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The most common interpolation techniques are moving average, trend surface and kriging (Kratze et al. 2006).
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Carcass locations from a single white-tailed deer, Odocoileus virginanus, outbreak were analyzed for spatial clustering using K-function analysis and directionality with trend surface analysis and the direction test.
Tanoak (Lithocarpus densiflorus) mortality was analyzed with respect to environmental and biotic factors using trend surface analysis and multivariate regression.
The model-estimated parameters for semivariograms are reported in Table 3. Interpolated surfaces were created from these models, with the trends in abundance due to latitude and longitude (trend surface) re-introduced and estimates back-transformed from the logarithm scale.
In order to predict the %clay across the landscape, a hierarchical spatial regression model (HSR) was developed using a composite signal variable [i.e., ln(EM34-10) + ln(EM34-40) + ln(EM38-h)] and first-order trend surface components (i.e., Easting and Northing).
DEMTSA is used to interpolate the scattered climatic data into raster data, by means of trend surface analysis (TSA) method and interpolation method.
The trend surface allowed for temperature and precipitation to have curvilinear or unimodal relationships with WNv incidence, and also captured possible interactions between temperature and precipitation.
Between two and three variables were obtained in addition to the trend surface depending on the year and pollutant, but population and altitude did not come into any of the models.
Vegetation pattern was described using trend surface analysis, spatial autocorrelation analysis and redundancy analysis.
Therefore, global spatial trends in abundance were first modeled using trend surface analysis, where abundance was modeled using polynomials for latitude and longitude [26].
Non-significant trend surface components were eliminated from the final model.
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