تحلیل رابطه فاصله از مرکز استان با ابعاد محرومیت چندگانه مطالعه موردی: استان آذربایجان شرقی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 گروه برنامه‌ریزی منطقه‌ای، دانشکده شهرسازی، دانشکدگان هنرهای زیبا، دانشگاه تهران، تهران، ایران

2 گروه مهندسی فضای سبز، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران

چکیده

تمرکز نامتوازن فرصت‌های توسعه، نقش مهمی در شکل‌گیری الگوهای محرومیت منطقه‌ای ایفا می‌کنند. اما، اثر فاصله فضایی بر ابعاد مختلف محرومیت، به‌صورت متفاوت موردبررسی قرار نگرفته است. هدف این پژوهش، تحلیل رابطه میان فاصله از مرکز استان و ابعاد مختلف محرومیت چندگانه در سطح شهرستان‌های استان آذربایجان شرقی در سال 1395 با استفاده از شاخص‌های مستخرج از ادبیات جهانی است. در این راستا، ابتدا با استفاده از روش تحلیل عاملی اکتشافی، ابعاد مختلف محرومیت استخراج و امتیاز عاملی هر شهرستان محاسبه‌شده است. درصد واریانس برای هرکدام از ابعاد قابل‌قبول (5/0-7/0) حاصل‌شده است و از طرفی مقادیر KMO، قابلیت تفسیر مدل شده و امتیازات عاملی استانداردشده‌ای برای هر شهرستان محاسبه‌شده است. سپس، به‌منظور بررسی رابطه میان فاصله فضایی و هر یک از ابعاد محرومیت، از مدل رگرسیون خطی ساده استفاده گردیده است. همچنین آزمون Moran’s I برای سنجش خودهمبستگی فضایی در داده‌ها مورداستفاده قرارگرفته است. مقادیر به‌دست‌آمده از نتایج رگرسیون برای هر بعد متفاوت بوده به‌طوری‌که اعداد ضریب تعیین (R²)، در بازه (0.05-0.65) قرار داشته است. بنابراین نتایج نشان داد که فاصله فضایی تأثیر مثبت و معناداری بر محرومیت‌های اشتغال، آموزش و مسکن دارد. در مقابل، رابطه فاصله با محرومیت بهداشتی ضعیف بوده و برای محرومیت دسترسی، رابطه معناداری مشاهده نشده است. این یافته‌ها نشان می‌دهد که اثر فاصله فضایی بر محرومیت، ماهیتی غیریکنواخت دارد و تمامی ابعاد محرومیت به یک‌میزان تحت تأثیر موقعیت فضایی قرار نمی‌گیرند. این پژوهش درک دقیق‌تری از سازوکارهای فضایی نابرابری منطقه‌ای فراهم می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

Spatial Patterns of Multiple Deprivation and Their Association with Distance from the Provincial Capital: A case study of East Azerbaijan

نویسندگان [English]

  • Asma Farshforoush Imani 1
  • Pouya Joodi Gollar 2
1 Department of Regional Planning, Faculty of Urban Planning, University of Tehran, Tehran, Iran
2 Department of Landscape Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
چکیده [English]

A B S T R A C T
The unbalanced concentration of development opportunities plays an essential role in shaping patterns of regional deprivation. However, the impact of spatial distance on various dimensions of deprivation has not been distinctly examined. This research aims to analyze the relationship between distance from the provincial capital and various dimensions of multiple deprivation at the city level in East Azerbaijan Province (2016), utilizing indicators derived from international literature. Therefore, Exploratory Factor Analysis was primarily employed to extract the dimensions of deprivation and calculate factor scores for each city. The variance explained for each dimension reached acceptable levels (0.5–0.7), while KMO values confirmed the interpretability of the model, resulting in standardized factor scores for each city. Subsequently, a Simple Linear Regression model was utilized to investigate the relationship between spatial distance and each deprivation dimension. Additionally, Moran’s I test was applied to assess spatial autocorrelation within the data. Regression results varied across dimensions, with the coefficient of determination (R2) ranging from 0.05 to 0.65. The results indicate that spatial distance exerts a positive and significant influence on deprivation in employment, education, and housing. Conversely, the relationship between distance and health deprivation was weak, and no significant relationship was observed for access-related deprivation. These findings suggest that the impact of spatial distance on deprivation is non-uniform, as not all dimensions are equally affected by spatial positioning. This study provides a more nuanced understanding of the spatial mechanisms underlying regional inequality.
 
