Spatial Data Mining, Spatial Data Warehousing, and Spatial OLAP

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Author(s): Amira M. Idrees (Fayoum University, Egypt), Mostafa Lamlom Ahmed Khaled (Institute of Statistical Studies and Research, Egypt)and Amal Hassan Ali Talkhan (Institute of Statistical Studies and Research, Egypt)
Copyright: 2018
Pages: 36
Source title: Emerging Trends in Open Source Geographic Information Systems
Source Author(s)/Editor(s): Naveenchandra N. Srivastava (Bhaskaracharya Institute for Space Applications and Geo-Informatics, India)
DOI: 10.4018/978-1-5225-5039-6.ch005

Keywords: Computer Engineering / Engineering Science Reference / Geographic Information Systems / Science & Engineering

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Abstract

Data mining is one of the current vital fields for all types of data including spatial data. An example of useful extracted patterns from spatial data is to describe changes in metropolitan poverty rates based on city distances from major highways. Geospatial is a term that has recently been gaining in popularity; moreover, many applications on geospatial have different uses in different fields such as geo-tagging, geospatial technology, and geo-fencing. Analyzing spatial data is considered a complex task due to its details as it is related to locations with a special representation such as longitude and latitude. Other attributes are involved in the description of objects which can be analyzed using different data mining techniques. In this chapter, a demonstration of the basic information is performed considering spatial data and spatial data mining including all aspects such as the different type of data, different methods of analysis, different mining techniques, and other related topics.

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