Special Issue "Spatial Analysis and Data Mining"

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 January 2013)

Special Issue Editor

Guest Editor
Prof. Dr. Brian Lees (Website)

School of Physical, Environmental and Mathematical Sciences, UNSW Canberra, PO Box 7916, Canberra, BC 2610, Australia
Fax: +61 2 6268 8017
Interests: global change; predictive mapping of land cover and land degradation

Special Issue Information

Dear Colleagues,

Traditional spatial analyses grew up in an era of sparse data and very weak computational power. Today, both of those circumstances are reversed and many of the old solutions are no longer suitable. The title of this Special Issue, "Spatial Analysis and Data Mining", reflects this change and combines two things which, until recently, engaged quite different groups of researchers and practitioners. Together, they require particular techniques and a sophisticated understanding of the special problems associated with spatial data. This geographic data mining, or Geographic Knowledge Discovery (GKD), is not new, but is developing and changing rapidly as both more, and different, data becomes available, and people see new applications. The days of ‘Big Data’ require fresh thinking.

The aim of geographic data mining (GKD) is to assist in the generation of hypotheses, which can be tested, about interesting or anomalous spatial patterns which may be discovered in very large databases. It is important that the patterns discovered should not be statistical or sampling artifacts, and should be nontrivial and useful. The intent is not to build a system that makes decisions or interpretations automatically, but supports humans in these tasks. Also GKD is not synonymous with statistical analyses, such tools have a role in the testing of hypotheses generated by GKD but not in GKD itself.

We seek original and innovative papers which address this fusion of “Spatial Analysis and Data Mining” and present research which advances theory, demonstrates application and evaluates the approach taken.

Professor Brian Lees
Guest Editor

Keywords

  • geographic data mining
  • geographic knowledge discovery
  • spatio-temporal data mining
  • spatial analysis
  • knowledge discovery

Published Papers (2 papers)

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Research

Open AccessArticle Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover
ISPRS Int. J. Geo-Inf. 2013, 2(2), 531-552; doi:10.3390/ijgi2020531
Received: 3 April 2013 / Revised: 29 May 2013 / Accepted: 29 May 2013 / Published: 7 June 2013
Cited by 2 | PDF Full-text (686 KB) | HTML Full-text | XML Full-text
Abstract
Associative methods for content-based image ranking by semantics are attractive due to the similarity of generated models to human models of understanding. Although they tend to return results that are better understood by image analysts, the induction of these models is difficult [...] Read more.
Associative methods for content-based image ranking by semantics are attractive due to the similarity of generated models to human models of understanding. Although they tend to return results that are better understood by image analysts, the induction of these models is difficult to build due to factors that affect training complexity, such as coexistence of visual patterns in same images, over-fitting or under-fitting and semantic representation differences among image analysts. This article proposes a methodology to reduce the complexity of ranking satellite images for associative methods. Our approach employs genetic operations to provide faster and more accurate models for ranking by semantic using low level features. The added accuracy is provided by a reduction in the likelihood to reach local minima or to overfit. The experiments show that, using genetic optimization, associative methods perform better or at similar levels as state-of-the-art ensemble methods for ranking. The mean average precision (MAP) of ranking by semantic was improved by 14% over similar associative methods that use other optimization techniques while maintaining smaller size for each semantic model. Full article
(This article belongs to the Special Issue Spatial Analysis and Data Mining)
Open AccessArticle A New Algorithm for Identifying Possible Epidemic Sources with Application to the German Escherichia coli Outbreak
ISPRS Int. J. Geo-Inf. 2013, 2(1), 155-200; doi:10.3390/ijgi2010155
Received: 21 December 2012 / Revised: 19 February 2013 / Accepted: 19 February 2013 / Published: 11 March 2013
Cited by 5 | PDF Full-text (3069 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we describe a recently developed algorithm called Topological Weighted Centroid (TWC). TWC takes locations of an event of interest and analyzes the possible associated dynamics using the ideas of free energy and entropy. This novel mathematical tool has been [...] Read more.
In this paper we describe a recently developed algorithm called Topological Weighted Centroid (TWC). TWC takes locations of an event of interest and analyzes the possible associated dynamics using the ideas of free energy and entropy. This novel mathematical tool has been applied to a real world example, the epidemic outbreak caused by Escherichia coli that occurred in Germany in 2011, to point out the real source of the outbreak. Other four examples of application to other epidemic spreads are described: Chikungunya fever of 2007 in Italy; Foot and mouth disease of 1967 in England; Cholera of 1854 in London; and the Russian influenza of 1889–1890 in Sweden. Comparisons have been made with other already published algorithms: Rossmo Algorithm, NES, LVM, Mexican Prob. The TWC results are significantly superior in comparison with other algorithms according to four independent indexes: distance from the peak, sensitivity, specificity and searching area. They are consistent with the idea that the spread of infectious disease is not random but follows a progression based on inherent, but as yet undiscovered, mathematical laws. The TWC method could provide an additional powerful tool for the investigation of the early stages of an epidemic and novel simulation methods for understanding the process through which a disease is spread. Full article
(This article belongs to the Special Issue Spatial Analysis and Data Mining)

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