Akademik Veri Yönetim Sistemi
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, cilt.36, sa.2, ss.715-726, 2021 (SCI-Expanded)
Spatial analysis has become more important today and is used in several application domains. Geographically Weighted Regression (GWR) method, which is one of the widely used spatial analysis methods, is the local spatial regression technique used to model the changing relationships on geography. Geographically and Temporal Weighted Regression (GTWR) is an approach developed by including temporal relations into the GWR approach. Although the GTWR approach produces much better models than the GWR approach in the dataset containing spatial-temporal heterogeneity, there are still challenges given the complexity of spatialtemporal approaches. Because of this reason, in the literature, GTWR algorithms can able to handle limited number of data. In this study, we proposed the FastGTWR approach to reduce the algorithmic complexity of GTWR approach and overcome data size restriction. The proposed FastGTWR approach was run on real and synthetic dataset. The performance of the proposed FastGTWR approach was compared with the performances of the classical GWR and GTWR approaches. Experimental results showed that the proposed FastGTWR approach works faster than the GWR and GTWR approaches.