London
Introduction/Business
Problem
London, the capital of
England and the United Kingdom, London is considered to be one of the world's
most important global cities and has been termed the world's most powerful,
most desirable, most influential, most visited, most expensive, innovative, sustainable,
most investment friendly, and most popular for work, London ranks 26th out of
300 major cities for economic performance, It is the most-visited city as
measured by international arrivals and has the busiest city airport system as
measured by passenger traffic. It is the leading investment destination
In respect to this I will
be considering London as regards which location is suitable for to relocate to,
since London is a big City and as such travelers who have different thought and
purpose at heart should be able to make decision WHERE or which set of places
is in my best interest, using FourSquare
Locations to explore the city and borough around the city, giving
information of the city her constituent.
This is targeted towards
making decision as regards where is best to make a journey, where is best to
have a tourist visit, where is best to set up a business.
Explanation
of DataSets and How it would solve problem
The data set used
includes the following:
1. london-borough-profiles.csv
from the https://data.london.gov.uk/ : London Dataset, it is a csv file with 8
rows and 84 colums: which includes: Area_name, Inner/_Outer_London,
GLA_Population_Estimate_2017, Population_density_(per_hectare)_2017
Average_Age,_2017, Proportion_of_population_aged_0-15,_2015,
Proportion_of_population_of_working-age,_2015,
Proportion_of_population_aged_65_and_over,_2015, Net_internal_migration_(2015)
Net_international_migration_(2015), Net_natural_change_(2015),
%_of_resident_population_born_abroad_(2015) Largest_migrant_population_by_country_of_birth_(2011)
, %_of_largest_migrant_population_(2011)
Second_largest_migrant_population_by_country_of_birth_(2011)
%_of_second_largest_migrant_population_(2011)
Third_largest_migrant_population_by_country_of_birth_(2011) %_of_third_largest_migrant_population_(2011)
%_of_population_from_BAME_groups_(2016)
%_people_aged_3+_whose_main_language_is_not_English_(2011_Census,
New_migrant_(NINo)_rates,_(2015/16),
Largest_migrant_population_arrived_during_2015/16, Second_largest_migrant_population_arrived_during_2015/16,
Third_largest_migrant_population_arrived_during_2015/16,
Employment_rate_(%)_(2015) Male_employment_rate_(2015),
Female_employment_rate_(2015), Unemployment_rate_(2015) e.t.c
FourSquare Location
Search: Exploring the area London: giving the Borough and the information of
specified
https://en.wikipedia.org/wiki/List_of_London_boroughs
which contained the list Borough in
London alongside it’s latitude and Longitude though in W & E format,
therefore Cleaning the Data is paramount
This Above data would be
used to solve the Problem such that it would be a pointer to decision making in
the city and how travelers can make decision as regards Location to specified
areas in the city to ensure, good decision making for business personnel’s, to
travelers, and visitors where can they visit as regards tourism
METHODOLOGY
As a database, I used
GitHub repository in my study. My master data which has the main components Borough
Latitude, Longitude, Mortality rate from preventable cause, Median House,
Price, Crime rates, Active businesses, Gross Annual Pay, Unemployment rate,
Average Age.
I got the neighborhood
data from Foursquare as regards the venue category of the City in relation to
Borough.
Then I grouped the
visited places in a format of most visited venue within a Borough in London.
I used machine learning
technique K Clustering to form a group the closely related venue together
within Clustes in the Borough.
I used Follium to
visualize it with different color indincating the various
Data used to indicate
Location with the first cluster which also includes information like the Crime
rate, Average Age, Active Business, Employment Rate, all within the 1st cluster
so that any individual who to migrate can also pick the best place within the
cluster that will suit his/her purpose of migration into London.
DISCUSSION
London is
considered to be one of the world's most important global cities, as I have mentioned above. This Project
is aimed at equipping travelers to know adequately depending on his/her purpose
of going to London to have a full view of what is attainable and also where is
appreciated for the purpose of the journeying.
For Example: A young man
coming from India to London for the purpose of tourism is likely to go to Harrow, where the most visited place
is Indian Resturant and Indian Movie theather is the 3rd most visited, also a
Yoga studio is also very visited. And the age bracket is about 40.
CONCLUSION
From this Project, people
who are turning to big cities to start a business or work, to do tourism, to
open a restaurant etc, can achieve better outcomes through their access to the
platforms where such information is provided.
For them to make decision
of where is suitable to accommodate them with utmost profit and Convenience.
References:
[1] https://data.london.gov.uk/
[2]
https://en.wikipedia.org/wiki/List_of_London_boroughs
[3] Forsquare API
