Optimal Bandwidth for Geographically Weighted Regression to Model the Spatial Dependency of Land Prices in Manado, North Sulawesi Province, Indonesia

Abstract

Bandwidth plays a crucial role in the Geographically Weighted Regression modelas it affects the model’s ability to describe spatial dependencies. If the bandwidth is too large, the model will be similar to a normal regression model. Conversely, if it is too small, the model will be too rough. Bandwidth can be selected in several ways, e.g. manually determined by experts or using Akaike Information Criteria, Cross-Validation, and Lagrange Multiplier methods. This study offers an alternative approach to choosing bandwidth based on the covariance function representing a linear combination between the Bessel and Gaussian-Type functions. We applied this function to analyze the land price in Manado with four infrastructure accessibility variables, such as accessibility to government offices, education facilities, shopping centers, and healthcare facilities. Therefore, the proposed method is different from the index methods (AIC and CV) which have been used by other researchers. The results showed that the non-parametric covariance function provides a smaller bandwidth than conventional methods, specifically Akaike Information Criteria and Cross-Validation. In addition, the value of R2(adjusted) given by the covariance function is greater than the one given by the proportional method. This means that the optimal bandwidth obtained using the covariance function is more suitable to explain the land price in the city of Manado.

Performa Kernel pada Model Geographically Weighted Regression untuk Menentukan Faktor-faktor Yang Mempengaruhi Indeks Pembangunan Manusia di Provinsi Sulawesi Selatan

“…Untuk mencegah terjadinya heterogenitas spasial pada parameter regresi, maka model regresi dikembangkan menjadi Geographically Weighted Regression (GWR) (Isbiyantoro et al, 2014) GWR adalah teknik yang membawa kerangka dari model regresi sederhana ke model regresi terboboti (Fotheringham et al, 2002). Hasilnya, menghasilkan model regresi linier lokal yang menghasilkan model penduga parameter lokal untuk setiap titik atau lokasi pengamtan (Weku et al, 2022). Pada model GWR, variabel dependen diprediksi dengan variabel independen yang masing-masing nilai parameter regresinya bergantung pada lokasi dimana data tersebut diamati (Weku, 2020).…”