Indexing Structure For Handling Uncertain Spatial Data
Bir Bhanu, Rui Li, Chinya Ravishankar, Michael Kurth and Jinfeng Ni
Center for Research in Intelligent Systems
University of California, Riverside, 92521
bhanu, email@example.com ; ravi, kurthm, firstname.lastname@example.org
Consideration of uncertainty in manipulation and management of spatial data is important. Unlike traditional fuzzy approaches, in this paper we use a probability-based method to model and index uncertain data in the application of Mojave desert endangered species protection. The query is a feature vector describing the habitat for certain species, and we are interested in finding geographic locations suitable for that species. We select appropriate layers of the geo-spatial data affecting species life, called habitat features, and model the uncertainty for each feature as a probability density function (PDF). We partition the geographic area into grids, assign an uncertain feature vector to each cell, and develop a filter-and-refine indexing method. The filter part is a bottom-up binary tree based on the automated clustering result obtained using the EM algorithm. The refine part processes the filtered results based on the “ similarity” between the query and properties of each cell. We compare the performance of our proposed indexing structure with the intersection method from Mojave Desert Ecosystem Program (MDEP); our method is more selective and efficient.
Keywords: geographic information system, uncertainty, probability density function, optimizedGaussian mixture hierarchy, mixture model, R-tree, Mojave desert, desert tortoise
In: McRoberts, R. et al. (eds). Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.