Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland
By Mariusz Szymanowski, Maciej Kryza. Theoretical and Applied Climatology, 2012
100 citations recorded by the source corpus.
Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI) in the city of Wrocław, SW Poland. Seven UHI cases, measured during various meteorological conditions and characteristic of different seasons, were selected for analysis. GWR results were compared with global regression models (MLR), using various statistical procedures including corrected Akaike Information Criterion, determination coefficient, analysis of variance, and Moran’s I index. It was found that GWR is better suited for spatial modeling of UHI than MLR models, as it takes into account non-stationarity of the spatial process. However, Monte Carlo and F3 tests for spatial stationarity of the independent variables suggest that for several spatial predictors a mixed GWR–MLR approach is recommended. Both local and global models were extended by the interpolation of regression residuals and used for spatial interpolation of the UHI structure. The interpolation results were evaluated with the cross-validation approach. It was found that the incorporation of the spatially interpolated residuals leads to significant improvement of the interpolation results for both GWR and MLR approaches. Because GWR is better justified in terms of statistical specification, the combined GWR + interpolated regression residuals (GWR residual kriging; GWRK) approach is recommended for spatial modeling of UHI, instead of widely applied MLR models.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/7e50eb81de66450f31773bb0cb538d280f7858e5
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Citations in the corpus
- Mapping the mean annual precipitation of China using local interpolation techniques (2014, background): “…at high resolution, such as inverse distance weighting (Zhan et al. 2011; Kizza et al. 2012), linear or nonlinear regression (Lee et al. 2011; Mariusz and Maciej 2012), geographically weighted regression (Kamarianakis et al. 2008; Bostan et al. 2012), artificial neural networks (Ramirez et…”
- Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures (2018, methodology): “As a result, researchers also utilised local-based model, namely geographically weighted regression (GWR) to assess the relationship (Deilami et al., 2016; Szymanowski and Kryza, 2012).”