In my first post detailing my attempt to understand social-mobility by observing the different strata of society I outlined in a fairly potted way my reasoning for using the tube as a means to observe.
In this post I will explain my research methods, which in part have been based on my assumptions. However, I came across research carried out by Oliver O’Brien, a researcher and software developer at UCL Department of Geography, which dovetails perfectly with my area of study and have decided to use his results.
His focus was to map the most common languages, after English, spoken within a 200 metre radius of the tube station, resulting in his alternative London Underground map Tube Tongues. O’Brien explains his methods here.
In addition he used the same premise to create another map of the tube, this time asking the question, What do the locals do?
Before coming across O’Brien’s maps my idea was to come to a conclusion on the demographics of the commuters observed (see photos below) by looking at nationality and occupation. Although not a perfect fit I have decided to use his research into languages spoken, as opposed to nationalities, in order to utilise the wonderful resource that he has created. As O’Brien, O. (2014) suggests; Language correlates well with some ethnicities (e.g. South Asian) but not others (e.g. African), in London. Therefore, just because a tube station has French as its second language, doesn’t mean that French citizens live in the area in any great number, rather that people from Francophone countries do. Contrast this with Lithuanian as a second language. A priori knowledge leading to the conclusion that Lithuanians live in the area.
So, on to my assumptions:
Leaving Stanmore at 6am I sit in a carriage with people starting an early shift. Mainly manual labourers they will come from a mix of countries; Poland, Romania, the Baltic countries and from the Indian sub continent. This range of nationalities will continue to get on between Stanmore and Kilburn. There might also be the odd white-collar worker too. At Swiss Cottage and St Johns Wood Japanese men, and they will mainly be men, get on, on their way to work in the City and Canary Wharf. As the train journeys on towards the retail hub of the city, cleaners and shop workers will get on and off.
Towards Canary Wharf bleary-eyed bankers will get on and for a brief moment share space with cleaners and manual labourers in a democratic environment. Over half of the line has been covered and as the train enters Bermondsey it will be populated by night shift workers on their way home in the suburbs of the city.
NB: As previously mentioned, English is the most common language spoken in the target area of each station, and as such I will only include the most commonly spoken foreign language. The lowest percentage of English spoken is in Queensbury, Kingsbury, Neasden and Wembley Park with percentages of; 50.1%, 52.7, 55.7% and 58.2% respectively.
Gujarati 6.4% Business Administration 7.4%
Romanian 1.7% Manager/proprietor 4.6%
Tamil 1.6% Health 4.9%
Gujarati 8.6% Business Administration 6.6%
Tamil 2.6% Health 5.3%
Romanian 2.3% Sales assistant 4.9%
Gujarati 18.5% Sales assistant 10%
Romanian 8% Construction 7.5%
Tamil 6.2% IT 4.4%
Gujarati 19.1% Sales assistant 8.5%
Romanian 5.3% Construction 6.4%
Polish 3% Records administration 4.7%
Gujarati 6.8% Sales assistant 7.5%
Polish 4.8% IT 7.2%
Arabic 3.1% Cleaning 4.8%
Polish 6.7% Construction 7%
Arabic 4% Sales assistant 6.8%
Somali 4% Cleaning 5.6%
Polish 6% Sales assistant 6.5%
Portuguese 4.4% Other elements 6%
Gujarati 3.2% Cleaning 5.6%
Polish 4% Education 6.1%
Portuguese 3.4% Art/media 5.1%
Arabic 2% Marketing 4.7%
Arabic 2.6% Marketing 6.6%
French 2.4% Business administration 5.7%
Polish 1.7% Art/media 5.1%
French 2.6% Business administration 9.3%
Italian 1.6% Business 6.7%
Polish 1.3% Marketing 6.5%
Japanese 3.4% Business administration 8%
French 2.6% Business 6.6%
Spanish 2% Education 5.1%
Japanese 4.8% Business 8.6%
Albanian 2.4% Business administration 7.1%
Persian 2.2% Legal 5.6%
Arabic 5.2% Business 9%
Japanese 4.1% Managing Director 7%
French Business administration 6.8%
French 5.3% Business 10.9%
Arabic 4% Managing Director 10%
Italian 2.1% Business administration 7.6%
French 4% Business 9.3%
Arabic 3.9% Managing Director 7.2%
Spanish 3.4% Manager/proprietor (misc) 6.5%
French 5.1% Business 14%
Arabic 3.4% Managing Director 7.7%
Italian 2.5% Manager/proprietor (misc) 7.2%
Mandarin/Cantonese 4.7% Business 8.4%
German 1.9% Education 7.5%
Russian 1.7% Managing Director 6.9%
Tagalog 5.5% Nursing 12.2%
Mandarin/Cantonese 4% Business administration 7.5%
Cantonese 2.4% Business 5.8%
French 2% Art/media 5.7%
Spanish 1.6% Business 5.4%
Business administration 6.1%
Tagalog 1.8% Nursing 7.5%
French 1.6% Business 8.1%
Italian 1.5% Business administration 7.1%
Spanish 2.8% Business administration 5.8%
French 2.2% Other elementary services 5.8%
Polish 2.1% IT 3.8%
Mandarin/Cantonese 4.2% Business administration 8.6%
Spanish 2.3% Business 6.9%
French 1.5% IT 5.6%
Mandarin/Cantonese 3.6% Business 17.9%
French 3.2% Business administration 13.3%
German 2.4% Managing Director 12.9%
Interestingly large Bengali communities live close to Canary Wharf, but do not show up on statistics of languages spoken in Canary Wharf itself.
English 78.2% Other elementary services 8.2%
Mandarin/Cantonese 3.8% Sales assistant 6.1%
Lithuanian 3.6% Business 6.4%
Bengali 2.8% IT 6.1%
Mandarin/Cantonese 2.3% Business administration 4.9%
Bengali 6% Sales assistant 7.1%
Lithuanian 2.3% Cleaning 5.6%
Romanian 2.3% Other elementary services 5.2%
Bengali 10.4% Sales assistant 7.7%
Portuguese 4.3% Cleaning 6.5%
Romanian 3.2% Records administration 5.7%
There are two types of data which must be considered when looking at my research question ‘ How can a[n] (embodied) space be socially mobile?’ The first is the data crunched by the *ONS from its 2011 Census. The second type of data is far more subjective, the visual data present in the photos. By mining the photos for clues I will take both into consideration and will present my thoughts and conclusions in my next post.
* Office for National Statistics
The information below each photo has been arranged in two columns for ease of viewing and should be read as a column, and not left to right.
The employment information came from data sets created by the ONS from the 2011 census. This information was collated by Oliver O’Brien.
Photos taken on November 14th, 2014