Identifying the Sustainable Development Corridor of Sanandaj Using a Scenario-Based GIS–MCDA–Fuzzy and OWA Approach

Articles in Press

Document Type : Original Article

Authors
Tofigh Forughi 1
Omid Ghaffari Aliabad 2
Reza Jaberi 3
1 Department of Geography and Urban Planning, Payame Noor University, Mahabad, Iran.
2 Department of Geomatics Engineering, University of Zanjan, Zanjan, Iran.
3 Department of Urban Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
10.22034/he.2026.583594.1215
Abstract
The global acceleration of urbanization in mountainous cities, together with the shortage of low-slope land, has intensified the phenomenon of “slope creep” and settlement in high-risk zones. Due to its valley morphology, steep slopes, and active fault zones, the city of Sanandaj is a prominent example of such conditions. This study aims to present an integrated framework for identifying the directions of Sanandaj’s future development that simultaneously considers geomorphological safety, physical efficiency, and spatial justice. In this framework, hard constraints—including the permissible slope threshold, buffer zones around active faults, riverbeds and river buffers, and selected safety buffers around hazardous facilities—were first applied as binary masks in a GIS environment to eliminate unsafe cells before any scoring. Then, natural, environmental, accessibility, safety, and socio-physical criteria were standardized using appropriate fuzzy membership functions, and their relative weights were determined through group AHP. Weighted Linear Combination (WLC) was implemented as a fully compensatory reference scenario. In contrast, several Ordered Weighted Averaging (OWA) scenarios with different α values were applied to represent varying levels of risk tolerance. Suitability maps were then produced in five classes. The results showed that the share of “very suitable” lands decreased from approximately 62.8% in the highly optimistic scenario (α = 0.5) to 10.5% in the highly cautious scenario (α = 5). This indicates that OWA effectively moderates WLC's inherent optimism. A radial analysis of the spatial pattern of very suitable zones showed that, across all scenarios, the northeast–east–northwest corridor consistently emerges as the “sustainable development corridor” of Sanandaj. In contrast, the southern and southwestern directions are highly sensitive to risk levels, and even a slight increase in strictness removes them from the set of top-priority zones. Overall, the proposed GIS–MCDA–Fuzzy–OWA framework, by practically integrating hard constraints into the model's core, provides a scenario-based tool to guide Sanandaj's development toward safer, more efficient, and more equitable directions. The depth of shallow cores (without undermining permeability) becomes a key objective.

Highlights

  1. حجازی، اسدالله، و نجف‌وند، سمیرا. (۱۳۹۹). مکان‌یابی مناطق مستعد توسعه فیزیکی شهری با استفاده از روش‌های OWA و ANP: مطالعه موردی شهر بیجار. جغرافیا و روابط انسانی، ۲(۴)، ۳۸۶–۳۹۹.
  2. نگهبان، سعید، گنجائیان، حمید، فریدونی‌کردستانی، مژده، و چشمه‌سفیدی، زیبا. (۱۳۹۸). ارزیابی توسعه فیزیکی شهرها و گسترش به سمت مناطق ممنوعه ژئومورفولوژیکی با استفاده از LCM: مطالعه موردی شهر سنندج. مخاطرات محیط طبیعی، ۸(۲۰)، ۳۹–۵۲. doi: 10.22111/jneh.2018.21943.1317
  3. نیری، هادی، سالاری، ممند، گنجاییان، حمید، و امانی، خبات. (۱۳۹۶). ارزیابی ژئومورفولوژیکی تناسب زمین برای گسترش کالبدی شهر سنندج با اعمال مناطق ممنوعه. پژوهش‌های جغرافیای برنامه‌ریزی شهری، ۵(۱).
  4. یزدانی، محمدحسین، و زارنجی، فاطمه‌سادات. (۱۴۰۲). مکان‌یابی بهینه گسترش کالبدی شهر سردشت با استفاده از روش‌های ترکیبی و فرایند انتقال شبکه فازی سامانه اطلاعات مکانی. دانش پیشگیری و مدیریت بحران، ۱۳(۴)، ۴۷۴–۴۸۹.

