Toplu taşıma ağı mesafesine dayalı çok kriterli konum değerlendirme endeksi: İzmir örneği

Öz

Konut ve kiralama piyasası ülke ekonomilerinde önemli bir ekonomik yere sahiptir. Konut fiyatları genel olarak evin alanı, oda sayısı, bulunduğu kat gibi özelliklere göre belirlenmektedir. Ancak evin bu özelliklerinin yanı sıra konumu da evin pazarlama değerlendirmesi üzerinde önemli bir etkiye sahiptir. Bu bakımdan subjektif fiyat spekülasyonlarının önüne geçebilmek için, konutun konumunu objektif kriterlere göre değerlendirebilmek oldukça önemlidir. Bu çalışmada bir nesnenin konumunun değerlendirilmesi, çok kriterli karar verme problemi olarak ele alınmıştır. Bu konumdan toplu taşıma araçlarıyla şehrin diğer noktalarına ulaşım kolaylığının yanı sıra metro istasyonları, hastaneler, alışveriş merkezleri, yeşil park alanları gibi önemli noktalara ulaşımın kolaylığını da göz önünde bulundurmaktadır. Bu bağlamda, bir toplu taşıma ağı (TTA) mesafe kavramı tanıtılmış ve Konum Değerlendirme Endeksi (KDE) adı verilen bir endeks önerilmiştir. Konutun konumuna göre piyasa değerinin tahmin edilmesinde bu endeksin önemi vurgulanmıştır. Önerilen model, Türkiye'nin üçüncü büyük metropol şehri olan İzmir şehrinin toplu ulaşım ağı verileri üzerinde çalıştırılmış ve şehrin KDE ısı haritası oluşturulmuştur.

Anahtar Kelimeler

• Toplu taşıma ağı (TTA)
• TTA mesafesi
• çok kriterli karar verme
• konum değerlendirme endeksi
• konut piyasası

A multicriteria location valuation index based on the public transport network distance: case study in Izmir

Öz

Housing and rental market has an important economic place in the economy of countries. Housing prices are generally determined by the characteristics such as area of the house, the number of rooms, the floor it is located on, etc. However, in addition to these characteristics of the house itself, the location of the house also has a significant impact on the marketing evaluation of the house. In this respect, it is important to be able to evaluate the housing location according to objective criteria in order to prevent subjective price speculations. The evaluation of the location of an object is handled as a multi-criteria decision-making problem in this study. It considers the ease of transportation from this location to other points of the city via public transport, as well as to reach important points such as subway stations, hospitals, shopping centers, green park areas, etc. In this context, a public transport network (PTN) distance concept is introduced and an index called Location Valuation Index (LVI) is proposed. The importance of this index in estimating the market value according to the location of the residence was emphasized. The proposed model was run on the data of the public transportation network of the Izmir city, the third largest metropolitan city of Turkey, and the LVI heatmap of the city was created.

Anahtar Kelimeler

• Public transportation network (PTN)
• PTN distance
• multi-criteria decision making
• housing market
• location valuation index

Kaynakça

1. 1. Smith S.J., Munro M., Christie H., Performing (Housing) Markets, Urban Studies, 43 (1), 81–98, 2006.
2. 2. Ferrero A., House Price Booms, Current Account Deficits, and Low Interest Rates, Journal of Money A., Credit and Banking, 47 (no. S1), 261-293, 2015.
3. 3. Nursoleh N., Location Analysis of Interest in Buying Housing in South Tangerang City. AKADEMIK: Jurnal Mahasiswa Ekonomi &Amp; Bisnis, 2 (1), 35–42, 2022.
4. 4. Jiang Y., Lv A., Yan Z., Yang Z., A GIS-Based Multi-Criterion Decision-Making Method to Select City Fire Brigade: A Case Study of Wuhan, China. ISPRS Int. J. Geo-Inf. 10, 777, 2021.
5. 5. Herath S., Elevating the Value of Urban Location: A Consumer Preference-Based Approach to Valuing Local Amenity Provision. Land, 10, 1226, 2021.
6. 6. Aydin N., Seker S., Özkan B., Planning Location of Mobility Hub for Sustainable Urban Mobility, Sustainable Cities and Society, 81, 103843, 2022.
7. 7. Shafii M., Afrazandeh S.M., Charrahi Z., Al-Modaresi S.A., Askari R., The Optimized Location of Hospitals Using an Integrated Approach GIS and Analytic Hierarchy Process: A Case Study in Iran, Iran J Health Sci, 11 (3), 195-206, 2023.
8. 8. Zhang H., Wei G., Wei C., TOPSIS Method for Spherical Fuzzy MAGDM Based on Cumulative Prospect Theory and Combined Weights and Its Application to Residential Location, Journal of Intelligent and Fuzzy Systems, 42 (3), 1367 – 1380, 2022.

