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The module teaches quantitative skills, with an emphasis on the context and use of data. Students learn to focus on datasets which will allow them to explore questions in society – in arts, humanities, sports, criminal justice, economics, inequality, or policy. The student will be expected to work with Python to carry out data manipulation (cleaning and segmentation), analysis (for example, deriving descriptive statistics) and visualisation (graphing, mapping and other forms of visualisation). They will engage with literatures around a topic and connect their datasets and analyses to explore and decide wider arguments, and link their results to these contextual considerations.
The module is assessed by a group research project, using data analysis and visualisation to explore a “real-world” question. The literature-research question-data-analysis-presentation-conclusion model follows the path of typical data-driven research projects which take place at a postgraduate and postdoctoral level.
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https://emapop28.wixsite.com/qm2group15
Access to good air quality is prioritised in current environmental agendas (COP26): it is equally a political issue. The negative health impacts are well-known— air pollution may cause heart disease, stroke, and lung cancer— yet these effects disproportionately impact economically deprived communities (WHO, 2022). Similarly, subjective well-being (happiness) is a metric strongly linked to economic inequality: if there is more inequality, there tends to be lower perceived levels of happiness (Wienk, et al., 2022).
This enquiry will explore the relationship between different indicators of air pollution (PM2.5 & PM10 levels) and happiness (using scored ‘happy means' data), across the years 2011-2021. The relationship will be explored on two scales: nationally, to assess the net change in relative levels of pollution and happiness (from 2011 to 2021), and regionally, across Local Authority sites in the North and South of England, to assess changes in relative levels of pollution and happiness in two key years—2011 and 2021.
The statistical significance of the relationship between pollution and happiness will be assessed using a regression analysis and a correlation matrix. The spatial variation of the relationship between pollution and happiness will be visualised geospatially, using QGIS. A successful project should indicate where interventions should take place to minimise health inequalities (if applicable) across the North-South divide.
With all of the above considered, the research question we seek to answer is: how are happiness metrics and air quality indices related? And what does this mean in the context of the English North/South divide?
https://emapop28.wixsite.com/qm2group15/about-8
https://emapop28.wixsite.com/qm2group15/correlation-matrix
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