In Mike Kuniavsky inspires himself from PARC’s inch/foot/yard scale to come up with a user-centered hierarchy for which we can design and then assign a term to. :

If you look at the articles in the Ubicomp conference proceedings, you’ll find them dominated by location sensing and tracking. Clearly, ubicomp is still about figuring where you are in a space. But what happens when you’ve done that? What happens to designing the user experience when you know location?
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My goal is to create a user-centered hierarchy (rather than hardware-centric) as a way of talking about the perceived effects of ubiquitous computing technologies. In other words, this is an attempt to talk (roughly) about end users’ radius of focus in the moment as a way to design for that moment.

In his Granularities of User Experience in Ubicomp, Mike focuses on the tangible and more physical aspect of ubicomp. My work has a similar user-centered approach on how ubiquitous technologies are perceived and how to design according to their granularity. One of my research question is “how certain do positional and tracking systems have to be in order to be useful and acceptable?”. Today, I ran a small experiment on Flickr’s map service. Each geotagged image has a related accuracy representing the level of granularity in which the user located the image on the map. Results show my and my contact’s use of the accuracy.

My use of “accuracy” in Flickr’s geotagging service

The use of “accuracy” by my contacts

Relation to my thesis: Investigating the use of granularity in spatial annotation, being inspired by Mike’s Granularities of User Experience.

On a side academic project, I am investigation of visualization techniques of recommendation for lifelong competence development programs. Two issues relate to my research interests and could be evaluated in parallel with my real-world field studies.

• Use a map metaphor to position a learner in a competence landscape and support his/her awareness and navigation.
• There is an inherent uncertainty in the recommendations (the algorithm is a black box). Visualizing the quality/accuracy of the recommendations could improve the usability of the tool.

A type of map that could support users to define competences to acquire. (Example of Search queries in 2003 in the swiss search engine search.ch. Source Schweizer Cyberlandschaft, produced by the University of Zürich Sotomo group)

Relation to my thesis: Usability studies or experiments on positioning in the information space could (eventually) support my “living lab“-based field studies.

From m-Location presentation UK Mobile Phone Location Technology - The Current Status at the Royal Institute of Navigation Forum - Conventry in May 2004

GSM cell coverage (in reality)

Area of uncertainty for Cell ID positioning

Approximate GSM cell boundaries

Accuracy and maps

Relation to my thesis: Gathering visualizations of issues with mobile phone location technologies

The Laboratory for Ubiquitous Computing and Interaction at UC Irvine released the first version of a UbiComp reading list. This reading list is necessary for graduate students advancement to candidacy.

Relation to my thesis: keeping track on my literature review. I spotted a Hightower-Borriello article I was not aware of:

Hightower, J. Brumitt, B. and Borriello, G. 2002. The Location Stack: A Layered Model for Location in Ubiquitous Computing. Proc. of the Fourth IEEE Workshop on Mobile Computing Systems and Applications, IEEE Computer Society.

Based on five design principles extracted from a survey of location systems, they present the Location Stack, a layered software engineering model for location in ubiquitous computing.

In the future work section, they mention the challenge of uncertainty representation:

While it is clear that representing the precise nature of a sensor’s measurement uncertainty is critical, a general mechanism for this remains elusive. Traditional Gaussian representations [18] suffer from problems with nonlinear transformation between coordinate frames and the scalability of particle filters to large domains remains a challenge, although scalable state estimation techniques used in mobile robotics [8] are an excellent place to start and are the approach taken by our reference implementation.

Audio from every talk at last week’s IDEA2006 conference (conference on designing complex information spaces of all kinds) are now available online.

Urban Mapping’s Ian White gave a talk on the design of data (MP3).

Ian White: The Design of Data (PDF)

In short, Ian talks about mapping complex georeferenced data and how the design of data influences the experiences the users will have. Prior to any product definition he categorizes data in their context of use:

• Mode (driving, public transit, bicyle)
• Environment (urban, rural, indoor)
• Domain opportunity (mixing mode and environmeent, e.g. public transit urban)
• Constraint (tempoal, technical logisical)

From his experience with print (i.e. Panamap) and his “quest to sell polygones” (i.e. a geospatial database of neighborhood boundaries they license) Ian mentioned a couple of issues that are right on the spot with what I do:

‘How Location Aware are You?’

