Spatial Zoning of Housing Quality by Combining CRITIC Weighting Method and Fuzzy Logic; Case Study: Qaen City
Aims: The purpose of this research is to zone housing quality in Qaen city spatially. Materials & Methods: The statistical population of the research is the statistical blocks of Qaen city. The indicators of housing quality in this research include the quality of the building skeleton, the quali...
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| Main Author: | |
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| Format: | Article |
| Language: | fas |
| Published: |
Hakim Sabzevari University
2025-08-01
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| Series: | مطالعات جغرافیایی مناطق خشک |
| Subjects: | |
| Online Access: | https://jargs.hsu.ac.ir/article_210431_8577e2362d4359f826194b0d8d36714e.pdf |
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| Summary: | Aims: The purpose of this research is to zone housing quality in Qaen city spatially.
Materials & Methods: The statistical population of the research is the statistical blocks of Qaen city. The indicators of housing quality in this research include the quality of the building skeleton, the quality of materials, the area of residential units, and the density of people and households within each residential unit. The weighting of indicators was done with the CRITIC method, and zoning was done with fuzzy logic in Arc GIS. In addition, Moran's Index and multiple regression were used for supplementary analyses.
Findings: Based on the CRITIC method, the weights of the indicators are as material quality (0.249), skeleton (0.246), household density (0.198), residential area (0.174), and the density of people in a residential unit (0.133). After fuzzy zoning and layer combination, the final map of Qaen city housing quality was produced. The results showed that housing quality in 39.52% of statistical blocks is below average, 33.6% is average, and 26.88% is above average. Moran's statistics revealed a cluster pattern in the quality of the skeleton, materials quality, and area. In contrast, the two indicators of population density and household density within residential units exhibited a random pattern. In other words, statistical blocks with similar housing quality are grouped. Based on the multiple regression, the average age, literacy rate, and employment rate in each block have a positive and significant effect on housing quality. However, on the contrary, household size hurts housing quality.
Conclusion: The result is that blocks with better economic and social conditions have better housing quality, while poorer blocks have poorer conditions.
Innovation: The innovation of the research is in the use of the objective method of weighting, while in most similar studies, subjective methods of weighting have been used, which are more influenced by the subjective attitudes of the respondents. |
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| ISSN: | 2228-7167 2981-1910 |