Abstract
The increasing availability of motion sensors and video cameras in living spaces has made possible the analysis of motion patterns and collective behavior in a number of situations. The visualization of this movement data, however, remains a challenge. Although maintaining the actual layout of the data space is often desirable, direct visualization of movement traces becomes cluttered and confusing as the spatial distribution of traces may be disparate and uneven. We present proximity-based visualization as a novel approach to the visualization of movement traces in an abstract space rather than the given spatial layout. This abstract space is obtained by considering proximity data, which is computed as the distance between entities and some number of important locations. These important locations can range from a single fixed point, to a moving point, several points, or even the proximities between the entities themselves. This creates a continuum of proximity spaces, ranging from the fixed absolute reference frame to completely relative reference frames. By combining these abstracted views with the concrete spatial views, we provide a way to mentally map the abstract spaces back to the real space. We demonstrate the effectiveness of this approach, and its applicability to visual analytics problems such as hazard prevention, migration patterns, and behavioral studies.
| Original language | English (US) |
|---|---|
| Title of host publication | VAST 09 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings |
| Pages | 11-18 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2009 |
| Event | VAST 09 - IEEE Symposium on Visual Analytics Science and Technology - Atlantic City, NJ, United States Duration: Oct 12 2009 → Oct 13 2009 |
Other
| Other | VAST 09 - IEEE Symposium on Visual Analytics Science and Technology |
|---|---|
| Country | United States |
| City | Atlantic City, NJ |
| Period | 10/12/09 → 10/13/09 |
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Keywords
- Linked views
- Movement patterns
- Principal component analysis
- Proximity
- Spatio-temporal visualization
- Temporal trajectories
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Information Systems
Cite this
Proximity-based visualization of movement trace data. / Crnovrsanin, Tarik; Muelder, Chris; Correa, Carlos; Ma, Kwan-Liu.
VAST 09 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings. 2009. p. 11-18 5332593.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Proximity-based visualization of movement trace data
AU - Crnovrsanin, Tarik
AU - Muelder, Chris
AU - Correa, Carlos
AU - Ma, Kwan-Liu
PY - 2009/12/1
Y1 - 2009/12/1
N2 - The increasing availability of motion sensors and video cameras in living spaces has made possible the analysis of motion patterns and collective behavior in a number of situations. The visualization of this movement data, however, remains a challenge. Although maintaining the actual layout of the data space is often desirable, direct visualization of movement traces becomes cluttered and confusing as the spatial distribution of traces may be disparate and uneven. We present proximity-based visualization as a novel approach to the visualization of movement traces in an abstract space rather than the given spatial layout. This abstract space is obtained by considering proximity data, which is computed as the distance between entities and some number of important locations. These important locations can range from a single fixed point, to a moving point, several points, or even the proximities between the entities themselves. This creates a continuum of proximity spaces, ranging from the fixed absolute reference frame to completely relative reference frames. By combining these abstracted views with the concrete spatial views, we provide a way to mentally map the abstract spaces back to the real space. We demonstrate the effectiveness of this approach, and its applicability to visual analytics problems such as hazard prevention, migration patterns, and behavioral studies.
AB - The increasing availability of motion sensors and video cameras in living spaces has made possible the analysis of motion patterns and collective behavior in a number of situations. The visualization of this movement data, however, remains a challenge. Although maintaining the actual layout of the data space is often desirable, direct visualization of movement traces becomes cluttered and confusing as the spatial distribution of traces may be disparate and uneven. We present proximity-based visualization as a novel approach to the visualization of movement traces in an abstract space rather than the given spatial layout. This abstract space is obtained by considering proximity data, which is computed as the distance between entities and some number of important locations. These important locations can range from a single fixed point, to a moving point, several points, or even the proximities between the entities themselves. This creates a continuum of proximity spaces, ranging from the fixed absolute reference frame to completely relative reference frames. By combining these abstracted views with the concrete spatial views, we provide a way to mentally map the abstract spaces back to the real space. We demonstrate the effectiveness of this approach, and its applicability to visual analytics problems such as hazard prevention, migration patterns, and behavioral studies.
KW - Linked views
KW - Movement patterns
KW - Principal component analysis
KW - Proximity
KW - Spatio-temporal visualization
KW - Temporal trajectories
UR - http://www.scopus.com/inward/record.url?scp=72849115846&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=72849115846&partnerID=8YFLogxK
U2 - 10.1109/VAST.2009.5332593
DO - 10.1109/VAST.2009.5332593
M3 - Conference contribution
SN - 9781424452835
SP - 11
EP - 18
BT - VAST 09 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings
ER -