TY - CHAP
T1 - Interpreting pedestrian behaviour by visualising and clustering movement data
AU - McArdle, Gavin
AU - Demšar, Urška
AU - Van Der Spek, Stefan
AU - McLoone, Seán
PY - 2013/4/11
Y1 - 2013/4/11
N2 - Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.
AB - Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.
KW - Clustering
KW - Geovisual Analysis
KW - Movement Data Analysis
KW - Space-time Cube
UR - http://www.scopus.com/inward/record.url?scp=84875895221&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37087-8_6
DO - 10.1007/978-3-642-37087-8_6
M3 - Chapter
AN - SCOPUS:84875895221
SN - 9783642370861
VL - 7820
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 64
EP - 81
BT - Web and Wireless Geographical Information Systems
T2 - 12th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2013
Y2 - 4 April 2013 through 5 April 2013
ER -