Extended Abstract
Introduction
Spatial inequality, as one of the fundamental issues of regional development, has attracted extensive attention in recent years, because significant differences has delineated in the level of development among various regions. These spatial inequalities have devitalized the development and resilience capacities of disadvantage districts and intensified regional gaps by restricting access to basic services such as education, health care, infrastructure, and economic opportunities. Thus, spatial deprivation and unbalanced concentration of development opportunities play a vital role in shaping patterns of regional deprivations. The spatial situation of the regions and their distance from the chief centers of economic activities, in the meantime, has been brought up as one of the potential factors affecting deprivation. However, the effect of spatial distance on different dimensions of deprivation has not been investigated equally. The purpose of this study is to analyze the relationship between the distance from the center of the province and the diverse dimensions of multiple deprivation in the cities of East Azerbaijan province by using indicators extracted from the world literature in 2016. The discrepancy between advantage and disadvantage districts is obvious in the province. Tabriz, as the hub of population and economic activities, has attracted an immense share of infrastructure and investments that increased the gap between the center and the periphery.
 
Methodology
In this regard, using exploratory factor analysis method, initially, various dimensions of deprivation including employment, education, housing, healthcare and accessibility extracted and factor score of each city calculated. It should be noted that the normality of the data has been examined to initiate factor analysis in each dimension. Factor analysis, according to the existing literature, has been conducted considering the non-normality of 20 percent of the data. The percentage of variance has been obtained for each of the appropriate and acceptable dimensions (0.5-0.7) and on the other hand, the appropriate values of KMO, modeled interpretability and standardized factor scores have been calculated for each city. These scores are normalized between 0 and 1 to enable the comparability between the index and the various dimensions. Meanwhile, the spatial distribution maps of these factor privileges have been prepared to spot the geographical location of different dimensions of deprivation. A simple linear regression model has been use in order to investigate the relationship between the spatial distance and each of the dimensions of deprivation. Moran's I test has also used to measure spatial autocorrelation in data. The calculated value of (R-Score) is 2.59, which indicates a confidence level of 99 percent. The obtained values from the regression results were unique for each dimension, so that the determine coefficients numbers (R) were in the range (0.05-0.65). For example, healthcare deprivation with R² of 0.070 has a weak relationship and accessibility deprivation has a non-significant relationship with distance and correlation.
 
Results and Discussion
This result demonstrated that spatial distance, although an essential factor, is not able to explain all the dimensions of deprivation alone. Autocorrelation could be described with Watson's camera test. These values vary in different dimensions. The presence of spatial autocorrelation in the accessibility dimension with the Watson camera value of 0.859 means that the values of this index in neighboring cities are not independent and have a dependent spatial pattern; In such circumstances, the assumption of independence of observations in the linear regression model is violated and, as a result, estimating the coefficients and statistical significance may be associated with error. The results indicated, on the other hand, that the spatial distance has a positive and significant effect on the deprivations of employment, education and housing, so that with increasing the distance from the provincial center, the level of these deprivations increases. In contrast, the relationship between distance and healthcare deprivation was weak and no significant relationship was observed for accessibility deprivation.
 
