CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
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id DDSS2006-HB-325
authors Jean Oh, Jie-Eun Hwang, Stephen F. Smith, and Kimberle Koile
year 2006
title Learning from Main Streets - A machine learning approach identifying neighborhood commercial districts
source Van Leeuwen, J.P. and H.J.P. Timmermans (eds.) 2006, Innovations in Design & Decision Support Systems in Architecture and Urban Planning, Dordrecht: Springer, ISBN-10: 1-4020-5059-3, ISBN-13: 978-1-4020-5059-6, p. 325-340
summary In this paper we explore possibilities for using Artificial Intelligence techniques to boost the performance of urban design tools by providing large scale data analysis and inference capability. As a proof of concept experiment we showcase a novel application that learns to identify a certain type of urban setting, Main Streets, based on architectural and socioeconomic features of its vicinity. Our preliminary experimental results show the promising potential for the use of machine learning in the solving of urban planning problems.
keywords Main street approach, Community development, Artificial intelligence, Machine learning, Active learning algorithm
series DDSS
full text file.pdf (406,415 bytes)
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