The word limit for this assignment is 600. This limit includes EVERYTHING apart from: i) the overall title of the piece of work; ii) where appropriate, an opening contents page; iii) the reference list* or bibliography, iv) tables and figures (although captions for figures and tables are part of the word count), and v) any appendices. Tables should not contain lengthy passages of text in an attempt to circumvent the word limit; such cases will be investigated for academic malpractice.
Please note that there is no leeway for word length. Marks will be deducted for work which is over the limit as set out in the Code of Practice on Assessment (see Minerva > Organisations > Geography > Assessment coursework and exams > Code of Practice on Assessment).
* You are expected to use the Harvard system for referencing on your assignments for this module. Guidance on how to include citations within your text and how to reference different types of material using Harvard is provided here: .
This assignment asks you to conduct a distributive analysis of environmental data and discuss your results. As discussed in class, most environmental appraisal addresses efficiency (reduced environmental impact per unit activity) but neglects equity how impacts are distributed amongst people. Distributional Impact Assessment examines this equity dimension and is required in all USA Federal policy and projects and is routine for UK transport projects such as railways or highways (Department for Transport 2017, 2020).
This assignment does not ask for appraisal of a specific project (e.g., a new highway or a local land use plan). It does ask for a social distribution analysis of environmental data, of the sort that an equity impact appraisal might generate. The scale is regional (a large urban area) so any changes you find over time in the data will not be due to any single project. Thes assignment has two parts.
Part 1. Assess the social distribution of air quality for Greater London.
A spreadsheet containing data accompanies this text. These data are for:
air quality (as annual average concentration for NO2), for three years and
social deprivation measured using the Townsend index (a measure of poverty). The read me file on the spreadsheet contains additional information on the variables. Note the following:
The data is for Greater London – see map below of Local Authority Districts (LADs). The data is provided by LAD and the Lower-layer Super Output Areas (LSOA) within
them. LSOAs are small areas intended to be of roughly similar population size (about 1,500 residents each) but in practice their population size varies quite a lot.
The air quality data is the annual average (NO2) concentration in ug/m3 by LSOA, produced by DEFRA (a UK department of State) on a 1km grid for the UK, and amalgamated to the LSOA geography for you, within a GIS.
The social metric is deprivation, measured by the Townsend index. A high positive Townsend score indicates high social deprivation (i.e., higher poverty). Other deprivation indexes exist, but the Townsend index does not change over time, and so allows for comparison across years.
Data is provided for 2001, 2011 and 2021, to allow an assessment of how the social distribution of air quality (i.e., the environmental inequality) changes over time.
You are required to show the relationship between NO2 and social deprivation for Greater London, and how this changed from 2001 to 2021. The simple statistical approach described below (Box 1) is a suitable method. This method (a population weighted histogram) is simple but powerful, and is used in similar national analyses (e.g., Mitchell et al, 2015).
4Box 1. Producing a population weighted air quality-deprivation histogram
1. Copy the master sheet to a new sheet and give this new sheet a name to reflect the year (e.g., 2001).
2. Sort your data sheet by the Townsend deprivation variable so that the most deprived LSOAs (high positive Townsend score) are at the top. Make sure to keep all variables together when sorting!
3. Calculate the total population in London and divide by 10 to calculate the population per decile (this will be the same value for all deciles).
4. Group LSOAs (rows) into deciles of equal population (the population size calculated in step 3). You will have ten groups with roughly equal population (the number of rows/LSOAs will vary). You will not be able to produce deciles of identical size, but each decile should be as close to 10% of the total population of as possible. [Nb. The sum total at the foot of the Excel spreadsheet is a useful aid if you decide to select rows manually. With the data sorted by Townsend score, select the first population cell and drag down until the sum shown in the footer is as close to the value in 3 as possible. This is the population for that Townsend decile).
5. For each decile in step 4, calculate descriptive statistics for the air quality (e.g., decile average NO2).
6. Produce a graphic(s) (e.g.. bar chart, box plots) of air quality values in step 5 by deprivation decile (air quality on Y axis, deprivation on the X axis).
7. Clearly label deciles, axes and provide a short title.
Analysis can also be conducted to examine the social distribution of air quality that does not meet air quality standards (these might be legal limits or the tougher WHO guide values). This will help your interpretation. To examine the social distribution of these air quality failures / more serious concentrations, the analysis above can be repeated, with the additional step, after step 4, of excluding LSOAs with NO2 levels below the standard (i.e.. compliant). Statistics for NO2 above the standard, and for the number of people affected per decile, can then be calculated.
Part 2. Interpret your results
Discuss what your results show. Is there any evidence of an unequal distribution of air quality by deprivation (a social distribution of air quality)? How does the social distribution of air quality change, 2001-2021?
Do your results support a conclusion of an unfair (unjust) social distribution of air quality in your conurbation, or not? Explain your reasoning.
NOTE:
I need Turnitin plagiarism and AI report with the final submission. Plagiarism should be below 10% and AI, negligible.
