I am looking into Spatial Neighbors to address autocorrelation in my dataset, but I find it difficult to find arguments as to which method to prefer. I am using the R package "spdep" and functions dnearneigh and knearneigh to determine the distance-based neighbors and k-nearest neighbors, respectively. However, could someone advise me on the main differences between the two methods, as well as on how to determine d2 (upper distance bound) and k (number of nearest neighbors).
Spatial autocorrelation (clustering) of bio-geographic phenomena may be expected following Tobler's first Law of Geography. Consequently, a spatial model showing spatial autocorrelation should be the null hypothesis, the lack thereof an alternative hypothesis.
Spatial autocorrelation of sampling/observation points/pixels is a different issue.
Obviously, spatial autocorrelation may be spatial and/or time scale-dependent, both for input data and output. Probably, DNN captures the spatial scale impact better. Why not try both DNN and KNN and discuss the results?
The election of either option can also be informed by theory. DNN can be useful when the spatial dispersion is limited by access -- for instance, travel distances/costs/frontiers. In general, it gives you more control over the parameters of the model.
"semivariograms and distance",there are two problems with Fararini's comment.
With only data values you can only estimate and model the variogram (using an empirical variogram). This is not an exact process, i.e. there is no way to be able to claim that you have chosen "the" model and have exactly estimated the parameters of the model. Moreover for a given (finite) data set the empirical variogram is not unique, you must choose the lag spacings and also you must distinguish between an omnidirectional empirical variogram and directional empirical variograms.
Secondly, the variogram is only indirectly related to correlation (unlike a covariance function or (auto)correlation function). A variogram model need not have a true range, e.g. the exponential and Gaussian models do not, moreover the Power models do not even have a sill. It is common to refer to a range for both the exponential and Gaussian models but this is not a theoretical result, rather it is the distance at which the value of the variogram model is 95% of the sill (95% is an arbitrary choice). If the variogram has a geometric anisotropy then the range depends on the direction (in 3D there are two direction parameters)
However the suggestion of using the empirical variogram to choose a maximum distance is not a bad one but recognize that you are choosing a distance, you are certainly NOT determining the "exact distance at which the autocorrelation stops/starts". It is critical to distinguish between an empirical variogram and a theoretical model.
Finally, "semivariogram" is long out dated terminology (since about 1988 when G. Matheron stopped using it)
To Chris Broekhoven, since you are using R packages, you might want to check references on the author of those packages. Do a search in Google for "R package dnearneigh", you will find a lot of documentation and some tutorial examples as well as for "R package knearneigh"
Both dnearestneighbor and knearestneighbor pertain to data sets where the interest is mostly or completely in the locations of the data points, variograms pertain to data sets where there is a value (for some variable, e.g. hydrologic parameter) at each data location and the interest is in how that value relates to the position coordinates.
- Badges
- Science topic
- Similar topics
- Filing