This function calculates basic and robust Geographically weighted summary statistics (GWSS). This includes geographically weighted means,standard deviations and skew. Robust alternatives include geographically weighted medians, interquartile ranges and quantile imbalances. This function also calculates basic geographically weighted covariances together with basic and robust geographically weighted correlations
Variables
: a vector of variable names to be evaluated.
Kernel
: a set of five commonly used kernel functions;
Distance bandwidth
: bandwidth used in the weighting function. It has two options, automatic
which is calculated in the Bandwidth selection module and manual
in which the user enter the value.
Power (Minkowski distance)
: the power of the Minkowski distance (p=1 is manhattan distance, p=2 is euclidean distance).
Figure 2. Minkowski distance
Theta (Angle in radians)
: an angle in radians to rotate the coordinate system, default is 0
longlat
: if TRUE, great circle distances will be calculated
quantile
:if TRUE, median, interquartile range, quantile imbalance will be calculated
a SpatialPointsDataFrame (may be gridded) or SpatialPolygonsDataFrame object with local means,local standard deviations,local variance, local skew,local coefficients of variation, local covariances, local correlations (Pearson’s), local correlations (Spearman’s), local medians, local interquartile ranges, local quantile imbalances and coordinates.
In the plot tab, the values obtained in the summary can be plotted, customized and downloaded in .pdf
or .png
format (see video)
Brunsdon C, Fotheringham AS, Charlton ME (2002) Geographically weighted summary statistics -a framework for localised exploratory data analysis. Computers, Environment and Urban Systems 26:501-524. https://doi.org/10.1016/S0198-9715(01)00009-6
Fotheringham S, Brunsdon, C, and Charlton, M (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Chichester: Wiley.
Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models. Journal of Statistical Software, 63(17), 1–50. https://doi.org/10.18637/jss.v063.i17
Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2014) Geographically weighted methods and their use in network re-designs for environmental monitoring. Stochastic Environmental Research and Risk Assessment 28: 1869-1887. https://doi.org/10.1007/s00477-014-0851-1