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Year 2020, Volume: 5 Issue: 1, 24 - 40, 25.04.2020
https://doi.org/10.33457/ijhsrp.670014

Abstract

References

  • 1. Erjem, Y. [A Sociological Research on Traffic System Operation and Traffic Accidents]. Polis ve Sosyal Bilimler Dergisi 2005: 3(1), 69-94. 2. Alp S. ve Engin T. [Analysis and Evaluation of the Relation Between the Reasons and Consequences of the Traffic Accidents by using TOPSIS And AHP Methods İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 2011: 10(19), 65-87. 3. Sungur İ., Akdur R. ve Piyal B. [Analysis of Traffic Accidents in Turkey] Ankara Med J. 14(3): 114 – 124. 4. WHO (2018). Findings from the Global Burden of Disease Study 2017 5. Türkiye İstatistik Kurumu (2017). Ulaşım İstatistikleri 6. Karayolları Genel Müdürlüğü (2018). Trafik Kazaları Özeti 2017. www.kgm.gov.tr. 7. OECD Road Accident data (2018). https://data.oecd.org/transport/road-accidents.htm 8. Özdamar K. [Multi-Dimensional Scaling]. Paket Programlar ile İstatistiksel Veri Analizi 2 (Çok Değişkenli Analizler). 5. Baskı. Eskişehir: Kaan Kitabevi; 2004. p.501-5. 9. Bülbül, S. ve Köse, A. Türkiye’de bölgelerarası iç göç hareketlerinin çok boyutlu ölçekleme yöntemi ile incelenmesi 2010: 39 (1), 75-94 10. Etikan, İ., Erkorkmaz, Ü. Sanisoğlu, S.Y., Demir, O., Kuyucu, Y.E. [Multidimensional Scaling analysis of Judicial Statistics for Crimes Against Persons and Properties in 2008 in 81 Provinces in Turkey]. Türkiye Klinikleri J Med Sci, 2012: 32(5), 1295-1306. 11. İbicioğlu, M. [Multidimensional Scaling Analysis of Relations Among Returns of Investment Instruments]. The International Journal of Economic and Social Research, 2012, 8(2), 8:45-55 12. Acar, A.B. [Comparison of Turkey and the Other OECD Countries in Terms of Labour Markets’ Main Indicators Using Multi Dimensional Scale Analysis]. Faculty of Business Administration Institute of Business Administration Journal of Management 2013: 24 (75), 121-144. 13. Ersöz F. [Analysis of health levels and expenditures of Turkey and OECD countries]. İstatistikçiler Dergisi 2008;1(2):95-104. 14. Alpar, R. (2013). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, Ankara. 15. Aydın, D. ve Başkın B. [The Classification Structures of Banks by Their Capital Adequacy Ratios as the Results of Clustering Analysis and Multidimensional Scaling]. (2013). BSAD Bankacılık ve Sigortacılık Araştırmaları Dergisi 2013:1(5-6), 29-47. 16. Ersöz, T., Türkoğlu Elitaş MN. ve Ersöz, F. [Investigation of Multidimensional Scaling Analysis of Biomass Energy Generation in OECD Countries]. TÜBAV Bilim 2015 8 (3) 2015 1-11. 17. Tinsley, H.E. A. Brown, S.D. Handbook of Applied Multivariate Statistics and Mathematical Modeling. Academic Press. USA, 2000, p.345. Türkiye İstatistik Kurumu (2017). Ulaşım İstatistikleri. 18. Tüzüntürk S. [Multidimensional scaling: an application on crime statistics]. Uludağ University Journal of Economics and Administrative Sciences 2009;28(2):71-91. 19. Büyüker İşler, D. 2014.[ Examining between regional internal migration movements in Turkey with multidimensional scaling] .Adıyaman Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 7(17), 447-484. 20. Fırat, Ü.O. ve Demirhan, A. [Performance Analysis of Commercial Banks]. ODTÜ İktisat Kongresi 2001. 21. Johnson RA, Wichern DW. [Multidimensional Scaling]. Applied Multivariate Statistical Analysis. 4th ed. New Jersey: Pearson Prentice Hall; 1999. p.741-63. 22. Gürçaylılar Yenidoğan T. [Multidimensional scaling in marketing research: a study on university students' perceptions of brand]. Akdeniz University Journal of Economics and Administrative Sciences 2008;8(15):138-69 23. Tatlıdil H. (1996). [Multi-Dimensional Scaling]. Uygulamalı Çok Değişkenli İstatistiksel Analiz. 2. Baskı. Ankara: Akademi Matbaası; 1996. p.279-90. 24. Oğuzlar A. [Locating factors affecting membership of European Union Using Multidimensional Scaling]. Uludağ University Journal of Economics and Administrative Sciences 2005;24(1):33-43. 25. Kruskal, J. B. (1964). Multıdimensional Scaling By Optimizing Goodness Of Fit to a Nonmetrıc Hypothesis. Psychometrika, 9(1), 1-27.

