Determine statistically significant results in a GWR in R

Question

I'm running a geographically weighted regression (GWR) in R using the spgwr library. I know it's possible to retrieve the local coefficients and standard errors of each observation with gwr_fit$SDF.

Now how do I use this info to determine which local coefficients are statistically significant so I can plot them on a map?

reproducible example

library(spgwr)
library(UScensus2000tract)
library(parallel)

# load data
  data("oregon.tract")


# calculate Optimal kernel bandwidth
  GWRbandwidth <- gwr.sel( log(med.age) ~ log(white) + log(black), data=oregon.tract, adapt=T)

# detect number of CPU cores to go parallel
  no_cores <- detectCores() - 1 # Calculate the number of cores
  cl <- makeCluster(no_cores)# Initiate cluster 

# run GWR Model
  gwr_fit <- gwr( log(med.age) ~ log(white) + log(black), data=oregon.tract, adapt= GWRbandwidth, hatmatrix=TRUE, se.fit=TRUE, cl=cl)


# return Sp object with coefficients and standard errors
  df <- gwr_fit$SDF