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


Tasyurek M., ÇELİK M.

NEUROCOMPUTING, cilt.399, ss.258-270, 2020 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 399
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.neucom.2020.02.058
  • Dergi Adı: NEUROCOMPUTING
  • Sayfa Sayıları: ss.258-270

Özet

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.