Artificial Intelligence in geospatial analysis: applications of self-organizing...

• Author(s): Henriques, Roberto André Pereira
• Date: 2011
• Persistent ID: http://hdl.handle.net/10362/5723
• Origin: Repositório Institucional da UNL
• Subject(s): Geocomputation; Geovisualization; Neural Networks; Self-organizing Maps; Spatial Clustering
• Document Type: Doctoral Thesis
• Language: English
• Advisor(s): Bacao, F.; Lobo, Víctor

Description

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems. The size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...)