TY - JOUR
T1 - Community design of a light rail transit-oriented development using casewise visual evaluation (CAVE)
AU - Bailey, Keiron
AU - Grossardt, Ted
AU - Pride-Wells, Michaele
N1 - Funding Information:
This project was funded by Transportation Research Board Transit-IDEA Grant T-33. The research team would like to thank our partners at the Transit Authority of River City for their bold decision to take this new method to the citizens of local neighborhoods, and to their partners in this project. We also thank the architectural students who participated in the Urban Design Studio during the Spring 2002 semester for their assistance. This manuscript has been benefited from the incisive and helpful comments of Professor Carl Steinitz of the Department of Landscape Architecture, Graduate School of Design, Harvard University; from the detailed commentary provided by two anonymous expert reviewers; and from the attention of Dr. Barnett R. Parker, Editor-in-Chief, whose suggestions helped us clarify the focus of the paper, and improve its flow.
PY - 2007/9
Y1 - 2007/9
N2 - This paper proposes the casewise visual evaluation or CAVE, methodology and discusses its application to the participatory design of a transit-oriented development (TOD) in Louisville, Kentucky. CAVE is a fuzzy logic-based non-linear visual preference modeling system designed to provide design element guidance from composite visual scenarios under conditions of sparse data. The context of application in a low-income urban neighborhood is detailed. An architectural expert's design vocabulary allows model input and output to be structured. A small set of image samples was scored for preference using anonymous electronic polling in distributed neighborhood forums. Using fuzzy set theoretic software a community preference knowledge base (PKB) was built and interrogated. Four critical TOD design dimensions were selected: height, typology, density, and open space type. Preferred TOD design combinations were identified using the PKB and discussed. This project shows that CAVE can provide context-specific guidance for urban designers and that its strengths in effectively devolving design input and capturing local preferences are recognized by the community. The paper highlights the necessity for advanced geovisual analytic methods to be embedded into a structured public involvement (SPI) process.
AB - This paper proposes the casewise visual evaluation or CAVE, methodology and discusses its application to the participatory design of a transit-oriented development (TOD) in Louisville, Kentucky. CAVE is a fuzzy logic-based non-linear visual preference modeling system designed to provide design element guidance from composite visual scenarios under conditions of sparse data. The context of application in a low-income urban neighborhood is detailed. An architectural expert's design vocabulary allows model input and output to be structured. A small set of image samples was scored for preference using anonymous electronic polling in distributed neighborhood forums. Using fuzzy set theoretic software a community preference knowledge base (PKB) was built and interrogated. Four critical TOD design dimensions were selected: height, typology, density, and open space type. Preferred TOD design combinations were identified using the PKB and discussed. This project shows that CAVE can provide context-specific guidance for urban designers and that its strengths in effectively devolving design input and capturing local preferences are recognized by the community. The paper highlights the necessity for advanced geovisual analytic methods to be embedded into a structured public involvement (SPI) process.
KW - Casewise visual evaluation
KW - Design vocabulary
KW - Fuzzy set
KW - Structured public involvement
KW - Transit-oriented development
KW - Visual preference
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U2 - 10.1016/j.seps.2006.04.002
DO - 10.1016/j.seps.2006.04.002
M3 - Article
AN - SCOPUS:34249019595
SN - 0038-0121
VL - 41
SP - 235
EP - 254
JO - Socio-Economic Planning Sciences
JF - Socio-Economic Planning Sciences
IS - 3
ER -