Conclusion
The results illustrated that the center-peripheral model does not work the same in all aspects of development, but is more prominent in economic and production sects such as employment and housing. Second, the findings indicated that some dimensions of deprivation, particularly healthcare and accessibility, are not necessarily subject to spatial distance. This outcome does not necessarily confirm other conducted studies that have introduced distance as a dominant factor in the formation of regional inequalities. Consequently, the effect of spatial distance on deprivation has a multidimensional and non-uniform nature and not all dimensions of deprivation are equally affected by the spatial position. These results paved the way to prioritize the intervention for cities with inferior conditions. This study introduced a more precise understanding of the spatial mechanisms of regional inequality by providing a separate analysis of the dimensions of deprivation, which could be a basis for targeted policy-making in reducing regional inequalities.
Keywords: Multiple Deprivation, Spatial Inequality, Spatial Distance, Factor Analysis, Linear Regression.
 
Funding
There is no funding support.
 
Authors’ Contribution
(First author) worked and contributed to the introduction, theoretical foundations, data collection, and methodology sections. The second author also collaborated and contributed to the analysis of results, discussion, and conclusion sections.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.

کلیدواژه‌ها [English]

  • Multiple Deprivation
  • Spatial Inequality
  • Spatial Distance
  • Exploratory Factor Analysis
  • Linear Regression
  • East Azerbaijan Province