Geo-aware device often offer a “range of uncertainty” and we know that we do not know how certain you are (knowledge typologies). It would be nice if you could make it explicit.

Centroid issue

In practice, a neighbor is defined with average centroid based on population density and then a radial curve is drawn. This barely represents reality in many cases and in the context of use many time useless.

In the field of building application taking advantage of geodata, Ian advises to

• Study the context of use of the users (qualitative data)
• then segment it rigorously
• Design is a process
• iterate!

It is really very similar to my design-science research approach.

Relation to my thesis: I am hardly interested in how the design of data influences the experiences the users will have. I am not sure I will apply methods that can answer that. Context of use is something I plan to categorize as well. The temporal (4th) dimension of data was barely mentioned, while I see it as one of the 3 main source of uncertainty. The problematic of defining neighborhood and how it should match the user’s perception of this space provides me of an example of uncertainty that comes from the processing of the geodata.

The WSJ has an article on the new generation of GPS-based personal navigatoin devices or so called “personal companions” or “travel companions. In the hopes of encouraging people to carry them wherever they go, many companies have added instant messaging, photo viewers, travel guides with information like restaurant reviews and MP3 players. Featured GPS/PDA hybrid devices that are meant to be used meant to be used by walkers as well as drivers include the Garmin Nüvi, the HP iPAQ rx5900 and the Mio DigiWalker H610.
Drawbacks of such devices are:

• small screens that can be hard to see
• lack of detailed maps (topographical contour maps)
• service continue to route you on streets as if you were a car
• no update of information (look for an ATM and find out that the system sent the user to an ATM that had be removed)

No mention about the quality and coverage of the positioning technologies and how these devices handle them. The same day, Rich of Mapping Hacks wrote a post on plotting waypoints and track logs showing how spotty GPS coverage was in Amsterdam during his last visit.

But I have become partially disillisioned with how really very bad the coverage really is. I still believe in position, but the GPS is not the ultimate answer.

Relation to my thesis: Personal navigation devices compete with GPS-based cellphones in offering services to pedestrians and car drivers. The shortcomings of positioning technologies are not taken into consideration for the review. Details in the maps and accuracy of the information of the points of interests seem to be the most important.

Map of Europe for the Spanish Railway (RENFE) network connections as seen in Barcelona. I had never seen Switzerland shaped like this.

Relation to my thesis: the need of precision to deliver spatial information. In that case, a distorted country borders does not seem to really influence the reading of the map and the user cognitive map is not challenged.

The last 40 days, I sadly increased my contribution to global warming in releasing NOx, CO, HC and SO2 to propel me around Europe and California. Below I try to compare the “geotraces” I left using (semi-implicitly) Plazes and (implicitly) Flickr. I generate the Plazes’ traces using Pascal’s jplazes.

Relation to my thesis: I am interested in the use of implicitly and explicitly collected “geotraces”. More specifically I would like to understand what level of quality/granualrity of the location information users need (when and in what context). and their visualizations.
Currently, Paul is running a little experiment on tracking his days. I aim at similar little side experiment with the daily use of a GlobalSat BT338 and GSM cell-id based positioning. The purpose here is to understand the potential of implicit trace generation with behavior inference based on location data.

Rukzio, E., Hamard, J., Noda, C., and De Luca, A. 2006. Visualization of uncertainty in context aware mobile applications. In Proceedings of the 8th Conference on Human-Computer interaction with Mobile Devices and Services (Helsinki, Finland, September 12 - 15, 2006). MobileHCI ‘06, vol. 159. ACM Press, New York, NY, 247-250.

Unlike previous research that argue that a context-aware system usability can be improved by displaying the uncertainty to the user, this study shows that the user needs slightly more time and produces slightly more errors when the confidence of the system is visualized.

The user study evaluating the effect of the visualization of uncertainty consisted of three phases which were conducted by every participant: preliminary interview, the test with the prototype and a post interview. The test consisted of mobile web forms pre-filled with the user’s personal information. The input forms were mapped with colors with similar meanings as the traffic lights. As a result many users mentioned that they were distracted by the colors. Most participants stated that they did not take the visualized probabilities into account. Therefore, the authors conclude that in general the visualization of uncertainty in context-aware system is still questionable.

