Public Transport in Tower Hamlets

It all starts with transport: In Tower Hamlets (TH) around 60% of journeys are made by sustainable travel (“Local Plan 2031 TH", 2020), such as walking, cycling and public transport. Although TH has a well-developed public transport system, this StoryMap will show that there is still much to be done using multiple research methods as transport is crucial to various areas of urban planning. I will map out accessibility of public transport, explore how occupation relates to the method of transport used and analyse TH's current and future public transport policy. I will make the argument that accessibility and affordability of transport is key to improving the borough’s liveability and inclusive development, especially in the future.

Improved transport can be a catalyst for development and increase connectivity between and within communities. However, it can also be the driver of residential gentrification and displacement. It is therefore crucial to recognise which area needs which type of transport (Song et al., 2019). Cost is often the main barrier to using transport; introducing fare subsidies for socio-economic groups that need them, such as older people or students, is important to reduce inequality, including in terms of e.g. inclusion (Gates et al., 2019). Gómez-Lobo (2011) builds on the work of Serebrisky et al. (2009) and argues that there are different definitions of public transport affordability and emphasises the need for a more nuanced approach to fare subsidies and the inclusion of wider sections of an urban community. But what could this look like in the case of Tower Hamlets?

How is accessibility distributed over the borough and what does it look like on the map?

As further described in the qualitative research section, the Council proposes to expand transport primarily along development sectors, but also promises to make more remote areas more accessible. Firstly, it is important to understand what is meant by geographic information systems (GIS). Longley et al. (2005) argue that interactive maps can reflect social reality, but they also reveal their weaknesses as data is nowadays mainly collected via the internet, which is not always a reliable source - I have had a similar experience. Steinberg and Steinberg (2015) explain how the audience, message and personal bias influence the map presented. They describe the strengths of GIS maps, but also warn against including too much information, as interactive maps sometimes can be more of a hindrance than a benefit.

These next two maps, based on data from the TfL GIS Hub, show the "cycle superhighways" and key bus routes in and around the borough. The "cycle superhighways" are particularly disappointing as there are only three in TH. However, the bus route grid shows an extensive system as almost all areas are within walking distance of a bus route, with the north of the borough still lagging behind and the east being more heavily served by buses when compared to other modes of transport.

To what extent does one’s occupation influence the chosen method of transport to travel to work?

As already mentioned, Gates et al. (2019) state that one’s occupation and therefore one’s income does influence the chosen method of transport. To discuss this question in the case of TH, I collected two datasets from the 2021 census by Nomis. The first shows the percentage of underground users per ward in TH; this data is slightly different from 2011 due to the Covid-19 pandemic. The other shows the employed population by wards in TH, categorised into nine different occupation levels, with one being the highest earning and nine being the lowest earning group of occupation.

This data is analysed using descriptive and inferential statistics. The former identifies patterns in the data that are not immediately apparent, the latter uses population sampling techniques to draw conclusions (Abbott, 2016). Spearman’s rank, which is applied here to look for a correlation between the two data sets, is a rank-based non-parametric method often used to tell how well the relationship between two variables can be described (Acevedo, 2012).

This visualied data shows the distribution of the working population across nine different categories in the wards and across TH, with categories 1-3 strongly represented across the borough (62.7%). The next chart compares the two wards with the highest percentage of categories 1-3 with the wards with the lowest percentage of categories 1-3 and the boroughwide average ward. Showing the apparent difference of income depending on where you live in the borough. Whilst in areas such as Canary Wharf 75.4% work in generally well-paid occupations, in Shadwell it is only 47.6%.

To find a correlation between the percentage of people in the first occupational group and the percentage of people who use the tube to get to work, I used the non-parametric Spearman’s rank, since this data is not normally distributed. Due to poorer accessibility to the Tube the Weavers, St Kathrins & Wapping and Island Gardens wards were excluded from this analysis as they are considered to be outliers (more than 2 standard deviations outside the interquartile range).

H0: There is no correlation between one's level of occupation and the usage of the underground as the chosen method to travel to work.

H1: There is a correlation between one's level of occupation and the usage of the underground as the chosen method to travel to work.

Ranking the data and using the Excel formula for Spearman's correlation, I found that there is a significant (p-value: 0.031) result that there is a significant correlation between the highest occupational category and the use of the underground to get to work per ward .