مراجع

  1. زیاری، کرامت اله؛ زنجیرچی، سید محمود و سرخ کمال، کبری. (1389). بررسی و رتبه بندی درجه توسعه‌یافتگی شهرستان‌های استان خراسان رضوی, با استفاده از تکنیک تاپسیس. پژوهش‌های جغرافیای انسانی (پژوهش‌های جغرافیایی)، 42(72)، 30-17.
  2. زارعیان، ساریه. (1404). برنامه‌ریزی شهری استعماری و عدالت فضایی در شهرهای پسااستعماری: مطالعه تطبیقی الجزیره، تونس و بمبئی از قرن نوزدهم تا امروز. جغرافیا و توسعه فضایی، 2(2)، 19-44. doi: 10.22098/gsd.2026.18799.1108.
  3. سرور، رحیم؛ موسوی، میرنجف و مبارکی، امید. (1389). تحلیل فضایی نابرابری‌های ناحیه‌ای در استان آذربایجان شرقی. جغرافیا (برنامه‌ریزی منطقه‌ای)، 1(2)، 39-50. doi: 10.23478/gsd.1996.18799.1108.
  4. شیخ بیگلو، رعنا؛ تقوایی، مسعود و وارثی، حمیدرضا. (1391). تحلیل فضایی محرومیت و نابرابری‌های توسعه در شهرستان‌های ایران. رفاه اجتماعی، 12(46)، 189-214. doi: 10.23478/gsd.2012.18799.1108.
  5. شالی، محمد و ایمانی، حبیبه. (1403). تحلیل ناهمگونی‌های فضایی در کلان‌شهر تبریز. جغرافیا و توسعه فضایی، 1(1)، 1-13. doi: 10.22098/gsd.2024.2981
  6. طاهرپور، فاطمه؛ واعظی، موسی؛ خرمی، هابیل و اکبری، مجید. (1399). ارزیابی شهرستان‌های استان آذربایجان شرقی از لحاظ شاخص‌های زیربنایی با استفاده از تحلیل رابطه خاکستری. فصلنامه برنامه‌ریزی منطقه‌ای، 10(38)، 33-50. doi: 18.89078/gsd.2020.18799.1108.
  7. طولابی نژاد، مهرشاد؛ بذرافشان، جواد و قنبری، سیروس. (1396). تحلیل ارتباط شاخص‌های منطقه‌ای محرومیت با پایداری محیطی (موردمطالعه: روستاهای شهرستان پلدختر). تحقیقات کاربردی علوم جغرافیایی (علوم جغرافیایی)، 17(46)، 45-72.
  8. محمودیان، حسین و قاسمی اردهایی، علی. (1391). شبکه‌های اجتماعی مهاجران و بازتولید فرهنگ مهاجرت در مناطق روستایی: مطالعه‌ای کیفی بر جریان‌های مهاجرت از استان آذربایجان شرقی به استان تهران. توسعه محلی روستایی - شهری (توسعه روستایی)، 4(1)، 109-128.
  9. لطفی، حیدر و مرادی، آرزو. (1403). تحلیلی بر نابرابری‌های فضایی بر پایه شاخص‌های اجتماعی در کلان‌شهر تبریز. جغرافیا و توسعه فضایی،1(3)، 99-113، doi: 10.22098/gsd.2025.16622.1077.
  10. Ahmad, W., & Ahmad, A. (2025). Linking misery and identity: Exploring Coetzee’s novel Disgrace from the perspective of relative deprivation theory. Contemporary Journal of Social Science Review, 3(3), 808-815. https://doi.org/10.1016/j.enbuild.2025.09.015
  11. Avdikos, V. (2017). Understanding geographies of polarization and peripheralization: Perspectives from Central and Eastern Europe and beyond. Taylor & Francis. https://doi.org/10.1016/j.eiar.2017.08.007
  12. Barbero, J., & Rodríguez-Crespo, E. (2022). Technological, institutional, and geographical peripheries: Regional development and risk of poverty in the European regions. The Annals of Regional Science, 69(2), 311-332. https://doi.org/10.1016/j.scs.2022.103786
  13. Bathelt, H., Buchholz, M., & Storper, M. (2024). The nature, causes, and consequences of inter-regional inequality. Oxford University Press, 24, 353-374. https://doi.org/10.1177/23998083241257263
  14. Brady, D., Fullerton, A., & Cross, J. (2010). More than just nickels and dimes: A cross-national analysis of working poverty. Social Problems, 57(4), 559-585.
  15. Catalano, G., Khan, M., Chatzipanagiotou, O., & Pawlik, T. (2024). Pharmacy accessibility and social vulnerability. JAMA Network Open, 7(8), e2429755. https://doi.org/10.1080/10095020.2021.1978276
  16. Chen, A., & Partridge, M. (2013). When are cities engines of growth in China?. Regional Studies, 47(8), 1313-1331. https://doi.org/10.1080/15481603.2024.1940739
  17. Chen, M., Robinson, C., & Singleton, A. (2025). Mapping multidimensional energy deprivation. Computers, Environment and Urban Systems, 121, 102324. https://doi.org/10.1016/j.habitatint.2025.102306
  18. Chen, S., Wang, S., Sun, Y., & Liu, J. (2024). Development of a contextualized index of multiple deprivation. Applied Geography, 167, 103285. https://doi.org/10.1061/JUPDDM.0000874
