TY - JOUR
T1 - Discovering personally meaningful places
T2 - An interactive clustering approach
AU - Zhou, Changqing
AU - Frankowski, Dan
AU - Ludford, Pamela
AU - Shekhar, Shashi
AU - Terveen, Loren
PY - 2007/7/1
Y1 - 2007/7/1
N2 - The discovery of a person's meaningful places involves obtaining the physical locations and their labels for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, home, work or Northwest Health Club. It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places. Previous work has explored algorithms to discover personal places from location data. However, we know of no systematic empirical evaluations of these algorithms, leaving designers of location-aware applications in the dark about their choices. Our work remedies this situation. We extended a clustering algorithm to discover places. We also defined a set of essential evaluation metrics and an interactive evaluation framework. We then conducted a large-scale experiment that collected real users' location data and personally meaningful places, and illustrated the utility of our evaluation framework. Our results establish a baseline that future work can measure itself against. They also demonstrate that that our algorithm discovers places with reasonable accuracy and outperforms the well-known K-Means clustering algorithm for place discovery. Finally, we provide evidence that shapes more complex than points are required to represent the full range of people's everyday places.
AB - The discovery of a person's meaningful places involves obtaining the physical locations and their labels for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, home, work or Northwest Health Club. It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places. Previous work has explored algorithms to discover personal places from location data. However, we know of no systematic empirical evaluations of these algorithms, leaving designers of location-aware applications in the dark about their choices. Our work remedies this situation. We extended a clustering algorithm to discover places. We also defined a set of essential evaluation metrics and an interactive evaluation framework. We then conducted a large-scale experiment that collected real users' location data and personally meaningful places, and illustrated the utility of our evaluation framework. Our results establish a baseline that future work can measure itself against. They also demonstrate that that our algorithm discovers places with reasonable accuracy and outperforms the well-known K-Means clustering algorithm for place discovery. Finally, we provide evidence that shapes more complex than points are required to represent the full range of people's everyday places.
KW - Clustering algorithms
KW - Field studies
KW - Location-aware applications
KW - Place discovery
KW - Ubiquitous computing
UR - http://www.scopus.com/inward/record.url?scp=34547410460&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547410460&partnerID=8YFLogxK
U2 - 10.1145/1247715.1247718
DO - 10.1145/1247715.1247718
M3 - Article
AN - SCOPUS:34547410460
SN - 1046-8188
VL - 25
JO - ACM Transactions on Information Systems
JF - ACM Transactions on Information Systems
IS - 3
M1 - 1247718
ER -