INVESTIGATION OF OECD COUNTRIES WITH MULTI-DIMENSIONAL SCALING ANALYSIS IN TERMS OF TRAFFIC ACCIDENT INDICATORS

Year 2020, Volume: 5 Issue: 1, 24 - 40, 25.04.2020
https://doi.org/10.33457/ijhsrp.670014

Abstract

This study tries to compare similarities and differences in OECD countries in terms of traffic accidents utilizing Multidimensional Scale Analysis (MDS). In the study, MDS analysis was used utilizing basic indicators such as the number of injuries, deaths and the number of accidents resulting in material damage in the traffic accidents that happened in 2017. As a result of analysis, stress values and RSQ values turned out to be 0.0000 and 1.0000, respectively. That the stress value has resulted as zero shows that there is no inconsistency; and the fact that RSQ value has been found to be 1 indicates that the accuracy rate of this analysis is high and the values are in excellent coherence. According to results obtained from the analyses, it is seen that Malta and Liechtenstein, in particular, have appeared to be in a very different position from other countries when the counties are compared in terms of traffic accidents. In the second dimension, the countries do not have positive load over 1. However, Mexico, which has the value of 0.6378 the closest positive value to 1, can be considered as the most important parser for this dimension. When the matrix of the differences is examined; Turkey and Liechtenstein have seemed to be the two countries very different from each other. It poses great importance in terms of both for individual and public health that the necessary precautions be taken by evaluating our country, Turkey, and other countries to decrease traffic accidents taking places at the top of the list as the most important death causes. It is clear that traffic accidents, a global public health problem, have a great influence on individuals and communities and national economies. It is necessary that especially, the countries such as the US, Japan, India, Germany, Korea and Turkey which take place in high ranks in terms of traffic accident indicators should develop national and international projects in order to face to this problem coming together. In addition, in local basis, taking serious precautions (substructure services, increasing traffic fines and education etc.) will help reduce human and economic losses to the lowest levels.

