Spatial evaluation of the quality of urban life The case study of the district 2 of Tehran

Document Type : Research article

Authors

1 Department of Urban Planning, Faculty of Non-Active Defense, Higher National Defense University, Tehran, Iran

2 Department of Human Geography, Faculty of Geography, University of Tehran, Tehran, Iran

3 Department of Urban Planning, Pardis Branch, Islamic Azad University, Pardis, Iran

10.22059/jurbangeo.2024.366022.1864

Abstract

ABSTRACT
Evaluation and quantification of the quality of urban life is one of the important and key prerequisites in urban planning and development. The purpose of this study is to provide a strategy based on a multi-criteria decision-making system to evaluate the quality of urban life. For this purpose, 8 environmental criteria and 6 infrastructure criteria were used. The weight of the effective criteria was calculated based on the opinion of experts using the BWM. Then, the OWA method was exerted to produce urban life quality maps in different mental scenarios. Finally, the spatial distribution of the population in different classes of quality of urban life was evaluated. Among the infrastructural and environmental criteria, population density and air pollution had the most significant impact on the quality of urban life. The quality of urban life maps in different scenarios showed that with the increase in the degree of optimism, the area of the classes with high and very high quality of urban life increases, and with the decrease of the degree of optimism, the area of the classes with high and very high quality of urban life decreases. The results of this study showed that a high percentage of the population in the urban environment lives in conditions of low quality of urban life, which indicates the necessity of evaluating the current state of the quality of urban life and planning to improve the existing state.
Extended Abstract
Introduction
The growth of cities since the beginning of the 20th century, the exponential increase of the urban population, the inappropriate and disorganized physical growth, and the quantitative approach to urban planning and neighborhoods have changed the culture of neighborhoods, inter-neighborhood migrations, and dissatisfaction with the quality of urban environments, which has been accompanied by the loss of identity and sense of belonging among residents. Therefore, to deal with such a trend, an approach called the quality of urban life has been proposed to create a desirable urban life. This approach is based on the belief that urban places, as part of human identity, also form an emotional bond between people and their surroundings. Therefore, paying attention to the quality of urban life can create a sense of belonging and identity in citizens towards the urban environment. Thus, this study aims to evaluate the quality of urban life in District 2 of Tehran by insisting on risk in decision-making. To achieve this goal, a risk-based expert system based on a geographic information system will be used. This system's ability is flexible in creating a wide range of scenarios (very optimistic to very pessimistic).
 
Methodology
The spatial data in this study includes satellite-based data, location-based field data, and spatial layers collected from related organizations. The research method in this study includes five steps. In the first stage, using past studies and experts' opinions, effective criteria for the quality of urban life were identified, and a spatial database of these data was created. In the second stage, due to the different sources of the collected data, the pre-processing operation was done on the data first. Then, according to the type of criterion, a map of the criteria was prepared using spatial analysis in GIS. Normalization of the criteria map, calculation of weight, and importance of criteria and sequential weights were done in the third stage. In the fourth step, using the ordered weighted averaging method, quality of urban life maps was prepared in different decision-making scenarios for environmental and infrastructure dimensions. Also, at this stage, the final quality of urban life maps was prepared by combining the two considered dimensions. Finally, in the fifth stage, an assessment of population distribution in urban quality of life classes was done.
 
Results and discussion
In this study, in the environmental dimension, air pollution, discomfort index, and vegetation cover criteria had the highest weight, and elevation, distance from stream network, and distance from fault had the lowest weight. Also, in terms of infrastructure, population density and distance from the road network have the most weight, and the criteria of distance from industrial areas and medical centers have the least weight. The visual evaluation of the quality of urban life maps in the environmental dimension shows that the eastern and central half of the studied area has a better quality of urban life than other regions. This is due to the adjustment of the urban microclimate in accordance with the increase of vegetation, reduction of air pollution, and improvement of the condition of discomfort index towards the eastern and central parts, directly affecting other environmental indicators. Regarding infrastructural criteria, the areas located north of the study area have the worst quality of urban life, and most areas with very low quality of urban life are located in these areas. However, the central parts of the studied region have a more favorable quality of urban life than other parts. The road network infrastructure, subway stations, medical centers, and parks are more concentrated in these areas than in others.
The combination of urban life quality maps in environmental and infrastructure dimensions showed that with the increase in the degree of optimism, the area of the areas located in the high and very high quality of urban life class increases, and the area of the areas located in the low and very low quality of urban life class decreases. So, the very low, low, medium, high, and very high quality of urban life in the very pessimistic scenario was 11.84, 20.07, 11.17, 3.73, and 1.58 km2, respectively. In a very optimistic scenario, these values would reach 3.46, 6.42, 13.79, 15.78, and 8.96 km2.
 
