A couple of weeks ago I was invited to participate in a workshop at NYU’s CUSP, or Center for Urban Science and Progress. As they describe themselves:

The Center for Urban Science + Progress (CUSP) is a unique public-private research center that uses New York as its laboratory and classroom to help cities around the world become more productive, livable, equitable, and resilient. CUSP observes, analyzes, and models cities to optimize outcomes, prototype new solutions, formalize new tools and processes, and develop new expertise/experts. These activities will make CUSP the world’s leading authority in the emerging field of “Urban Informatics.”

The theme of the workshop was ‘mobile sensing’ - with, of course, a particular focus on how it may support urban science.

Talks

The invited speakers were from diverse backgrounds and institutions, making for a very interesting line up. I did not take any notes, so my summary here is vastly unfair to each talk:

• Rob van Kranenburg (@robvank) naturally spoke about the Internet of Things, and how it fits into the broader ecosystem of cities.
• Mischa Dohler (@mischadohler)’s talk covered urban sensors, and gave rise to a big debate about where the boundary between crowd-sourcing and urban sensing should lie.
• Jacqueline Lu (NYC Parks and Recreation) spoke about how data is supporting efforts to maintain and promote green spaces in the city. This was particularly interesting since it made me realise how a seemingly ‘trivial’ problem (maintaining trees) is actually vastly complex when placed into urban settings.
• Vivek Singh (MIT) spoke about ongoing mobile sensing experiments that investigate how behaviours can be promoted between social groups.
• Margaret Martonosi (Princeton) spoke about her work using Call Detail Record data (e.g., see this paper). The data gives fantastic geographical coverage and potential to study many facets of mobility, while presenting very difficult challenges with regards to inference and privacy.
• Weisi Guo (Warwick University), the workshop organiser, spoke about his research about understanding cities through mobile sensors.
• Jarlath O’Neil-Dunne (University of Vermont, @jarlathond) gave a talk about geographic analysis using satellite data - I learned about how LiDAR data (e.g., this blog post) can be used to, for example, find trees that would otherwise be hidden by shade: a very challenging data feature extraction task.
• Raz Schwartz (Rutgers, @razsc) is the co-creator of the Livehoods project, which is a great example of attempts to uncover the structure of the city via social media analysis.
• Eiman Kanjo (King Saud University) discussed her work with mobility and affective sensing using smartphones (see her publications here)
• Andrew Eckford (York University - Canada not UK!) gave a very interesting talk about molecular communication for harsh environments (say, flooded subway tunnels!) - something I had never heard of.
• Graham Cormode (AT&T) discussed his work on distributed data monitoring and mining. See his personal page here.
• Lin Zhang (Tsinghua University) talked about his work with sensors on Beijing’s taxi cabs for pollution monitoring (MobiSys paper here). The dataset is available on request.

Finally, I briefly talked (with very sparse slides) about open challenges in mobile sensing - ranging from energy efficiency to data inference and behaviour change measurement.

Open Idea

The fact that this broad range of researchers all agreed to come to a workshop on ‘urban sensing’ shows how this field is still in its infancy; I very much enjoyed the fact that everyone spoke about very different things. In fact, we even differed on the basics:

• What is urban? It is one of those words that could mean tunnels in a subway, parking sensors on a road, or community driven tree maintenance. The ‘where’ of all the research above certainly agreed on cities: but within this context, there is a hierarchy than ranges from metropolitan-scale analysis down to individual citisen’s sensors.
• What is sensing? It seems that ‘sensor’ is quickly becoming a term to mean ‘a source of data;’ while this is consistent with the past, my gut tells me that historically this would not have been the case. Both tweets, accelerometers, and satellites are sensors, albeit very different in nature: and there is ample space for research both within the scope of individual sensors and finding links/building systems that bridge between them.