Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation

Tu, Jun; Xia, Zong-Guo

doi:10.1016/j.scitotenv.2008.09.031

Cited by 336 publications

(173 citation statements)

References 29 publications

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“…GWR calculates the optimal distance for fixed kernel or optimal number of neighbors for the adaptive kernel. Unlike the literature using adaptive bandwidth (Tu and Xia, 2008;Pratt and Chang, 2012;Tu, 2013), due to the distribution of monitoring sites, we found that the fixed bandwidth method had a significant advantage in developing GWR models for the Wen-Rui Tang River watershed.…”

Section: Modeling Methodscontrasting

confidence: 62%

“…These global statistical methods express the average of existing relationships, which may neglect some significant spatial characteristics and hide local variations (Tu and Xia, 2008;Tu, 2013). Geographically weighted regression (GWR) models (Fotheringham et al, 1996;Boots, 2003) were first applied to assess the relationship between land use and water quality by Tu and Xia (2008).…”

Section: Introductionmentioning

confidence: 99%

“…Geographically weighted regression (GWR) models (Fotheringham et al, 1996;Boots, 2003) were first applied to assess the relationship between land use and water quality by Tu and Xia (2008). They demonstrated better results for GWR models than from traditional linear regression models, such as ordinary least squares (OLS) regression.…”

Section: Introductionmentioning

confidence: 99%

“…In studies applying the GWR technique to evaluation of land use on surface water quality (e.g., Tu and Xia, 2008;Tu, 2011;Brown et al, 2012;Tu, 2013;Sun et al, 2014), it is common that only a single landuse indicator was selected as the independent variable because of the high potential for multicollinearity among different land-use variables (Griffith, 2008). This would result in an invalid GWR model when variables experiencing collinearity are selected.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Chen

Mei

Dahlgren

et al. 2016

Science of The Total Environment

168

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• The modified GWR models had higher R 2 and reflected the actual spatial features.• A manual variable excluding-selecting method is explored to avoid multicollinearity.• Influences of the dominant indicator on water quality varied with space and seasons.• Protection policies need consider sitespecific conditions and seasonal variations. G R A P H I C A L A B S T R A C Ta b s t r a c t a r t i c l e i n f o As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2~5 9.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use Contents lists available at ScienceDirectScience of the Total Environment j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s c i t o t e n v conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations.

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Section: Modeling Methodscontrasting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Chen

Mei

Dahlgren

et al. 2016

Science of The Total Environment

168

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“…In an autocorrelation situation, the value of a variable (e.g., UHI) in a location is impacted by the value of the same variable at nearby locations-i.e., if an area is surrounded by several high temperature zones, that area will automatically be experiencing high temperatures. The spatial non-stationarity explains how the relationship between an independent and dependent variable varies over space [34]. As a result, parameters estimated using the OLS method are an average over an entire area of interest rather than location-specific within an area.…”

Section: The Application Of Gwr To Model the Uhi Effectmentioning

confidence: 99%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1-cross-sectional ordinary least square (OLS) regression-to examine the association between the UHI effect and land cover patterns in 2013; Model 2-cross-sectional geographically weighted regression (GWR)-to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3-longitudinal OLS-to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4-longitudinal GWR-to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.

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Using Moran's I and geostatistics to identify spatial patterns of soil nutrients in two different long‐term phosphorus‐application plots

Zhao

Zhang

et al. 2011

Z. Pflanzenernähr. Bodenk.

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The spatial variation of soil nutrients especially the soil test phosphorus (STP) in grassland soils is becoming important because of the use of soil‐nutrients information as a basis for policies such as the recently EU‐introduced Nitrates Directive. Up to now, the small‐scale spatial variation of soil nutrients in grassland has not been studied. The main aim of this study was to investigate the spatial patterns of soil nutrients in two grazed grassland plots with a long‐term (38 y) P‐application experiment, in order to better understand the spatial variation of soil nutrients and the correlation among soil nutrients in grasslands. Two small areas (one from a high‐P background and the other from a medium‐P background) were selected. Soil samples (304 per study area) were collected based on a 1 m × 1 m grid system. The samples were analyzed for STP, Mg, K, pH, and lime requirement (LR). The results were analyzed using conventional statistics, Moran's I, geostatistics, and a GIS.Based on the global Moran's I values, significant positive spatial autocorrelations were found for STP, Mg, pH, and LR in both study areas. Spatial clusters and spatial outliers were detected using the local Moran's I index. Clear linear‐shaped high‐high or low‐low value clusters of the studied variables except K were observed in the study areas due to long‐term usage of machine spreader or other agricultural‐management methods in the past. The corresponding linear patterns were further found in the spatial‐distribution maps. Small spatial patches were found for soil K revealing that it had a random spatial distribution related to the relatively uniform K fertilizer in the study areas. The spatial clusters revealed by local Moran's I were in line with the spatial patterns in the distribution maps.

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Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation

Cited by 336 publications

References 29 publications

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Using Moran's I and geostatistics to identify spatial patterns of soil nutrients in two different long‐term phosphorus‐application plots

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