Supporting Web-Based Visual Exploration of Large-Scale Raster Geospatial Data Using Binned Min-Max Quadtree

Traditionally environmental scientists are limited to simple display and animation of large-scale raster geospatial data derived from remote sensing instrumentation and model simulation outputs. Identifying regions that satisfy certain range criteria, e.g., temperature between [t1,t2) and precipitation between [p1,p2), plays an important role in query-driven visualization and visual exploration in general. In this study, we have proposed a Binned Min-Max Quadtree (BMMQ-Tree) to index large-scale numeric raster geospatial data and efficiently process queries on identifying regions of interests by taking advantages of the approximate nature of visualization related queries. We have also developed an end-to-end system that allows users visually and interactively explore large-scale raster geospatial data in a Web-based environment by integrating our query processing backend and a commercial Web-based Geographical Information System (Web-GIS). Experiments using real global environmental data have demonstrated the efficiency of the proposed BMMQ-Tree. Both experiences and lessons learnt from the development of the prototype system and experiments on the real dataset are reported.

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