An Interactive Framework for Raster Data Spatial Joins ABSTRACT

must handle large geospatial datasets stored in raster representation. Spatial joins over raster data are important queries in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of these datasets. In this paper we propose a new interactive framework that allows users to get approximate answers in near instantaneous time, thus allowing for truly interactive data exploration. Our method utilizes two proposed statistical approaches: probabilistic joins and quad-tree based incremental sampling. Our probabilistic join method provides speedups of two orders of magnitude with no correctness guarantee, while our sampling based method provides an order of magnitude improvement over the full quad-tree join and also provides running confidence intervals. We propose a framework that combines the two approaches to allow end users to trade-off speed versus bounded accuracy. The two approaches are evaluated empirically with real and synthetic datasets. 1