Document Type : Original Article

Subjects

• Urban geography, urban planning

Extended Abstract

1. Introduction

The purpose of this research is spatial zoning of housing quality in Qaen city. A preliminary survey (based on 2016 census data) indicates that the city's housing conditions are appropriate in some indicators and inappropriate in others; The persons per dwelling unit is approximately 1.02, suggesting a slight housing shortage. Regarding the type of construction materials used, , about 89 percent of residential units have more or less suitable materials (brick and iron together), which is acceptable. However, in the  frame structure index, only 44.1 percent of the city's dwelling units have a resistant structural famework (steel or Concrete ), which is completely unacceptable given the history of devastating earthquakes in the region. In the residential unit area index, about 57 percent of residential units are under 100 square meters, which affects the quality of life.

When these positive and negative aspects are considered together, it becomes difficult to present an accurate picture of housing quality in different parts of the city. Therefore, it is necessary to apply a scientific method to determine the appropriate weight of these indicators, combine them to achieve the final housing quality index and find their spatial pattern in the city, which can ultimately help to understand urban decision-makers better.

Thus, the purpose of this research is to answer the following questions:

 - What is the zoning of the city of Qaen city based on housing quality indicators?

 - What is the spatial pattern of housing quality in Qaen city?

 - What is the connection between the socio-economic characteristics of residents and the quality of their housing?

2. Materials and methods

The statistical population of the study is the statistical blocks of Qaen city in the 2016  population and housing census. The indicators of housing quality in this research include the type of frame structure, the quality of materials, the area of the residential units, and the density of persons and households per dwelling 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.

3. Results and Discussion

Based on the CRITIC method, the weights of the indicators are as follows: material quality (0.249), frame structure (0.246), households per dwelling unit  (0.198), residential area (0.174) ) and the density of persons per dwelling unit (0.133). For fuzzy modeling, the first step is to normalize the layers through fuzzy membership functions. The type of fuzzy membership function is selected based on the nature of the index and its connection with the research objective, and on this basis, two linear and small functions were used for this research. The linear function was used for the layers of frame structure quality, material quality, and dwelling unit area because these indicators have a direct link to housing quality, but since the two indicators of persons per dwelling unit and households per dwelling unit have a negative role in housing quality, the small function was used.

The results suggested that the greatest spatial difference among statistical blocks is in the frame structure index, and the status of this index is comparatively weaker than other indices. After fuzzy membership, the weights obtained for each index via the CRITIC method were applied to each layer. Finally, using the Fuzzy Overlay and selecting a gamma function of 0.9, the five layers were integrated, resulting in the final zoning map. According to the map, blocks with substandard housing quality are scattered throughout all parts of the city. However, overall, the southern parts of the city, which contain older neighborhoods, have lower quality.

Also, in terms of relative frequency, housing quality in 39.52% of statistical blocks is below average, 33.6% is average, and 26.88% is above average. Moran's statistics also showed that the frame structure quality, materials quality, and area have a cluster pattern, but the two indicators of the density of persons and households per dwelling unit have a random pattern. In other words, statistical blocks with similar housing quality are placed next to each other. 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.

4. Conclusion

The results indicate that approximately one-third of the statistical blocks in Qaen city have inadequate average housing quality in their residential units, a significant finding that underscores the need for increased attention to these areas in urban development planning and projects. Another result is that blocks with better economic and social conditions have better housing quality, while poorer blocks have poorer conditions.

5. Acknowledgement & Funding

• The author would like to acknowledge the financial support of Bozorgmehr University of Qaenat for this research under contract number
• Bozorgmehr University of Qaenat supported this work.

6. Conflict of Interest

• Author declared no conflict of interest.