  5. Akıncı, H., Özalp, A. Y., & Turgut, B. (2013). Agricultural land use suitability analysis using GIS and AHP technique. Computers and Electronics in Agriculture, 97, 71–82. doi: 10.1016/j.compag.2013.07.006
  6. Allen, J., & Lu, K. (2003). Modeling and prediction of future urban growth in the Charleston region of South Carolina: A GIS-based integrated approach. Conservation Ecology, 8(2), 2. doi: 10.5751/ES-00595-080202
  7. Angel, S., Lamson-Hall, P., Blei, A. M., Shingade, S., & Kumar, S. (2021). Densify and expand: A global analysis of recent urban growth. Sustainability, 13(7), 3835. doi: 10.3390/su13073835
  8. Aydınoğlu, A. Ç., Iqbal, A. S., & Şişman, S. (2025). Parking suitability and site selection analysis using GIS-based multi-criteria decision analysis techniques: AHP, TOPSIS, and VIKOR—A case study of Pendik District (Istanbul). ICONARP International Journal of Architecture and Planning, 13(1), 22–50. doi: 10.15320/ICONARP.2025.314
  9. Boroushaki, S., & Malczewski, J. (2008). Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers & Geosciences, 34(4), 399–410. doi: 10.1016/j.cageo.2007.04.003
  10. Brans, J. P., & Vincke, P. (1985). A preference ranking organisation method: The PROMETHEE method for MCDM. Management Science, 31(6), 647–656. doi: 10.1287/mnsc.31.6.647
  11. Buchsbaum, R., & Jackson, S. (2012). The scientific basis for protecting wetland buffers. Massachusetts Audubon Society.
  12. Burrough, P. A., & McDonnell, R. A. (1998). Principles of geographical information systems. Oxford University Press.
  13. Burrough, P. A., van Gaans, P. F., & MacMillan, R. A. (2000). High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems, 113(1), 37–52. doi: 10.1016/S0165-0114(99)00011-1
  14. Churchman, A. (1999). Disentangling the concept of density. Journal of Planning Literature, 13(4), 389–411. doi: 10.1177/08854129922092478
  15. Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: Principles and practices (3rd ed.). CRC Press. doi: 10.1201/9780429052729
  16. Doulay Seydou, K., Morenikeji, W., Diouf, A., Dicko, K., Erdanaev, E., Loewner, R., & Okhimamhe, A. A. (2024). Dynamics of Zinder’s urban landscape: Implications for sustainable land use management and environmental conservation. Sustainability, 16(23), 10263. doi: 10.3390/su162310263
  17. Duncan, B. N., Lamsal, L. N., Thompson, A. M., Yoshida, Y., Lu, Z., Streets, D. G., & Pickering, K. E. (2016). A space-based, high-resolution view of notable changes in urban NO₂ pollution around the world (2005–2014). Journal of Geophysical Research: Atmospheres, 121(2), 976–996. doi: 10.1002/2015JD024121
  18. Eastman, J. R. (1999). Multi-criteria evaluation and GIS. In P. A. Longley, M. F. Goodchild, D. J. Maguire, & D. W. Rhind (Eds.), Geographical information systems (Vol. 1, pp. 493–502). Wiley.
  19. Ferretti, V., & Pomarico, S. (2013). Ecological land suitability analysis through spatial indicators: An application of the Analytic Network Process technique and Ordered Weighted Average approach. Ecological Indicators, 34, 507–519. doi: 10.1016/j.ecolind.2013.06.005
  20. Gesch, D. B. (2018). Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. Frontiers in Earth Science, 6, 230. doi: 10.3389/feart.2018.00230
  21. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. doi: 10.1016/j.rse.2017.06.031
  22. Hosseini, S. H., & Hajilou, M. (2019). Drivers of urban sprawl in urban areas of Iran. Papers in Regional Science, 98(2), 1137–1158. doi: 10.1111/pirs.12381
  23. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer. doi: 10.1007/978-3-642-48318-9
  24. Jiang, H., & Eastman, J. R. (2000). Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173–184. doi: 10.1080/136588100240903
  25. MacMillan, R. A., Pettapiece, W. W., Nolan, S. C., & Goddard, T. W. (2000). A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems, 113(1), 81–109. doi: 10.1016/S0165-0114(99)00014-7
