Spatial zoning of the quality of life in urban neighborhoods using FANP (Case study: areas 3,, 4,, 5, and 6 of Mashhad)

Document Type : Original Article

Authors

• Hossein Ghahremanpour ¹
• Hani Rezayan ²
• JAVAD SADIDI ²

¹ collegian of Remote Sensing And GIS of kharazmi University. tehran. Iran.

² Assistant Professor of Remote Sensing and GIS of kharazmi University. tehran. Iran.

https://doi.org/10.22067/jgusd.2022.74461.1152

Abstract

Extended Abstract

Introduction

Evaluation of the quality of life would identify the weaknesses of the disadvantaged areas. Evaluation can be used as an effective tool in urban management and planning to enable the authorities to decide more effectively on increasing the quality of life, satisfaction among citizens, and ranking places. Recent studies have used RS and GIS methods to model urban quality considering such criteria as census, income, and housing values more effectively. The results show that these criteria are spatially correlated and strongly influenced by their relative geographical location. The importance of the environmental variables is also highlighted. While most studies use the hierarchical structure of urban environments, the network structure is more adaptable. This paper uses network models and fuzzy sets considering spatial autocorrelation to model urban quality of life for Regions 3, 4, 5, and 6 of Mashhad City, Iran. The elements affecting the quality of urban life that were obtained from distance measurement data, statistics, and spatial data are categorized into 5 groups: economic, social, physical, access to services, and environment. They are weighed using the analytic network process. The elements were combined using the gamma fuzzy model.

Method

This study used ANP which is a multi-criteria decision-making to spatially model the quality of life in an urban environment. ANP, which is an extension of the well-known analytic hierarchy process (AHP), enables us to model network-based structures that are more complicated than the hierarchical structures. Adopting this method, 21 criteria were selected as the most important influential factors affecting the quality of urban life. The criteria are categorized into 5 groups used to define the model. These categories are economic, social, physical, access to public services, and access to environmental services. The required data were obtained from census information, satellite images, organizational data, and some algorithms applied. The census data were from the Iranian Center of Statistics for 2006. The environmental data such as the surface temperature, air pollution, and greenness were extracted from satellite images. The rest of the data were obtained as vector maps like streets and stations. Some criteria were also calculated like the access to the public and environmental services using the streets network, buffer, and near analyses. All the vector data were converted to raster to be able to combine them with raster data. These data were normalized using fuzzy sets based on small, linear, and near membership functions. The data were weighed constructing a questionnaire based on the ANP. The questionnaires were filled up by 13 experts. Finally, the gamma fuzzy function was used overlapping the criteria applying the weight derived from ANP. Moran’s Index is used to evaluate the spatial autocorrelation of the results. It was calculated for all the 21 criteria. While the positive value of Moran’s Index represents presence of the spatial autocorrelation, the negative value shows that data are not related spatially.

Results

The results showed that the quality of life is reduced near the regions’ boundaries. This effect is represented by the level of spatial autocorrelation that exists as it is higher at the center of the regions and has more randomness near regions’ boundaries. It depicts that while neighborhood districts have similar quality of life the dissimilarity increases at boundaries. Among the criteria used, occupation, building ownership, literacy, access to public services, and air pollution had higher weights. It shows that the economic and social criteria had the most impact and environmental elements had the least impact on the quality of urban life of the 5 categories of the criteria studied. They represent positive Moran’s Index, high z-score, and near-zero p-values, which are correlated with a 99% and 90% confidence level at the center and boundary of the regions, respectively.

Discussion and conclusion

About 16.03% of the study area has a suitable level of quality of life, 14.08% has an average quality of life, and 69.89% has an unfavorable quality of life. As mentioned, the results indicate that the quality of life in the central areas of the study area is higher, but by moving away from the central areas to the outskirts of the study area, the quality of life gradually decreases. This indicates a clear gap between the central and outskirt neighborhoods. The existing gap could be considered in the priorities of urban management planning and decision-making for providing the required preparedness of the urban spaces. Moran’s spatial autocorrelation index of 0.57 with 99% confidence shows that the distribution of urban quality of life characteristics is mostly clustered spatially. So, it is expected to increase the quality of life of a neighborhood by introducing local improvements that will affect its surrounding area and will cause an increase in the overall quality of life in urban areas. Providing better access to public services is the most viable improvement that municipalities can follow to realize a better quality of life in urban areas. Adopting coherent decisions at regional boundaries can also improve the situation.
 
