Cell-based
positioning for improving LBS Markus
Ray
Figure 3: Example for visualizing
prominent places in GoogleEarth. Yellow
rectangles indicate the composition
of cell-based positions for prominent
places.
Step-by-step, for each hour of day, the visiting
frequencies of the prominent places are compared
and the prominent place with the highest representation
is selected for the sequence. In order to
avoid toggling in the sequence at transition
times of one prominent place to another, smoothed
distributions are used for prominent place
selections.
A typical daily routine of a work day of an
Austrian employee (All volunteers are Austrian)
is manually defined as
being at home (at night/early morning),
being at work (in the morning)
being somewhere else (in the afternoon/evening)
and
being at home (in the evening/night)
From this daily routine three classes of prominent
places ‘home’, ‘work’ and ‘spare time’ are
derived. Hence, the sequence which is to be
compared to the computed sequence is {‘home’,
‘work’, ‘spare time’, ‘home’}.
For classification, the first element of the
computed sequence is taken and labeled with
the first element of the manually defined
sequence. By assuming the computed sequence
is {‘unknown 3’, ‘unknown 2’, ‘unknown 1’,
‘unknown 3’} - ‘unknown3’ is labeled to ‘home’.
The same is done with the next element (here
‘unknown2’ is labeled to ‘work’). Finally
all other elements of the computed sequence
are classified as ‘spare time’ untilthe end
is reached (Here ‘unknown 1’ is labeled as
‘spare time’). Once-classified prominent places
are not re-classified.
Experimental results
validated by a 250 000 cell-based positioning
data set obtained during a half year of permanent
observation. Eleven of twelve home locations
(92%) and nine of ten work locations (90%)
have been found and correctly classified (Two
volunteers moved their home during observation
phase). Each volunteer has validated the result
based on her provided cell-based positioning
data with respect to the correctness of found
and classified prominent places. All found
prominent places are close to the real location.
Geographical accuracy of the found places
mainly depends on the cell-network distribution
in the surrounding area and cannot be influenced
by the used method. Hence, no quantitative
validation about the localization quality
was performed.
In Figure 3 is an example for visualizing
the results in Google Earth. This visualization
was used to validate the results together
with the volunteers.
The demonstrated grouping and classification
results are promising and can be used as basis
for improved LBS.
Acknowledgements
The author gratefully acknowledges the support
of the Austrian Mobile Phone Provider “mobilkom
austria” by providing a web interface for
obtaining cell-based positioning data for
free. The author also thanks the volunteers
for providing their position information over
this long time period.
References
[1] Ray, M. and Schrom-Feiertag,
H.: Cell-based Finding and Classification
of Prominent Places of Mobile Phone Users,
In: Proceedings 4th International Symposium
on Location Based Services & TeleCartography,
November 2007
[2] Zhou C., Frankowski D., Ludford P.,
Shekhar S. and Terveen L.: Discovering Personally
Meaningful Places: An Interactive Clustering
Approach, In: ACM Transactions on Information
Systems, Vol. 25, No.3, Article 12, July
2007
[3] Hightower J., Consolvo S., LaMarca
A., Smith I. and Hughes J.: Learning and
Recognizing the Places We Go, In: Proceedings
of Ubicomp’05, 2005
[4] Ashbrook D. and Starner, T.: Using
GPS to learn significant locations and predict
movement across multiple users, In: Proceedings
of Personal and Ubiquitous Computing 7,
2003
[5] Marmasse N. and Schmandt
C.: Location-aware information delivery
with commotion, In: Proceedings Second International
Symposium on Handheld and Ubiquitous Computing
(HUC).
Volume 1927., Springer-Verlag, 2000
[6] Nurmi P. and Koolwaaij J.: Identifying
meaningful locations, Submitted to: Conference
on Mobile and Ubiquitous Systems: Networking
and Services, 2006.
[7] Laasonen K.: Clustering and prediction
of mobile user routes from cellular data,
In: Proceedings of the 9th European Conference
on Principles and Practice of Knowledge
Discovery in Databases (PKDD). Lecture Notes
in Artificial Intelligence, vol. 3721. Springer-Verlag,
2005.
Markus Ray
Human Centered Mobility
Technologies, Arsenal
research Vienna, Austria Markus.Ray@arsenal.ac.at
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