Mixed qualitative and quantitative data-intensive approaches were taken to the analysis as appropriate to the data. For the online research, data-intensive topic modelling, term frequencies and associations, sentiment and cluster analysis was conducted, alongside close reading and qualitative discourses analysis of select examples.
The sections below provide an introduction to individual methods, explaining what they involve, why you might use them, and how they can be used.
For details about the online methods and the results achieved with their application on the Deep Cities case studies, please have a look at the PRACTICAL EXAMPLE section (Woolwich, Canongate, San Donato). Furthermore you can find the case studies reports in the Participatory Methods RESOURCES page.
Social Media Analysis is a method to identify and investigate people’s interactions with the urban area.
The first important step is mapping the presence of references to the selected urban area on social media platforms, detailing location and content available. This mapping is also very useful for dissemination purposes, see Crowdsourcing and Survey section.
After the mapping, it is necessary to select the sites for the analysis, based on data access restrictions, research time constraints and content type relevance to the case study. We suggest focusing only on sub-environments of these sites where content is posted publicly and people posting have a reasonable expectation to be observed or read by strangers, such as public Facebook Pages for Facebook.
The data can be retrevied from social media manually or in an automated way using the R Free Software (a language and environment for statistical computing and graphics), then analysed with a mixed qualitative and quantitative data-intensive approach.
Some R plots of Flickr Analysis on Woolwich and Canongate Deep Cities case studies ©University of Stirling
According to recent studies on participatory urban planning, the crowdsourcing method seemed to be a suitable tool to unveil what values the citizens associate with the urban environment they interact with. Crowdsourcing implies a crowd, in this case an urban one, and the accomplishment of a task to solve a problem, here understanding what heritage preserve through time. To solve this, it is important to gather not just the stakeholders' views but also the non-expert local knowledge, bridging the gap between planners and citizens.
The main objectives for the Deep Cities project's crowdsourcing were creating some apps able to collect these values and local knowledge, as place-based memories, related to an urban area, and to enhance participation in the co-design of the urban space. Furthermore, we aimed to:
NB The crowdsourcing apps developed for this project can be considered as Participatory Mapping; this method can also take place offline. See here
We developed the following apps:
For further details and learn how to develop this app for your research, go to RESOURCES section.
Your City, Your Place App interface ©University of Stirling
For further details and learn how to develop this app for your research, go to RESOURCES section.
Some results of the app and the interactive board of the co-design activity during the workshop ©University of Stirling
An anonymous online survey can be useful to investigate heritage values associated with the case study in greater depth while also acquiring information that could help to contextualise the social media data previously collected and analysed. We suggest distributing the survey to potential participants through suitable social media environments identified in the mapping. Descriptive statistics can be produced using answers from close questions, whereas answers to open questions need to be analysed qualitatively.
The Deep Cities online survey comprised 16 open and close questions on social media use, values associated with the site and the wider urban area, and demographic data (about 5–10-minute completion). .
The Deep Cities online survey was developed on Google Forms.
Deep Cities Survey ©University of Stirling
Last update
09.01.2023