pub school playground kindergarten pub park playground park',
'pub bar restaurant restaurant restaurant restaurant pub hairdresser supermarket restaurant fast_food fast_food bar beauty_shop hairdresser restaurant doityourself restaurant hairdresser bar pharmacy pub hairdresser bank fast_food clinic beverages cafe bakery restaurant fast_food fast_food beauty_shop cafe doityourself cafe hairdresser fast_food bar beauty_shop beauty_shop doctors school pub fast_food cafe nursing_home clothes hairdresser bookshop beverages sports_centre optician cafe restaurant clinic laundry clothes hairdresser cafe cafe supermarket gift_shop clothes convenience restaurant restaurant restaurant bar pub restaurant restaurant hairdresser beauty_shop cafe hairdresser greengrocer beverages',
'ruins ruins ruins ruins archaeological ruins ruins playground community_centre park attraction playground playground park community_centre pitch cafe convenience pharmacy fast_food community_centre hairdresser fast_food convenience convenience archaeological pitch playground',
'park pub convenience',
'pitch pitch fast_food convenience community_centre pitch kindergarten']
Then, we can use the fit_transform() function provided by TfidfVectorizer to get the tfidf scores:
tfidf_response = tfidf.fit_transform(docList)
We can also convert the result of a specific document into a dataframe as well:
tfidf_pd = pd.DataFrame(tfidf_response[0].T.todense(), index=tfidf.get_feature_names(), columns=["TF-IDF"])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[37], line 1
----> 1 tfidf_pd = pd.DataFrame(tfidf_response[0].T.todense(), index=tfidf.get_feature_names(), columns=["TF-IDF"])
AttributeError: 'TfidfVectorizer' object has no attribute 'get_feature_names'
tfidf_pd
| TF-IDF | |
|---|---|
| archaeological | 0.0 |
| arts_centre | 0.0 |
| artwork | 0.0 |
| attraction | 0.0 |
| bakery | 0.0 |
| ... | ... |
| veterinary | 0.0 |
| video_shop | 0.0 |
| wastewater_plant | 0.0 |
| water_tower | 0.0 |
| water_works | 0.0 |
105 rows × 1 columns
As can be seen here, since the list of words is long, many of the rows are not showing here. A trick to show the full table with all rows being listed is to use the code below:
pd.set_option('display.max_rows', None)
tfidf_pd
| TF-IDF | |
|---|---|
| archaeological | 0.000000 |
| arts_centre | 0.000000 |
| artwork | 0.000000 |
| attraction | 0.000000 |
| bakery | 0.000000 |
| bank | 0.000000 |
| bar | 0.000000 |
| beauty_shop | 0.000000 |
| beverages | 0.000000 |
| bicycle_shop | 0.000000 |
| bookshop | 0.000000 |
| butcher | 0.000000 |
| cafe | 0.000000 |
| camp_site | 0.000000 |
| car_dealership | 0.000000 |
| car_rental | 0.000000 |
| car_wash | 0.000000 |
| caravan_site | 0.000000 |
| castle | 0.000000 |
| chemist | 0.000000 |
| cinema | 0.000000 |
| clinic | 0.000000 |
| clothes | 0.000000 |
| college | 0.000000 |
| comms_tower | 0.000000 |
| community_centre | 0.000000 |
| computer_shop | 0.000000 |
| convenience | 0.000000 |
| courthouse | 0.000000 |
| dentist | 0.000000 |
| department_store | 0.000000 |
| doctors | 0.000000 |
| doityourself | 0.000000 |
| fast_food | 0.000000 |
| fire_station | 0.000000 |
| florist | 0.000000 |
| food_court | 0.000000 |
| fountain | 0.000000 |
| furniture_shop | 0.000000 |
| garden_centre | 0.000000 |
| gift_shop | 0.000000 |
| golf_course | 0.000000 |
| graveyard | 0.000000 |
