SavedAdd your journal

Journals / Acta Infologica / 2021 / Cilt: 5 - Sayı: 2

Comparision of Kernel Functions in Geographically Weighted Regression Model: Suicide Data as an Application

Coğrafi Ağırlıklı Regresyon Modelinde Kernel Fonksiyonlarının Karşılaştırılması: Bir Uygulama Olarak İntihar Verileri

Pelin AKIN
Tuba KOC
Pages
333–340
Publication Date
2022-09-04
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The province with the highest number of suicides across the country was Istanbul.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The traumatic traces of suicide in a society and the emotional devastation due to these losses make it very important to determine the causes of suicide.
📊
Key Findings & Data:In this study, the number of suicides data was used for Turkey’s 81 provinces in 2019.The effects of factors affecting suicide and spatial differences on suicide were analyzed and predicted with geographically weighted regression models (GWR).
💡
Conclusion & Impact:The province with the highest number of suicides across the country was Istanbul.
🔬Design:Modeling & Simulation
📍Scope:2019 Verileri
⚙️Variables:Domain Parameters

Abstract

The traumatic traces of suicide in a society and the emotional devastation due to these losses make it very important to determine the causes of suicide. In this study, the number of suicides data was used for Turkey’s 81 provinces in 2019.The effects of factors affecting suicide and spatial differences on suicide were analyzed and predicted with geographically weighted regression models (GWR). GWR models were applied with different kernel functions, and the best GWR model was found with the bisquare kernel function. Factors affecting suicide numbers were established as human development index, proportion of internet users, and numbers of unemployment. When the results were examined, it was seen that the number of suicides in the provinces was affected by different factors. In addition, the 2019 suicide numbers and predicted values were mapped, and the results were found to be quite similar. The province with the highest number of suicides across the country was Istanbul.

Özet

İntiharın toplumda bıraktığı travmatik izler, kayıplara bağlı yaşanılan duygusal yıkımlar intihar nedenlerinin belirlenmesini oldukça önemli kılmaktadır. Bu çalışmada 2019 yılına ait Türkiye’nin 81 ili için intihar sayısı verisi kullanılmıştır. İntiharı etkileyen faktörler ve mekansal farklılıkların intihar üzerindeki etkileri coğrafi ağırlıklı regresyon modeller (GWR) ile analiz edilmiş ve tahmin yapılmıştır. Farklı kernel fonksiyonları ile birlikte GWR modelleri uygulanmış ve en iyi GWR modeli bisquare kernel fonksiyonu ile bulunmuştur. İntihar sayılarını etkileyen faktörler insani gelişim endeksi, internet kullanıcı oranı ve işsizlik sayısı olarak elde edilmiştir. Sonuçlar incelendiğinde illerin yerleşim yerlerine göre intihar sayılarının farklı faktörlerden etkilendiği görülmektedir. Ayrıca 2019 yılı intihar sayıları ve tahmin değerlerinin haritalandırılması yapılmış ve sonuçlar oldukça benzer bulunmuştur. Ülke genelinde intihar sayısının en yüksek olduğu il İstanbul’dur.

🔖

Cite This Article

AKIN, P., KOC, T. (2022). Comparision of Kernel Functions in Geographically Weighted Regression Model: Suicide Data as an Application. Acta Infologica, Cilt: 5 - Sayı: 2, 333–340. https://ajindex.com/articles/425964

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (8)

  1. Dennet A. (2014). An Introduction to Geographically Weighted Regression in R. [Internet] https://rpubs.com/adam_dennett/44975
  2. Bektaş, M. (2015). 2002 ve 2012 Yıllarında Türkiye’de Meydana Gelen İntihar Vakası Nedenlerinin Mekansal Analizi (Yayınlanmamış Yüksek Lisans Tezi). Fatih Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul Bowman, A. W. (1984). An alternative method of cross-validation for the smoothing of density estimates. Biometrika, 71(2), 353-360.
  3. Bulut, E., & Aydın, V. G. (2020). İntiharı Etkileyen Sosyal ve Ekonomik Faktörlerin Beta Regresyon Analizi ile Belirlenmesi. Ahi Evran Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(2), 422-436.
  4. Devrimci-Ozguven, H., & Sayıl, I. (2003). Suicide attempts in Turkey: results of the WHO—EURO multicentre study on suicidal behavior. The Canadian Journal of Psychiatry, 48(5), 324-329.
  5. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons. Frutos, A. M., Sloan, C. D., & Merrill, R. M. (2018). Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression. PloS one, 13(12), e0206992. Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2013). GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint arXiv:1306.0413. Ha, H., & Tu, W. (2018). An ecological study on the spatially varying relationship between county-level suicide rates and altitude in the United States. International journal of environmental research and public health, 15(4), 671. Iyanda, A. E., Chima-Adaralegbe, N., Adeleke, R., & Lu, Y. (2021) Covariation of suicide and HIV in 186 countries: a spatial autoregressive and multiscale geographically weighted regression analyses. Journal of Public Health, 1-11.
  6. Kyonne, J. (2019). Impact of Social Service Expenditures on the Suicide Rate: The Cases of Asia Countries. Journal of Social Service Research, 45(1), 12-15.
  7. Lu, B., Yang, W., Ge, Y., & Harris, P. (2018). Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths. Computers, Environment and Urban Systems, 71, 41-57.
  8. Sedgwick, R., Epstein, S., Dutta, R., & Ougrin, D. (2019). Social media, internet use and suicide attempts in adolescents. Current opinion in psychiatry, 32(6), 534. Stojanova, D., Debeljak, M., Ceci, M., Appice, A., Malerba, D., & Džeroski, S. (2