Spatial Data in the Sensor Web

Posted: January 22nd, 2008 | No Comments »

A couple of paper that discuss the emergence of the large data generated by sensor now sharing our lives:

First, Data management in the worldwide sensor web draws the big picture in mentioning that now too much attention has been placed on the networking issues of distributed sensing and too little on tools to manage, analyze and understand the data. The authors ask the question weather we can design sensor networks with data quality in mind? They ask a very crucial question, but as often in location-aware computing, it is very unclear on who can claim what quality in location information is or in other words who can answer “how good is good enough?”. Of course it is important to manage temporal and spatial data and handle their inherent uncertainty (e.g. via probabilistic theory) or mask it (e.g. via interpolation) or play with it (seamful design). It seems clear now that my thesis is about acknowledging that situation (uncertainty in the location information, fluctuant quality in the data), but instead of aiming to produce “perfect data”, I plan to provide an understanding and solutions from a human and urban perspective. It comes, at the first place, with the observation of people experiencing location-aware systems in CatchBob!, and making use of location information, in my taxi driver (co-evolution, context and granularity). This observations help me accumulating evidences on the contextual factors influencing the granularity (≈human expectation of quality) of the location information used.
Balazinska, M., Deshpande, A., Franklin, M. J., Gibbons, P. B., Gray, J., Hansen, M., Liebhold, M., Nath, S., Szalay, A., and Tao, V. (2007). Data management in the worldwide sensor web. IEEE Pervasive Computing, 6(2):30–40.

Second, Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0 discusses that the most powerful sensor web is made of the 6 billion humans occupying Earth’s surface. This large collection of mobile and intelligent sensors will affect the processes by which geographic information acquisition and compilation (VGI: volunteered geographic information). The data generated suffer similar issues as a top down (authoritarian, centrist paradigm) when it comes to the fluctuating quality in the data and trust. However, the notion that citizens with means of taking measurements is at the source of the solution to the problems mentioned above. The analysis of how these “citizens” handle and annotate their measurements and observations allow to further understand the influencing factors in the use of location granularity. This is why I study Flickr users in their spatial annotation practice and in their use of geographic semantics.
Goodchild, M. F. (2007). Citizens as voluntary sensors: Spatial data infrastructure in the world of web 2.0. International Journal of Spatial Data Infrastructures Research, 2:24–32.

Third, the digital traces (shared measurements/observations) left by people in space allow to define a human description of space (e.g. citizen definition of a neighborhood). This type of sensor web data can only make sense with geovisualization, as the ones presented in Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup. The authors explore the new opportunities for visualizing sensor web data to explain user behaviors. Tools such as Google Earth provide a quick support for visual synthesis and preliminary investigation of digital traces.
Wood, J., Dykes, J., Slingsby, A., and Clarke, K. (2007). Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup. IEEE Transactions on Visualization and Computer Graphics, 13(6):1176–1183.

Relation to my thesis: Each of these three paper give an overview of the main themes of my thesis that aims to take a human and urban perspective to define the quality of location information:
1. Issues in the quality in the location data (uncertainty).
2. New data to observe people handle/experience location granularity in order to collecting evidences.
3. New visualization to reveal how people perceive and describe the urban space