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A comprehensive geospatial knowledge...

The University of North Carolina at Charlotte., Geography and Urban Regional Analysis (PhD).

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A comprehensive geospatial knowledge discovery framework for spatial association rule mining.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A comprehensive geospatial knowledge discovery framework for spatial association rule mining./
Author:	Dao, Thi Hong Diep.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2013,
Description:	297 p.
Notes:	Source: Dissertations Abstracts International, Volume: 75-08, Section: A.
Contained By:	Dissertations Abstracts International75-08A.
Subject:	Geography. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3608812
ISBN:	9781303672514

A comprehensive geospatial knowledge discovery framework for spatial association rule mining.
Dao, Thi Hong Diep.

A comprehensive geospatial knowledge discovery framework for spatial association rule mining. - Ann Arbor : ProQuest Dissertations & Theses, 2013 - 297 p.

Source: Dissertations Abstracts International, Volume: 75-08, Section: A.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2013.

This item is not available from ProQuest Dissertations & Theses.

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space-time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining.

ISBN: 9781303672514Subjects--Topical Terms:

524010
Geography.
Subjects--Index Terms:

Big-data

A comprehensive geospatial knowledge discovery framework for spatial association rule mining.
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245 1 0 $a A comprehensive geospatial knowledge discovery framework for spatial association rule mining.
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300 $a 297 p.
500 $a Source: Dissertations Abstracts International, Volume: 75-08, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Thill, Jean-Claude.
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2013.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space-time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining.
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710 2 $a The University of North Carolina at Charlotte. $b Geography and Urban Regional Analysis (PhD). $3 3556271
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