 

Keywords

• Urban Quality of Life
• Spatial Information System
• FANP Model
• Moran's Index
• Mashhad City

Main Subjects

• urban planinnin

References

1. اسدی، ا.، و اکبری، ا. (1399). تحلیل فضایی کیفیت زندگی شهروندان در محیط های شهری با استفاده از سیستم اطلاعات جغرافیایی(GIS) (مطالعه موردی: منطقه 2 شهر مشهد). تحقیقات کاربردی علوم جغرافیایی، 20(58)، 52-35.
2. حاتمی نژاد، ح.، منوچهری میاندوآب، ا.، بهارلو، ا.، ابراهیم پور، ا.، و حاتمی نژاد، ح. (1391). شهر و عدالت اجتماعی: تحلیلی بر نابرابری های محله ای (مطالعه موردی: محله های قدیمی شهر میاندوآب). پژوهشهای جغرافیای انسانی (پژوهش های جغرافیایی)، 44(80)، 63-41.
3. حاج یوسفی، ع. (1385). کیفیت زندگی و راهبرد های اصلاحی. نشریه دهاتی، 28، 38-30.
4. ربانی خوراسگانی، ع.، و کیانپور، م. (1386). مدل پیشنهادی برای سنجش کیفیت زندگی (مطالعه موردی: شهر اصفهان). مجله دانشکده ادبیات و علوم انسانی (دانشگاه خوارزمی)، 15(59/58)، 108-67.
5. رضاعلی، م.، حاتمی نژاد، ح.، فرجی سبکبار، ح.، علوی، س.، و قائدرحمتی، ص. (1395). تحلیل فضایی کیفیت زندگی در ایران. آمایش جغرافیایی فضا، 6(21)، 192-174.
6. قالیباف، م.، روستایی، م.، رمضان زاده لسبویی، م.، و طاهری، م. (1390). ارزیابی کیفیت زندگی شهری (مطالعه موردی: محله یافت آباد). جغرافیا، 9(31)، 53-33.
7. قدیری، م.، حکمت‌نیا، ح.، و سیف الهی، م. (1400). تحلیل شاخص‌های کیفیت زندگی در محلات شهری (مطالعۀ موردی: شهر کوهبنان). پژوهش های جغرافیای انسانی، 53(2)، 426-409.
8. کاظم زاده، ع.، نیسانی سامانی، ن.، درویشی بلورانی، ع.، تومانیان، آ.، و پوراحمد، ا. (1397). ارزیابی کیفیت زندگی شهری با استفاده از سنجش از دور وGIS. اطلاعات جغرافیایی، 27(107)، 132-113.
9. متکان، ع.، و پوراحمد، ا.، و منصوریان، ح.، و حسینی اصل، ا. (1388). سنجش کیفیت مکان های شهری با استفاده از روش ارزیابی چندمعیاره در GIS (مورد مطالعه: شهر تهران). سنجش از دور و GIS ایران، 1(4)، 20-1.
10. مرکز آمار ایران. (1395). سرشماری عمومی نفوس و مسکن. تهران: مرکز آمار ایران.
11. نسترن، م.، احمدی، ق.، و آقازاده مقدم، س. (1394). ارزیابی کیفیت زندگی شهری در محلات بافت قدیم و جدید شهر ارومیه. مطالعات توسعه اجتماعی ایران، 7(27)، 33-7.

 

12. Harpham, T., Burton, S., & Blue, I. (2001). Healthy City Project in Developing Countries: The First Evaluation. Health Promotion International, 16(2), 14.
13. Haslauer, E., Delmelle, E. C., Keul, A., Blaschke, T., & Prinz, T. (2015). Comparing Subjective and Objective Quality of Life Criteria: A Case Study of Green Space and Public Transport in Vienna, Austria. Social Indicators Research, 124(3), 17.
14. Jensen, J. R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective (3 ed.). Prentice Hall.
15. Jensen, R., Gatrell, J., Boulton, J., Harper, B. (2004). Using Remote Sensing and Geographic Information Systems to Study Urban Quality of Life and Urban Forest Amenities. Ecology and Society, 9(5).
16. Jiménez-Muñoz, J. C., Sobrino, J. A., Skoković, D., Mattar, C., & Cristóbal, J. (2014). Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters, 11(10), 1840 - 1843.
17. Klee, P. (2011). The core of GIScience: A process-based approach. Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC).
18. Lee, Y. J. (2008). Subjective quality of life measurement in Taipei Author links open overlay panel. Building and Environment, 43(7), 1205-1215.
19. Li, G., & Weng, Q. (2007). Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data. International Journal of Remote Sensing, 28(2), 249-267.
20. Massam, B. H. (2002). Quality of life: Public planning and private living. Progress in Planning, 58(3), 141-227.
21. Parker, M. K. (1997). Loss in the Lives of Southeast Asian Elders. Minneapolis: University of Minnesota.
22. Rosu, L., Corodescu, E., & Blăgeanu, A. (2015). Does geographical location matter? Assessing spatial patterns in perceived quality of life in European Cities. European Journal of Geography, 6(2), 15-34.
23. Sluiter, R. (2008). Interpolation methods for climate data. KNMI.
24. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.