processing and presentation of data

• This is where you select the appropriate techniques to show your raw data in, usually graphical form. This will make it easier to interpret. What you use depends on what you are trying to find out and what data you have and you need to know why you picked it.
• Method: Bi-Polar Analysis (primary) (quantitative)

  Equipment: Data recording sheet, pen
• How bi-polar analysis is measured:

  • Stratified to find each site (one fully renovated and the other still showing the past state)
  • Find a space and slowly turn around on the spot. Think about what you can hear, see, sense, smell.
  • Tick the box for each category with the score you think that section deserves
  • Add up all scores to have one total
• Justification of bi-polar analysis:

  To understand what the environment is like and what facilities there may or may not be. This will help to understand social features, such as quality of life for residents and the experience that people who visit will have. Nicer areas are likely to attract more people and therefore increase economy.
• Limitations of bi-polar analysis:

  • It’s one person’s opinion which is very subjective.
  • The scores are likely to be very different dependent on the day and time that you come. As there are many restaurants and shops, it is likely to be far busier at a weekend than a weekday morning when we went.
• Method: Questionnaire (secondary) (quantitative + qualitative)

  Equipment: questionnaire/ pen
• How questionnaires are measured: look at the 2018 questionnaire data and use to make data presentation
• Justification of questionnaire:

  • To determine why people are visiting – for social or economic reasons?
  • To find out how much money on average people spend (this links to economy)
  • To find out how the area makes people feel (social factors).
• questionnaire limitations:

  • The 2018 data is 6 years ago so people may feel differently about it now as some shops and restaurants have opened whilst others have closed down