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International Conference on Scientific and Statistical Database Management

SSDBM 2010: Scientific and Statistical Database Management pp 379–396Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6187))

Abstract

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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Author information

Authors and Affiliations

  1. Department of Computer Science, The City College of the City University of New York, 138th Convent Avenue, New York, NY 10031

    Jianting Zhang

  2. Department of Computer Science, The Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY 10006

    Jianting Zhang & Simin You

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  1. Jianting Zhang
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  2. Simin You
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Editors and Affiliations

  1. Institute of Computer Science, University of Heidelberg, 69120, Heidelberg, Germany

    Michael Gertz

  2. Dept. of Computer Science and Genome Center, University of California, One Shields Avenue, 95616, Davis, CA, USA

    Bertram Ludäscher

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Zhang, J., You, S. (2010). Supporting Web-Based Visual Exploration of Large-Scale Raster Geospatial Data Using Binned Min-Max Quadtree. In: Gertz, M., Ludäscher, B. (eds) Scientific and Statistical Database Management. SSDBM 2010. Lecture Notes in Computer Science, vol 6187. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13818-8_27

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  • DOI: https://doi.org/10.1007/978-3-642-13818-8_27

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  • Online ISBN: 978-3-642-13818-8

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