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.