Modeling Spatial Dependencies for Mining Geospatial Data: A Statistical Approach
2000-01-10
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Modeling Spatial Dependencies for Mining Geospatial Data: A Statistical Approach
Authors
Published Date
2000-01-10
Publisher
Type
Report
Abstract
Geo-spatial data mining is a process to discover interesting and potentially useful spatial patterns embedded in spatial databases. Efficient tools for extracting information from geo-spatial data sets can be of importance to organizations which own, generate and manage large geo-spatial data sets. The current approach towards solving spatial data mining problems is to use classical data mining tools after "materializing" spatial relationships and assuming independence between different data points. However, classical data mining methods often perform poorly on spatial data sets which have high spatial auto-correlation. This approach often leads to poor results because it does not take into account the fundamental notion of spatial autocorrelation. In this paper we will overview statistical techniques which can effectively model the notion of spatial-autocorrelation. We will also present a "roadmap" for extending classical data mining techniques to manage geo-spatial data which will the serve as basis for future research.
Keywords
Description
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Chawla, Sanjay; Shekhar, Shashi; Wu Li, Wei. (2000). Modeling Spatial Dependencies for Mining Geospatial Data: A Statistical Approach. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215408.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.