Evsel katı atık oluşum miktarlarının küresel ve yerel mekânsal otokorelasyon yöntemleri ile incelenmesi

Katı atık oluşumlarının mekânsal olarak incelenmesi evsel katı atık sorunun bölgesel olarak nasıl davrandığını belirtmek açısından oldukça önemlidir. Bu çalışmada, ortalama günlük kişi başı katı atık miktarları(kg) 2014,2016 ve 2018 yılları için küresel ve yerel mekansal otokorelasyon yöntemleri ile incelenmiştir. Küresel otokorelasyon yöntem sonuçlarına göre katı atık oluşum oranları, istatistiksel olarak anlamlı pozitif otokorelasyon göstermiştir. Moran’nın I indeksi en yüksek 2014 yılı için 0,2980 olarak hesaplanmıştır. Yerel mekansal otokorelasyon yöntemlerinden, yerel Moran’nın I indeksi (LISA) kullanılmıştır. LISA sonuçlarına göre katı atık oluşum oranı düşük seviye olarak Güneydoğu Anadolu Bölgesi ve Doğu Akdeniz bölgelerinde kümelendiği görülmüştür. Yüksek seviyede kümelenme ise Kuzey Batı Trakya ve Ege Bölgelerinde görülmüştür.

Anahtar Kelimeler:

Mekansal Otokorelasyon, Moran’nın I indeksi, LISA, Geary’nin C oranı

Investigation of generation of municipal solid waste with global and local spatial autocorrelations

The spatial analysis of the municipal solid waste is significant to clearfy how this issue behaves regionally. The munisipal solid waste (kg) per capita per day was tested both global and local spatial autocorrelation methods. According to the global autocorrelation analysis results, solid waste generation rates showed statistically significant positive autocorrelation. The highest of the calculated Moran's I indexes was found for 2014. Among the local spatial autocorrelation methods, the local Moran's I index (LISA) was used. According to the LISA results, it was observed that the solid waste generation rate was clustered with low leveled values in the Southeastern Anatolia Region and the Eastern Mediterranean regions. The high leveled values were showed a clustered pattern in North Western Thrace and Aegean Regions.

Keywords:

