What are some of the advantages and limitations of geostatistical models in GIS?

1 What are geostatistical models?

Geostatistical models are mathematical models that describe the spatial variation of a variable across a geographic area. They use a concept called the variogram, which measures how the similarity between two values of a variable changes with the distance between them. The variogram can be used to estimate the spatial correlation of the data, and to fit a function that represents the spatial structure of the variable. This function can then be used to interpolate values at locations where there are no measurements, using a technique called kriging.

2 What are the advantages of geostatistical models?

Geostatistical models have several advantages over other methods of spatial interpolation, such as inverse distance weighting or spline interpolation. For example, they take into account the spatial autocorrelation of the data, meaning that the values of a variable are not assumed to be independent of each other. Furthermore, geostatistical models provide estimates of the prediction error and the confidence intervals for each interpolated value, which can help you assess the reliability and uncertainty of your results. Additionally, they are able to handle complex spatial patterns, like anisotropy or multi-scale variation, by using different types of variogram models or transformations. Moreover, they can incorporate auxiliary information into the interpolation process, such as covariates or trends, through co-kriging or regression kriging.

3 What are the limitations of geostatistical models?

Geostatistical models have some limitations to consider before using them for GIS analysis. For example, they require a large and representative sample of data to capture the spatial variability of the variable and fit an appropriate variogram model. Additionally, they can be sensitive to outliers, measurement errors, or spatial clustering of the data, which can affect variogram estimation and interpolation accuracy. Furthermore, geostatistical models tend to be computationally intensive and time-consuming, particularly for large datasets or high-resolution grids. Finally, they can be difficult to interpret and communicate due to complex mathematical concepts and terminology such as semivariance, nugget, sill, range, or anisotropy.

4 How to overcome some of the challenges of geostatistical models?

Geostatistical models can be an effective tool for spatial analysis in GIS, provided that you adhere to certain best practices and recommendations. Exploratory data analysis and quality control are essential for identifying and removing outliers, errors, or biases in your data. Additionally, you should select a suitable sampling design and size to ensure that your data covers the spatial domain and variability of your variable of interest. Furthermore, testing different variogram models and parameters to find the best fit for your data, as well as validating your model using cross-validation or other methods is recommended. Additionally, it is important to compare different interpolation methods and criteria to select the most appropriate one for your objective and data characteristics. Finally, it is important to visualize and report your results using maps, graphs, and statistics that show the interpolated values, the prediction errors, and the confidence intervals.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?