International Journal of Computational Intelligence Systems

Volume 7, Issue 4, August 2014, Pages 796 - 808

Evaluation on Functions of Urban Waterfront Redevelopment Based on Proportional 2-Tuple Linguistic

Authors
Ting Da, Yejun Xu
Corresponding Author
Ting Da
Available Online 1 August 2014.
DOI
https://doi.org/10.1080/18756891.2014.960293How to use a DOI?
Keywords
Function, Urban waterfront redevelopment, Assessment index system, proportional 2-tuple linguistic, group decision making
Abstract
As a planning strategy, “connectivity” has been used to promote the functions of Urban Waterfront space to revitalize the city. By analyzing the hierarchy of functions of Urban Waterfront, namely ecological function, social function and context function, an index system of assessing its functions is proposed. In order to overcome the drawbacks which are only used numerical scales or simple linguistic values in the traditional expert evaluation for the functions of Urban Waterfront, a proportional 2-tuple linguistic representation model is introduced. Some proportional 2-tuple linguistic aggregation operators are proposed, such as PTWA, PTOWA, PTHA operators. A group decision making procedure to evaluate the functions of urban waterfront by the proposed proportional 2-tuple linguistic operators is developed. A case study of two typical redevelopment projects in West Lake Waterfront of Hangzhou of China is also provided.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
7 - 4
Pages
796 - 808
Publication Date
2014/08
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2014.960293How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Ting Da
AU  - Yejun Xu
PY  - 2014
DA  - 2014/08
TI  - Evaluation on Functions of Urban Waterfront Redevelopment Based on Proportional 2-Tuple Linguistic
JO  - International Journal of Computational Intelligence Systems
SP  - 796
EP  - 808
VL  - 7
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.960293
DO  - https://doi.org/10.1080/18756891.2014.960293
ID  - Da2014
ER  -