| Popis: |
With the rapid rise in site-specific data collection, many research efforts have been directed towards finding optimal sampling and analysis procedures. However, the absence of widely available high quality precision agriculture data sets makes it difficult to compare results from separate experiments and to assess the optimality and applicability of procedures. To provide a tool for spatial data experimentation, we have developed a spatial data generator that allows users to produce data layers with given spatial properties and a response variable (e.g. crop yield) dependent upon user specified functions. Differences in response functions within fields can be simulated by assigning different models to regions in coordinate-(x and y) or feature space (multidimensional space of attributes that may have an influence on response). Noise, either unexplained variance or sensor error, can be added to all spatial layers. Sampling and interpolation error is modeled by sampling a continuous data layer and interpolating values at unsampled locations. The program has been successfully tested for up to 15000 grid points, 10 features and 5 models. As an illustration of the potential uses of generated data, the effect of sampling density and kriging interpolation on neural network prediction of crop yield was assessed. Yield prediction accuracy was highly related (correlation coefficient 0.98) to the accuracy of the interpolated layers indicating that unless data are sampled at very high densities relative to their geostatistical properties, one should not attempt to build highly accurate regression functions using interpolated data. By allowing users to generate large amounts of data with controlled complexity and features, the spatial data generator should facilitate the development of improved sampling and analysis procedures for spatial data. |
Full text from SpringerLink