Readings

Reading Reflection: On Trails

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Part of understanding our theme of Technology on the Trail demands an exploration into the history and nature of trails.  Robert Moor provides that in his book, On Trails: An Exploration. Inspired by hiking the Appalachian Trail, Moor sets out on a quest to “find the nature of trails”, including their earliest pre-human history, their establishment by animals ranging from ants to elephants, and their adoption by humans.  Most relevant to our goals, Moor includes a discussion of “how trails and technology…connect us in previously unimaginable ways”.

Moor acknowledges our fickle relationship with technology, noting that many hikers embrace GPS devices but scorn mobile phones, that technology is a malleable and agnostic vehicle for conveying knowledge yet inadequate without extensive time outdoors. He seems like a late adopter of technology, only willing to step in when he feels he’s missing out on an experience; e.g., he only bought a smartphone when he started needing to view online videos and when he recognized a need for a GPS-enhanced online map.  Yet he’s clearly done his homework about the history and continuing impact of technology on trails and hiking, providing a valuable resource for anyone interested in trails.

Reading Group Summary: Asocial Hiking App

Gracie Fields3 Comments

Readings:

This week’s paper is by Maaret Posti and her colleagues examines social issues related to hiking, presenting a design-focused approach to understanding the tensions and desires to be asocial while hiking.

Agenda:

  • Brief revisit of reading group and introductions of anyone new
    • Attendance: 6 people (1 professor, 3 graduate students, 2 undergraduate student)
  • Discussion of next week (the week before Thanksgiving break) and beyond
  • Summarize papers
  • Discuss papers

Discussion:

Gracie opened up with a comment about how it was a shame we couldn’t download an app to try out, but such is the way with research projects.  A bit more planning ahead and we could email the authors, alas.

Several times, the group came back to the issue of which trails had enough of a network to make an app like this feasible. In our experience, a lot of trails nearby have one and only one route, meaning it would be impossible to avoid someone without hiding in the bushes (which the paper explicitly said users didn’t prefer doing). Where the app was tested, there was enough of a network of trails that pieces could be mixed and matched at intersections of routes in order to avoid people or otherwise accomplish a “lonely” walk. However, it’s not like such networked trails don’t exist in the US (Gracie has hiked one in Indiana’s Turkey Run State Park) – it just seems less common to us locally.

Some of us felt there wasn’t demand for an app to avoid people on the trail. Some people are comfortable just walking by and not acknowledging others on the trail when hiking “alone,” but others note that people with considerations like social anxiety could benefit from avoiding people entirely. However, we did all relate to some degree with the notion of avoiding contact with a specific person (maybe your professor that you see across the quad, or an old classmate who you see in line at a shop) by actively avoiding them. We talked about little tricks like walking behind a different person or acting distracted.

We also talked at length about privacy and security issues, which we felt the original paper didn’t do justice to. This app could very easily tell someone with malicious intentions that they’re alone on a remote trail with just one other person. We connected this to a talk here at VT recently by our new faculty Gang Wang who discusses similar security issues with a driving app that revealed exact locations of others in traffic (which his group proved made it possible to stalk specific people). In general, very few people want everyone else around them to know their exact location.

Instead of an app with specific nearby people marked, we talked about the utility of a tool for estimating projected loneliness of a trail at a specific time. You’re at the mercy of chance if you wait until you’re already hiking to see if others are hiking the same paths. Projected popularity could be based on things like social media traffic relating to an area, but there’s confounding factors of a boring area not getting much social media attention.

In terms of the app itself, we talked a bit about the user preferences discussed in the paper. We thought it interesting that most preferred the version with an actual map integral to the view – something with utility beyond just the lonely hike use case. Needing the visual cue to detect others nearby made us talk about how a user might handle checking the device, being distracted by checking it, or choosing only to engage the app at intersections. We envisioned use over a longer period of time than just 84 minute hike. This would enable an understanding of hikers use it over time and how usage changes.

Reading Group Summary: Bridging Urban and Woods Technology

mccricks1 Comment

 Readings:

With this week’s paper we examine the connections between urban and trail technology use, considering how lessons from sensor-based science the woods affect urban sensing efforts.

  • Dana Cuff, Mark Hansen, and Jerry Kang. 2008. Urban sensing: Out of the woods. Communications of the ACM 51, 3 (March 2008), 24-33.

Agenda:

  • Brief revisit of reading group and introductions of anyone new
    • Attendance: 4 people (1 professor, 2 graduate students, 1 undergraduate student)
  • Summarize papers
  • Discuss papers

Discussion:

This paper, authored by an architect/urban planner, a statistician, and a lawyer, outlined challenges regarding sensing efforts in urban areas.  They began by describing successes in sensor-based science in the woods, where air, water, soil, etc. sensors could collect data continually without objection from the birds or worms.  They contrasted that with efforts to do sensing in urban areas, where there are legal issues (people object to being sensed more than birds) and a greater likelihood of junk data and garbage analysis.

The issues from this paper resonated with the people in the discussion group, particularly in reflecting on past papers.  Several people noted parallels with the challenges encountered with the Dix data, for which even a careful scientist and a conscientious science community generated faulty data (e.g., GPS malfunctions, forgetting to turn on or off a sensor) or questionable analysis (e.g., is there a “happy” day, or is that the wrong unit of time to examine).  Challenges stemming from large data sets, including human-generated data, certainly have grown in importance in the 8 years since this paper appeared.

