CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.02 - February
Visualization of Spatiotemporal Behavior of Discrete Maps via Generation of Recursive Median Elements
Issue No.02 - February (2010 vol.32)
B.S. Daya Sagar , Indian Statistical Institute-Bangalore Centre, Bangalore
Spatial interpolation is one of the demanding techniques in Geographic Information Science (GISci) to generate interpolated maps in a continuous manner by using two discrete spatial and/or temporal data sets. Noise-free data (thematic layers) depicting a specific theme at varied spatial or temporal resolutions consist of connected components either in aggregated or in disaggregated forms. This short paper provides a simple framework: 1) to categorize the connected components of layered sets of two different time instants through their spatial relationships and the Hausdorff distances between the companion-connected components and 2) to generate sequential maps (interpolations) between the discrete thematic maps. Development of the median set, using Hausdorff erosion and dilation distances to interpolate between temporal frames, is demonstrated on lake geometries mapped at two different times and also on the bubonic plague epidemic spread data available for 11 consecutive years. We documented the significantly fair quality of the median sets generated for epidemic data between alternative years by visually comparing the interpolated maps with actual maps. They can be used to visualize (animate) the spatiotemporal behavior of a specific theme in a continuous sequence.
GISci, spatial interpolation, mathematical morphology, thematic maps, dilation, erosion, interpolation formulas, spatial databases and GIS, cartography, morphological image representation, visualization techniques and methodologies, geometrical problems and computations, set theory.
B.S. Daya Sagar, "Visualization of Spatiotemporal Behavior of Discrete Maps via Generation of Recursive Median Elements", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 2, pp. 378-384, February 2010, doi:10.1109/TPAMI.2009.163