It's not the first time I post something about visualization of real-time events, I must admit it: I am a great fun of it. Under the term locative and animations in this blog it is possible in fact to find other posts of this type.
I think there is a clear trend today of visualizing the flood of data streams that are generated everyday from us and the object that surround us and the thing becomes more and more intriguing when data are displayed on a map in real-time time, where objects can appear, move, leave traces, and fade away.
This is the case of Wiki City Rome a real-time view of a living city.
Wiki City Rome has been publicly projected on Sept 8 2007 during the "Notte Bianca". Data obtained from cell-phones, GPSs, and other sources is used to display the dynamic behavior of people, events, transportation systems, etc. In the map one can see the density of active cell-phones and other additional information and observe their evolution during that night.
The project is of particular interest to me because: (1) it is developed in my city of origin Rome, which I deeply love, (2) because it is maybe the most mature project I have seen in this area.
The project is part of a larger Wiki City project where the goal is to give people access to the information of a living city through powerful visualizations and to let people act as agents that can influence the evolution of the city through cooperative and competitive behaviors. From the project's web page:
"Although the city already contains several classes of actuators such as traffic lights and remotely updated street signage, a much more flexible actuator would be the city’s own inhabitants."
"Consequently, we are creating a new platform for storing and exchanging data which are location and time-sensitive, making them accessible to users through mobile devices, web interfaces and physical interface objects. This platform enables people to become distributed intelligent actuators, which pursue their individual interests in cooperation and competition with others, and thus become prime actors themselves in improving the efficiency of urban systems."
Among the many challenges related to the project (e.g., data extraction and integration from multiple heterogeneously sources) the ones related to visualization and interaction are of specific interest to this blog.
I see (at least) the following big challenges. For each I add one or more references to potentially useful research:
- Data density and sampling: with such an amount of data to handle it is mandatory to visualize only a sample of the available information. Unfortunately selecting the "right" sampling is a hard task because the distribution of objects can be very skewed and some areas might become either over or under represented. In addition, different levels of sampling should be applied when changing from one zoom level to another. When zooming on a specific area it is necessary for example to retrieve more data to maintain a constant density.
- Level of detail: most likely the users will want to visualize the data at different zoom levels, as is common in any map navigation, and adapting the level of detail will be necessary to present as much information as possible, depending on how crowded a given area will be. Under the name "semantic zoom" reside a series of studies in visualization that address exactly this problem but deciding what's the appropriate detail is is not trivial in this case because the data is transient and density can change.
- Frank, A.U. and Timpf, S., 1994. Multiple Representations for Cartographic Objects in a Multiscale Tree - an Intelligent Graphical Zoom. Computers and Graphics, 18(6): 823-829.
- Visual feature overloading: one of the explicit goals of the project is to visualize heterogeneous data, that is, data object pertaining to different semantic entities (e.g., places, people, events, messages, etc.). In this case the problem is (1) how to distinguish one object from another and (2) how to remember the meaning of a visual items with respect to the entity it represents. In this case the whole theory of preattentive processing can be of great help in selecting the right visual feature. Another useful branch of research is the one related to the creation of Visual IDs, that is, icons that can be easily recognized and hardly mixed up.
- Lewis, J. P., Rosenholtz, R., Fong, N., and Neumann, U. VisualIDs: automatic distinctive icons for desktop interfaces. In ACM SIGGRAPH 2004.
- Rosenholtz, R., Li, Y., Mansfield, J., and Jin, Z. Feature congestion: a measure of display clutter. In Proceedings of CHI '05.
- Healey, C. G., Booth, K. S., and Enns, J. T. 1996. High-speed visual estimation using preattentive processing. ACM Trans. Comput.-Hum. Interact. 3, 2 (Jun. 1996)
- Change blindness: the problem of change blindness raises in the visual perception of moving objects. Under certain circumstances the human perceptual system misses some changes and thus it is blind to them. In vision science there is a long tradition of studies ti understand this kind of phenomenon. What matters here is that the visualization of moving object in real-time can be affected by blindness and this must be considered in the design of the visualization. Some initial studies exist on how to design visualization that explicitly cope with this problem, see the reference below.
- Exposing correlation and causation: this last one is the biggest challenge is see. If the visualization is used to understand how certain events influence some others (correlation) or to observe the consequence of some deliberate actions (causation) the visualization must be able to aid the user in finding these patterns. One obvious method to see these trends is to replay the visualization at different speeds and with different settings but I don't think this is enough to analyze these data. This is one of those cases where the joint use of visualization and mining techniques can really make the difference. The whole new mindset of Visual Analytics can be a starting point to deal with it.
So ... in summary I think this is a very challenging domain with a lot of research to do soon. I expect to see a lot more of this kind of visualizations in the future, especially when a certain critical mass of users will be developed.