The calls for action certainly seem prescient today.  The call for more data commons efforts is seen in data sets like the Dix data, government datasets, and the NSF requirement that all proposals include a data plan that explains how data generated in the proposed work would be made available for others to analyze.  The call for distributed citizen-initiated sensing is seen in efforts like Google traffic (Waves), Foursquare, bar tracking, Facebook check-ins, openstreet map, CMU bus schedule project (Zimmerman), and lots of other crowdsourcing efforts.

And it was encouraging that the authors noted that user interfaces are both important and hard.  Again, we saw that in the Dix data, which was made available but was reformatted  and cleaned up multiple times–and gaining insights from it is still hard: McCrickard’s class had difficulty identifying interesting findings for a week-long homework assignment. 

There were lots of ways forward that emerged from the discussion and paper.  Many were related to the difficulty in knowing the right question to ask about data sets?  A bottom up examination reveals trends and top down reveals questions, but there’s a danger in just finding what you’re looking for without a scientifically rigorous approach.  The paper seems prescient 8 years later, and worthy of consideration moving forward.

Reading Group Summary: Alan Walks Wales

Gracie Fields2 Comments

Readings:

Agenda:

  • Brief revisit of reading group and introductions of anyone new
    • Our time this week overlapped with faculty meetings
    • Core ideas for TotT; see previous week
    • Attendance: 7 people(1 professor, 4 graduate students, 2 undergraduate students, including 2 new faces)
  • Summarize papers
  • Discuss papers

Discussion:

We started with an overall discussion of the Alan Walks Wales project from miscellaneous outside readings by a few of the reading group members. This week, our two papers were very recent, so we only touched briefly on whether new tools or techniques (most specifically to dealing with open, large data sets) existed since the time of the paper’s publishing.PubsofBlacksburg-Poster-Maroon

Following the suggested definition of a wonky map from Alan Dix’s paper, we put forth as our own example the PubsOf poster shown here of our own local bars drawn by Brian McKelvey. It is “wonky”, as it doesn’t faithfully represent the town of Blacksburg but includes distortions that highlight key destination points (i.e., pubs).

With this example and the paper in mind, we talked about the definition of a “map.” One person suggested that a map depicted places that are roughly co-located with an emphasis on directionality, and another suggested a map was a graphical representation of physical places.

From the discussion of maps, a few interesting threads of conversation arose. We debated, especially for the purposes of travel or hiking, whether a map based on time-to-travel rather than physical distance might be possible. Traveling the distance up a mountainside takes far longer than traveling the same distance on flat ground. We also talked about examples like subway maps which commonly shrink the map of rails into more of a conceptual understanding of intersections, especially to fit on smaller pieces of paper or near the ceiling of a subway car.

We also discussed what it’s like to describe directions using landmarks to someone unfamiliar with the area, especially in the context of back routes or walking through alleys and grassy areas; sometimes we just say “follow me” to lead a stranger through campus instead of trying to describe such a thing. We also talked about on-the-ground assistance people give each other that traditional maps can’t provide, such as a trailhead marker that has a slider for its condition.

We talked about what it would be like if trail maps were read the same way as street maps, or if they were described solely in terms of landmarks. One of us suggested there are three ways to navigate by landmarks: solely on landmarks, landmarks plus direction, and purely on a sense of distance between landmarks. We also hit on the significance of general map literacy, going again back to A Walk in the Woods in which the author went into the experience with fairly robust map-reading skills, such as when he found a logging road to get around an impassable mountain in a storm.

We connected the two readings when talking about what maps could be generated from information collected on the ground. A map of the difficulty of the terrain could be generated based on the people actually traversing it. We talked about how user-generated maps might also capture those back routes that traditional maps miss that would normally spread by word of mouth only, or other issues like seasonal closures.

When discussing open data in classrooms, we hit upon the pros and cons of giving students entirely unexplored sets of data as open-ended puzzles to figure out. We talked about the need, also stressed in the paper, to clean the data beforehand. We felt a big issue could be the student not knowing how or where to get started with such an open problem. The data feeling irrelevant to the student could also be an issue. We talked about whether working with the data of hundreds of people in the set might make it more interesting to work with. However, that still might not make the data feel relevant to the student, and more isn’t always better. What the student cares about might not be something numerical data can capture.

We talked about two ways to approach data: either look for insights in an open-ended manner, or try to verify a hypothesis. The two are not always mutually exclusive. We talked about how readers of a blog might feel more connected to the story if they had access to tools to analyze and gain insights about the data contained in it, and specifically whether that would make readers feel more connected to the Alan Walks Wales story when they might not have before.

We talked about the potential of tying personal data sets, such as one generated by Fitbit, into publicly accessible data sets, such as meteorological data. Enriching data after the fact with additional public data would be an interesting area of study. However, we talked about the downside of highly localized data; going back to the meteorological data, weather can be very different in one area than it is only half a mile away, such as in a valley and on top of a foothill, and it’s less likely that publicly available data sets would match your exact experiences. Aggregating the data of many people with similar private data might crowdsource some of that specificity in an interesting way.