Conclusion
From an environmental point of view, air pollution and distance from the fault criteria, respectively, have the most and the least impact on modeling the quality of urban life. Also, in terms of infrastructure, the population density criterion has the most impact, and the distance from industrial areas criterion has the least impact in modeling the quality of urban life. The central and eastern regions of the studied region have better urban life quality conditions than other regions. With the increase in the degree of optimism, the area of the areas located in the class of high and very high quality of urban life increases, and the area of the areas located in the class of low and very low quality of urban life decreases. In the very pessimistic, pessimistic, intermediate, optimistic, and very optimistic scenarios, respectively, 5.31, 7.94, 10.24, 14.66, and 24.73 square kilometers of the studied area have suitable urban life quality conditions (high and very high class). In a very pessimistic state, 650,963 and 9,162 thousand people of the study area population are located in areas with unsuitable (low and very low class) and suitable (high and very high) quality of urban life, respectively. The results of this study can be useful for managers and planners to implement plans and programs to improve the quality of urban life.
 
Funding
There is no funding support.
 
Authors’ Contribution
All authors contributed equally to the preparation of this manuscript.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.

Keywords

References

  1. Abd El Karim, A., & Awawdeh, M. M. (2020). Integrating GIS accessibility and location-allocation models with multicriteria decision analysis for evaluating quality of life in Buraidah city, KSA. Sustainability, 12(4), 1412. https://doi.org/10.3390/su12041412
  2. Afsari, R., Nadizadeh Shorabeh, S., Bakhshi Lomer, A. R., Homaee, M., & Arsanjani, J. J. (2023). Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran. Remote Sensing, 15(5), 1248. https://doi.org/10.3390/rs15051248
  3. Afsari, R., Nadizadeh Shorabeh, S., Kouhnavard, M., Homaee, M., & Arsanjani, J. J. (2022). A spatial decision support approach for flood vulnerability analysis in urban areas: A case study of Tehran. ISPRS International Journal of Geo-Information, 11(7), 380. https://doi.org/10.3390/ijgi11070380
  4. Ahmad Akhoundi, A., Barakpur, N., Khalili, A., Sedaghatnia, S., & Safiyari, R. (2014). Measuring Quality of Urban Life in Tehran Metropolitan. Journal of Fine Arts: Architecture & Urban Planning, 19(2), 5-22. doi.org/10.22059/JFAUP.2014.55385 [In Persian].
  5. Arcila-Arango, J. C., Castro-Sánchez, M., Espoz-Lazo, S., Cofre-Bolados, C., Zagalaz-Sánchez, M. L., & Valdivia-Moral, P. (2020). Analysis of the dimensions of quality of life in colombian university students: Structural equation analysis. International Journal of Environmental Research and Public Health, 17(10), 3578. https://doi.org/10.3390/ijerph17103578
  6. Badach, J., Dymnicka, M., & Baranowski, A. (2020). Urban vegetation in air quality management: A review and policy framework. Sustainability, 12(3), 1258. https://doi.org/10.3390/su12031258
  7. Boloorani, A. D., Kazemi, Y., Sadeghi, A., Shorabeh, S. N., & Argany, M. (2020). Identification of dust sources using long term satellite and climatic data: A case study of Tigris and Euphrates basin. Atmospheric Environment, 224, 117299. https://doi.org/10.1016/j.atmosenv.2020.117299
  8. Boloorani, A. D., Shorabeh, S. N., Samany, N. N., Mousivand, A., Kazemi, Y., Jaafarzadeh, N., ... & Rabiei, J. (2021). Vulnerability mapping and risk analysis of sand and dust storms in Ahvaz, IRAN. Environmental Pollution, 279, 116859. https://doi.org/10.1016/j.envpol.2021.116859
  9. Bowling, A., & Gabriel, Z. (2007). Lay theories of quality of life in older age. Ageing & Society, 27(6), 827-848. https://doi.org/10.1017/S0144686X07006423
  10. Carbonara, S., Faustoferri, M., & Stefano, D. (2021). Real estate values and urban quality: a multiple linear regression model for defining an urban quality index. Sustainability, 13(24), 13635. https://doi.org/10.3390/su132413635