| greengrocer | 0.000000 |
| guesthouse | 0.000000 |
| hairdresser | 0.000000 |
| hospital | 0.000000 |
| hostel | 0.000000 |
| hotel | 0.000000 |
| jeweller | 0.000000 |
| kindergarten | 0.000000 |
| laundry | 0.000000 |
| library | 0.000000 |
| mall | 0.000000 |
| market_place | 0.000000 |
| memorial | 0.000000 |
| mobile_phone_shop | 0.000000 |
| monument | 0.000000 |
| museum | 0.000000 |
| nan | 0.000000 |
| newsagent | 0.000000 |
| nightclub | 0.000000 |
| nursing_home | 0.000000 |
| observation_tower | 0.000000 |
| optician | 0.000000 |
| outdoor_shop | 0.000000 |
| park | 0.194603 |
| pharmacy | 0.000000 |
| picnic_site | 0.000000 |
| pitch | 0.000000 |
| playground | 0.249640 |
| police | 0.000000 |
| post_office | 0.000000 |
| prison | 0.000000 |
| pub | 0.000000 |
| public_building | 0.000000 |
| recycling | 0.000000 |
| recycling_glass | 0.000000 |
| recycling_metal | 0.000000 |
| restaurant | 0.000000 |
| ruins | 0.000000 |
| school | 0.000000 |
| shelter | 0.000000 |
| shoe_shop | 0.000000 |
| sports_centre | 0.000000 |
| sports_shop | 0.000000 |
| stadium | 0.000000 |
| stationery | 0.000000 |
| supermarket | 0.000000 |
| swimming_pool | 0.948583 |
| theatre | 0.000000 |
| theme_park | 0.000000 |
| toilet | 0.000000 |
| tourist_info | 0.000000 |
| tower | 0.000000 |
| town_hall | 0.000000 |
| toy_shop | 0.000000 |
| track | 0.000000 |
| travel_agent | 0.000000 |
| university | 0.000000 |
| veterinary | 0.000000 |
| video_shop | 0.000000 |
| wastewater_plant | 0.000000 |
| water_tower | 0.000000 |
| water_works | 0.000000 |
We can also order the tfidf_pd and only show the top 25 (or other numbers as you wish) records. As we have learnt from the lecture, the greater the TF-IDF, the more relevant/unique the word is to characterize the document.
tfidf_pd = tfidf_pd.sort_values('TF-IDF', ascending=False)
tfidf_pd.head(25)
| TF-IDF | |
|---|---|
| swimming_pool | 0.948583 |
| playground | 0.249640 |
| park | 0.194603 |
| recycling_metal | 0.000000 |
| recycling | 0.000000 |
| public_building | 0.000000 |
| pub | 0.000000 |
| prison | 0.000000 |
| post_office | 0.000000 |
| police | 0.000000 |
| pitch | 0.000000 |
| picnic_site | 0.000000 |
| pharmacy | 0.000000 |
| archaeological | 0.000000 |
| outdoor_shop | 0.000000 |
| optician | 0.000000 |
| observation_tower | 0.000000 |
| nursing_home | 0.000000 |
| nightclub | 0.000000 |
| newsagent | 0.000000 |
| nan | 0.000000 |
| museum | 0.000000 |
| monument | 0.000000 |
| mobile_phone_shop | 0.000000 |
| memorial | 0.000000 |
Part 6: Relevance Ranking#
With TF-IDF for each word in the document, we can then compute the relevance of a query in respect to the candidate documents. A real-world scenaria can be like this: a user might want to find similar regions that have POIs with place types including “park” and “car dealership” (this is the query). What we can do is to compare the vector of query with the vector of each document, and rank their relevances. The top documents then can be selected to responde to users’ query. The vector here is exactly the TF-IDF vector we have just computed (like the tfidf_pd).
For the query, we regard it as a single document. Its TF-IDF vector is computed using the same way as what we have done (namely, we can directly multiple the IDF (same as before) with the TF of this specific query document). Below are the codes.
