H1. Our algorithm discovers personal places accurately.
Why interesting
 Place learning [Hightower, 2003] Useful algorithm to discover user’s personal places in
location-aware applications; provide place candidates to users for various location-aware
application needs.
 Empirical testing which was not done in related study. [Natalia 2000][Liao
2004][Ashbrook 2003]
Data





Collect places of subjects each day
Compile places of each day into a list of unique places (A) for each subject during the
experiment period.
Collect subject’s GPS data every minute
Run DJ Cluster algorithm on GPS data and discover places (B)
Plot the discovered places on color maps with multiple level of details
Testing Method
 Present both the accumulated list of places (A) and the maps to the subject
 (Q)For each place on list (A), match it to the places (B) on maps. This generates C.
 (Q)Do you have places on list (A) that can not be matched to (B)?
 (Q)Do you find places on map (B) that are not on your list (A)? Are they meaningful(D)?
Not meaningful?(E)
 C is the set f places that are in both A and B
 D is the set of places that are discovered by the system, confirmed by the subject, but not
in the subject’s record
 E is the set of places that are discovered by the system, unrecognizable to the subject
 Calculate precision and recall
 Calculate average precision and recall, and standard deviation.
Accept of hypothesis
 average recall > 80%
 average precision > 80%
Recorded
Baseline
A
Discovered
Placess
B
Uninteresting
Places
E
Interesting
Baseline
Places
Coverage
D
C
H2. Certain place descriptions are highly personal and idiosyncratic; some are
comprehensible by close family members, friends or co-wokers; others are comprehensible
by other people; People can tell which descriptions are of which type and can tailor their
descriptions for social purposes as necessary.
Why interesting
 Place labeling [Hightower, 2003]
 Place name ontology on a personal level [Vögele 2002][Weaver 2003]
 Because we need to understand whether place descriptions generated by one person will
make sense to other people. If I tell you to meet me at “Tom’s house”, you would have
no idea what I meant. If I tell you to meet me at “the Dunn Brothers across from the
Uptown Lunds”, you could figure it out.
Testing Method
 (Q)If you were to describe this place to <X>, what description would you use (if not the
one you supplied previously)? (Where <X> could be filled in various ways, e.g., a friend
or family member from the Twin Cities, a friend or family member from elsewhere, a
work colleague from the Twin Cities or elsewhere, a new acquaintance from the Twin
Cities or elsewhere, …
 (Q- optional)Would other people be likely to come up with the same description of this
location?
 (Q - optional)How understandable would other people find this label?
 Calculate the percentage of place descriptions that are personal.
Accept of hypothesis
H3. The number of distinct visits that person makes to a place has higher coefficient to the
importance of the place to that person than the amount of time a person spends in a place.
Dan: I believe you should list all the 'objective' factors you can think of, and have a
couple of hypotheses. The grand-daddy is "objective factors can predict the importance of
a place", then grand-children of the form "factor X is good/bad at predicting the
importance of a place". Factors include:






# of distinct visits (per unit time, probably)
amount of time
time of day/week (any calendaring experts out there?)
variability of time of day (are you punctual, is it a habit)
whether other people consider the place important (is this 'objective'? Not sure.
Nevertheless an interesting factor.)
Other information associated with lat-long coordinates. Not sure what. Urban vs.
rural, if you could tell? Street address? # of hits on Google? Web site exists with
that street address? Etc. This is tough, maybe not explorable now, but interesting
nonetheless.
(This seems like a “compound” hypothesis. At the least, I think it should be broken into two
parts: (a) the number of distinct visits to and the amount of time spent in a place predict the
importance of that place, and (b) the number of distinct visits is a better predictor than the
amount of time spent. Or perhaps we don’t think that the number of time spent is a good
predictor?)
Why interesting
Design guidelines for location-aware applications: it is the number of visits that counts,
not the time spent.
Data
GPS reading with timestamp, personal places
Testing Method
Subjects: rate the importance of the places using weights -2, -1, 0, 1 2.
