code: def kde(data, bw, cmap='hot', method='hist', scaling='sqrt', alpha=220,
                   cut_below=None, clip_above=None, binsize=1, cmap_levels=10, show_colorbar=False):
      
      from geoplotlib.layers import KDELayer
      _global_config.layers.append(KDELayer(data, bw, cmap, method, scaling, alpha,
                   cut_below, clip_above, binsize, cmap_levels, show_colorbar))
docs: Kernel density estimation visualization
  
      :param data: data access object
      :param bw: kernel bandwidth (in screen coordinates)
      :param cmap: colormap
      :param method: if kde use KDEMultivariate from statsmodel, which provides a more accurate but much slower estimation.
          If hist, estimates density applying gaussian smoothing on a 2D histogram, which is much faster but less accurate
      :param scaling: colorscale, lin log or sqrt
      :param alpha: color alpha
      :param cut_below: densities below cut_below are not drawn
      :param clip_above: defines the max value for the colorscale
      :param binsize: size of the bins for hist estimator
      :param cmap_levels: discretize colors into cmap_levels levels
      :param show_colorbar: show colorbar