Visuale

The SENSEable City Lab @ MIT has an interesting project about using visualization to analyze how footbol players move on the field during a match:

Information about the movement of soccer players on the field during a match can be useful for strategic and physiologic analysis, directed at improving the performance of the players and that of the team.

There are some interesting challenges like the fact that "the sensing area is large compared to the moving actors (players)" and that a strong occlusion is always present due to the paths that are most frequantly traced by the players.

There are a number of other visualizations tracking people moving in a space and, interestingly enough, they always have the same recognizable shape (you can say ah ah, this is people!) and share similar challenges. Notably, Katy Broner's work on Visualizing 3D Virtual Worlds and Their Users (some images here)

Yet another locative information visualization. Cabspotting tracks the position of taxi cabs in San Francisco and produces visualizations to show interesting social and economic patterns.

Cab Tracker, the main project, uses the last four hours of tracking data and presents them as lines on a map. Then it selects 10 taxi cabs and overlay them to the map showing their movements in real time.

The patterns traced by each cab create a living and always-changing map of city life. This map hints at economic, social, and cultural trends that are otherwise invisible.

A beatuful web application of the cab tracker can be accessed from the home page. Time Lapse, another related project aims at revealing time-varying patterns like rush hours and traffic jams.

Here is another interesting visualization stemming from the analysis of people moving in urban environments. Trace visualizes wireless networks encountered while moving around in the city, easily revealing their overlay, strength and the swapping between one to another.

As a participant walks through the city, wireless networks are sensed by the PDA. Each time a new network is encountered, a new vertical bar is drawn. As each new network is encountered, its marker moves along the color spectrum. The first network is always red and on the left hand side, the last one is always purple and on the right side, and networks along the way get new colors as they come within range. The height of each bar represents the combined strength of the wireless networks currently in range.

With a simple tweak it is also possible to distinguish between private and open networks. The black shaded area represents private ones.

It would be really interesting to see it in action. Unfortunately, the website does not provide any video or demo. Anyway, it's interesting to note how the visualization permits to reveal the number of different networks encountered while moving, that is, the number of different colors. Interestingly, if one color disappears and reappear later, it means the device has returned to the same coverage area.

Since each network has its own color assigned, one possible limitation might be the number of different networks that can be represented at the same time. Reusing the color of a network not showing up for a while, might be useful to save some colors (maybe it is already like that, who knows). Interestingly, the often criticized rainbow color scale seems to work very nice here.

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.
  • Woodruff, A., Landay, J., and Stonebraker, M. Constant density visualizations of non-uniform distributions of data. In Proceedings of UIST '98.

  
  

• 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.
  
  
  
  • Nowell, L., Hetzler, E., and Tanasse, T. 2001. Change Blindness in Information Visualization: A Case Study. In Proceedings of the InfoVis Symposium 2001.
    

  
  

• 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.
  
  
  
  • A. Pattath, B. Bue, Yun Jang, D. Ebert, X. Zhong, A. Ault, E. Coyle. Interactive Visualization and Analysis of Network and Sensor Data on a Mobile Device. In Proceedings of VAST 2006.
    
  
  

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.