GWR | Shahabuddin Amerudin @ UTM

From AHP to GWR in Sinkhole Susceptibility Modeling with Advanced GIS Methods

By Dr. Shah • August 27, 2024 • Jurnal, Knowledge, Project, Research • AHP, GWR, mapping, model, susceptibility • 5 minutes of reading

Introduction Rosdi et al. (2017) made significant strides in understanding sinkhole susceptibility in Kuala Lumpur and Ampang Jaya by combining Geographic Information Systems (GIS) with the Analytical Hierarchical Process (AHP). Their work laid a solid foundation for assessing sinkhole risk, but there remains an opportunity to refine and enhance these models using more advanced spatial analysis techniques. One promising approach is Geographically Weighted Regression (GWR), which has the potential to improve both the accuracy and granularity of sinkhole susceptibility assessments. This article examines how incorporating GWR, along with other advanced GIS methodologies, could lead to more precise and insightful analyses […]

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Unveiling Spatial Relationships: Predictive Applications of Regression Analysis

By Dr. Shah • August 14, 2023 • Knowledge, Research, Teaching • analysis, GIS, GWR, OLS, prediction, regression, relationships, spatial • 6 minutes of reading

By Shahabuddin Amerudin Introduction In the realm of data analysis, regression analysis stands as a powerful tool that facilitates the exploration, understanding, and prediction of spatial relationships. By unraveling the intricate connections between variables, it provides insights into the factors driving observed spatial patterns. In this article, we delve into the fascinating world of regression analysis, focusing on its predictive applications through two distinct examples: the prediction of human deaths and the analysis of grave demand. Regression analysis forms the cornerstone of modern statistical analysis, enabling us to move beyond mere correlation and into the realm of causation. As we

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Geographically Weighted Regression (GWR)

By Dr. Shah • January 31, 2023 • Public, Research, Teaching • GIS, GWR, prediction, spatial, statistics • 11 minutes of reading

Geographically Weighted Regression (GWR) is a spatial statistical method used for predicting outcomes based on geographical data. To conduct prediction using GWR, you can follow these steps: Note: It is essential to validate the GWR results with independent validation data and assess the model performance using appropriate validation metrics. Geographically Weighted Regression (GWR) is a powerful statistical tool for predicting outcomes based on geographical data. Its ability to account for spatial heterogeneity in the relationships between independent and dependent variables makes it an attractive alternative to traditional regression methods such as Ordinary Least Squares (OLS). The quality of GWR results

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