Computer based pedestrian landscape design using decision tree templates

Raman, Baranidharan; Naderi, Jody Rosenblatt

doi:10.1016/j.aei.2005.08.002

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Disaggregating the landscape into component units has been an important method for landscape studies (Ergin et al, 2004;Li and Will, 2005;McHarg, 1969;Raman and Naderi, 2006). In this study we disaggregated the sidewalk environments into sidewalk trees, type of surface material, surface condition, type of land use, shrubs, signage, sidewalk width, and percentage of green area and sky area in the photograph.…”

Section: The Field Surveymentioning

confidence: 99%

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Linking People's Perceptions and Physical Components of Sidewalk Environments—An Application of Rough Sets Theory

Wang

Namgung

2010

Environ Plann B Plann Des

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This paper aims to develop a new approach to investigating the relationships between people's perceptions and physical components of sidewalk environments. A psychological survey composed of semantic differential items was administered to 112 participants in order to assess their perceptions of 20 sidewalk environments in Iksan city, South Korea. A field survey of the selected sidewalks was conducted to survey the physical components of the sidewalk environments. Because conventional statistical methods are not appropriate owing to the qualitative data, small sample size, and uncertainty, a new approach based on an artificial intelligence technique—rough sets theory—is applied to deal with the collected data. The application of the rough sets theory outputs the most important attributes of people's perceptions, minimal attribute sets without redundancy, and a series of decision rules that represent the relationships between perceptions and physical components of sidewalk environments. The analytical approach helps to understand better people's perceptions to sidewalk environments in a small city and then to establish a useful and constructive ground of discussion for walking environment design and management.

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Section: The Field Surveymentioning

confidence: 99%

“…Mougiakakou et al (2005) applied neural network technology for the classification of landscape images. Naderi and Raman (2005) [and Raman and Naderi (2006)] used decision tree learning to design pedestrian landscapes.…”

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confidence: 99%