  26. Malczewski, J. (1999). GIS and multicriteria decision analysis. Wiley.
  27. Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1), 3–65. doi: 10.1016/j.progress.2003.09.002
  28. Malczewski, J. (2006). Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International Journal of Applied Earth Observation and Geoinformation, 8(4), 270–277. doi: 10.1016/j.jag.2006.01.003
  29. Malczewski, J., & Rinner, C. (2015). Multicriteria decision analysis in geographic information science. Springer. doi: 10.1007/978-3-540-74757-4
  30. Mallick, J., Ibnatiq, A. A., Ben Kahla, N., Alqadhi, S., Singh, V. P., Hoa, P. V., Hang, H. T., Hong, N. V., & Le, H. A. (2022). GIS-based decision support system for safe and sustainable building construction site in a mountainous region. Sustainability, 14(2), 888. doi: 10.3390/su14020888
  31. Miller, H. J., & Shaw, S. L. (2001). Geographic information systems for transportation: Principles and applications. Oxford University Press.
  32. Mulliner, E., Malys, N., & Maliene, V. (2016). Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, 59, 146–156. doi: 10.1016/j.omega.2015.05.013
  33. Noth, T., & Rinner, C. (2021). Prioritization in wildfire restoration using GIS-based ordered weighted averaging (OWA): A case study in southern California. AIMS Environmental Science, 8(5), 481–497. doi: 10.3934/environsci.2021031
  34. Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. doi: 10.1016/S0377-2217(03)00020-1
  35. Rinner, C., & Malczewski, J. (2002). Web-enabled spatial decision analysis using ordered weighted averaging (OWA). Journal of Geographical Systems, 4(4), 385–403. doi: 10.1007/s101090300095
  36. Robison, R. M., Nelson, C. V., & Christenson, G. E. (1990). Geologic hazards and land-use planning (Open-File Report No. 198). Utah Geological and Mineral Survey.
  37. Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
  38. Saaty, T. L. (2001). Decision making with dependence and feedback: The analytic network process. RWS Publications.
  39. Saaty, T. L., & Vargas, L. G. (2001). Models, methods, concepts & applications of the analytic hierarchy process. Springer. doi: 10.1007/978-1-4615-1665-1
  40. Sharifi, M. A., & Murayama, Y. (2015). Neighborhood knowledge representation in GIS-based land-use planning. International Journal of Geographical Information Science, 29(8), 1310–1328. doi: 10.1080/13658816.2015.1019886
  41. Shi, K., Yu, B., Ma, J., Cao, W., & Cui, Y. (2023). Impacts of slope climbing of urban expansion on global sustainable development. The Innovation, 4(6), 100529. doi: 10.1016/j.xinn.2023.100529
  42. Sister, C., Wolch, J., & Wilson, J. (2010). Got green? Addressing environmental justice in park provision. GeoJournal, 75(3), 229–248. doi: 10.1007/s10708-009-9303-8
  43. Suzuki, H., Cervero, R., & Iuchi, K. (2013). Transforming cities with transit: Transit and land-use integration for sustainable urban development. World Bank. doi: 10.1596/978-0-8213-9745-9
  44. Suthar, N., Das, D., & Mallik, J. (2024). Land-use suitability assessment for urban development using multi-criteria decision-making analysis in the Himalayan districts of Shimla, Nainital, and Darjeeling, India. Discover Environment, 2, 90. doi: 10.1007/s44274-024-00134-1
  45. Tachikawa, T., Hato, M., Kaku, M., & Iwasaki, A. (2011). Characteristics of ASTER GDEM version 2. In 2011 IEEE International Geoscience and Remote Sensing Symposium (pp. 3657–3660). doi: 10.1109/IGARSS.2011.6050017
  46. Tatem, A. J. (2017). WorldPop, open data for spatial demography. Scientific Data, 4, 170004. doi: 10.1038/sdata.2017.4
  47. UN-Habitat. (2020). World cities report 2020: The value of sustainable urbanization. United Nations Human Settlements Programme.