  11. Carpentieri, G., Guida, C., & Masoumi, H. E. (2020). Multimodal accessibility to primary health services for the elderly: A case study of Naples, Italy. Sustainability, 12(3), 781. https://doi.org/10.3390/su12030781
  12. Chen, S., Cerin, E., Stimson, R., & Lai, P. C. (2016). An objective measure to assessing urban quality of life based on land use characteristics. Procedia Environmental Sciences, 36, 50-53. https://doi.org/10.1016/j.proenv.2016.09.009
  13. Das, D. (2008). Urban quality of life: A case study of Guwahati. Social indicators research, 88, 297-310. https://doi.org/10.1007/s11205-007-9191-6
  14. Djouani, I., Dehimi, S., & Redjem, A. (2022). Evaluation of the efficiency and quality of the tram route of Setif city, Algeria: Combining AHP and GIS approaches. Journal of the Geographical Institute" Jovan Cvijic", SASA, 72(1), 85-102. https://doi.org/10.2298/IJGI2201085D
  15. Faka, A., Kalogeropoulos, K., Maloutas, T., & Chalkias, C. (2021). Urban quality of life: Spatial Modeling and indexing in Athens metropolitan area, Greece. ISPRS International Journal of Geo-Information, 10(5), 347. https://doi.org/10.3390/ijgi10050347
  16. Feneri, A. M., Vagiona, D., & Karanikolas, N. (2015). Multi-criteria decision making to measure quality of life: an integrated approach for implementation in the urban area of Thessaloniki, Greece. Applied Research in Quality of Life, 10, 573-587. https://doi.org/10.1007/s11482-014-9335-1
  17. Firozjaei, M. K., Nematollahi, O., Mijani, N., Shorabeh, S. N., Firozjaei, H. K., & Toomanian, A. (2019). An integrated GIS-based Ordered Weighted Averaging analysis for solar energy evaluation in Iran: Current conditions and future planning. Renewable Energy, 136, 1130-1146. https://doi.org/10.1016/j.renene.2018.09.090
  18. Firozjaei, M. K., Sedighi, A., Kiavarz, M., Qureshi, S., Haase, D., & Alavipanah, S. K. (2019). Automated built-up extraction index: A new technique for mapping surface built-up areas using LANDSAT 8 OLI imagery. Remote Sensing, 11(17), 1966. https://doi.org/10.3390/rs11171966
  19. Fotso-Nguemo, T. C., Vondou, D. A., Diallo, I., Diedhiou, A., Weber, T., Tanessong, R. S., ... & Yepdo, Z. D. (2022). Potential impact of 1.5, 2 and 3 C global warming levels on heat and discomfort indices changes over Central Africa. Science of the Total Environment, 804, 150099. https://doi.org/10.1016/j.scitotenv.2021.150099
  20. Guida, C., & Carpentieri, G. (2021). Quality of life in the urban environment and primary health services for the elderly during the Covid-19 pandemic: An application to the city of Milan (Italy). Cities, 110, 1-15. https://doi.org/10.1016/j.cities.2020.103038
  21. Iamtrakul, P., Chayphong, S., Kantavat, P., Hayashi, Y., Kijsirikul, B., & Iwahori, Y. (2023). Exploring the Spatial Effects of Built Environment on Quality of Life Related Transportation by Integrating GIS and Deep Learning Approaches. Sustainability, 15(3), 2785. https://doi.org/10.3390/su15032785
  22. Isakov, V., Arunachalam, S., Batterman, S., Bereznicki, S., Burke, J., Dionisio, K., ... & Vette, A. (2014). Air quality modeling in support of the near-road exposures and effects of urban air pollutants study (NEXUS). International journal of environmental research and public health, 11(9), 8777-8793. https://doi.org/10.3390/ijerph110908777
  23. Joseph, M., Wang, F., & Wang, L. (2014). GIS-based assessment of urban environmental quality in Port-au-Prince, Haiti. Habitat International, 41, 33-40. https://doi.org/10.1016/j.habitatint.2013.06.009
  24. Jun, B. W. (2006). Urban quality of life assessment using satellite image and socioeconomic data in GIS. Korean journal of remote sensing, 22(5), 325-335. https://doi.org/10.7780/kjrs.2006.22.5.325
  25. Karimi, M., & Brazier, J. (2016). Health, health-related quality of life, and quality of life: what is the difference? Pharmacoeconomics, 34, 645-649. https://doi.org/10.1007/s40273-016-0389-9
  26. Leogrande, A., Saponaro, A., Massaro, A., & Galiano, A. M. (2020). A GIS based estimation of quality of life in italian regions. American Journal of Humanities and Social Sciences Research (AJHSSR) e-ISSN.
