Thank you very much to everyone who submitted an entry to Competition AH4, based on Professor Gillian Rose’s lecture, ‘How is Digital Data Transforming How Cities Work?’. Below you can read the winning entry, as selected by our team of markers.
Think about the neighbourhood where you live. Identify a problem that you would like to see solved there, then ask yourself:
- What kind of digital data might be gathered to help solve that problem? i.e. what would you need to measure to help you analyse the problem and work out a solution to it?
- How could that data be gathered by a digital device? From a sensor on a pavement or a streetlight? From smartphone use?
- Would you be worried about who saw that data?
Ebrahim’s winning entry:
- One problem in my area is littering. This is something that damages the aesthetics of the area and, more seriously, can reach and consequently pollute the oceans. Digital data on things like traffic/number of visits that an area receives or the availability or usage of a specific area or facility etc., can aid in gauging which areas are likely to have the most litter and what exactly may cause this e.g., a lack of places to dispose of rubbish.
- This data can be gathered in various ways. The easiest but more controversial method would be by tracking smartphone data more specifically location and GPS tools. This would give an idea of the amount of people that would be in an area at a certain time, which could then be linked to the levels of litter within the vicinity. However, one must appreciate that not everyone would the appreciate the analysis of personal data and some may even argue that this collection and analysis of data could cause security problems. Another way data could be gathered is via motion sensors. These sensors could measure the fill of bin and other disposal facilities to understand the usage of these resources and ways to increase and improve the use of these assets.
- There is not necessarily a worry as to who may see the data as it doesn’t dictate who exactly it may belong to.