Notice of Violation of IEEE Publication Principles: Data augmented design: Urban planning and design in the new data environment

Abstract

Notice of Violation of IEEE Publication Principles<br><br> "Data Augmented Design: Urban Planning and Design in the New Data Environment"<br> by Junyan Dong, Chuanpeng Ma, Wen Cheng, Lisen Xin<br> in the Proceedings of the 2nd Annual Conference on Big Data Analysis (ICBDA), October 2017, pp. 508-512<br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br> This paper is a duplication of the original text from the paper cited below. The original text was translated and copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br> Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br> "Data Augmented Design: Urban Planning and Design in the New Data Environment"<br> by Long Ying and Shen Yao<br> in Shanghai Urban Planning Review, 2015, pp.81-87<br><br> <br/> The new data environment composed by big data and open data has descripted urban physical and social space in a more detailed way. Currently, numerous quantitative urban studies have been conducted under new data environment. However, most studies concentrated on status quo evaluation and problem identification of urban system, and few of them have a perspective into future-oriented urban planning and design. A new planning and design methodology termed Data Augmented Design (DAD) is presented in this paper. Empowered by quantitative urban analysis, utilizing approaches such as data analyzing, modeling and forecasting, DAD provides supporting tools covering the whole planning and design process from investigation, analysis, project design, evaluation and feedbacks. Empirical data analysis in DAD improves the scientific level of planning and design, and inspires the creativity of planners and designers. This paper illustrates our knowledge and understanding of DAD from the following aspects: its definition, theory & practice, features & conceptual distinctions, frequently used approaches & tools, as well as its expected applicable situations. Case studies of DAD both in research and design are presented in the last section of the paper.