First, we get the term frequency dictionary of the query document:
## rank the relevance score of a query to the documents (region)
query = ["park", "car_dealership"]
# get the vector of the query (same to what we have done before)
query_wordDict = dict.fromkeys(wordSet, 0)
for word in query:
query_wordDict[word]+=1
query_wordDict
{'clothes': 0,
'butcher': 0,
'dentist': 0,
'recycling': 0,
'sports_shop': 0,
'furniture_shop': 0,
'bicycle_shop': 0,
'recycling_glass': 0,
'hospital': 0,
'track': 0,
'chemist': 0,
'garden_centre': 0,
'veterinary': 0,
'beauty_shop': 0,
'car_wash': 0,
'supermarket': 0,
'tower': 0,
'outdoor_shop': 0,
'playground': 0,
'college': 0,
'fountain': 0,
'car_rental': 0,
'park': 1,
'wastewater_plant': 0,
'swimming_pool': 0,
'beverages': 0,
'hostel': 0,
'nursing_home': 0,
'mobile_phone_shop': 0,
'memorial': 0,
'nan': 0,
'newsagent': 0,
'artwork': 0,
'jeweller': 0,
'cafe': 0,
'water_works': 0,
'ruins': 0,
'arts_centre': 0,
'market_place': 0,
'university': 0,
'kindergarten': 0,
'monument': 0,
'convenience': 0,
'community_centre': 0,
'guesthouse': 0,
'mall': 0,
'camp_site': 0,
'greengrocer': 0,
'bank': 0,
'graveyard': 0,
'shoe_shop': 0,
'car_dealership': 1,
'gift_shop': 0,
'stadium': 0,
'hotel': 0,
'bakery': 0,
'restaurant': 0,
'shelter': 0,
'hairdresser': 0,
'post_office': 0,
'public_building': 0,
'department_store': 0,
'comms_tower': 0,
'fire_station': 0,
'stationery': 0,
'library': 0,
'theme_park': 0,
'castle': 0,
'water_tower': 0,
'picnic_site': 0,
'tourist_info': 0,
'golf_course': 0,
'school': 0,
'cinema': 0,
'optician': 0,
'prison': 0,
'florist': 0,
'food_court': 0,
'travel_agent': 0,
'attraction': 0,
'pub': 0,
'fast_food': 0,
'museum': 0,
'video_shop': 0,
'archaeological': 0,
'observation_tower': 0,
'pitch': 0,
'recycling_metal': 0,
'clinic': 0,
'bar': 0,
'computer_shop': 0,
'bookshop': 0,
'nightclub': 0,
'police': 0,
'pharmacy': 0,
'caravan_site': 0,
'doityourself': 0,
'toilet': 0,
'sports_centre': 0,
'laundry': 0,
'courthouse': 0,
'town_hall': 0,
'toy_shop': 0,
'theatre': 0,
'doctors': 0}
query_tf = computeTF(query_wordDict)
query_tf
{'clothes': 0.0,
'butcher': 0.0,
'dentist': 0.0,
'recycling': 0.0,
'sports_shop': 0.0,
'furniture_shop': 0.0,
'bicycle_shop': 0.0,
'recycling_glass': 0.0,
'hospital': 0.0,
'track': 0.0,
'chemist': 0.0,
'garden_centre': 0.0,
'veterinary': 0.0,
'beauty_shop': 0.0,
'car_wash': 0.0,
'supermarket': 0.0,
'tower': 0.0,
'outdoor_shop': 0.0,
'playground': 0.0,
'college': 0.0,
'fountain': 0.0,
'car_rental': 0.0,
'park': 0.5,
'wastewater_plant': 0.0,
'swimming_pool': 0.0,
'beverages': 0.0,
'hostel': 0.0,
'nursing_home': 0.0,
'mobile_phone_shop': 0.0,
'memorial': 0.0,
'nan': 0.0,
'newsagent': 0.0,
'artwork': 0.0,
'jeweller': 0.0,
'cafe': 0.0,
'water_works': 0.0,
'ruins': 0.0,
'arts_centre': 0.0,
'market_place': 0.0,
'university': 0.0,
'kindergarten': 0.0,
'monument': 0.0,
'convenience': 0.0,
'community_centre': 0.0,
'guesthouse': 0.0,
'mall': 0.0,
'camp_site': 0.0,
'greengrocer': 0.0,
'bank': 0.0,
'graveyard': 0.0,
'shoe_shop': 0.0,
'car_dealership': 0.5,
'gift_shop': 0.0,
'stadium': 0.0,
'hotel': 0.0,
'bakery': 0.0,
'restaurant': 0.0,
'shelter': 0.0,
'hairdresser': 0.0,
'post_office': 0.0,
'public_building': 0.0,
'department_store': 0.0,
'comms_tower': 0.0,
'fire_station': 0.0,
'stationery': 0.0,
'library': 0.0,
'theme_park': 0.0,
'castle': 0.0,
'water_tower': 0.0,
'picnic_site': 0.0,
'tourist_info': 0.0,
'golf_course': 0.0,
'school': 0.0,
'cinema': 0.0,
'optician': 0.0,
'prison': 0.0,
'florist': 0.0,
'food_court': 0.0,