However, the same analysis for the lowest earning occupational category number nine showed a significant (p-value: 0.00093) result that there is no correlation between the number of people working in occupational category nine and the use of the underground as a method for travelling to work (see scatter plot).

Although case studies such as the one about the DLR in east London by Song et al. (2019) and Gates et al. (2019) argue for a link between occupation and chosen mode of transport, it must be recognised that many more factors play a role in this decision, as will be shown in the next section. TH is a diverse borough, and so are the occupations of its population. Therefore, more in-depth quantitative research is needed to find out what factors influence the way people travel in TH.

Who is the future of transport in Tower Hamlet for – and what is the council’s attitude towards different groups?

To explore this question, two council policy documents on the 'Transport Strategy 2019-41' and a statement by the new Mayor of Tower Hamlet (TH), Lutfur Rahman, were analysed using discourse analysis.

Firstly, what is discourse analysis? Assuming that language matters as it has a huge impact on our daily lives, it must be recognised that it can be easily manipulated. Hardy & Philips (2004) explain that discourse analysis is a methodology enabling us to recognise the social realities behind a text and the way it produces them. Accordingly, Hastings (2000) explains that one must sensitise oneself to the language used and recognise how the author’s position and the zeitgeist have influenced the story told in the text.

The TH Council adopted its new strategy document “Local Plan 2031” in January 2020, shortly before the pandemic. The introduction and ‘borough portrait’ provide an overview of the ambitious plan, which promises the best for communities and aims to promote community development. Section 16, “improving connectivity and travel choices', looks at transport, parking and development. This strategy document talks about infrastructural development areas, growth and the importance of public transport to this development. However, the neutral language and broad terms do not allow the voices of those who are not well connected to the transport system to be heard. The term "cycle superhighway" conveys a notion of importance to their purpose. Promising words like “will” are mainly used for development, not specifying what kind of development and for whom, and “would” or “should” for improving the transport system.

Similarly, the “Transport Strategy 2019-41” makes many promises but does not explain how they will be realised. Ambitious projects, such as the construction of more charging stations for e-cars, do not reflect the social realities of many people in the borough who cannot afford such an e-car. Issues such as the affordability of fares are briefly discussed, but no explicit policy is formulated to help those who need it. Broad terms and many promises without specifics on how they will be implemented make the texts appear as if they are not aimed at the general population of TH.

The mayor’s statement goes in a rather different direction, deciding to abolish Low Traffic Neighbourhoods (LTNs) despite the fact that the majority of people voted for them as they have been key to lower pollution, safer streets and have created an overall neighbourly atmosphere for many residents. The mayor’s words have a defining effect and seem to prioritise the political agenda over the municipal will. The new mayor also contradicts some parts of the local plan implemented by the previous administration.

Overall, these texts are either too broad or not profound enough, as they are not aimed at its supposed main target audience, the inhabitants of TH. Loose promises or goodwill as well as the lack of definitive numbers or statistics often leave just enough room to avoid being held accountable for these policies should some of these lofty plans not be realizable. An additional effective, clearly communicated, and easy to understand overview would be much more convincing.

This StoryMap addressed a broader research question, and while this broad overview is a strength, it also means that it was not possible to consider narrower sub-aspects of the question in detail. Using multiple research methods allowed me to analyze the topic of public transport at different levels to understand its multiple impacts on the population. Some key findings were: Occupation has an impact on mode choice, mapping accessibility data reveals areas with poorer connectivity in the east and north of TH, and analyzing policies for the future shows that there is an overall concept, but the future will tell if these exact promises will be implemented as the council wants to keep its options open. Furthermore, public, and sustainable transport have many facets and policy development should always be done together with other stakeholders: A multi-stakeholder approach. The scope of this project was rather small, issues like this require a more in-depth analysis of their causes.

Exploring the impact of certain policies on different socio-economic groups or how fare subsidies can reach those who need them most are just two examples of what further research could look like. In the case of Tower Hamlet, it is important to assess what type of transport is needed in which location, as not every area has a need for the next high-tech underground line and its far-reaching effects. But, for example, making streets more cycle-friendly could lead the borough into its urban mobility future. Tower Hamlets has a well-developed transport system, but there are still some that are falling behind or are not recognized, more inclusion should be vital for future developments.