Subjective subway map

“Mon plan du métro de Paris” by Pierre Joseph is an interesting representation of the author’s memory of Paris:

Why do I blog this? Maps based on people’s recollection of souvenirs and past experiences are always insightful. They tell stories about the person’s subjectivity, what count for certain individuals and what is left out of the picture (metro lines/stations…). The use of the same graphical code as the real subway makes it even more intriguing than hand-drawn map as it give an awkward perspective on the city itself (see the real map below). In addition, the different between the two maps highlight the person’s perspective in a very coherent way.

When on the field, I enjoy asking informants to draw maps of their mobility patterns. It’d be curious to expand this method to such kind of representation too.

A study about mobile phone location data and recommendation systems:

People who played with location-based recommendation systems may have been confronted to a common issue: when you start using the application, you do not necessarily have a “location history” (no list of past “check-in” if we translate this in the Foursquare idiom), hence it’s difficult to get relevant recommendations. This phenomenon has been called “the mobile cold-start problem” in this paper.

This academic article written by Quercia et al. for the IEEE ICDM 2010 conference addresses this problem in the context of mobile recommendation systems, apps that can identify patterns in people’s movements in order to recommend events and services. The researchers investigated how social events can be recommended to a cold-start user based only on his home location. They conducted a quantitative study to investigate the relationship between preferences for social events and geography. They tested a different set of algorithms for recommending social events and evaluated their effectiveness.

Some excerpts of the results that caught my attention:

“In a situation of cold start (user preferences are unknown), recommending geographically close events produces the least effective recommendations, while the most effective recommendations are produced by recommending social events popular among residents of a specific area.
(…)
Interestingly, there are geographic areas that are more predictable than others, and this does not depend on the number of residents we consider in each area. We are trying to obtain sociodemographic data for Greater Boston to test whether sociodemographic factors such as income and inequality would explain those differences. If that would be the case, to produce effective recommendations, one would then need to complement real-time mobile data with historical sociodemographic data.”“

And this bit about the data themselves is relevant too:

“To infer attendance at social events, one needs large sets of data of location estimations. Often such sets of data are not made available to the research community, mainly for privacy concerns. Such fears are not misplaced, but they gloss over the benefits of sharing data. That is why our research agenda has been focusing on situations in which people benefit from making part of their private, aggregate data available. This paper put forward the idea that, by sharing attendance at social events, people are able to receive quality recommendations of future events.“

Why do I blog this? Working on the user experience of location-based services, I’ve always been curious about recommender systems and the problem designers face developing them. What’s so fascinating is that they are based on basic and somewhat intuitive ideas about the way city-dwellers behave. Studies about their usage often reveal the complexity such systems.

PHOTO/NYKTO: a game played by switching on and off the lights

PHOTO/NYKTO is a project designed by my colleague Annelore Schneider & Douglas Edric Stanley at HEAD in Geneva:

“« Photo/Nykto » is an experimental game conceived by Annelore Schneider and Douglas Edric Stanley as part of the « Unterplay » project at the Master Media Design —HEAD, Genève. It is a game for nyktophobes and photophobes. It is played by switching on and off the lights in order to avoid reaching the edge of the screen. The score increases exponentially near the edges, and speeds up with each change from light to dark and back.“

Why do I blog this? Fascinating gameplay!

About location-based advertising

Few articles raising doubts about location-based advertising:

Unni, R., Harmon, R. (2007) Perceived Effectiveness of Push vs. Pull Mobile Location-Based Advertising. In: Journal of Interactive Advertising, Vol. 7, Nr. 2:

“Pull LBA fared better than push LBA. However, value perceptions of LBA and intentions to try this service appear to be quite low. Also, privacy concerns relating to location data were high, and perceived benefits were low.
(…)
Interestingly, initial surveys by market research agencies such as Driscoll and In-Stat showed a high level of interest and willingness to pay for location-based services such as navigation (driving directions), maps and guides, and traffic updates. Unlike LBA, these services are perceived to be more utilitarian and hence benefits and perceived value are easier to communicate. Results of our study show that the perceived benefits from LBA are low.“

Banerjee, S. & Dholakia R.R. (2008) Mobile advertising: does location-based advertising work?, MMA International Journal of Mobile Marketing,

““location inertia” seems to characterize consumer responses from a private location. We use the term location inertia because this relative unwillingness to shop when advertised in private places has nothing to do with geographical distance from the store. In the LBS scenarios, private or public locations, the distances of the advertised store were specified as exactly the same (less than 0.1 mile away) but it appears that the actual distance does not matter; despite knowing that the store is the same distance away, a consumer is less likely to avail the offer when the ad is received at a private location than a public location.
(…)
The example of mobile advertising discussed in this paper can be simply viewed as an Internet pop-up ad that has traced the consumer’s location and accordingly appeared on his mobile phone.“

Why do I blog this? I’m not necessarily into this kind of application but I’m often asked by clients and journalists about the co-called “effectiveness” of using location-based ads in a “push” mode. My general understanding of these technology is that users find it intrusive and not very useful but it’s good to have more data up my sleeve to discuss the complexity of people’s perspective on this.

The main problem I see in the research papers about this is that they generally focus on projective methods (as opposed to following people using location-based advertising platforms).