9. 9. Hwang C.L., Yoon K., Multiple attribute decision making: methods and applications: a state-of-the-art survey, Springer-Verlag, New York, 1981.
10. 10. Triantaphyllou E., Multi-criteria decision making methods: a comparative study, Springer, US, 2000.
11. 11. Olson D., Comparison of weights in TOPSIS models, Mathematical and Computer Modelling, 40, 721–7, 2004.
12. 12. Hwang C-L., Lai Y-J., Liu T-Y., A new approach for multiple objective decision making, Computers & Operations Research, 20, 889–99, 1993.
13. 13. Lai Y-J., Liu T-Y., Hwang C-L., TOPSIS for MODM, European Journal of Operational Research, 76, 486–500, 1994.
14. 14. Saaty T.L., Decision making with dependence and feedback: the analytic network process, RWS Publications, Pittsburgh, PA, 2001.
15. 15. Saaty T.L., How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48, 9–26, 1990.
16. 16. Opricovic S., Tzeng G-H., Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research, 156, 445–55, 2004.
17. 17. Opricovic S., Tzeng G-H., Extended VIKOR method in comparison with outranking methods, European Journal of Operational Research, 178, 514–29, 2007.
18. 18. Roy B., The outranking approach and the foundations of ELECTRE methods, Theory and Decision, 31, 49–73, 1991.
19. 19. Brans J.P., Mareschal B., Vincke P., PROMETHEE: a new family of outranking methods in multicriteria analysis, In: Brans JP (Eds.), Operational research, IFORS 84, North Holland, Amsterdam, 477–90, 1984.
20. 20. Brans J.P., Vincke P., Mareschal B., How to select and how to rank projects: the PROMETHEE method, European Journal of Operational Research, 24, 228–38, 1986.
21. 21. Brans J.P., Vincke P., Note—A preference ranking organization method (The PROMETHEE method for multiple criteria decision-making), Management Science, 31, 647–56, 1985.
22. 22. Toloie-Eshlaghy A., Homayonfar M., Aghaziarati M., Arbabiun P., A subjective weighting method based on group decision making for ranking and measuring criteria values, Australian Journal of Basic and Applied Sciences, 5 (12), 2034-2040, 2011.
23. 23. Xu X., The SIR method: a superiority and inferiority ranking method for multiple criteria decision making, European Journal of Operational Research, 131, 587–602, 2001.
24. 24. Kersuliene V., Turskis Z., Integrated fuzzy multiple criteria decision making model for architect selection, Technological and Economic Development of Economy, 17, 645–66, 2011.
25. 25. Jessop A., IMP: a decision aid for multiattribute evaluation using imprecise weight estimates, Omega, 49, 18–29, 2014.
26. 26. Wallenius J., Dyer J.S., Fishburn P.C., Steuer R.E., Zionts S., Deb K., Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead, Management Science, 54 (7), 1336–1349, 2008.
27. 27. Karadağ A.A., Gültekin Y. S., Açık ve Yeşil Alanların Konut Seçimine Etkisinin Belirlenmesi Temelinde Bir Ölçek Geliştirme Çalışması, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7 (1), 223-238, 2019
28. 28. Shafiei M.W.M., Abadi H., Osman W.N., The indicators of green buildings for Malaysian property development industry, International Journal of Applied Engineering Research, 12 (10), 2182-2189, 2017.
29. 29. Goh Z.T., Low S.T., Choong W.W., Wee S.C., Do green features increase housing value in Malaysia?, International Journal of Housing Markets and Analysis, 15 (5), 1296-1312, 2022.
30. 30. Adamec J., Janoušková S., Hák T., How to Measure Sustainable Housing: A Proposal for an Indicator-Based Assessment Tool, Sustainability, 13, 1152, 2021.
31. 31. Morancho A.B., A hedonic valuation of urban green areas, Landscape and Urban Planning, 66, 35–41, 2003.