  48. UN-Habitat. (2024). MY Neighbourhood for mountainous cities. United Nations Human Settlements Programme.
  49. Vakilipour, S., Sadeghi-Niaraki, A., Ghodousi, M., & Choi, S. M. (2021). Comparison between multi-criteria decision-making methods and evaluating the quality of life at different spatial levels. Sustainability, 13(7), 4067. doi: 10.3390/su13074067
  50. Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., ... Levelt, P. F. (2012). TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120, 70–83. doi: 10.1016/j.rse.2011.09.027
  51. Wicaksono, A., & Lazuardi, W. (2018). Urban park suitability analysis using spatial multi-criteria fuzzy logic. IOP Conference Series: Earth and Environmental Science, 158, 012040. doi: 10.1088/1755-1315/158/1/012040
  52. Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190. doi: 10.1109/21.87068
  53. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. doi: 10.1016/S0019-9958(65)90241-X
  54. Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165–179. doi: 10.3846/20294913.2014.892037
  55. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. doi: 10.1080/01431160304987

Keywords
Urban development
development direction suitability analysis
GIS–MCDA fuzzy multi-criteria decision-making
Ordered Weighted Averaging (OWA)
Sanandaj
Subjects
  1. حجازی، اسدالله، و نجف‌وند، سمیرا. (۱۳۹۹). مکان‌یابی مناطق مستعد توسعه فیزیکی شهری با استفاده از روش‌های OWA و ANP: مطالعه موردی شهر بیجار. جغرافیا و روابط انسانی، ۲(۴)، ۳۸۶–۳۹۹.
  2. نگهبان، سعید، گنجائیان، حمید، فریدونی‌کردستانی، مژده، و چشمه‌سفیدی، زیبا. (۱۳۹۸). ارزیابی توسعه فیزیکی شهرها و گسترش به سمت مناطق ممنوعه ژئومورفولوژیکی با استفاده از LCM: مطالعه موردی شهر سنندج. مخاطرات محیط طبیعی، ۸(۲۰)، ۳۹–۵۲. doi: 10.22111/jneh.2018.21943.1317
  3. نیری، هادی، سالاری، ممند، گنجاییان، حمید، و امانی، خبات. (۱۳۹۶). ارزیابی ژئومورفولوژیکی تناسب زمین برای گسترش کالبدی شهر سنندج با اعمال مناطق ممنوعه. پژوهش‌های جغرافیای برنامه‌ریزی شهری، ۵(۱).
  4. یزدانی، محمدحسین، و زارنجی، فاطمه‌سادات. (۱۴۰۲). مکان‌یابی بهینه گسترش کالبدی شهر سردشت با استفاده از روش‌های ترکیبی و فرایند انتقال شبکه فازی سامانه اطلاعات مکانی. دانش پیشگیری و مدیریت بحران، ۱۳(۴)، ۴۷۴–۴۸۹.

  5. Akıncı, H., Özalp, A. Y., & Turgut, B. (2013). Agricultural land use suitability analysis using GIS and AHP technique. Computers and Electronics in Agriculture, 97, 71–82. doi: 10.1016/j.compag.2013.07.006
  6. Allen, J., & Lu, K. (2003). Modeling and prediction of future urban growth in the Charleston region of South Carolina: A GIS-based integrated approach. Conservation Ecology, 8(2), 2. doi: 10.5751/ES-00595-080202
  7. Angel, S., Lamson-Hall, P., Blei, A. M., Shingade, S., & Kumar, S. (2021). Densify and expand: A global analysis of recent urban growth. Sustainability, 13(7), 3835. doi: 10.3390/su13073835
  8. Aydınoğlu, A. Ç., Iqbal, A. S., & Şişman, S. (2025). Parking suitability and site selection analysis using GIS-based multi-criteria decision analysis techniques: AHP, TOPSIS, and VIKOR—A case study of Pendik District (Istanbul). ICONARP International Journal of Architecture and Planning, 13(1), 22–50. doi: 10.15320/ICONARP.2025.314
  9. Boroushaki, S., & Malczewski, J. (2008). Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers & Geosciences, 34(4), 399–410. doi: 10.1016/j.cageo.2007.04.003
  10. Brans, J. P., & Vincke, P. (1985). A preference ranking organisation method: The PROMETHEE method for MCDM. Management Science, 31(6), 647–656. doi: 10.1287/mnsc.31.6.647
  11. Buchsbaum, R., & Jackson, S. (2012). The scientific basis for protecting wetland buffers. Massachusetts Audubon Society.
