/Learning from Main Streets: A machine learning approach identifying neighborhood commercial districts

Learning from Main Streets: A machine learning approach identifying neighborhood commercial districts

Jean Hyaejin Oh, Jie-Eun Hwang, Stephen Smith and Kimberle Koile
Book Section/Chapter, Carnegie Mellon University, Innovations in Design & Decision Support Systems in Architecture and Urban Planning, pp. 325 - 340, June, 2006

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Abstract

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.

BibTeX Reference
@incollection{Oh-2006-9523,
author = {Jean Hyaejin Oh and Jie-Eun Hwang and Stephen Smith and Kimberle Koile},
title = {Learning from Main Streets: A machine learning approach identifying neighborhood commercial districts},
booktitle = {Innovations in Design & Decision Support Systems in Architecture and Urban Planning},
publisher = {Springer},
editor = {Jos. P. van Leeuwen, Harry J.P. Timmermans},
year = {2006},
month = {June},
pages = {325 - 340},
keywords = {statistical machine learning, urban design/planning},
}
2017-09-13T10:42:40+00:00