Investigator: sort places by time spent
Investigator: sort places by number of visits
Investigator: Statistically establish the coefficients and compare
H4. People perceive places on both generic and specific level
Comments
 Some people consider any Target store as their place, the Target close to work over lunch
time, any Target in the city on the way home, the Target close to home during weekend.
Some people consider the Target at a specific location as their place because it is close to
home, or because it always has better deal, etc. Other examples of generic places are:
driving range, party, lunch place, etc.
Why interesting
 Location-aware application design implication: a user may want to leave a note to pick up
something at a specific Target at a specific location, while another user may want to leave
a note to pick up something whenever she gets to a Target store.
Data
 Ask subjects to write down their regular places
 Ask subjects to categorize each place into specific and generic. Can you apply this
description to other places?
Testing Method
 Calculate the percent of subjects that have both generic and specific places
Accept of hypothesis
 Percentage > 80%
H5. Different functions of places distinguish different types of places.
Why interesting
 A personal place is somewhere where a person would spend some amount of time
doing one or more things. The users of a place have certain expectations as to the
objectives possibly realizable in a place. This would require some understanding
of the places’ functions in order to use them accordingly [Kramer, 1996]. For
example, one, e.g. a professor, might drink coffee in a “coffee shop”, study in the
“library”, read books at the “bookstore”, and refuel her car at the “gas station”,
etc. Another person, e.g., a college student, might study in a “coffee shop”, take a
nap in the “library”, drink coffee at the “bookstore”, and buy groceries at the “gas
station”, etc.
Data
Testing Method
Subject: categorize places into residential, services (institution and daily necessities),
leisure (physical and cultural), and socializing/entertainment
H6. Certain places are considered private, others public.
Comments
Why interesting
Testing Method:
Subjects: Rate privacy in the scale -2, -1, 0, 1, 2
Investigator: Draw 2 dimensional graph for all the places: privacy rating and different
types.
References
Canter, D. 1977. The psychology of place. New York, N.Y.: St. Martin's Press.
Kramer, B. 1995. Classification of generic places: explorations with implications for evaluation.
Journal of Environmental Psychology 15 (1):3-22.
Sime, J. D. "Creating places or designing spaces?" Journal of Environmental Psychology 6
(1986): 49-63.
Mike Hazas, James Scott and John Krumm, "Location-Aware Computing Comes of Age", IEEE
Computer Magazine, vol. 37, no. 2, February 2004.
Fredrik Espinoza, Per Persson, Anna Sandin, Hanna Nyström, Elenor Cacciatore, Markus
Bylund: GeoNotes: Social and Navigational Aspects of Location-Based Information Systems.
Ubicomp 2001: 2-17
Jeffrey Hightower, "From Position to Place," in Proceedings of The 2003 Workshop on LocationAware Computing, Oct. 2003.
Paulos, E. and Goodman, E. “The Familiar Stranger: Anxiety, Comfort, and Play in Public
Places.” ( Conference on Human Factors in Computing Systems, Vienna, Austria ) 04.04
Thomas Vögele, Christoph Schlieder, “Information Retrieval and Reasoning about Spatial
Relevance with Place Name Structures”, Workshop on Geo-Ontology, 16th & 17th September
2002 Ilkley, West Yorkshire, UK
M. Weaver, L. Delcambre, L. Shapiro, J. Brewster, A. Gutema and T. Tolle ,“A Digital
GeoLibrary: Integrating Keywords And Place Names,”, Proc. of the 7th European Conf. on
Research and Advanced Technology for Digital Libraries (ECDL 2003), Trondheim,
Norway, August 2003, Lecture Notes in Computer Science, Number 2769, SpringerVerlag, Berlin, 2003.
L. Liao, D. Fox, and H. Kautz. Learning and Inferring Transportation Routines. Proc.
of the National Conference on Artificial Intelligence (AAAI-04)
D. Ashbrook and T. Starner. Using gps to learn significant locations and predict movement across
multiple users. Personal Ubiquitous Computing, (7):275–286, 2003.
Natalia Marmasse , Chris Schmandt, Location-Aware Information Delivery with ComMotion,
Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing, p.157171, September 25-27, 2000, Bristol, UK