  12. Burrough, P. A., & McDonnell, R. A. (1998). Principles of geographical information systems. Oxford University Press.
  13. Burrough, P. A., van Gaans, P. F., & MacMillan, R. A. (2000). High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems, 113(1), 37–52. doi: 10.1016/S0165-0114(99)00011-1
  14. Churchman, A. (1999). Disentangling the concept of density. Journal of Planning Literature, 13(4), 389–411. doi: 10.1177/08854129922092478
  15. Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: Principles and practices (3rd ed.). CRC Press. doi: 10.1201/9780429052729
  16. Doulay Seydou, K., Morenikeji, W., Diouf, A., Dicko, K., Erdanaev, E., Loewner, R., & Okhimamhe, A. A. (2024). Dynamics of Zinder’s urban landscape: Implications for sustainable land use management and environmental conservation. Sustainability, 16(23), 10263. doi: 10.3390/su162310263
  17. Duncan, B. N., Lamsal, L. N., Thompson, A. M., Yoshida, Y., Lu, Z., Streets, D. G., & Pickering, K. E. (2016). A space-based, high-resolution view of notable changes in urban NO₂ pollution around the world (2005–2014). Journal of Geophysical Research: Atmospheres, 121(2), 976–996. doi: 10.1002/2015JD024121
  18. Eastman, J. R. (1999). Multi-criteria evaluation and GIS. In P. A. Longley, M. F. Goodchild, D. J. Maguire, & D. W. Rhind (Eds.), Geographical information systems (Vol. 1, pp. 493–502). Wiley.
  19. Ferretti, V., & Pomarico, S. (2013). Ecological land suitability analysis through spatial indicators: An application of the Analytic Network Process technique and Ordered Weighted Average approach. Ecological Indicators, 34, 507–519. doi: 10.1016/j.ecolind.2013.06.005
  20. Gesch, D. B. (2018). Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. Frontiers in Earth Science, 6, 230. doi: 10.3389/feart.2018.00230
  21. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. doi: 10.1016/j.rse.2017.06.031
  22. Hosseini, S. H., & Hajilou, M. (2019). Drivers of urban sprawl in urban areas of Iran. Papers in Regional Science, 98(2), 1137–1158. doi: 10.1111/pirs.12381
  23. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer. doi: 10.1007/978-3-642-48318-9
  24. Jiang, H., & Eastman, J. R. (2000). Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173–184. doi: 10.1080/136588100240903
  25. MacMillan, R. A., Pettapiece, W. W., Nolan, S. C., & Goddard, T. W. (2000). A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems, 113(1), 81–109. doi: 10.1016/S0165-0114(99)00014-7
  26. Malczewski, J. (1999). GIS and multicriteria decision analysis. Wiley.
  27. Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1), 3–65. doi: 10.1016/j.progress.2003.09.002
  28. Malczewski, J. (2006). Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International Journal of Applied Earth Observation and Geoinformation, 8(4), 270–277. doi: 10.1016/j.jag.2006.01.003
  29. Malczewski, J., & Rinner, C. (2015). Multicriteria decision analysis in geographic information science. Springer. doi: 10.1007/978-3-540-74757-4
  30. Mallick, J., Ibnatiq, A. A., Ben Kahla, N., Alqadhi, S., Singh, V. P., Hoa, P. V., Hang, H. T., Hong, N. V., & Le, H. A. (2022). GIS-based decision support system for safe and sustainable building construction site in a mountainous region. Sustainability, 14(2), 888. doi: 10.3390/su14020888
  31. Miller, H. J., & Shaw, S. L. (2001). Geographic information systems for transportation: Principles and applications. Oxford University Press.