Spatial autocorrelation, Moran’s I index, LISA, Geary’s C ratio,

PDF

___

C. O. Ugwu, C. G. Ozoegwu, and P. A. Ozor, Solid waste quantification and characterization in university of Nigeria, Nsukka campus, and recommendations for sustainable management. Heliyon, 6 (6), e04255, 2020. https://doi.org/https://doi.org/10.1016/j.heliyon.2020.e04255
E. Stunzenas and I. Kliopova, Optimizing municipal biodegradable waste management system to increase biogas output and nutrient recovery: a case study in Lithuania. Energy Procedia, 147, 641-648, 2018. https://doi.org/https://doi.org/10.1016/j.egypro.2018.07.083
L. R. Luttenberger, Waste management challenges in transition to circular economy – Case of Croatia. Journal of Cleaner Production, 256, 120495, 2020. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.120495
G. Gök, Estimation of methane generation and energy potential of Nigde landfill site using first order mathematical modelling approaches. journal of engineering sciences and design, 7 (1), 126-135, 2019.
S. Keser, S. Duzgun, and A. Aksoy, Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey. Waste Management, 32 (3), 359-71, 2012. https://doi.org/10.1016/j.wasman.2011.10.017
Y. Li, Y. Cui, B. Cai, J. Guo, T. Cheng, and F. Zheng, Spatial characteristics of CO2 emissions and PM2.5 concentrations in China based on gridded data. Applied Energy, 266, 2020.https://doi.org/10.1016/j.apenergy .2020.114852
F. Fan, H. Lian, X. Liu, and X. Wang, Can environmental regulation promote urban green innovation Efficiency? An empirical study based on Chinese cities. Journal of Cleaner Production, 2020. https://doi.org/10.1016/j.jclepro.2020.125060
C. Zhang, L. Luo, W. Xu, and V. Ledwith, Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Sci Total Environ, 398 (1-3), 212-21, 2008. https://doi.org/10.1016/ j.sci totenv.2008.03.011
M. Agovino, M. D'Uva, A. Garofalo, and K. Marchesano, Waste management performance in Italian provinces: Efficiency and spatial effects of local governments and citizen action. Ecological Indicators, 89, 680-695, 2018. https://doi.org/10.1016/ j.ecolind .2018.02.045
E. S. Thompson, P. Saveyn, M. Declercq, J. Meert, V. Guida, C.D. Eads, E.S.J. Robles, and M.M. Britton, Characterisation of heterogeneity and spatial autocorrelation in phase separating mixtures using Moran's I. J Colloid Interface Sci, 513, 180-187, 2018. https://doi.org/10.1016/j.jcis.2017.10.115
L. S. Premo, Local spatial autocorrelation statistics quantify multi-scale patterns in distributional data: an example from the Maya Lowlands. Journal of Archaeological Science, 31 (7), 855-866, 2004. https ://doi.org/https://doi.org/10.1016/j.jas.2003.12.002
E. M. Özgür and O. Aydın, Türkiye’de Evlilik Göçünün Mekânsal Veri Analizi Teknikleriyle Değerlendirilmesi (The Evaluation of Marriage Migration Using Spatial Data Analysis Techniques in Turkey). Coğrafi Bilimler Dergisi/Turkish Journal of Geographical Sciences, 9 (1), 29-40, 2011.
D. Yüncü, İ.O. Coşkun, Y. Mert Kantar, S. Günay Aktaş, and H. Sezerel, Turist Çekicilikleri Ve Turist Akişi Arasindaki Ilişkilerin Mekansal Bağimliliğa Dayali Olarak Incelenmesi. e-Journal of New World Sciences Academy, 12 (4), 232-247, 2017. https://doi .org/10.12739/nwsa.2017.12.4.3c0168
Ö. TÜRKŞEN, Mekansal istatistiklerin bir uygulaması: Simule edilmiş fay düzlemine ilişkin jeodezik noktaların mekansal analizi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 1-13, 2019. https://doi.org/ 10.25092/baunfbed.522967
Ç. L. Uslu, Provincial Income Inequality and Spatial Autocorrelation Across Turkish Provinces: 1992-2013. Sosyoekonomi, 25 (34), 197-211, 2017. https://doi.org/ 10.17233/sosyoekonomi.315759
M. A. Kalkhan, Spatial Statistics GeoSpatial Information Modeling and Thematic Mapping 2011, Boca Raton, FL: CRC Press. 178.
L. Anselin, Local Indicators of Spatial Association—LISA. 27 (2), 93-115, 1995. https://doi.org/https:// doi.org/10.1111/j.1538-4632.1995.tb00338.x
A. Getis and K. Ord, The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24, 189-206, 1992. https:// doi.org/10.1111/j.1538-4632.1992.tb00261.x
TÜİK. Çevre İstatistikleri. [cited 2020 22.11.2020]; Available from:https://tuikweb.tuik .gov.tr/PreTablo .do?alt_id=1019.
TÜİK. Nüfus İstatistikleri. [cited 2020 22.11.2020]; Available from: https://tuikweb.tuik.gov.tr/ UstMenu .do?metod=temelist.
K. Suryowati, R. D. Bekti, and A. Faradila, A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation. IOP Conference Series: Materials Science and Engineering, 335, 2018. https://doi.org/10.1088/1757-899x/335/1/012052
R. E. Plant, Spatial Data Analysis in Ecology and Agriculture Using R Vol. Second Edition. 2019, Boca Raton, FL: CRC Press Taylor & Francis Group. 685.
A. Ghasemi and S. Zahediasl, Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab, 10 (2), 486-9, 2012. https:// doi .org/10.5812/ijem.3505
G. Tepanosyan, L. Sahakyan, C. Zhang, and A. Saghatelyan, The application of Local Moran's I to identify spatial clusters and hot spots of Pb, Mo and Ti in urban soils of Yerevan. Applied Geochemistry, 104, 116-123, 2019.https://doi.org/ 10.1016/ j.apgeochem .2019.03.022
A. Neşe and M. Aytaç, Türkiye’de işsizliğin mekânsal analizi. Öneri Dergisi, 13 (49), 1-20, 2018.
K. M. Çubukçu, Planlamada ve Coğrafyada Temel İstatistik ve Mekansal İstatistik. Vol. 2. Basım. 2019: Nobel 323.
J. Lee, Wong, W. S., Statistical Analysis with ArcView GIS 2001, New York, NY: John Wiley & Sons.
S. Hafeez, M. Amin, and B.A. Munir, Spatial mapping of temporal risk to improve prevention measures: A case study of dengue epidemic in Lahore. Spat Spatiotemporal Epidemiol, 21, 77-85, 2017. https:// doi.org/10.1016/j.sste.2017.04.001
M. A. Dereli, Polat, Nizar, Boşanma Verilerinin Coğrafi Bilgi Sistemleri Destekli Mekânsal İstatistiksel Yöntemler ile İrdelenmesi. Harran Üniversitesi Mühendislik Dergisi, 3 (3), 112-118, 2018.