ÇOK DEĞİŞKENLİ HARİTALAMA İÇİN KÜMELEME YÖNTEMLERİNİN KULLANILMASI

Öz

   Çok değişkenli haritalama mekânsal objelere ait birden çok özelliğin harita kullanılarak görsel sunumudur. Çeşitli veri hazırlama ve istatistiksel sınıflandırma teknikleri kullanılarak mekânsal objelere ait birden çok özellik görsel olarak incelenebilir ve kartografik işaretlerle gösterilebilir. Bu kapsamda kümeleme analizi yöntemleri de çok değişkenli haritalama için kullanılabilir. Bu çalışmada kümeleme analiz yöntemlerinden k-ortalama yöntemi, k-temsilci yöntemi ve Birleştirici Hiyerarşik Kümeleme yöntemi ele alınmıştır. Bu yöntemlerle Türkiye’deki üç ayrı yıla ait trafik kaza verileri kullanılarak oluşturulan sınıflar ve üretilen çok değişkenli haritalar kullanılarak bu yöntemlerin karşılaştırılması yapılmış, bu yöntemlerle üretilen haritaların risk yönetimi ve planlamada kullanılabilirliği üzerinde durulmuştur.

Anahtar Kelimeler

• Çok değişkenli haritalama,veri madenciliği,kümeleme analizi

USING CLUSTER ANALYSIS METHODS FOR MULTIVARIATE MAPPING

Öz

   Multivariate mapping is the visual exploration of spatial objects with multiple attributes using a map. More than one attribute can be visually explored and symbolized using numerous statistical classification systems or data reduction techniques. In this sense, clustering analysis methods can be used for multivariate mapping. In this study, among clustering analysis methods, k-means method, k-medoids method and Agglomerative Hierarchical Clustering method were selected. For this purpose, multivariate maps created from traffic accident data of two different years in Turkey were used. The methods were compared using the maps produced with these methods and effectiveness of these maps in risk management and planning were discussed.

Anahtar Kelimeler

• Multivariate mapping,data mining,cluster analysis

Kaynakça

1. [1] BUCKLEY, A., Multivariate Mapping. In K. KEMP (Eds.) Encyclopedia of Geographic Information Science (pp. 300-303), 2008.
2. [2] SLOCUM, T.A., MCMASTER,,R.B., KESSLER, F.C., HOWARD, H.H.,Thematic Cartography and Geovisualization, Pearson Education Inc. Third Edition, USA, 2009.
3. [3] BREWER, C.A., Color Use Guidelines for Mapping and Visualization, In A.M. MACEACHREN D.R.F TAYLOR (Eds.), Visualization in Modern Cartography (pp. 123-147),1994.
4. [4] METTERNICHT, G., VESTOTT J., “Trivariate Spectral Encoding: A Prototype System for Automated Selection of Colours for Soil Maps Based on Soil Textural Composition”, Proceedings of the 21st International Cartographic Conference, Durban, CD, 2003.
5. [5] BYRON, J. R., “Spectral Encoding of Soil Texture: A New Visualization Method”, GIS/LIS Proceedings, Phoenix, Airz., 125-132, 1994.
6. [6] INTERRANTE, V., “Harnessing Natural Textures for Multivariate Visualization”, IEEE Computer Graphics and Applications, 20, 6-11, 2000.
7. [7] JENKS, G. F., “Pointillism as a Cartographic Technique”, The Professional Geographer, 5, 4-6, 1953.
8. [8] COX, D.J., “The Art of Scientific Visualization. Academic Computing”, 4, 20-22, 32-34, 36-38,1990.