  32. Mulliner, E., Malys, N., & Maliene, V. (2016). Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, 59, 146–156. doi: 10.1016/j.omega.2015.05.013
  33. Noth, T., & Rinner, C. (2021). Prioritization in wildfire restoration using GIS-based ordered weighted averaging (OWA): A case study in southern California. AIMS Environmental Science, 8(5), 481–497. doi: 10.3934/environsci.2021031
  34. Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. doi: 10.1016/S0377-2217(03)00020-1
  35. Rinner, C., & Malczewski, J. (2002). Web-enabled spatial decision analysis using ordered weighted averaging (OWA). Journal of Geographical Systems, 4(4), 385–403. doi: 10.1007/s101090300095
  36. Robison, R. M., Nelson, C. V., & Christenson, G. E. (1990). Geologic hazards and land-use planning (Open-File Report No. 198). Utah Geological and Mineral Survey.
  37. Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
  38. Saaty, T. L. (2001). Decision making with dependence and feedback: The analytic network process. RWS Publications.
  39. Saaty, T. L., & Vargas, L. G. (2001). Models, methods, concepts & applications of the analytic hierarchy process. Springer. doi: 10.1007/978-1-4615-1665-1
  40. Sharifi, M. A., & Murayama, Y. (2015). Neighborhood knowledge representation in GIS-based land-use planning. International Journal of Geographical Information Science, 29(8), 1310–1328. doi: 10.1080/13658816.2015.1019886
  41. Shi, K., Yu, B., Ma, J., Cao, W., & Cui, Y. (2023). Impacts of slope climbing of urban expansion on global sustainable development. The Innovation, 4(6), 100529. doi: 10.1016/j.xinn.2023.100529
  42. Sister, C., Wolch, J., & Wilson, J. (2010). Got green? Addressing environmental justice in park provision. GeoJournal, 75(3), 229–248. doi: 10.1007/s10708-009-9303-8
  43. Suzuki, H., Cervero, R., & Iuchi, K. (2013). Transforming cities with transit: Transit and land-use integration for sustainable urban development. World Bank. doi: 10.1596/978-0-8213-9745-9
  44. Suthar, N., Das, D., & Mallik, J. (2024). Land-use suitability assessment for urban development using multi-criteria decision-making analysis in the Himalayan districts of Shimla, Nainital, and Darjeeling, India. Discover Environment, 2, 90. doi: 10.1007/s44274-024-00134-1
  45. Tachikawa, T., Hato, M., Kaku, M., & Iwasaki, A. (2011). Characteristics of ASTER GDEM version 2. In 2011 IEEE International Geoscience and Remote Sensing Symposium (pp. 3657–3660). doi: 10.1109/IGARSS.2011.6050017
  46. Tatem, A. J. (2017). WorldPop, open data for spatial demography. Scientific Data, 4, 170004. doi: 10.1038/sdata.2017.4
  47. UN-Habitat. (2020). World cities report 2020: The value of sustainable urbanization. United Nations Human Settlements Programme.
  48. UN-Habitat. (2024). MY Neighbourhood for mountainous cities. United Nations Human Settlements Programme.
  49. Vakilipour, S., Sadeghi-Niaraki, A., Ghodousi, M., & Choi, S. M. (2021). Comparison between multi-criteria decision-making methods and evaluating the quality of life at different spatial levels. Sustainability, 13(7), 4067. doi: 10.3390/su13074067
  50. Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., ... Levelt, P. F. (2012). TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120, 70–83. doi: 10.1016/j.rse.2011.09.027
  51. Wicaksono, A., & Lazuardi, W. (2018). Urban park suitability analysis using spatial multi-criteria fuzzy logic. IOP Conference Series: Earth and Environmental Science, 158, 012040. doi: 10.1088/1755-1315/158/1/012040
  52. Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190. doi: 10.1109/21.87068
  53. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. doi: 10.1016/S0019-9958(65)90241-X
  54. Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165–179. doi: 10.3846/20294913.2014.892037
  55. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. doi: 10.1080/01431160304987
Human Ecology

Articles in Press, Accepted Manuscript
Available Online from 30 May 2026