Spatial patterns of the urban heat island in Zaragoza (Spain)

Vicente‐Serrano, Sergio M.; Prats, José María Cuadrat; Saz-Sánchez, Miguel Ángel

doi:10.3354/cr030061

Cited by 16 publications

(10 citation statements)

References 35 publications

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“…The method can be used to spatialize discrete point data on the assumption that auxiliary, independent variables are known and continuous in space, or, technically, they can be provided as raster layers. MLR has been successfully used for climatological purposes, as well as for UHI spatialization (Svensson et al 2002;Bottyán and Unger 2003;Vicente-Serrano et al 2005;Alcoforado and Andrade 2006;Szymanowski and Kryza 2009). Independent variables were selected for each UHI case from the set of potential predictors described in Section 4.1.…”

Section: Global Linear Regression Modelmentioning

confidence: 99%

“…The first attempts to analyze the UHI structure were based on manually interpolated isotherm maps (Duckworth and Sandberg 1954). More sophisticated interpolation algorithms became popular with the increasing access to effective computers and development of geographic information system (GIS) (Svensson et al 2002;Bottyán and Unger 2003;Vicente-Serrano et al 2005;Alcoforado and Andrade 2006). Most of the recent studies on spatial characteristic of UHI are based on multidimensional interpolation algorithms, with the multiple linear regression (MLR) being the most often applied (Unger et al 2010).…”

Section: Introductionmentioning

confidence: 99%

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Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland

Szymanowski

Kryza

2011

Theor Appl Climatol

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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.

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Section: Global Linear Regression Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland

Szymanowski

Kryza

2011

Theor Appl Climatol

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“…Detailed information on interpolation techniques applied was not included in these previous papers and only a few studies were supported by more advanced mathematical tools (PrestonWhyte 1970, Clarke & Peterson 1973. The development of GIS gave rise to a number of papers dealing with spatialization techniques and the UHI spatial structure (Svensson et al 2002, Bottyán & Unger 2003, Szymanowski 2004, Vicente-Serrano et al 2005, Alcoforado & Andrade 2006. Nowadays, computationally demanding methods are available and, more importantly, development of continuous information on potential UHI predictors, and sine qua non conditions for the use of multidimensional spatialization techniques, is feasible.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, computationally demanding methods are available and, more importantly, development of continuous information on potential UHI predictors, and sine qua non conditions for the use of multidimensional spatialization techniques, is feasible. Svensson et al (2002), Vicente-Serrano et al (2005), and Alcoforado & Andrade (2006) present the results of UHI spatialization using different environmental (climate and terrain) information as potential predictors. The 'urban' group of UHI predictors used in spatial interpolation is related to various features characteristic of the urban environment (Bottyán & Unger 2003, Szymanowski 2004, Szymanowski & Kryza 2006.…”

Section: Introductionmentioning

confidence: 99%

GIS-based techniques for urban heat island spatialization

Szymanowski¹,

Kryza²

2009

Clim. Res.

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This paper addresses the issue of usefulness of selected spatialization techniques for the characterization of an urban heat island (UHI). Five interpolation methods (including both deterministic and stochastic methods or their combination) -namely: inverse distance weighting (IDW), regularized spline with tension (RST), ordinary kriging (OK), multiple linear regression (MLR) and residual kriging (RK) -were evaluated for their ability to estimate air temperature in Wroc8aw, Poland, during 7 cases of the UHI. Spatial interpolation was performed based on time-adjusted air temperature data gathered by mobile measurements. Additional explanatory variables for multidimensional spatialization methods (MLR and RK) were developed based mainly on the land-use map and Landsat thematic mapper (TM) images. Statistically significant predictors were selected using a stepwise regression procedure. Parameters for optimal interpolation were chosen by cross-validation (CV) of results. The CV technique was also used to compare results obtained with the different algorithms together with evaluation of errors (e.g. root mean square error, RMSE; mean absolute error, MAE) and visual examination of the final maps. The least plausible maps, both in terms of error statistics and visually, were obtained with the IDW method. Inside the convex hull of sample points, the OK and RST techniques were characterized by simplified but acceptable air temperature surfaces. The MLR method expressed the land-use background of the UHI, even outside the convex hull, but distorted results when the process tended towards non-stationarity, e.g. due to wind influence. The most accurate results of the UHI spatialization were obtained with the RK technique. KEY WORDS: Spatialization · Spatial interpolation · Urban heat island · GIS · Wroc8awResale or republication not permitted without written consent of the publisher Clim Res 38: 171-187, 2009 This means that the isothermal pattern of an UHI is generally concentric but also strongly dependent on the spatial arrangement of the land-use types that produce local variation. The UHI shape can therefore differ from city to city and may be described as 'amoebic' (e.g. Seoul, South Korea; Park 1986) or 'multicellular ' (e.g. 1ódź, Poland;K8ysik & Fortuniak 1999).Although knowledge on the origin and consequences of UHIs has gradually increased in recent years, the accurate estimation of the UHI spatial structure, which is often needed by town planners, is still one of the most important problems. In addition, air temperature determines numerous aspects of the urban environment and data on its spatial structure is an essential input for various modelling studies (e.g. dispersion of air pollutants). However, sampling sites in the monitoring system are often sparse, limiting the application of interpolation techniques. Data gathered at meteorological stations can be supported by mobile measurements to solve data inadequacy, although some data-time adjustments are needed (Duckworth & Sandberg 1954, Kuttler ...

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“…More specifically, the mobile measurement method (Charabi and Bakhit, 2011;Murphy et al, 2007). Vicente-Serrano et al, 2005) is usually used to obtain a detailed horizontal distribution of climate variables. The methodology begins with the specification of the urban area under study and the points inside this area where the measurements will take place.…”

Section: Introductionmentioning

confidence: 99%

A Linear Model for Moving Measurements Estimation in Urban Climate Studies

Pezzuto¹

2011

American Journal of Applied Sciences

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Problem statement: Several methods can be adopted to study the variations in urban climate. The mobile measurement method is one of them, involving information provided by moving measurements of air temperature, that are taken in points defined along pre-established routes and also data from fixed-point temperature recording stations. Because moving measurements are made in different times along the measurement process, adjustments must be made in order to adequately analyze the air temperature measurements. Approach: Mobile measurements were taken in an urban area and contextualized in the domain of some fixed-point temperature recording stations. Therefore, a linear model proposed to investigate and represent the variables that influence moving measurements estimation in the urban context. Results: All proposed variables in the linear model were considered relevant, because all coefficients of the determined model were non null. Also, the identified model presents a good fit to the field data, as indicated by the resulting coefficient of determination (R²) that is 90.3%. Conclusion/Recommendations: The linear model described in this work is easy to apply, requiring few input variables. It is important to emphasize that the model was developed to estimate moving measurements as a function of fixed measurements and presents the potential to identify new input variables based on moving measurements, as shown by the fit among fixed and moving temperature measurements, in order to provide insight about other possible models of late time adjustment

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Spatial patterns of the urban heat island in Zaragoza (Spain)

Cited by 16 publications

References 35 publications

Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland

Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland

GIS-based techniques for urban heat island spatialization

A Linear Model for Moving Measurements Estimation in Urban Climate Studies

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