'travel_agent': 0.0,
'attraction': 0.0,
'pub': 0.0,
'fast_food': 0.0,
'museum': 0.0,
'video_shop': 0.0,
'archaeological': 0.0,
'observation_tower': 0.0,
'pitch': 0.0,
'recycling_metal': 0.0,
'clinic': 0.0,
'bar': 0.0,
'computer_shop': 0.0,
'bookshop': 0.0,
'nightclub': 0.0,
'police': 0.0,
'pharmacy': 0.0,
'caravan_site': 0.0,
'doityourself': 0.0,
'toilet': 0.0,
'sports_centre': 0.0,
'laundry': 0.0,
'courthouse': 0.0,
'town_hall': 0.0,
'toy_shop': 0.0,
'theatre': 0.0,
'doctors': 0.0}
Then, we get the TF-IDF using the function computeTFIDF():
# we often use the same idfs as the documents
query_idf = idfs
query_tfidf = computeTFIDF(query_tf, query_idf)
query_tfidf
{'clothes': 0.0,
'butcher': 0.0,
'dentist': 0.0,
'recycling': 0.0,
'sports_shop': 0.0,
'furniture_shop': 0.0,
'bicycle_shop': 0.0,
'recycling_glass': 0.0,
'hospital': 0.0,
'track': 0.0,
'chemist': 0.0,
'garden_centre': 0.0,
'veterinary': 0.0,
'beauty_shop': 0.0,
'car_wash': 0.0,
'supermarket': 0.0,
'tower': 0.0,
'outdoor_shop': 0.0,
'playground': 0.0,
'college': 0.0,
'fountain': 0.0,
'car_rental': 0.0,
'park': 0.08599124106397561,
'wastewater_plant': 0.0,
'swimming_pool': 0.0,
'beverages': 0.0,
'hostel': 0.0,
'nursing_home': 0.0,
'mobile_phone_shop': 0.0,
'memorial': 0.0,
'nan': 0.0,
'newsagent': 0.0,
'artwork': 0.0,
'jeweller': 0.0,
'cafe': 0.0,
'water_works': 0.0,
'ruins': 0.0,
'arts_centre': 0.0,
'market_place': 0.0,
'university': 0.0,
'kindergarten': 0.0,
'monument': 0.0,
'convenience': 0.0,
'community_centre': 0.0,
'guesthouse': 0.0,
'mall': 0.0,
'camp_site': 0.0,
'greengrocer': 0.0,
'bank': 0.0,
'graveyard': 0.0,
'shoe_shop': 0.0,
'car_dealership': 0.32455186842380684,
'gift_shop': 0.0,
'stadium': 0.0,
'hotel': 0.0,
'bakery': 0.0,
'restaurant': 0.0,
'shelter': 0.0,
'hairdresser': 0.0,
'post_office': 0.0,
'public_building': 0.0,
'department_store': 0.0,
'comms_tower': 0.0,
'fire_station': 0.0,
'stationery': 0.0,
'library': 0.0,
'theme_park': 0.0,
'castle': 0.0,
'water_tower': 0.0,
'picnic_site': 0.0,
'tourist_info': 0.0,
'golf_course': 0.0,
'school': 0.0,
'cinema': 0.0,
'optician': 0.0,
'prison': 0.0,
'florist': 0.0,
'food_court': 0.0,
'travel_agent': 0.0,
'attraction': 0.0,
'pub': 0.0,
'fast_food': 0.0,
'museum': 0.0,
'video_shop': 0.0,
'archaeological': 0.0,
'observation_tower': 0.0,
'pitch': 0.0,
'recycling_metal': 0.0,
'clinic': 0.0,
'bar': 0.0,
'computer_shop': 0.0,
'bookshop': 0.0,
'nightclub': 0.0,
'police': 0.0,
'pharmacy': 0.0,
'caravan_site': 0.0,
'doityourself': 0.0,
'toilet': 0.0,
'sports_centre': 0.0,
'laundry': 0.0,
'courthouse': 0.0,
'town_hall': 0.0,
'toy_shop': 0.0,
'theatre': 0.0,
'doctors': 0.0}
The dictionary can also be converted to a dataframe:
queryTFIDFMatrix = pd.DataFrame.from_dict(query_tfidf, orient='index').T
queryTFIDFMatrix
| clothes | butcher | dentist | recycling | sports_shop | furniture_shop | bicycle_shop | recycling_glass | hospital | track | ... | caravan_site | doityourself | toilet | sports_centre | laundry | courthouse | town_hall | toy_shop | theatre | doctors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
1 rows × 105 columns
Finally, we can write a function to compute the similarity between the TF-IDF vectors of candicate documents (doc_tfidf) with the TF-IDF vector of the query document (query_tfidf), and rank it using codes like:
import numpy as np
from numpy.linalg import norm
def rankingTFIDF(query_tfidf, doc_tfidf):
doc_cosine = []
for index, row in doc_tfidf.iterrows():
doc_tfidf_vec = row.to_numpy().flatten()[1:]
query_tfidf_vec = query_tfidf.to_numpy().flatten()