  19. Costello, A., & Osborne, J. (2005). Best practices in exploratory factor analysis. Practical Assessment, Research and Evaluation, 10(1), 1-9.
  20. Crescenzi, R., & Rodríguez-Pose, A. (2012). Infrastructure and regional growth in the European Union. Papers in Regional Science, 91(3), 487-513. https://doi.org/10.3389/frsc.2023.1084573
  21. Crescenzi, R., Di Cataldo, M., & Rodríguez-Pose, A. (2016). Government quality and economic returns of infrastructure investment. Journal of Regional Science, 56(4), 555-582. https://doi.org/10.1080/003434042000211114
  22. Darling-Hammond, L. (2000). Teacher quality and student achievement. Education Policy Analysis Archives, 8, 1-44. https://doi.org/10.1080/17538947.2000.2489731
  23. Dijkstra, L., Papadimitriou, E., Martinez, B., De Dominicis, L., & Kovacic, M. (2022). EU regional competitiveness index. European Commission.
  24. Dijkstra, L., Poelman, H., & Rodríguez-Pose, A. (2020). The geography of EU discontent. Regional Studies, 54(6), 737-753. https://doi.org/10.1016/j.scs.2017.06.009
  25. Dudek, H., & Wojewódzka-Wiewiórska, A. (2024). Housing deprivation among Polish households: Prevalence and associated factors. Real Estate Management and Valuation, 32(2), 58-69.
  26. Ezcurra, R., & Del Villar, A. (2021). Globalization and spatial inequality. The Annals of Regional Science, 67(2), 335-358. https://doi.org/10.1016/j.scs.2022.104873
  27. Fahmy, E., Gordon, D., & Patsios, D. (2011). Predicting fuel poverty in England. Energy Policy, 39(7), 4370-4377. https://doi.org/10.1016/j.buildenv.2014.03.014
  28. Farole, T., Goga, S., & Ionescu-Heroiu, M. (2018). Rethinking lagging regions. World Bank.
  29. Farole, T., Rodríguez-Pose, A., & Storper, M. (2011). Human geography and economic growth. Progress in Human Geography, 35, 58-80.
  30. Filandri, M., & Olagnero, M. (2014). Housing inequality and social class in Europe. Housing Studies, 29(7), 977-993. https://doi.org/10.1016/j.scitotenv.2014.01.060
  31. Friedrich, M., & Teichler, N. (2025). Temporary employees and material deprivation. Journal of European Social Policy, 35(2), 143-156. https://doi.org/10.1016/j.uclim.2025.101905
  32. Furlong, A. (2006). Labour market transitions. Work, Employment and Society, 20(3), 553-569.
  33. Fusco, A., Guio, A., & Marlier, E. (2012). Material deprivation index. Springer.
  34. Gachanja, J., & Yang, T. (2025). Built environment and multidimensional poverty. Habitat International, 160, 103402. https://doi.org/10.1016/j.scs.2023.104973
  35. Gesthuizen, M., Solga, H., & Künster, R. (2011). Economic marginalization. European Sociological Review, 27(2), 264-280.
  36. Gibson, M., Petticrew, M., Bambra, C., Sowden, A., Wright, K., & Whitehead, M. (2011). Housing and health inequalities. Health & Place, 17(1), 175-184. https://doi.org/10.3390/su15021020
  37. Gradín, C., Cantó, O., & Del Río, C. (2012). Employment deprivation. ECINEQ Working Paper Series.
  38. Guagliardo, M. (2004). Spatial accessibility of primary care. International Journal of Health Geographics, 3(1), 3. https://doi.org/10.1016/j.scs.2012.104657.
  39. Guio, A. (2005). Material deprivation in the EU. Statistics in Focus, 21.
  40. Guio, A., Marlier, E., Gordon, D., Fahmy, E., Nandy, S., & Pomati, M. (2016). Improving measurement of deprivation. Journal of European Social Policy, 26(3), 219-333. https://doi.org/10.1080/01944360108976228
  41. Hanselman, P. (2019). Access to effective teachers. The Sociological Quarterly, 60(3), 498-534.. https://doi.org/10.1177/2399808319845079
  42. Iammarino, S., Rodríguez-Pose, A., & Storper, M. (2019). Regional inequality. Journal of Economic Geography, 19(2), 273-298.