Slides of Enrico’s presentation at MobileHCI 2006.

Relation to my thesis: This study is very close to a type of user study on showing/not showing uncertainty I had in mind. However, I would probably apply it into a stronger contextual setting implying location awareness in a real-world environment. I would also question the way that the confidence is visualized. Moreover, the type of the task might have an impact on the usefulness of uncertainty visualization and the usability of the system. The authors of this study are working on the situations in which the confidence should be visualized or not in order to develop guidelines defining when the uncertainty should be shown and how it should look like. Can’t wait to see the results…

Alex Pang and Craig Wittenbrink and Suresh Lodha. “Approaches to Uncertainty Visualization“. In The Visual Computer, vol. 13, no. 8, pp 370–390, 1997.

This paper surveys techniques for presenting data together with uncertainty introduces as the data are derived, transformed, interpolated, and rendered. These uncertainty visualization techniques present data in such a manner that users are made aware of the locations and degree of uncertainty in their data so as to make more informed analyses and decision.

This research lies in the lack of methods that present uncertainty and data. The common underlying problem is visually mapping data and uncertainty together into a holistic view. The ultimate goal of uncertainty visualization is to provide users with visualizations that incorporate and reflect uncertainty information to aid in data analysis and decisions making. The authors define uncertainty to include statistical variation or spread, error and differences, minimum-mamixum range values, noise, or missing data. In this paper, 3 types of uncertainty are considered: statistical, error and range.

The sources of uncertainty, errors and ranges within data include:

• Uncertainty in acquisition: With instruments, there is an experimental variability whether the measurements are taken by a machine or by a scientist. The more times the measurements is taken, the more confident the measurement. But there will be a statistical variation in these measurements.
• Uncertainty in transformation: Data are rescaled, resampled, quantized prior or as part of the visualization stage. These transformations alter the data from its original form, and have the potential of introducing some uncertainty.
• Uncertainty in visualization: The rendering process introduces uncertainty arising form the data collecting process, algorithmic errors, and computational accuracy and precision.

The authors create a classification of uncertainty visualization techniques with five characteristics:

1. Value of datum and its associated value uncertainty (scalar, vector, tensor, multivariate)
2. Location of datum and its associated positional uncertainty (0D, 1D, 2D, 3D, time)
3. Extent of datum location and value (discrete or continuous)
4. Visualization extent (discrete or continuous)
5. Axes mapping defines visualization mapping (experimental or abstract)

The authors developed a variety of new uncertainty visualization methods. They are organized into a table showing general approach versus applications domain.

• Add glyphs: a glyph is a geometrically plotted specifier that encodes data values
• Add geometry: While glyphs do add geometry, they are placed at discrete locations. Adding geometry is used to denote a more continuous representation of data. Techniques include contour lines, isosurfaces, streamlines, and swept surfaces and volumes.
• Modify geometry: Geometry may be translated, scaled, rotated, or generally warped or distorted. They may also be displace, subdivided or refined.
• Modify attributes: uncertainty can be visualized by modifying attributes of geometrey in the rendered scene.
• Animation: Application to most applications, including comparison of animation data and techniques. Uncertainty information can be visualized by mapping them to animation parameters such as: speed or duration, motion blur, range or extend of motion.
• Sonification: Mapping uncertainty to sound.
• Psycho-visual approaches: stereo-pairs and subliminal messages

From the author’s exploration of uncertainty visualization techniques, they have found that continuous visualization extents are more challenging than discrete visualization techniques. This is based on a basic methodology that uses visual tests where users examine visualizations and decode the information within the graphics. The amount of errors between the user interpretation and the encoding is statistically evaluated to determine if the visualization is effective.

Similar papers include:
Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know and Visualizing Uncertainty in Geo-Spatial Data.
Relation to my thesis: the pipeline of the sources of uncertainty has similarities with my current categorization. The methodology to evaluate infoviz is based on visual tests in during which the user interpretation is evaluated. Interesting reference is the report which identifies four ways of expressing uncertainty:

Barry N Taylor and Chris E Kuyatt. Guidelines for evaluating and expressing the uncertainty of NIST measurement results. Technical report National Institute of Standards and Technology Technical Note. Gaithersburg MD January