  27. Liu, L., Silva, E. A., Wu, C., & Wang, H. (2017b). A machine learning-based method for the large-scale evaluation of the qualities of the urban environment. Computers, environment and urban systems, 65, 113-125. https://doi.org/10.1016/j.compenvurbsys.2017.06.003
  28. Liu, Y., Yue, W., Fan, P., Zhang, Z., & Huang, J. (2017a). Assessing the urban environmental quality of mountainous cities: A case study in Chongqing, China. Ecological Indicators, 81, 132-145. https://doi.org/10.1016/j.ecolind.2017.05.048
  29. Malczewski, J. (2006). Integrating multicriteria analysis and geographic information systems: the ordered weighted averaging (OWA) approach. International Journal of Environmental Technology and Management, 6(1-2), 7-19. https://doi.org/10.1504/IJETM.2006.008251
  30. Nadizadeh Shorabeh, S., Hamzeh, S., Zanganeh Shahraki, S., Firozjaei, M. K., & Jokar Arsanjani, J. (2020). Modelling the intensity of surface urban heat island and predicting the emerging patterns: Landsat multi-temporal images and Tehran as case study. International Journal of Remote Sensing, 41(19), 7400-7426. https://doi.org/10.1080/01431161.2020.1759841
  31. Nadizadeh Shorabeh, S., Neisany Samany, N., & Abdali, Y. (2019). Mapping the potential of solar power plants based on the concept of risk Case study: Razavi Khorasan Province. Scientific-Research Quarterly of Geographical Data (SEPEHR), 28(111), 129-147. https://doi.org/10.22131/sepehr.2019.37512 [In Persian].
  32. Nadizadeh Shorabeh, S., Neysani Samani, N., & Jelokhani-Niaraki, M. R. J. N. (2017). Determination of optimum areas for the landfill with emphasis on the urban expansion trend based on the combination of the Analytical Hierarchy Process and the Ordered Weighted Averaging model. Journal of Natural Environment, 70(4), 949-969. https://doi.org/10.22059/JNE.2017.231160.1365 [In Persian].
  33. Olsson, M., Järbrink, K., Divakar, U., Bajpai, R., Upton, Z., Schmidtchen, A., & Car, J. (2019). The humanistic and economic burden of chronic wounds: A systematic review. Wound Repair and Regeneration, 27(1), 114-125.  https://doi.org/10.1111/wrr.12683
  34. Pacione, M. (2003). Quality-of-life research in urban geography. Urban geography, 24(4), 314-339. https://doi.org/10.2747/0272-3638.24.4.314
  35. Pour Ahmad, A., Zayyari, K., & Zahedi, J. (2014). The Evaluation of Afghan Immigrant’s Urban Quality of Life by Mental Method (Case study: Robat Karim). Geographical Urban Planning Research (GUPR), 2(1), 1-15. https://doi.org/10.22059/JURBANGEO.2014.51479 [In Persian].
  36. Rahman, A., Kumar, Y., Fazal, S., & Bhaskaran, S. (2011). Urbanization and quality of urban environment using remote sensing and GIS techniques in East Delhi-India. Journal of Geographic Information System, 3(01), 62. https://doi.org/10.4236/jgis.2011.31005 
  37. Reginster, I., & Goffette-Nagot, F. (2005). Urban environmental quality in two Belgian cities, evaluated on the basis of residential choices and GIS data. Environment and Planning A, 37(6), 1067-1090. https://doi.org/10.1068/a3735a
  38. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
  39. Rezvani, M., Nickravesh, F., & Darban Astaneh, A. (2021). Assessing the Ecotourism Development Capability in Rural Areas With an Emphasis on Local Risk-Taking in Decision-Making: The Case Study of Mazandaran Province. Town and Country Planning, 13(2), 587-618. https://doi.org/10.22059/jtcp.2021.330884.670257 [In Persian].
  40. Rezvani, M., Nickravesh, F., Astaneh, A. D., & Kazemi, N. (2022). A risk-based decision-making approach for identifying natural-based tourism potential areas. Journal of Outdoor Recreation and Tourism, 37, 100485. https://doi.org/10.1016/j.jort.2021.100485
  41. Roy, S., Bose, A., Majumder, S., Roy Chowdhury, I., Abdo, H. G., Almohamad, H., & Abdullah Al Dughairi, A. (2022). Evaluating urban environment quality (UEQ) for Class-I Indian city: an integrated RS-GIS based exploratory spatial analysis. Geocarto International, 2153932. https://doi.org/10.1080/10106049.2022.2153932
  42. Saaty, T. (1980, November). The analytic hierarchy process (AHP) for decision making. In Kobe, Japan (Vol. 1, p. 69). https://doi.org/10.1007/978-1-4613-2805-6_12
