Follow-Ups at SENSEable

The meeting to report on my first results on tourist activities in Florence provided the opportunity to further plan my year at the MIT SENSEable City Lab. Prior to moving here, I extracted the keywords of my research: feedback loop, manual location disclosure, digital traces, granularity, uncertainty and co-evolution. Instead of finding complete coherence, its seems that now the completion of my thesis could take two separate avenues each related to some of these keywords:

Leveraging digital traces
In the first, I can build further upon the Tracing the Visitor’s Eye project and consider the analysis of digital traces or volunteer generated information to understand how they can be helpful to tourism (or more in general mobility?) and support decision making. It could be about forging new ways to describe tourism with a validation through second order analysis with other dynamic data such as cellphone data (flickr 70% and 30% cellphone data). Analysis could take place in Florence or Rome (better for statistical validity). Part of the analysis would focus on the accuracy of the data at hand and highlight the shortcomings and potentials. It would be about how flickr users (and maybe another dataset) describe the space (semantic analysis of the flickr dataset). The outcome would be a set of interactive tools and visualization to analyze the data and why not a model that could simulate the mobility of tourist from the flickr and cellphone datasets.

Research questions: How digital traces (or in a narrower way “volunteer generated information”) can enhance current tourism (or in a more extended way mobility) observations? With potential sub-questions as follow:

• What new information on mobility and tourism do these data bring? -> traces, scalability, richness of the explicit act of disclosing information, peope-defined area of influence of points of interests, people’s area of attention (digital footprints to improve the virtual representation of the space), geographic relevance
• How can we validate these data? -> use techniques to calibrate the flickr dataset with other mobility databases.
• What are the data quality (accuracy, noise, …) issues in volunteer generated information? This would be about revealing some factors that influence people’s decisions when they georeference information. ->In addition to Flickr data, I could setup a field experiment in Florence or as part of the WikiCity Rome project.
• How does automatic positioning influences location disclosure? Retrieve users who georeference automatically and study the semantic descriptions they use to disclose the information.
• How to visualize uncertain location information? This might involve setting-up an experiment with practitioners in urbanism/tourism or observe their current practices.

The appropriation of location information
The second avenue aims at building a coherence (a story) from the outcomes of CatchBob! and my taxi driver study and the semantic analysis of the flickr dataset. The main theme/question would be to better understand how do people relate to space (and its multiple spaces) through location information with a set of evidences each study would bring. CatchBob! indicated that technologies representation of the physical environment is uneven and fluctuant leading to feelings of uncertainty. Observations of taxi drivers revealed the importance of the prior experience of the space to appropriate a satnav system and the pitfalls of the discrepancies revealed in CatchBob!. In addition, current satnav systems do not fully support the practices of taxi drivers who need to access different levels of granularity of location information during a journey (trunked access to the information as if it was process through a funnel). This is for the reading/accessing part of location information. So what happens when we let people write and describe space. How does that translate to the different levels of granularity of multiple spaces (spatial semantics)? The semantic analysis of the flickr dataset could help understand how people manage multiple space. I could add a field study in Florence to bring another perspective to that question. The outcome of this research avenue could consist in a list of evidences revealing the issues around the granularity of information, a tool to study people-generated content. The sub-questions could be:

• What factors influence uncertainty in the use of a location-aware application? How is that related to the management of granularity and the reference to multiple spaces?
• What are the influence of automatic positioning on the the practice of manual location disclosure?
• How can people-generated content help define multiple spaces and different levels of granularity?
• …

Relation to my thesis: Avenues to discuss with my advisor, then take a decision, stick to it, trim and polish the research plan. I would love to integrate some of the velib and bicing data analysis to any research avenue, but it seems that for the moment it will stay as a parallel (fun) research endeavor.