TY - GEN
T1 - A spatiotemporal database for ozone in the conterminous U.S.
AU - Li, Xin
AU - Zhang, Xingyou
AU - Piltner, Reinhard
PY - 2006
Y1 - 2006
N2 - This paper considers a set of ozone data in the conterminous U.S., which records the ozone concentration levels at a set of monitoring sites during 1994 and 1999. Existing GIS techniques are insufficient in handling such kind of spatiotemporal data in terms of data interpolation, visualization, representation and querying. We adopt 3-D shape functions from finite element methods for the spatiotemporal interpolation of the ozone dataset and analyze interpolation errors. The 3-D shape function based method estimates ozone concentration levels with less than 10 percent Mean Absolute Percentage Error. We give two approaches for visualizing the data: (i) combining the ArcGIS visualization tool with shape function interpolation results to visualize the ozone data for each year from 1994 and 1999, (ii) using Matlab to visualize the interpolated ozone data in a 3-D vertical profile display. For the spatiotemporal data representation, we use the constraint data model, because it can give an efficient and accurate representation of interpolation results. Finally, we give some practical query examples.
AB - This paper considers a set of ozone data in the conterminous U.S., which records the ozone concentration levels at a set of monitoring sites during 1994 and 1999. Existing GIS techniques are insufficient in handling such kind of spatiotemporal data in terms of data interpolation, visualization, representation and querying. We adopt 3-D shape functions from finite element methods for the spatiotemporal interpolation of the ozone dataset and analyze interpolation errors. The 3-D shape function based method estimates ozone concentration levels with less than 10 percent Mean Absolute Percentage Error. We give two approaches for visualizing the data: (i) combining the ArcGIS visualization tool with shape function interpolation results to visualize the ozone data for each year from 1994 and 1999, (ii) using Matlab to visualize the interpolated ozone data in a 3-D vertical profile display. For the spatiotemporal data representation, we use the constraint data model, because it can give an efficient and accurate representation of interpolation results. Finally, we give some practical query examples.
UR - http://www.scopus.com/inward/record.url?scp=33751401566&partnerID=8YFLogxK
U2 - 10.1109/TIME.2006.3
DO - 10.1109/TIME.2006.3
M3 - Conference article
SN - 0769526179
SN - 9780769526171
T3 - Proceedings of the International Workshop on Temporal Representation and Reasoning
SP - 168
EP - 176
BT - Proceedings - Thirteenth International Symposium on Temporal Representation and Reasoning, TIME 2006
T2 - 13th International Symposium on Temporal Representation and Reasoning, TIME 2006
Y2 - 15 June 2006 through 17 June 2006
ER -