  43. Jackson, M., Huang, L., Xie, Q., & Tiwari, R. (2010). Moran's I spatial analysis. International Journal of Health Geographics, 9(1), 33. https://doi.org/10.1016/j.uclim.2010.102433
  44. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). Statistical learning. Springer.
  45. Jia, P., et al. (2022). Healthcare accessibility inequalities. Social Science & Medicine, 314, 115458. https://doi.org/10.1016/j.scs.2021.106450
  46. Kaiser, H. (1974). Factorial simplicity index. Psychometrika, 39(1), 31-36.
  47. Khachatryan, K., & Grigoryan, A. (2023). Labour market deprivation. Comparative Economic Studies. https://doi.org/10.1016/j.scitotenv.2024.173728
  48. Lang, T., Burneika, D., Noorkõiv, R., Plüschke-Altof, B., Pociūtė-Sereikienė, G., & Sechi, G. (2022). Socio-spatial polarization. European Urban and Regional Studies, 29(1), 21-44. https://doi.org/10.1016/j.scs.2022.103568
  49. Langemeyer, J., et al. (2025). Social-ecological justice. npj Urban Sustainability, 5(1), 46. https://doi.org/10.3389/fevo.2025.1065538
  50. Li, Y., & Feng, X. (2024). Poverty in Shanghai. Sustainability, 16(5), 2009.
  51. Lohmann, H. (2009). Working poor analysis. European Sociological Review, 25(4), 489-504.
  52. Lotfi, H., & Moradi, A. (2024). An analysis of spatial inequalities based on social indicators in Tabriz Metropolitan City. Spatial and Urban Development, 1 (3), 99-113. https:// doi: 10.22098/gsd.2025.16622.1077. [In Persian]
  53. Mak, H., Coulter, R., & Fancourt, D. (2021). Neighbourhood deprivation. BMC Public Health, 21, 1685. https://doi.org/10.3402/gha.v7.24928
  54. Mazenda, A., & Lubinga, M. (2024). Healthcare deprivation. Discover Social Science and Health, 4, 47. https://doi.org/10.1016/j.scs.2024.103568
  55. McCartney, G., Popham, F., McMaster, R., & Cumbers, A. (2019). Health inequalities. Public Health, 172, 22-30.
  56. McLennan, D., et al. (2025). English indices of deprivation. https://doi.org/10.1016/S0034-4257(03)00079-8
  57. Mohammadian, H., & Qasemi Ardabi, A. (2011). Migrant social networks and the reproduction of migration culture in rural areas: A qualitative study of migration flows from East Azarbaijan to Tehran. Rural-Urban Local Development (Rural Development), 4 (1), 109-128. [In Persian]
  58. Montgomery, D., Peck, E., & Vining, G. (2021). Linear regression analysis. Wiley.
  59. Murphy, J. (2019). Global production networks. Journal of Economic Geography, 19(4), 943-971. doi: 10.1038/s41598-024-77036-y. https://doi.org/10.1019/j.scs.2019.103568
  60. Nelson, R., Warnier, M., & Verma, T. (2024). Urban inequalities. Geographical Analysis, 56(2), 187-216.
  61. Nicoletti, L., Sirenko, M., & Verma, T. (2023). Infrastructure inequality. Environment and Planning B, 50(3), 831-849. https://doi.org/10.1016/j.scs.2023.106627
  62. Noble, M., Wright, G., Smith, G., & Dibben, C. (2006). Multiple deprivation. Environment and Planning A, 38(1), 169-185. https://doi.org/10.1016/j.cities.2006.105587
  63. Ouyang, W., Wang, B., Tian, L., & Niu, X. (2017). Urban service deprivation. Cities, 60, 436-445.
  64. Polimeni, J., Simionescu, M., & Iorgulescu, R. (2022). Energy poverty. International Journal of Environmental Research and Public Health, 19(18), 11459. https://doi.org/10.1016/j.scs.2022.106546
  65. Rodríguez-Pose, A. (2018). Revenge of places that don’t matter. Cambridge Journal of Regions, Economy and Society, 11(1), 189-209. https://doi.org/10.18737/srep11679
  66. Rodríguez-Pose, A. (2020). Rise of populism. LSE Public Policy Review, 1(1).
  67. Sarver, R., Mousavi, M. N., & Mobaraki, A. (2009). Spatial analysis of regional disparities in East Azarbaijan Province. Geography (Regional Planning), 1 (2), 39-50. https://doi.org/10.23478/gsd.1996.18799.1108. [In Persian]
