Geographically Weighted Regression (GWR) is a statistical method designed to analyze relationships between variables that can vary spatially, or in other words, relationships that depend on geographical location. GWR addresses the limitations of classical regression models, which assume that relationships between variables are constant across the study area. In GWR, the relationship between independent and dependent variables is explored based on geographical location, allowing for the identification of more specific spatial variations in the data. The application of GWR in the field of education is highly relevant for examining differences in educational quality between urban and rural areas, where various social, economic, and infrastructure factors can affect educational outcomes differently in each location.
The difference in educational quality between urban and rural areas has become a significant issue faced by many countries, including Indonesia. In urban areas, the quality of education is often higher compared to rural areas due to better infrastructure, easier access to educational resources, and the availability of technology to support learning. Educational facilities in urban areas are generally more complete, ranging from well-equipped school buildings to sufficient digital tools for supporting education. On the other hand, in rural areas, despite government efforts to improve access to education, many challenges remain, such as limited educational facilities, a shortage of qualified human resources, and minimal access to educational technology.
In this context, GWR can be used to delve deeper into how factors that influence education, such as unemployment rates, infrastructure, and family support, have different impacts in urban and rural areas. For instance, although unemployment rates are higher in rural areas, their impact on educational outcomes can vary greatly depending on local factors, such as the educational policies implemented, access to teacher training programs, and local government initiatives to improve education quality. In urban areas, the effect of unemployment on education may not be as significant as in rural areas due to the availability of resources and the diversity of jobs that support family economic stability.
One of the major advantages of using GWR in education is its ability to provide more detailed insights into how educational policies can be implemented more effectively. GWR allows for the examination of the differential effects of education policies across various locations. In urban areas, educational policies may focus more on enhancing teaching quality through technology and curriculum innovation, while in rural areas, policies might focus more on the physical development of schools and the accessibility of basic education. Thus, GWR enables policymakers to identify more precisely the locations that require specific interventions based on the varying impacts of policies found.
Moreover, GWR is also useful in analyzing other factors that affect educational quality, such as household income levels, parental literacy rates, and social support. In urban areas, these factors may have a lesser impact due to better access to social services and educational facilities. However, in rural areas, closer family support and strong social networks may have a more significant impact on educational outcomes. GWR allows researchers and policymakers to identify local relationships between these variables and optimize policies that can improve educational quality in both urban and rural areas.
The main advantage of GWR is its ability to more precisely depict the relationship between educational variables and the influencing factors in each location. By using GWR, policymakers can gain a deeper understanding of the local context and design education programs that are better suited to the specific needs of those areas. For example, if GWR shows that in rural areas, educational quality is significantly affected by the availability of infrastructure, policies focused on building educational facilities and providing access to technology would be more effective.
The application of GWR in education can provide a solid foundation for designing more equitable and fair policies, considering the local differences between urban and rural areas. By leveraging this method, researchers and policymakers can design more targeted policies, ensuring that efforts to improve educational quality are carried out more efficiently and effectively across regions.
Keywords: Geographically Weighted Regression (GWR), Educational Quality Differences, Urban and Rural Education
References:
- Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 28(4), 281-298.
- Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons.
- He, X., & Xie, Y. (2011). Spatial Variability of Education in China: A Case Study Using Geographically Weighted Regression. International Journal of Applied Geography, 31, 201-212.
Author: Meilinda Roestiyana Dewy