Green Transport in Tower Hamlets.

One assumption of  a parametric test is that the data is normally distributed. The box plots to the left suggest that the ‘on foot’ data set isn’t normally distributed. Thus, I carried out the Shapiro Wilk test on both data sets.

After undertaking the Shapiro Wilk test for the household deprivation data set, the W value, 0.97481108 is more than the p value at the 0.05 significance level (for a sample of 33), suggesting the data is normally distributed.

However, the calculated W value for the percentage of people who travel to work by foot is (0.83852846). This value is less than the P value (for n=33) at 0.05 significance level (0.9352), suggesting that the data is not normally distributed.

Thus, I could not use a parametric test (Pearson’s R.), and instead used Spearmen’s rank to measure the monotonic relationship between these variables (Schober et al, 2018).

Alternative hypothesis: There will be a correlation between the percentage of households who are deprived (in one dimension) and the percentage (%) of people in London who travel to work on foot.

Null hypothesis: There will be no correlation between the percentage of households in London who are deprived (in one dimension) and the percentage (%) of people in London who travel to work on foot.

After completing the Spearmen’s rank correlation in Excel, my Rho value is -0.432, indicating a negative correlation. My calculated value is close to 0, and so it's significance can be questioned.

I tested its significance by calculating the t- statistic in excel. My calculated value is (-)2.667. When comparing it to the critical value (of a two tailed test with a degree of freedom of 31 at 0.05 significance) which is (-) 2.0395, my calculated value is less than my critical. And so, I reject my null hypothesis and accept my alternative hypothesis.

A map showing the spatial variation in the accessibility to public transport across TH. This was made possible by visualising Ptal scores, which reflect reliability of services available, number of services, level of service, and walking time from one point to a mode of public transport.) Each ward has been graded an Attribute from 6 (excellent accessibility) to 0 (very poor accessibility), (London data store, 2015). I displayed the Attribute by the size of the symbol style, contained within the boundary of TH. Here geometric shapes represent spatial features (Chang, 2019), with the larger the symbol, the larger the Attribute (Ptal score). Thus the relationship between accessibility to the public transport network and geography can be visualised.

The larger symbols are concentrated in wards in the North West of TH, suggesting these areas have better accessibility to public transport. Areas in Canary Wharf and Eastern TH have lower Ptal scores, suggesting reduced accessibility to public transport. Another vector data layer can be seen on this map, displayed using an appropriate colour scheme, where darker areas represent areas with a higher population of residents who use the Underground to travel to work and lighter areas where this number is lower.

Although there seems to be no geographical trend, there are ‘pockets’ where larger numbers of people use the Underground frequently despite poor accessibility (low ptal scores). It is in the interests of these residents to improve accessibility to public transport.

A join is useful in displaying a one-to-one relationship (Longley et al 2015). I used an Attribute join to illustrate the association between Ptal scores and a vector layer of mean IMD (index of multiple deprivation) within the boundary of TH. The higher the score, the more deprived the ward is. This relationship was displayed by using an appropriate colour scheme showing mean IMD scores, with darker red illustrating higher scores, and proportionally sized symbols displaying ptal scores.

The analysis we can make from joining these vector layers is that areas of higher deprivation are concentrated in Northern TH, in wards like Weavers and Shadwell. These areas also appear to have better accessibility to the public transport network compared to less deprived areas in Southern TH. It could be suggested that residents in deprived wards have better accessibility to the public transport network, compared to wards with lower mean IMD scores.

It appears that those who are economically isolated and living in deprived areas have crucial accessibility to the public transport network. However, it is important to recognise that wards such as Bow still have relatively high mean IMD scores and relatively low accessibility to the public transport network. And so, future efforts made to improve accessibility should be in these areas.

Despite the value of ptal scores in displaying accessibility, some wards are missing from this map, as ptal are from 2014 and outdated an relatively outdated. Wards have since changed. More recent ptal scores from every ward should be produced for greater accuracy.

These documents use maps and figures to show which areas have more/ less access to the Green Grid, enabling visualisation of and easier interpretation of the Green Grid.

By using word and number analysis, it is evident that both documents use numbers and emotive language such as 'significantly' and 'extremely' (TowerHamletsCouncil, pp 8-19) along with the use of statistics to emphasise a message of uncertainty and threat. A story is created where the Green Grid 'saves' TH. Really emphasising the the need for the Green Grid to residents.

Narrative analysis: when talking about a benefit, each benefit is split into 4 paragraphs. (benefit and what this means) (existing green grid and issues) (how they are going to improve this) this creates an almost chronological storyline for the audience to follow. suggesting that the Green Grids implementation will be as simple and easy as that.

This document lists 9 common issues of resident concerns, the voices of residents can be heard in this document.

Table and quotes from: (TowerHamlets.gov, 2017, pg18-19)

Map from:

(TowerHamlets.gov, 2010, pg28)

However, in other parts of the report, use of technical language can alienate the some ember of the audience, such as the use of "0.3ha".

Furthermore, the report ignores different social groups. Young children were the only group identified when talking about Green Grid benefits (as seen on the right.) As social groups are not identified, it conveys an area in which everyone has the same needs which will be equally met.

(Manmohan Dayal, 2009)