Using Hierarchical Analysis Process (AHP) in Ranking the Assessment Indices of Identity Criteria of Valuable Old Buildings, Case Study: The Old Sagharisazan Neighborhood of Rasht City

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Article Type:
Case Study (دارای رتبه معتبر)
Abstract:

The majority of new residential units in the historically valuable urban areas lack a sense of belonging to a specific place and identity. This is despite the fact that human beings always require an identity place regardless of their historical, social, or geographical position. This paper is a practical guide for architects and urban development planners for evaluating identity criteria effectively and efficiently. The goal was to achieve the most important identity criteria evaluation indicators and rank them using the AHP technique. At present, unfortunately, due to the lack of accurate knowledge of the indicators for evaluating the identity criteria of housing, so far, no technical and positive research has been done in this area; only in some studies the indicators have been collected. The present study was conducted in the old Saghrisazan neighborhood in Rasht. Data analysis was performed based on logical reasoning. First, indicators for the evaluation of criteria and initial sub-criteria were identified using relevant documents. Then a questionnaire was prepared for measuring indicators, and it was distributed among 47 residents of this old neighborhood. The collected information was analyzed using SPSS software. Then, using Expert Choice11 software, the identity indicators of valuable old houses were ranked and evaluated based on the set criteria through the Analytic Hierarchy Process (AHP) technique. The results show that among the 9 sub-criteria of identity index in the studied neighborhood, the location identification and sense of belonging to the place of residence has a priority level of 0.537, which has the highest coefficient compared to other criteria.

Language:
Persian
Published:
Geographical Studies of Coastal Ereas Journal, Volume:2 Issue: 4, 2022
Pages:
133 to 160
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