Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling
Several studies in the hydrology field have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized tha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | http://www.mdpi.com/2220-9964/7/2/64 |
| _version_ | 1811201785836077056 |
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| author | Lorrayne Miralha Daehyun Kim |
| author_facet | Lorrayne Miralha Daehyun Kim |
| author_sort | Lorrayne Miralha |
| collection | DOAJ |
| description | Several studies in the hydrology field have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. We selected ten watersheds in the USA and analyzed if water quality variables with higher Moran’s I values undergo greater increases in the coefficient of determination (R2) and greater decreases in residual SAC (rSAC). We compared non-spatial ordinary least squares to two spatial regression approaches, namely, spatial lag and error models. The predictors were the principal components of topographic, land cover, and soil group variables. The results revealed that water quality variables with higher inherent SAC showed more substantial increases in R2 and decreases in rSAC after performing spatial regressions. In this study, we found a generally linear relationship between the spatial model outcomes (R2 and rSAC) and the degree of SAC in each water quality variable. We suggest that the inherent level of SAC in response variables can predict improvements in models before spatial regression is performed. |
| first_indexed | 2024-04-12T02:27:48Z |
| format | Article |
| id | doaj.art-69b2099f95ac4489a564862eece3470b |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-12T02:27:48Z |
| publishDate | 2018-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-69b2099f95ac4489a564862eece3470b2022-12-22T03:51:55ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-02-01726410.3390/ijgi7020064ijgi7020064Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality ModelingLorrayne Miralha0Daehyun Kim1Department of Geography, University of Kentucky, Lexington, KY 40508, USADepartment of Geography, Seoul National University, Seoul 08826, KoreaSeveral studies in the hydrology field have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. We selected ten watersheds in the USA and analyzed if water quality variables with higher Moran’s I values undergo greater increases in the coefficient of determination (R2) and greater decreases in residual SAC (rSAC). We compared non-spatial ordinary least squares to two spatial regression approaches, namely, spatial lag and error models. The predictors were the principal components of topographic, land cover, and soil group variables. The results revealed that water quality variables with higher inherent SAC showed more substantial increases in R2 and decreases in rSAC after performing spatial regressions. In this study, we found a generally linear relationship between the spatial model outcomes (R2 and rSAC) and the degree of SAC in each water quality variable. We suggest that the inherent level of SAC in response variables can predict improvements in models before spatial regression is performed.http://www.mdpi.com/2220-9964/7/2/64spatial autocorrelationwater qualityspatial modelingcoefficient of determinationresidual autocorrelation |
| spellingShingle | Lorrayne Miralha Daehyun Kim Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling ISPRS International Journal of Geo-Information spatial autocorrelation water quality spatial modeling coefficient of determination residual autocorrelation |
| title | Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling |
| title_full | Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling |
| title_fullStr | Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling |
| title_full_unstemmed | Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling |
| title_short | Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling |
| title_sort | accounting for and predicting the influence of spatial autocorrelation in water quality modeling |
| topic | spatial autocorrelation water quality spatial modeling coefficient of determination residual autocorrelation |
| url | http://www.mdpi.com/2220-9964/7/2/64 |
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