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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