Urban data-mining: spatiotemporal exploration of multidimensional data
Urban data-mining: spatiotemporal exploration of multidimensional data
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DOI：
10.1080/09613210903189343
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发表时间：
2009-01-01
影响因子：
3.9
通讯作者：
Ultsch, Alfred
中科院分区：
文献类型：
--
作者：
Behnisch, Martin;Ultsch, Alfred
“城市数据挖掘”描述了一种方法论方法，用于揭示一组地理空间数据中的模式和布局的逻辑或数学和部分复杂的描述。循环方法论的程序特点是六个主要任务后，数据收集的初始步骤：数据检查，结构可视化，结构定义，结构控制，操作化，知识转换。地理可视化和空间分析补充了知识转换和交流的过程。多维挖掘方法作为一个案例研究，适用于12 430德国社区，分析1994年至2004年之间的多动态特性。特别是，紧急自组织映射（ESOM）进行聚类和分类的适当方法。它们的优点是可视化数据的结构，然后定义一些可行的聚类。对多层面数据进行时空探索，从而在动态社区行为的背景下得出具体细节、解释和抽象概念，将是决策者和规划工具实施的良好证据基础。所提出的技术预计将越来越感兴趣的管理和发展的建筑库存，以及城市和区域规划过程。
'Urban data-mining' describes a methodological approach to reveal logical or mathematical and partly complex descriptions of patterns and regularities inside a set of geospatial data. The cyclical methodology procedure is characterized by six main tasks following the initial step of data collection: data inspection, structure visualization, structure definition, structure control, operationalization, and knowledge conversion. Geovisualization and spatial analysis supplement the process of knowledge conversion and communication. The multidimensional mining approach is presented as a case study applied to 12 430 German communities to analyse multidynamic characteristics between 1994 and 2004. In particular, Emergent Self Organizing Maps (ESOM) are performed as an appropriate method for clustering and classification. Their advantage is to visualize the structure of data and later on to define a number of feasible clusters. A good evidence-base for decision-makers and the implementation of planning tools would be the spatiotemporal exploration of multidimensional data leading to specific details, explanations and abstractions in the context of dynamic community behaviour. The presented techniques are expected to be of increasing interest for the management and development of building stocks, as well as for urban and regional planning processes.