  68. Scheuer, S., Haase, D., & Meyer, V. (2011). Flood vulnerability analysis. Natural Hazards, 58(2), 731-751.
  69. Schubertová, M., & Antalová, M. (2023). Working poverty. International Journal of Social Quality, 13(1), 93-113. https://doi.org/10.1038/srep11160
  70. Shali, M., & Imani, H. (2024). Analysis of spatial heterogeneities in Tabriz Metropolitan City. Spatial and Urban Development, 1 (1), 1-13. [In Persian]
  71. Sheikh Beiglou, R., Taqvayi, M. S., & varesi, H. R. (2011). Spatial analysis of deprivation and development inequalities in Iranian counties. Social Welfare, 12 (46), 189-214. [In Persian]
  72. Stringer, T., Clavel, E., & Burelo, M. (2025). Road accessibility and development. Journal of Transport Geography, 125, 104206.
  73. Taher pour, F., Vaezy, M., khorrami, H., & Akbari, M. (2020). Assessment of Counties in East Azarbaijan Province with Respect to Infrastructure Indicators Using Grey Relational Analysis, 10(38), 33-50. https://doi: 18.89078/gsd.2020.18799.1108. [In Persian]
  74. Tolabi Nezhad, M. H., Bazrafshan, J., & Qanbari, C. (2017). Analysis of the relationship between indicators of regional deprivation with environmental sustainability (Case Study: Villages of Poldokhtar County). Applied Geographic Sciences (Geographic Sciences), 17 (46), 45-72. [In Persian]
  75. Tu, Y., et al. (2025). Infrastructure inequality and health. Nature Human Behaviour, 9(8), 1669-1682. https://doi.org/10.1016/j.uclim.2022.101278
  76. Tunstall, B. (2025). Housing inequalities. Journal of Social Welfare and Family Law, 47(4), 464-478.
  77. Van Vulpen, B., & Bock, B. (2020). Spatial justice review. Romanian Journal of Regional Science, 14(2). https://doi.org/10.3390/rs15030709
  78. Vasishtha, G., & Mohanty, S. (2021). Poverty in India. Spatial Demography, 9.
  79. Wang, Y., Chen, L., Liu, B., & Tao, Z. (2025). Healthcare inequality. ISPRS International Journal of Geo-Information, 14(4), 168. https://doi.org/10.1016/j.ufug.2024.128219
  80. Wheeler, D. (2021). Geographically weighted regression. Handbook of Regional Science, 1895-1921.
  81. Woodward, A., et al. (2024). Socioeconomic deprivation and health. Health Expectations, 27(3). https://doi.org/10.1016/j.buildenv.2022.109062
  82. Wooldridge, J. (2016). Introductory econometrics. Cengage.
  83. Wu, M., Zhang, R., & Liu, W. (2025). Industrial structure inequality. Journal of the Knowledge Economy, 1-30. https://doi.org/10.1016/j.scs.2023.104613
  84. Ying, B., & Hatta, Z. (2025). Educational inequality. International Journal on Recent Trends in Business and Tourism (IJRTBT), 9(1), 1-13.
  85. Yong, A., & Pearce, S. (2013). Exploratory factor analysis guide. Tutorials in Quantitative Methods for Psychology, 9(2), 79-94. https://doi.org/10.1016/j.landurbplan.2014.01.017
  86. Zareian, S. (2014). Urban colonial planning and spatial justice in post-colonial cities: A comparative study of Algiers, Tunisia and Mumbai from the 19th century to today. Spatial and Urban Development, 2 (2), 19-44. https://doi.org/10.22098/gsd.2026.18799.1108. [In Persian]
  87. Zhang, Q., Wang, R., Ribeiro, A., Demszky, D., & Loeb, S. (2025). Educator attention inequality. arXiv preprint. https://doi.org/10.1029/2024GL112711
  88. Ziarei, K., Zanjirieh, S. M., & Sorkh Komeil, K. (2009). Examination and ranking of the degree of development of counties in the province of Khorasan-e-Shodrang, using the technique of Topos. Human Geographic Research (Geographic Research), 42 (72), 30-17. [In Persian]
جغرافیا و توسعه فضایی
دوره 2، شماره 3
آبان 1404
صفحه 33-49

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