Temporal And Spatial Analysis Of Flooding And Landslide Disasters In Turkey For Period 1960-2018

Yıl 2020, Cilt: 5 Sayı: 2, 146 - 153, 30.06.2020

Güzide Miray Perihanoğlu , Himmet Karaman

https://doi.org/10.35229/jaes.682827

Cited By: 2

Öz

Turkey, a country affected by natural disasters is natural disasters such as flooding and landslides are seen frequently. Floods and landslides are one of the common natural disasters in Turkey. The use of GIS-based spatial statistical analysis in disaster studies has increased in recent years. From this point, it is essential to examine the spatial distribution of disasters to prevent and mitigate disasters. In this study, flood and landslides disasters that occurred between 1960 and 2018 in Turkey is aimed to determine the spatial analysis using spatial autocorrelation methods. The problem of how these two disaster types distribute is the most pressing thing in disaster risk assessment. Data set was taken from the Disaster Knowledge Base system (TABB) of the Republic Of Turkey Ministry Of Interior Disaster And Emergency Management Presidency. Various indices related to spatial autocorrelation were classified and also they were explained mathematically. Spatial autocorrelation indices such as Moran's I, Getis Ord G, and Geary C were used to determine whether there were clustering in the data set. The rates of flood and landslide in the provinces showing clustering and their distribution are examined and also Flood and landslide distributions in clustered provinces were examined, tested with indices and compared and the results are presented in detail.

Anahtar Kelimeler

Natural Disasters, Spatial Analysis, Spatial Autocorrelation, Temporal Analysis

Kaynakça

• Anselin, L. (2002).Under the hood Issues in the specification and interpretation of spatial regression models. Agricultural Economics 27(3), 247-267. Doi: (https://doi.org/10.1111/j.1574-0862.2002.tb00120.x)
• Anselin, L. (2003). Spatial Externalities, Spatial Multipliers, and Spatial Econometrics. International Regional Science Review 26(2), 153-166. Doi:(https://doi.org/10.1177/0160017602250972)
• Anselin, L. (2019). A Local Indicator of Multivariate Spatial Association: Extending Geary's c. Geographical Analysis 51(2), 133-150. Doi:(https://doi.org/10.1111/gean.12164)
• Feng, M., Feng, M.J. & Sheng, Z.W. (2008). Earthquake Disaster GIS and Spatial-Temporal Pattern Of Earthquake Disasters In China. 14th World Conference On Earthquake Engineering, 2008, Beijing, China.
• Fischer, M. & Wang, J. (2011). Spatial Data Analysis Models, Methods and Techniques. Springer Briefs In Regional Science, 82p.
• Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician 5(3), 115–145.
• Getis, A. & J. K. Ord (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis 24(3), 189-206.
• Goodchild, M. (1986). Spatial Autocorrelation. GeoBooks, Norwich, UK, 57p.
• Gökçe, O., Özden, Ş., Demir, A. (2008). Spatial and Statistical Distribution of Disasters in Turkey, The Ministry of Public Works and Settlement. 26-36.
• Griffith, D. A. (1992). What is Spatial Autocorrelation? Reflections on the Past 25 Years of Spatial Statistics. L’Espace Ge´ographique 21, 265–80.
• Levine, N. (2013). CrimeStat IV: Spatial Autocorrelation Statistics. Ned Levine & Associates, Houston, T X, Washington DC.
• Mathur, M. (2015). Spatial autocorrelation analysis in plant population: An overview.Journal of Applied and Natural Science 7 (1), 501 – 513. Doi: (https://doi.org/10.31018/jans.v7i1.639).
• Mitchell, A. (2005). The ESRI Guide to GIS Analysis, Volume 2., Spatial Measurements. California: ESRI Press, 238p.
• Moran, P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society, Series B 37(2): 243–251.
• Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17-23.
• Salima, B., Bellefon, M. (2018). Spatial autocorrelation indices. Handbook of Spatial Analysis. 52-68p, Insee, Eurostat.
• Smith, T. E. (2008). Spatial Weight Matrices. Computer Science Published in Encyclopedia of GIS 2008.
• Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–40.
• TSI. (2006). The Data: Coverage, Periodicity, and Timeliness. Turkish Statistical Institute, Turkey, 8p.
• Wu, A. M., & Kemp, K. K. (2019). Global Measures of Spatial Association. The Geographic Information Science & Technology Body of Knowledge, Doi: 10.22224/gistbok/2019.1.12.