TY - GEN
T1 - TopoGroups
T2 - 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
AU - Zhang, Jiawei
AU - Malik, Abish
AU - Ahlbrand, Benjamin
AU - Elmqvist, Niklas
AU - Maciejewski, Ross
AU - Ebert, David S.
N1 - Funding Information:
This work was funded by the U.S. Department of Homeland Security VACCINE Center under Award Number 2009-ST-061-CI0003.
Publisher Copyright:
© 2017 ACM.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - Spatial datasets, such as tweets in a geographic area, often exhibit different distribution patterns at multiple levels of scale, such as live updates about events occurring in very specific locations on the map. Navigating in such multi-scale data-rich spaces is often inefficient, requires users to choose between overview or detail information, and does not support identifying spatial patterns at varying scales. In this paper, we propose TopoGroups, a novel context-preserving technique that aggregates spatial data into hierarchical clusters to improve exploration and navigation at multiple spatial scales. The technique uses a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates. Our user study explores multiple visual encoding strategies for To-poGroups including color, transparency, shading, and shapes in order to convey the hierarchical and statistical information of the geographical aggregates at different scales.
AB - Spatial datasets, such as tweets in a geographic area, often exhibit different distribution patterns at multiple levels of scale, such as live updates about events occurring in very specific locations on the map. Navigating in such multi-scale data-rich spaces is often inefficient, requires users to choose between overview or detail information, and does not support identifying spatial patterns at varying scales. In this paper, we propose TopoGroups, a novel context-preserving technique that aggregates spatial data into hierarchical clusters to improve exploration and navigation at multiple spatial scales. The technique uses a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates. Our user study explores multiple visual encoding strategies for To-poGroups including color, transparency, shading, and shapes in order to convey the hierarchical and statistical information of the geographical aggregates at different scales.
KW - Context preservation
KW - Geospatial visualization
KW - Multi-scale analysis
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85044853365&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044853365&partnerID=8YFLogxK
U2 - 10.1145/3025453.3025801
DO - 10.1145/3025453.3025801
M3 - Conference contribution
AN - SCOPUS:85044853365
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 2940
EP - 2951
BT - CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 6 May 2017 through 11 May 2017
ER -