From the internet site description:

Urban Tapestries is a research project exploring social and cultural uses of the convergence of place and mobile technologies. To investigate these issues we are conducting transdisciplinary research as well as building an experimental platform that allows people to author and access place-based content (text, audio and pictures). It is a framework for exploring and sharing experience and knowledge, for leaving and annotating ephemeral traces of peoples’ presence in the geography of the city.

The Urban Tapestries software platform allows people to author their own virtual annotations of the city, enabling a community’s collective memory to grow organically, allowing ordinary citizens to embed social knowledge in the new wireless landscape of the city. People can add new locations, location content and the ‘threads’ which link individual locations to local contexts, which are accessed via handheld devices such as PDAs and mobile phones.

Today, we brainstormed a lot about the possibility of combining the wireless network at EPFL with an ad-hoc interaction. Mainly we are interested in this topic to study the location awareness effect on the reasoning and problem solving of a group structured task.

In this context we are looking for a specific task to give to the user of such system so that we can measure different performances and reflect on the topic.

The choice of a mixed architecture seemed to enhance the kind of interaction we can expect and lead us to a nice definition of the scope. However, we still did not come out with a definition of the particular task we want to work around and subsequently developing the interface for an undefined problem is highly complicated … 🙂

Any idea, please shout!

This site in maintained by the National Centers of Competences in Research. The center’s goal is to study fundamental and applied questions raised by new generation mobile communication and info services based on self-organization. This systems have become important with the advent of ad-hoc networks and peer2peer networking.

[more]

After a brief period of gestation, the analysis of the TeamFrames questionnaire is drawing to an end. Among the many interesting insight we had looking at the results, there is the definition of three categories of respondent which we have called 1) the undecided; 2) the collaborators; 3) the cooperators. I will give later on a brief description for each category.

1) The undecided. People in this category mostly have no opinion on what kind of study style they prefer. They may like group work for preparing exams but not to address new problems. People belonging to this category have not clear in mind what kind of strategy they follow to study. When they find themselves in a group environment, they fear a lack of control over the situation and they tend to assume not a strong role in the group. In fact, they find rather difficult to start talking when they are in a group. People in this category, finally, are more group players, although they prefer splitting the job and working individually on his/her part.

2) The collaborators. People in this category are distinct group players, and particularly they prefer this environment because they can tackle new problems and deal with practical work. People in this category don’t have a particular study style because usually they prefer working in groups where they find themselves quite comfortable without facing any particular problem. They have a slightly more active role when the work in groups where they like to assign tasks to assure an equal participation. For them is usually easier to speak in public. Although we call them “collaborators”, they have still the tendency to work individually os divided parts of the work and re-assemble the results at the end.

3) The cooperators. People in this category privilege the division of tasks. They have a better defined individual study style and they expressed a preference to work individually. While inside a group the fear mostly organizational problems, and for this reason they usually tend to have a passive role in the group, invoking frequent debriefing sessions with their colleagues to ensure a constant commitment of their peers. For this reason, the ask often for a division of tasks so they can work individually on their own part.

Below is represented the division of the questionnaire respondent in the clusters/categories defined above.

Recently, I have been reading about this concept of Mesh network: “wireless devices that talk to each other instead of to central towers”. Although the idea has been out there for ages, no big deal of application has been build out of it. Most of the effort into this domain concentrated on the middle-ware effort to put everything in touch. So, I thought that maybe one interesting to do is to think about a new possible application to start seeing the effect of such network philosophy.

The idea is simple: everybody (or almost) is carrying around a Bluetooth enabled device. Fine. What if while you walk around and you get in touch with someone else, you exchange something with that person? This something may be, for instance, a virtual pill containing a question or an idea. Then the person who got the pill may add a contribution to the idea, or an answer to the question.

Next phase is that the pill never stops but is always circulating. Fine. The point is how can the author get in touch with the contribution/answers? Well, a possible answer is that whenever an user connects to the internet with the phone, or pass by a bluetooth device connected to the internet, all the pills’ contained on the phone get transmitted to an internet site where they can be retrieved and viewed by everyone.

Possible features of the system is to include a kind of social/physical positioning to the propagation of the pills in the network so to unveil interesting aspect of the place where we live and of the people we pass by.

My research interest into this concept is to enquiry how people form opinions through the development of the messages contained in the pills, and how people use unconventional emergent technologies in a spontaneous collaborative way.

The following picture represent the clustering of a subset of the teamframes questionnaire respondents. What I did is to select only the people with a s(i) above the first quartile. In this way was possible to define a decent structure in the groups. Our next goal is to give to this groups a meaning comparing their members with the questions of the questionnaire and finding a definition for their profile.

Yesterday I managed to get the distribution of dissimilarities into an array object. I was considering using this distribution to find a cut-off point or level of confidence to get rid of users who filled the form but who are creating noise in the definition of clusters. As far as I understand the lower part of this distribution curve is represented by people who are very similar to the rest of the population. The upper part is the contrary.

My weekend in Rome at my parent’s place. The spring here is full of color

1. One of possible directions to find clusters and to compare them with usage pattern is (a) to fix arbitrary value of s(i) (which is the dissimilarity of measure/user x).

Once defined this value, is possible to get rid of all the user whose dissimilarity coefficient is lees than s(i), so to restrict the proximity of the clusters.

Other methods to define this value of s(i) may be: b) we can use the program AGNES to suggest acceptable value; c) eventually we can use the distribution of the dissimilarity values to define the acceptable level of confidence to restrict the wrong data.

2. Once defined acceptable clusters we can compare the cluster with each question in the set so to have an understanding of the profile. To do this we have to carry a oneway test of each question to the clustering division, to define the influence of each question to the definition of the cluster.

[So far, I could not find if it is possible to know from the method which factor most influenced a certain dissimilarity distribution]

3. The last step of this analysis is to compare the obtained profile with the social competences section and usage pattern style.

4. We can compare the results with an EXTERNAL VALIDATION.

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Questions:

1. Which kind of “Matching Method” are we using in DAISY? [Is the method for measuring the similarity or dissimilarity of two measures. The most common method is to use the SIMPLE MATCHING approach: s(i,j)=n/p and d(i,j)=(p-n)/p , where n is the number of matches, or in other words the number of variables for which for which i and j happen to be in the same state.]

2. Do we use the nominal parameter in DAISY? [I tried to use the parameter “symm” but it returned some mistakes. In addition I was using the “as.ordered” method to say that that variable was an interval scaled one. Unfortunately the syntax I used returned some errors. Finally, it was not clear to me whether I had to specify that a variable could be binary in the DAISY method (using the symm parameter) or using the as.factor parameter. ]

3. What are the medoids of the questionnaire? [Finally I could answer this question using the component “$medoids” of the PAM method. I discovered that the modoids of the questionnaire change if we subset the responses.]

4. Is there a difference between PAM and FANNY output?

5. How can we have the system suggesting the best k number of clusters? [There should be a CLUSFIND method contained in the CLUSTAN package that should allow this. www.clustan.com – apparently is not free]

6. Can we use an agglomerative method for defining the best k? [look above]