9. [9] ELLSON, R., “Visualization at Work”, Academic Computing, 4, 6, 26-28,54-56, 1990.
10. [10] DORLING, D., “The Visualization Of Local Urban Change Across Britain”, Environment and Planning B: Planning and Design, 22, 269 -290, 1995.
11. [11] GRINSTEIN, G., SIEG, J.C.J., SMITH, S., WILLIAMS M.G., “Visualization for Knowledge Discovery”, International Journal of Intelligent Systems, 7, 637-648, 1992.
12. [12] HEALEY, C.G., ENNS, J.T., “Large Datasets at A Glance: Combining Textures And Colors In Scientific Visualization”, IEEE Transactions on Visualization and Computer Graphics, 5, 2, 145-167, 1999.
13. [13] MILLER, J.R., “Attribute Blocks: Visualizing Multiple Continuously Defined Attributes”, IEEE Computer Graphics and Applications, 27, 3, 57-69, 2007.
14. [14] ZHANG X., PAZNER, M., “The Icon Imagemap Technique for Multivariate Geospatial Data Visualization: Approach and Software System”, Cartography and Geographic Information Science, 31, 1, 29-41, 2004.
15. [15] NELSON, E. S., GILMARTIN, P. P., An Evaluation Of Multivariate, Quantitative Point Symbols For Maps, In C. H. WOOD, C. P. KELLER (Eds.) Cartographic Design: Theoretical and Practical Perspectives (pp. 191-203), 1996.
16. [16] DIBIASE, D., “Designing Animated Maps for A Multimedia Encyclopedia”, Cartographic Perspectives, 19, 3-7, 1994.
17. [17] NELSON, E.S., “Designing Effective Bivariate Symbols: The Influence of Perceptual Grouping Processes”, Cartography and Geographic Information Science, 27, 4, 261-278, 2000.
18. [18] GUO, D., GAHEGAN, M., ALAN M. M., BILIANG Z., “Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach”, Cartography and Geographic Information Science, 32, 2, 113-132, 2005.
19. [19] MENNIS, J., GUO, D., “Spatial Data Mining And Geographic Knowledge Discovery-An Introduction”, Computers, Environment and Urban Systems, 33, 403-408, 2009.
20. [20] MURRAY, A. T., GRUBESIC, T. H., Exploring Spatial Patterns Of Crime Using Non-Hierarchical Cluster Analysis, In M. Leitner (Eds.) Crime Modeling And Mapping Using Geospatial Technologies, 105-124, Springer, Netherlands, 2013.
21. [21] GRUBESIC, T. H., WEI, R., MURRAY, A. T., “Spatial Clustering Overview and Comparison: Accuracy, Sensitivity, and Computational Expense”, Annals of the Association of American Geographers, 104, 6, 1134-1156, 2014.
22. [22] BRIMICOMBE, A.J., “A Dual Approach to Cluster Discovery in Point Event Data Sets”, Computers, Environment and Urban Systems, 31, 4-18, 2007.
23. [23] ANDERSON T.K, “Kernel Density Estimation and K-Means Clustering to Profile Road Accident Hotspots”, Accident Analysis and Prevention 41, 359-364, 2008.
24. [24] LU, Q., CHEN, F., HANCOCK, K., “On Path Anomaly Detection in a Large Transportation Network”, Computers, Environment and Urban Systems, 33, 6, 448-462, 2009.
25. [25] WENG J., WENXIN, Q., XIAOBO, Q., XUEDONG, Y., “Cluster-Based Lognormal Distribution Model For Accident Duration”, Transportmetrica A: Transport Science, 11, 4, 345-363,2015.
26. [26] GUO, F., FANG, Y., “Individual Driver Risk Analysis Using Naturalistic Driving Data”, 3rd International Conference on Road Safety and Simulation, Indianapolis, USA, 2011.
27. [27] YALCIN G., DUZGUN, H.S., “Spatial Analysis of Two-Wheeled Vehicles Traffic Crashes: Osmaniye in Turkey”, KSCE Journal of Civil Engineering, 19, 7, 2225-2232, 2015.
28. [28] FENG, S, ZHENNING, L., YUSHENG, C., GUOHUI, Z., “Risk Factors Affecting Fatal Bus Accident Severity: Their Impact On Different Types Of Bus Drivers”, Accident Analysis and Prevention 86, 29-39, 2016.
29. [29] ERDOGAN, S., “Explorative Spatial Analysis Of Traffic Accident Statistics And Road Mortality Among The Provinces Of Turkey”, Journal of Safety Research 40, 341-351, 2009.
30. [30] MARTINUSSEN, L.M., MØLLER, M., PRATO, C.G., “Assessing The Relationship Between The Driver Behavior Questionnaire And The Driver Skill Inventory: Revealing Sub-Groups Of Drivers”, Transportation Research Part F 26, 82-91, 2014.
31. [31] DINÇER, E.Ş. Veri Madenciliğinde K-means Algoritması ve Tıp Alanında Uygulanması, Yüksek Lisans Tezi, Kocaeli Universitesi Fen Bilimleri Enstitüsü, Kocaeli, 2006.
32. [32] HAN, J., LEE, J.G., KAMBER, M., An Overview of Clustering Methods in Geographic Data Analysis, In Miller H.J., Han H. (Eds.) Geographic Data Mining and Knowledge Discovery, Taylor & Francis Group, LLC, 2009.
33. [33] HAN, J., KAMBER M., Data Mining: Concepts and Techniques. San Francisco, 2006.
34. [34] SİLAHTAROĞLU, G., Veri Madenciliği (Kavram ve Algoritmaları). Papatya Yayıncılık, İstanbul, 2013.
35. [35] AKIN, Y.K., Veri Madenciliğinde Kümeleme Algoritmaları ve Kümeleme Analizi, Doktora Tezi, Marmara Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul, 2008.
36. [36] http://www.ibm.com/analytics/us/en/technology/spss/ (erişim tarihi 15.12.2016).
37. [37] https://rapidminer.com/ (erişim tarihi 15.12.2016).
38. [38] https://www.arcgis.com/features/index.html (erişim tarihi 15.12.2016).
39. [39] ROMESBURG H.C., Cluster Analysis for Researchers, Belmont, CA: Lifetime Learning Publications, 1984.