Urban data and urban design: A data mining approach to architecture education
Urban data and urban design: A data mining approach to architecture education
复制标题
DOI：
10.1016/j.tele.2017.09.015
复制
发表时间：
2018-07
期刊：
影响因子：
--
通讯作者：
F. Valls;E. Redondo;D. Fonseca;R. T. Kompen;Sergi Villagrasa;N. Martí
中科院分区：
文献类型：
--
作者：
F. Valls;E. Redondo;D. Fonseca;R. T. Kompen;Sergi Villagrasa;N. Martí
使用信息和通信技术的城市项目配置是未来建筑师教育的一个重要方面。学生必须了解有助于他们的学术和专业发展的技术，以及预测市民的需求和他们设计的要求。在本文中，采用基于项目的学习策略，使用数据挖掘方法来概述建筑课程中城市设计项目的战略要求。与获奖公共空间（英国伦敦吉列广场）相关的非正式数据来自两个社交网络（Flickr和Twitter）及其官方网站。分析的重点是语义、时间和空间模式，这些方面在传统方法中通常被忽视。文本挖掘技术用于关联语义和时间数据，重点关注季节和每周（工作-休闲）周期，并通过地理标记图片和地理编码用户位置提取地理模式。结果表明，为了确定城市空间的不同用途和建筑要求，可以获取和提取有价值的数据和信息，但这些数据和信息的检索、结构、分析和可视化可能具有挑战性。本文的主要目标是概述一种策略，并以一种对学生和专业人士（即使没有技术背景）都具有吸引力和信息丰富的方式呈现结果的可视化，因此所进行的分析可以在其他城市数据环境中重现。
The configuration of urban projects using Information and Communication Technologies is an essential aspect in the education of future architects. Students must know the technologies that will facilitate their academic and professional development, as well as anticipating the needs of the citizens and the requirements of their designs. In this paper, a data mining approach was used to outline the strategic requirements for an urban design project in an architecture course using a Project-Based Learning strategy. Informal data related to an award-winning public space (Gillett Square in London, UK) was retrieved from two social networks (Flickr and Twitter), and from its official website. The analysis focused on semantic, temporal and spatial patterns, aspects generally overlooked in traditional approaches. Text-mining techniques were used to relate semantic and temporal data, focusing on seasonal and weekly (work-leisure) cycles, and the geographic patterns were extracted both from geotagged pictures and by geocoding user locations. The results showed that it is possible to obtain and extract valuable data and information in order to determine the different uses and architectural requirements of an urban space, but such data and information can be challenging to retrieve, structure, analyze and visualize. The main goal of the paper is to outline a strategy and present a visualization of the results, in a way designed to be attractive and informative for both students and professionals – even without a technical background – so the conducted analysis may be reproducible in other urban data contexts.