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Knowledge Extraction from Geographical Databases for Land Use Data Production

Knowledge Extraction from Geographical Databases for Land Use Data Production

Hana Alouaoui, Sami Yassine Turki, Sami Faiz
Copyright: © 2017 |Pages: 23
ISBN13: 9781522509370|ISBN10: 1522509372|EISBN13: 9781522509387
DOI: 10.4018/978-1-5225-0937-0.ch012
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MLA

Alouaoui, Hana, et al. "Knowledge Extraction from Geographical Databases for Land Use Data Production." Handbook of Research on Geographic Information Systems Applications and Advancements, edited by Sami Faiz and Khaoula Mahmoudi, IGI Global, 2017, pp. 321-343. https://doi.org/10.4018/978-1-5225-0937-0.ch012

APA

Alouaoui, H., Turki, S. Y., & Faiz, S. (2017). Knowledge Extraction from Geographical Databases for Land Use Data Production. In S. Faiz & K. Mahmoudi (Eds.), Handbook of Research on Geographic Information Systems Applications and Advancements (pp. 321-343). IGI Global. https://doi.org/10.4018/978-1-5225-0937-0.ch012

Chicago

Alouaoui, Hana, Sami Yassine Turki, and Sami Faiz. "Knowledge Extraction from Geographical Databases for Land Use Data Production." In Handbook of Research on Geographic Information Systems Applications and Advancements, edited by Sami Faiz and Khaoula Mahmoudi, 321-343. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0937-0.ch012

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Abstract

Our study focuses on the task of land use evolution in urban environment which is fundamental in revealing the territorial planning. It refers crucially to the use of spatial data mining tools due to their high potential in handling with spatial data characteristics. The results of our knowledge discovery process are spatial and spatiotemporal association rules referring to the land use and its evolution. Three proposals based on different knowledge extraction techniques are detailed. The first approach aims to extract spatiotemporal association rules by introducing time into the attributes. The second approach forecasts the extracted rules at different dates. The third approach is devoted to the mining of spatiotemporal association rules. This proposal looks for rules that relate properties of reference objects with properties of other spatial relevant objects. The extracted patterns are relationships involving the spatial objects during time periods. To prove the applicability of each approach, experimentations are conducted on real world data. The obtained results are promising.

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