An interactive framework for spatial joins: A statistical approach to data analysis in GIS

Shayma Alkobaisi, Wan D. Bae, Petr Vojtěchovský, Sada Narayanappa

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Many Geographic Information Systems (GIS) handle a large volume of geospatial data. Spatial joins over two or more geospatial datasets are very common operations in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of datasets. In this paper, we propose an interactive framework that provides faster approximate answers of spatial joins. The proposed framework utilizes two statistical methods: probabilistic join and sampling based join. The probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while the sampling based method provides an order of magnitude improvement over the full indexing tree joins of datasets and also provides running confidence intervals. The framework allows users to trade-off speed versus bounded accuracy, hence it provides truly interactive data exploration. The two methods are evaluated empirically with real and synthetic datasets.

Original languageEnglish
Pages (from-to)329-355
Number of pages27
Issue number2
Publication statusPublished - Apr 2012


  • GIS
  • Incremental sampling
  • Interactive queries
  • Join probability
  • Probabilistic joins
  • Quad-tree
  • R-tree
  • Spatial join

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development


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