Spatial Distribution and Modelling of the Total Fertility Rate in Turkey Using Spatial Data Analysis Techniques

dc.authorwosid Aydin, Olgu/I-3117-2018
dc.authorwosid Ozgur, Murat/B-9654-2019
dc.authorwosid Bostan, Pınar/Aaa-5913-2021
dc.contributor.author Aydin, Olgu
dc.contributor.author Aslantas Bostan, Pinar
dc.contributor.author Ozgur, Ertugrul Murat
dc.date.accessioned 2025-05-10T17:11:08Z
dc.date.available 2025-05-10T17:11:08Z
dc.date.issued 2018
dc.department T.C. Van Yüzüncü Yıl Üniversitesi en_US
dc.department-temp [Aydin, Olgu] Ankara Univ, Dil & Tarih Cografya Fak, Cografya Bolumu, Ankara, Turkey; [Aslantas Bostan, Pinar; Ozgur, Ertugrul Murat] Van Yuzuncu Yil Univ, Mimarlik & Tasarim Fak, Peyzaj Mimarligi Bolumu, Van, Turkey en_US
dc.description.abstract The fertility rate has been declining for over four decades in Turkey. However, the fertility rate has shown regional variability due to ethno-cultural differences. While the fertility rate is low in the Western part of Turkey, the Eastern and Southeastern parts have still shown moderate to high rates. This study focuses on the spatial patterns of the total fertility rate. Using variables that may affect the fertility rate, such as economic and socio-cultural parameters, we performed spatial data analysis techniques to represent, analyze, and model the spatial data. The results show that according to Moran's scatter plot, Turkey's total fertility rate falls into two groups: high-high and low-low. On the other hand, local Moran's I results have shown that while the East and Southeastern regions have positive auto-correlations, Marmara, the Aegean, the West Black Sea, and the Middle Anatolia regions have negative auto-correlations. In this study, we applied both the ordinary least square (OLS) and geographically weighted regression (GWR) models and compared the results. In GWR analysis, variance of the dependent variable was shown to be 93%, and we achieved a high success rate in modeling Turkey's total fertility rate. In the limitation of this study, using an illiterate female population rate and Kurdish female population rate variables, one can obtain more accurate models that show the total fertility rate and show where the fertility rate is high. As a conclusion, spatial data analysis methods bring a new perspective to socio-demographic studies. en_US
dc.description.woscitationindex Emerging Sources Citation Index
dc.identifier.doi 10.26650/JGEOG434650
dc.identifier.endpage 45 en_US
dc.identifier.issn 1302-7212
dc.identifier.issn 1305-2128
dc.identifier.issue 37 en_US
dc.identifier.scopusquality N/A
dc.identifier.startpage 27 en_US
dc.identifier.uri https://doi.org/10.26650/JGEOG434650
dc.identifier.uri https://hdl.handle.net/20.500.14720/7649
dc.identifier.wos WOS:000454899100003
dc.identifier.wosquality N/A
dc.language.iso tr en_US
dc.publisher Istanbul Univ, Fac Letters, dept Geography en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Total Fertility Rate en_US
dc.subject Fertility Differences en_US
dc.subject Spatial Data Analysis en_US
dc.subject Spatial Weight Matrix en_US
dc.subject Ordinary Least Square (Ols) en_US
dc.subject Geographically Weighted Regression (Gwr) en_US
dc.title Spatial Distribution and Modelling of the Total Fertility Rate in Turkey Using Spatial Data Analysis Techniques en_US
dc.type Article en_US
dspace.entity.type Publication

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