2012
DOI: 10.1007/978-3-642-29063-3_18
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Abstract: International audienceUrban fabric characterization is very useful in urban design, planning, modeling and simulation. It is traditionally considered as a descriptive task mainly based on visual inspection of urban plans. Cartographic databases and geographic information systems (GIS) capabilities make possible the analytical formalization of this issue. This paper proposes a renewed approach to characterize urban fabrics using buildings' footprints data. This characterization method handles both architectural… Show more

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Cited by 23 publications
(21 citation statements)
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“…From the analysis of the state-of-the-art on the application of the clustering and knowledge model by means of the SOM methodology and corroborated by our own experience, it can be concluded that the SOM methodology is useful to carry out an exploratory analysis [98] to make the descriptive classifications more powerful, robust, and more complete [102], and to help understand the patterns of spatial distribution [88], facilitating explorations and visual evaluations [88,100], effectively analyzing complex geographic and demographic data sets. It also allows inferring spatial considerations from the taxonometric groups found [88], coding classifications in a GIS to approximate them to a wider audience not familiar with AI [24], overcoming the traditional challenges associated with studies of the complexity of environmental communities and showing their value by integrating SOM and GIS [91].…”
Section: Discussionmentioning
confidence: 67%
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“…From the analysis of the state-of-the-art on the application of the clustering and knowledge model by means of the SOM methodology and corroborated by our own experience, it can be concluded that the SOM methodology is useful to carry out an exploratory analysis [98] to make the descriptive classifications more powerful, robust, and more complete [102], and to help understand the patterns of spatial distribution [88], facilitating explorations and visual evaluations [88,100], effectively analyzing complex geographic and demographic data sets. It also allows inferring spatial considerations from the taxonometric groups found [88], coding classifications in a GIS to approximate them to a wider audience not familiar with AI [24], overcoming the traditional challenges associated with studies of the complexity of environmental communities and showing their value by integrating SOM and GIS [91].…”
Section: Discussionmentioning
confidence: 67%
“…As indicated, the state of the art shows that SOM is very often used as a methodology for reduction and classification [102] and also for entity labeling [103]. Compared to other dimensional reduction methods such as PCA (principal component analysis) or MDS (multidimensional scaling), the ability of SOM to preserve the topology of the data results in more efficient use of the available space in the map representation, with the consequence of greater distortion in relative distances [104].…”
Section: Models Of Knowledge Discovery and Clustering Through Non-supmentioning
confidence: 99%
“…(3) they show much more powerful representations than methods using classical linear analysis [48]; (4) they cluster in a more robust and complete way than traditional descriptive methodologies [72], specifically than K-means [50]; (5) they allow an effective visual exploration and validation of the results [73]; and (6) facilitate a powerful visualization easy to interpret [47], maintaining the topological relationships of the data [48]. The SOM can be used with both quantitative and qualitative variables [68,74,75] and results similar to those achieved with a panel of experts [76] can be obtained, thus allowing for improved results and savings in operating costs in the process.…”
Section: Introduction To Artificial Intelligence Methods Used In Resementioning
confidence: 99%
“…Thomas et al (2010) utilizan la dimensión fractal y las curvas de comportamiento escalar para caracterizar las áreas urbanas a partir de la formación de clústeres kmedoid, demostrando que paquetes de ciudad con formas de crecimiento similares se agrupan conjuntamente. Hamaina et al (2012) realizan una caracterización de la forma urbana y del espacio libre usando como único dato la huella de las edificaciones, para un análisis de clústeres a partir de self-organized maps. Gil et al (2012) utilizan el algoritmo k-means para definir clústeres de manzana y de segmentos de red viaria -callesa partir de variables de geometría, densidad, forma, uso del espacio público y topología de la red viaria.…”
Section: Estado De La Cuestiónunclassified