cosine = np.dot(doc_tfidf_vec,query_tfidf_vec)/norm(doc_tfidf_vec)*norm(query_tfidf_vec)
doc_cosine.append([row[0], cosine])
doc_cosine_df = pd.DataFrame(doc_cosine, columns=["LSOA11 Code", "Cosine Similarity"])
doc_cosine_df = doc_cosine_df.sort_values(by=['Cosine Similarity'],ascending=False)
return doc_cosine_df
To apply the function and only show the top 10 results:
doc_cosine_df_test = rankingTFIDF(queryTFIDFMatrix, doc2wordTFIDFMatrix)
doc_cosine_df_test.iloc[0:9]
| LSOA11 Code | Cosine Similarity | |
|---|---|---|
| 203 | E01014537 | 0.106043 |
| 22 | E01014569 | 0.090785 |
| 207 | E01014565 | 0.090718 |
| 51 | E01014679 | 0.086994 |
| 121 | E01014506 | 0.082671 |
| 72 | E01014675 | 0.077745 |
| 147 | E01014682 | 0.060071 |
| 116 | E01033352 | 0.058472 |
| 47 | E01014617 | 0.058310 |
To evaluate the results, we check the TF-IDF vector of the document who is ranked the top. We can find that this document (region) contains pois with types of “car dealership” and “park” as well (as what our query requests):
pd.set_option('display.max_columns', None)
doc2wordTFIDFMatrix.loc[doc2wordTFIDFMatrix['LSOA11 Code'] == "E01014537"]
| LSOA11 Code | clothes | butcher | dentist | recycling | sports_shop | furniture_shop | bicycle_shop | recycling_glass | hospital | track | chemist | garden_centre | veterinary | beauty_shop | car_wash | supermarket | tower | outdoor_shop | playground | college | fountain | car_rental | park | wastewater_plant | swimming_pool | beverages | hostel | nursing_home | mobile_phone_shop | memorial | nan | newsagent | artwork | jeweller | cafe | water_works | ruins | arts_centre | market_place | university | kindergarten | monument | convenience | community_centre | guesthouse | mall | camp_site | greengrocer | bank | graveyard | shoe_shop | car_dealership | gift_shop | stadium | hotel | bakery | restaurant | shelter | hairdresser | post_office | public_building | department_store | comms_tower | fire_station | stationery | library | theme_park | castle | water_tower | picnic_site | tourist_info | golf_course | school | cinema | optician | prison | florist | food_court | travel_agent | attraction | pub | fast_food | museum | video_shop | archaeological | observation_tower | pitch | recycling_metal | clinic | bar | computer_shop | bookshop | nightclub | police | pharmacy | caravan_site | doityourself | toilet | sports_centre | laundry | courthouse | town_hall | toy_shop | theatre | doctors | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 203 | E01014537 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.171982 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.258588 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.245519 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.286417 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.399695 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.26462 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.703952 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Great that you have followed the instructions till here. Note that it is just an example to show you how some introduced techniques in the lecture can be implemented in Python. Hope it is interesting and have inspired you on your own projects. Also note that the dataset we are using - place types of POIs - is relatively clean. In most geo-text analysis, you might need to use a lot natural language preprocessing such as stemming, lemmatisation, punctuation, etc. Don’t forget that spaCy can help you on it (Lab 2).