  43. Sadler, R. C., Hippensteel, C., Nelson, V., Greene-Moton, E., & Furr-Holden, C. D. (2019). Community-engaged development of a GIS-based healthfulness index to shape health equity solutions. Social Science & Medicine, 227, 63-75. https://doi.org/10.1016/j.socscimed.2018.07.030
  44. Schalock, R. L., Verdugo, M. A., Gomez, L. E., & Reinders, H. S. (2016). Moving us toward a theory of individual quality of life. American journal on intellectual and developmental disabilities, 121(1), 1-12. https://doi.org/10.1352/1944-7558-121.1.1
  45. Schuessler, K. F., & Fisher, G. A. (1985). Quality of life research and sociology. Annual review of sociology, 11(1), 129-149. https://doi.org/10.1146/annurev.so.11.080185.001021
  46. Shafer, C. S., Lee, B. K., & Turner, S. (2000). A tale of three greenway trails: user perceptions related to quality of life. Landscape and urban planning, 49(3-4), 163-178. https://doi.org/10.1016/S0169-2046(00)00057-8
  47. Shahpari Sani, D., Heidari, M. T., Tahmasebi Mogaddam, H., Nadizadeh Shorabeh, S., Yousefvand, S., Karmpour, A., & Jokar Arsanjani, J. (2022). An Assessment of Social Resilience against Natural Hazards through Multi-Criteria Decision Making in Geographical Setting: A Case Study of Sarpol-e Zahab, Iran. Sustainability, 14(14), 8304. https://doi.org/10.3390/su14148304
  48. Shorabeh, S. N., Argany, M., Rabiei, J., Firozjaei, H. K., & Nematollahi, O. (2021). Potential assessment of multi-renewable energy farms establishment using spatial multi-criteria decision analysis: A case study and mapping in Iran. Journal of Cleaner Production, 295, 126318. https://doi.org/10.1016/j.jclepro.2021.126318
  49. Shorabeh, S. N., Firozjaei, H. K., Firozjaei, M. K., Jelokhani-Niaraki, M., Homaee, M., & Nematollahi, O. (2022). The site selection of wind energy power plant using GIS-multi-criteria evaluation from economic perspectives. Renewable and Sustainable Energy Reviews, 168, 112778. https://doi.org/10.1016/j.rser.2022.112778
  50. Shorabeh, S. N., Firozjaei, M. K., Nematollahi, O., Firozjaei, H. K., & Jelokhani-Niaraki, M. (2019). A risk-based multi-criteria spatial decision analysis for solar power plant site selection in different climates: A case study in Iran. Renewable Energy, 143, 958-973. https://doi.org/10.1016/j.renene.2019.05.063
  51. Smith, T., Nelischer, M., & Perkins, N. (1997). Quality of an urban community: a framework for understanding the relationship between quality and physical form. Landscape and Urban planning, 39(2-3), 229-241. https://doi.org/10.1016/S0169-2046(97)00055-8
  52. Teklay, R. (2012). Adaptation and Dissonance in Quality Of Life: Indicators for urban planning and policy making (Master's thesis, University of Twente).
  53. Tercan, E. (2021). Land suitability assessment for wind farms through best-worst method and GIS in Balıkesir province of Turkey. Sustainable Energy Technologies and Assessments, 47, 101491. https://doi.org/10.1016/j.seta.2021.101491
  54. Tian, Y., Jim, C. Y., & Wang, H. (2014). Assessing the landscape and ecological quality of urban green spaces in a compact city. Landscape and urban planning, 121, 97-108. https://doi.org/10.1016/j.landurbplan.2013.10.001
  55. Weng, Q., Firozjaei, M. K., Kiavarz, M., Alavipanah, S. K., & Hamzeh, S. (2019). Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Science of the Total Environment, 650, 515-529. https://doi.org/10.1016/j.scitotenv.2018.09.027
  56. Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics, 18(1), 183-190. https://doi.org/10.1109/21.87068
  57. Yagoub, M. M., Tesfaldet, Y. T., Elmubarak, M. G., & Al Hosani, N. (2022). Extraction of Urban Quality of Life Indicators Using Remote Sensing and Machine Learning: The Case of Al Ain City, United Arab Emirates (UAE). ISPRS International Journal of Geo-Information, 11(9), 458. https://doi.org/10.3390/ijgi11090458
  58. Zhang, J. F., & Deng, W. (2010). Industrial structure change and its eco-environmental influence since the establishment of municipality in Chongqing, China. Procedia Environmental Sciences, 2, 517-526. https://doi.org/10.1016/j.proenv.2010.10.056
Geographical Urban Planning Research (GUPR)
Volume 11, Issue 3
October 2023
Pages 213-237

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