References

  • 1. Erjem, Y. [A Sociological Research on Traffic System Operation and Traffic Accidents]. Polis ve Sosyal Bilimler Dergisi 2005: 3(1), 69-94. 2. Alp S. ve Engin T. [Analysis and Evaluation of the Relation Between the Reasons and Consequences of the Traffic Accidents by using TOPSIS And AHP Methods İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 2011: 10(19), 65-87. 3. Sungur İ., Akdur R. ve Piyal B. [Analysis of Traffic Accidents in Turkey] Ankara Med J. 14(3): 114 – 124. 4. WHO (2018). Findings from the Global Burden of Disease Study 2017 5. Türkiye İstatistik Kurumu (2017). Ulaşım İstatistikleri 6. Karayolları Genel Müdürlüğü (2018). Trafik Kazaları Özeti 2017. www.kgm.gov.tr. 7. OECD Road Accident data (2018). https://data.oecd.org/transport/road-accidents.htm 8. Özdamar K. [Multi-Dimensional Scaling]. Paket Programlar ile İstatistiksel Veri Analizi 2 (Çok Değişkenli Analizler). 5. Baskı. Eskişehir: Kaan Kitabevi; 2004. p.501-5. 9. Bülbül, S. ve Köse, A. Türkiye’de bölgelerarası iç göç hareketlerinin çok boyutlu ölçekleme yöntemi ile incelenmesi 2010: 39 (1), 75-94 10. Etikan, İ., Erkorkmaz, Ü. Sanisoğlu, S.Y., Demir, O., Kuyucu, Y.E. [Multidimensional Scaling analysis of Judicial Statistics for Crimes Against Persons and Properties in 2008 in 81 Provinces in Turkey]. Türkiye Klinikleri J Med Sci, 2012: 32(5), 1295-1306. 11. İbicioğlu, M. [Multidimensional Scaling Analysis of Relations Among Returns of Investment Instruments]. The International Journal of Economic and Social Research, 2012, 8(2), 8:45-55 12. Acar, A.B. [Comparison of Turkey and the Other OECD Countries in Terms of Labour Markets’ Main Indicators Using Multi Dimensional Scale Analysis]. Faculty of Business Administration Institute of Business Administration Journal of Management 2013: 24 (75), 121-144. 13. Ersöz F. [Analysis of health levels and expenditures of Turkey and OECD countries]. İstatistikçiler Dergisi 2008;1(2):95-104. 14. Alpar, R. (2013). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, Ankara. 15. Aydın, D. ve Başkın B. [The Classification Structures of Banks by Their Capital Adequacy Ratios as the Results of Clustering Analysis and Multidimensional Scaling]. (2013). BSAD Bankacılık ve Sigortacılık Araştırmaları Dergisi 2013:1(5-6), 29-47. 16. Ersöz, T., Türkoğlu Elitaş MN. ve Ersöz, F. [Investigation of Multidimensional Scaling Analysis of Biomass Energy Generation in OECD Countries]. TÜBAV Bilim 2015 8 (3) 2015 1-11. 17. Tinsley, H.E. A. Brown, S.D. Handbook of Applied Multivariate Statistics and Mathematical Modeling. Academic Press. USA, 2000, p.345. Türkiye İstatistik Kurumu (2017). Ulaşım İstatistikleri. 18. Tüzüntürk S. [Multidimensional scaling: an application on crime statistics]. Uludağ University Journal of Economics and Administrative Sciences 2009;28(2):71-91. 19. Büyüker İşler, D. 2014.[ Examining between regional internal migration movements in Turkey with multidimensional scaling] .Adıyaman Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 7(17), 447-484. 20. Fırat, Ü.O. ve Demirhan, A. [Performance Analysis of Commercial Banks]. ODTÜ İktisat Kongresi 2001. 21. Johnson RA, Wichern DW. [Multidimensional Scaling]. Applied Multivariate Statistical Analysis. 4th ed. New Jersey: Pearson Prentice Hall; 1999. p.741-63. 22. Gürçaylılar Yenidoğan T. [Multidimensional scaling in marketing research: a study on university students' perceptions of brand]. Akdeniz University Journal of Economics and Administrative Sciences 2008;8(15):138-69 23. Tatlıdil H. (1996). [Multi-Dimensional Scaling]. Uygulamalı Çok Değişkenli İstatistiksel Analiz. 2. Baskı. Ankara: Akademi Matbaası; 1996. p.279-90. 24. Oğuzlar A. [Locating factors affecting membership of European Union Using Multidimensional Scaling]. Uludağ University Journal of Economics and Administrative Sciences 2005;24(1):33-43. 25. Kruskal, J. B. (1964). Multıdimensional Scaling By Optimizing Goodness Of Fit to a Nonmetrıc Hypothesis. Psychometrika, 9(1), 1-27.
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Details

Primary Language English
Subjects Public Health, Environmental Health
Journal Section Article
Authors

Özlem Bezek Güre 0000-0002-5272-4639

Murat Kayri 0000-0002-5933-6444

Publication Date April 25, 2020
Submission Date January 3, 2020
Acceptance Date April 19, 2020
Published in Issue Year 2020 Volume: 5 Issue: 1

Cite

IEEE Ö. Bezek Güre and M. Kayri, “INVESTIGATION OF OECD COUNTRIES WITH MULTI-DIMENSIONAL SCALING ANALYSIS IN TERMS OF TRAFFIC ACCIDENT INDICATORS”, IJHSRP, vol. 5, no. 1, pp. 24–40, 2020, doi: 10.33457/ijhsrp.670014.

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