Using the Geographically Weighted Regression (GWR) Method to Find Out the Factors Causing Poverty in North Sumatra Province

Authors

  • Hilda Pradina Faradiba
  • aditya syaiful dhuhri

Keywords:

Poverty, Geographically Weighted Regression (GWR, North Sumatra Province, Factors Causing Poverty, Spatial Analysis

Abstract

Poverty is a complex problem that affects various aspects of life in various regions. In this study, we use the Geographically Weighted Regression (GWR) method to identify factors that cause poverty in North Sumatra Province. This approach allows a more detailed analysis of the relationship between independent variables and poverty levels in each geographic location. We use socio-economic and spatial data to build GWR models and identify possible hidden patterns in the distribution of poverty. The findings from this research provide valuable insights for stakeholders to formulate more effective policies in overcoming poverty in North Sumatra Province.

References

Agustianto, SP, Martha, S., & Satyahadewi, N. (2018). Modeling Factors Causing Traffic Accidents in West Kalimantan using the Geographically Weighted Regression (GWR) Method . 07 (4), 303 – 310.

Agustina, MF, Wanoso, R., & Darsyah, MY (2015). Geographically Weighted Regression (GWR) Modeling on Poverty Levels in West Java Province. Vol. 3.

Amalia, E., & Sari, LK (2019). Spatial Analysis to Identify Open Unemployment Rates Based on Regency/City on Java Island in 2017. Indonesian Journal of Statistics and Its Applications , 3 (3), 202–215. https://doi.org/10.29244/ijsa.v3i3.240

Astuti, P., Debataraja, NN, & Sulistianingsih, E. (2018). Poverty Analysis using Geographically Weighted Regression (GWR) Modeling in East Nusa Tenggara Province, Vol 07.

BPS. (2019). North Sumatra in Figures 2019 . Medan: BPS North Sumatra.

Caraka, RE, & Yasin, H. (2014). Geographically Weighted Regression (GWR). In Encyclopedia of Geographic Information Science . https://doi.org/10.4135/9781412953962.n81

Damayanti, R., & Chamid, MS (2016). Analysis of the Relationship Pattern of GRDP with Environmental Pollution Factors in Indonesia Using the Geographically Weighted Regression (GWR) Method, Vol. 5.

Dewi, PLA, & Zain, I. (2016). Modeling Factors Causing Traffic Accidents Based on the Geographically Weighted Regression Method in East Java , Vol. 5.

Fotheringham, A.S., Brunsdon, C., &Charlton, M. (2002). Geographically Weighted Regression The Analysis of Spatially Varying Relationships. England: John Wiley & Sons.

Sukanto, S., Juanda, B., Fauzi, A., & Mulatsih, S. (2019). Spatial Analysis of Poverty Using a Geographically Weighted Regression Approach: Case Study of Pandeglang and Lebak Regencies. Tataloka , 21 (4), 669. https://doi.org/10.14710/tataloka.21.4.669-677

Sukirno, S. (2016). Introductory Theoretical Macroeconomics (Third Edition). Jakarta: PT Raja Grafindo Persad.

Walpole, R. E. (1993). Introduction to Statistics (3rd ed.). Jakarta : PT Gramedia Pustaka Utama.

Yuhan, RJ, & Sitorus, JRH (2018). Geographically Weighted Regression Method on the Characteristics of the Nearly Poor Population in Districts/Cities on the Island of Java. Widya Eksakta E-Journal , 1 (1), 41–47.

Downloads

Published

2024-03-23

How to Cite

Hilda Pradina Faradiba, & aditya syaiful dhuhri. (2024). Using the Geographically Weighted Regression (GWR) Method to Find Out the Factors Causing Poverty in North Sumatra Province. International Journal of Science and Mathematics Education, 1(1), 18–28. Retrieved from https://international.arimsi.or.id/index.php/IJSME/article/view/13