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Artificial Intelligence in Geospatial Analysis: Applications of Self-Organizing Maps in the Context of Geographic Information Science

机译：人工智能在地理空间分析中的应用：自组织地图在地理信息科学领域的应用

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摘要

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. (...).
机译：可用的地理空间存储库的大小和维数每天都在增加，这给现有的分析工具带来了额外的压力，因为它们有望从这些数据库中提取更多的知识。这些工具大多数是在数据贫乏的环境中创建的，因此很少解决效率，维度和自动探索的问题。另外，传统的统计技术提出了一些在地理空间数据域中不现实的假设。这方面的一个例子是大多数经典统计方法所需的观测值之间的统计独立性，这与地理空间数据中存在的众所周知的空间依赖性相冲突。人工智能和数据挖掘方法构成了从地理空间数据中探索和提取知识的替代方法，它较少依赖假设。本文研究了现有通用数据挖掘工具对地理空间数据分析的可能适应性。地理空间数据集的特性似乎在许多方面与其他空间数据集相似，在这些空间数据集上已经使用多种数据挖掘工具成功检测了模式和关系。但是，似乎具有GIS意识的分析和目标所需要的不仅仅是这些通用工具所提供的结果，而且还需要进行调整才能满足地理信息科学家的要求。因此，我们提出了一种基于众所周知的数据挖掘方法，自组织地图（SOM）的地理空间应用程序，并分析了满足这些目标和需求的每种应用程序所需的适应性。本文涵盖了GIS科学的三个主要领域：制图表达；地理学；地理学。空间聚类和知识发现；和位置优化。 （...）。

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  • 作者

    Henriques, Roberto André Pereira.;

  • 作者单位

    Universidade NOVA de Lisboa (Portugal).;

  • 授予单位 Universidade NOVA de Lisboa (Portugal).;
  • 学科 Geographic information science and geodesy.;Artificial intelligence.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 452 p.
  • 总页数 452
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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