This function calculates basic and robust Geographically weighted summary statistics [1]. 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.
The arguments were taken from Gollini et al.[2]
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).
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
.
[1] 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
[2] 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
[3] Fotheringham S, Brunsdon, C, and Charlton, M (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Chichester: Wiley.