32. 32. Hosseini S.M.A., Ghalambordezfooly R., de la Fuente A., Sustainability Model to Select Optimal Site Location for Temporary Housing Units: Combining GIS and the MIVES–Knapsack Model, Sustainability, 14, 4453, 2022.
33. 33. Nebati E.E., Ekmekçi İ., Başlıgil H., Proposal of index model in performance measurement: Shopping mall application, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (3), 1403-1415, 2023.
34. 34. Heyman A.V., Law S., Pont M.B., How is Location Measured in Housing Valuation? A Systematic Review of Accessibility Specifications in Hedonic Price Models, Urban Science, 3 (1), 3, 2019.
35. 35. Dijkstra E.W., A note on two problems in connexion with graphs, Numer. Math., 1, 269–271, 1959.
36. 36. Bozyiğit A., Alankuş G., Nasiboğlu E., Public transport route planning: Modified Dijkstra’s algorithm, In: International Conference on Computer Science and Engineering, (UBMK), Antalya, Turkey, 502–505, 2017.
37. 37. Bozyigit A., Alankus G., Nasibov E., A public transport route recommender minimizing the number of transfers, Sigma J. Eng. Nat. Sci., 9 (4), 437–446, 2018.
38. 38. Nasibov E.N., Diker A., Nasibov E., A multi criteria route planning model based on fuzzy preference degrees of stops, Appl. Soft Comput., 49, 13–26, 2016.
39. 39. Nasiboglu R., Dijkstra solution algorithm considering fuzzy accessibility degree for path optimization problem, Applied Soft Computing, 130, Article 109674, 2022.
40. 40. Nasiboğlu R., Şehir içi toplu taşımada otobüs içi yoğunluğun OWA toparlama operatörü aracılığıyla modellenmesi, In: Gürbüz F. (Eds.), Fen Bilimleri ve Matematik Alanında Güncel Araştırmalar, Duvar Yayınları, 21–38, 2020. ISBN: 978-625-7680-07-3.
41. 41. Saaty T., Peniwati K., Group Decision Making: Drawing out and Reconciling Differences. Pittsburgh, RWS Publications, Pennsylvania, 2008. ISBN 978-1-888603-08-8.
42. 42. Saracoglu B.O., Selecting industrial investment locations in master plans of countries, European Journal of Industrial Engineering, 7 (4), 416–441, 2013.
43. 43. Forman E.H., Saul I.G., The analytical hierarchy process—an exposition, Operations Research, 49 (4), 469–487, 2001.
44. 44. Rezaei J., A Concentration Ratio for Non-Linear Best Worst Method, International Journal of Information Technology & Decision Making, 19 (3), 891-907, 2020.
45. 45. Rezaei J., Best-worst multi-criteria decision-making method, Omega, 53, 49-57, 2015.
46. 46. Pamucar D., Ecer F., Cirovic G., Arlasheedi M.A., Application of Improved Best Worst Method (BWM) in Real-World Problems, Mathematics, 8, Article 1342, 2020.
47. 47. Ceder A., Public transit planning and operation: theory, modelling and practice, Butterworth-Heinemann, Oxford, 2007.
48. 48. Costa L.F., Rodrigues F.A., Travieso G., Villas Boas P.R., Characterization of complex networks: A survey of measurements, Advances in Physics, 56 (1), 167-242, 2007.
49. 49. Ferber C., Holovatch T., Holovatch Y., Palchykov V., Network harness: metropolis public transport, Physica A: Statistical Mechanics and Its Applications, 380 (1-2), 585–591, 2007.
50. 50. Ferber C., Holovatch T., Holovatch Y., Palchykov V., Public transport networks: empirical analysis and modeling, The European Physical Journal B—Condensed Matter and Complex Systems, 68 (2), 261–275, 2009.
51. 51. Izmir PTN data, https://github.com/resilla/FuzzyDijkstra/blob/main/IzmirPTNData.rar, 2022 (erişim 15 Kasım 2022).
52. 52. Brummelen G.R., Heavenly Mathematics: The Forgotten Art of Spherical Trigonometry, Princeton University Press, 2013.
53. 53. Sinnott R.W., Virtues of the Haversine, Sky Telescope, 68 (2), 158, 1984.