RNN-GWR: A geographically weighted regression approach for frequently updated data

Tasyurek M., Çelik M.

NEUROCOMPUTING, vol.399, pp.258-270, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 399
  • Publication Date: 2020
  • Doi Number: 10.1016/j.neucom.2020.02.058
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, zbMATH
  • Page Numbers: pp.258-270
  • Keywords: Geographically weighted regression, Reverse nearest neighbor, Frequently updated data, Regression, Data mining, BANDWIDTH SELECTION, DENSITY, PATTERNS, INTERNET, MODEL, PRICE
  • Kayseri University Affiliated: No


Geographically weighted regression (GWR) is a local spatial regression technique to model varying relationships in many application domains, such as ecology, environmental management, public health, meteorology, and tourism. In the literature, most of the studies dealing with GWR do not take into account if the dataset is frequently updated and so these techniques are not efficient to handle such datasets. In this study, to handle frequently updated data on given locations, a computationally efficient GWR approach, RNN-GWR, which utilizes reverse nearest neighbor (RNN) strategy, is proposed. The performance of the proposed RNN-GWR approach is compared with the performances of a Naive-GWR and FastGWR approaches. Experimental evaluations show that the proposed approach is computationally efficient than the other approaches on handling frequently updated data. (c) 2020 Elsevier B.V. All rights reserved.