Estimate two-dimensional kernel density of points

Usage

hotspot_kde(
  data,
  cell_size = NULL,
  grid_type = "rect",
  bandwidth = NULL,
  bandwidth_adjust = 1,
  grid = NULL,
  weights = NULL,
  quiet = FALSE,
  ...
)

Arguments

data: sf data frame containing points.
cell_size: numeric value specifying the size of each equally spaced grid cell, using the same units (metres, degrees, etc.) as used in the sf data frame given in the data argument. Ignored if grid is not NULL. If this argument and grid are NULL (the default), the cell size will be calculated automatically (see Details).
grid_type: character specifying whether the grid should be made up of squares ("rect", the default) or hexagons ("hex"). Ignored if grid is not NULL.
bandwidth: numeric value specifying the bandwidth to be used in calculating the kernel density estimates. If this argument is NULL (the default), the bandwidth will be determined automatically using the result of bandwidth.nrd called on the co-ordinates of data.
bandwidth_adjust: single positive numeric value by which the value of bandwidth is multiplied. Useful for setting the bandwidth relative to the default.
grid: sf data frame containing polygons, which will be used as the grid for which densities are estimated.
weights: NULL or the name of a column in data to be used as weights for weighted counts and KDE values.
quiet: if set to TRUE, messages reporting the values of any parameters set automatically will be suppressed. The default is FALSE.
...: Further arguments passed to kde.

Value

An sf tibble of grid cells with corresponding point counts and kernel density estimates for each cell. This can be plotted using autoplot.

Details

This function creates a regular two-dimensional grid of cells (unless a custom grid is specified with grid) and calculates the density of points in each cell on that grid using functions from the SpatialKDE package. The count of points in each cell is also returned.

Coverage of the output data

The grid produced by this function covers the convex hull of the input data layer. This means the result may include KDE values for cells that are outside the area for which data were provided, which could be misleading. To handle this, consider cropping the output layer to the area for which data are available. For example, if you only have crime data for a particular district, crop the output dataset to the district boundary using st_intersection.

Automatic cell-size selection

If no cell size is given then the cell size will be set so that there are 50 cells on the shorter side of the grid. If the data SF object is projected in metres or feet, the number of cells will be adjusted upwards so that the cell size is a multiple of 100.

References

Yin, P. (2020). Kernels and Density Estimation. The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2020 Edition), John P. Wilson (ed.). doi:doi:10.22224/gistbok/2020.1.12

Examples

library(sf)

# Transform data to UTM zone 15N so that cell_size and bandwidth can be set
# in metres
memphis_robberies_utm <- st_transform(memphis_robberies_jan, 32615)

# Automatically set grid-cell size, bandwidth and neighbour distance
# \donttest{
hotspot_kde(memphis_robberies_utm)
#> Cell size set to 500 metres automatically
#> Bandwidth set to 8,877 metres automatically based on rule of thumb
#> Simple feature collection with 2715 features and 2 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 761986.2 ymin: 3876436 xmax: 794486.2 ymax: 3905936
#> Projected CRS: WGS 84 / UTM zone 15N
#> # A tibble: 2,715 × 3
#>        n   kde                                                          geometry
#>  * <dbl> <dbl>                                                     <POLYGON [m]>
#>  1     0 11.1  ((770486.2 3876436, 770486.2 3876936, 770986.2 3876936, 770986.2…
#>  2     0 11.2  ((770986.2 3876436, 770986.2 3876936, 771486.2 3876936, 771486.2…
#>  3     0 11.2  ((771486.2 3876436, 771486.2 3876936, 771986.2 3876936, 771986.2…
#>  4     0 11.1  ((771986.2 3876436, 771986.2 3876936, 772486.2 3876936, 772486.2…
#>  5     1 10.9  ((772486.2 3876436, 772486.2 3876936, 772986.2 3876936, 772986.2…
#>  6     0 10.5  ((772986.2 3876436, 772986.2 3876936, 773486.2 3876936, 773486.2…
#>  7     0 10.0  ((773486.2 3876436, 773486.2 3876936, 773986.2 3876936, 773986.2…
#>  8     0  9.45 ((773986.2 3876436, 773986.2 3876936, 774486.2 3876936, 774486.2…
#>  9     0  8.76 ((774486.2 3876436, 774486.2 3876936, 774986.2 3876936, 774986.2…
#> 10     0 10.1  ((768486.2 3876936, 768486.2 3877436, 768986.2 3877436, 768986.2…
#> # ℹ 2,705 more rows
# }

# Manually set grid-cell size and bandwidth in metres, since the
# `memphis_robberies_utm` dataset uses a co-ordinate reference system (UTM
# zone 15 north) that is specified in metres
# \donttest{
hotspot_kde(memphis_robberies_utm, cell_size = 200, bandwidth = 1000)
#> Simple feature collection with 16133 features and 2 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 762136.2 ymin: 3876586 xmax: 794136.2 ymax: 3905386
#> Projected CRS: WGS 84 / UTM zone 15N
#> # A tibble: 16,133 × 3
#>        n    kde                                                         geometry
#>  * <dbl>  <dbl>                                                    <POLYGON [m]>
#>  1     0 0.0658 ((771936.2 3876586, 771936.2 3876786, 772136.2 3876786, 772136.…
#>  2     0 0.315  ((772136.2 3876586, 772136.2 3876786, 772336.2 3876786, 772336.…
#>  3     0 0.618  ((772336.2 3876586, 772336.2 3876786, 772536.2 3876786, 772536.…
#>  4     0 0.867  ((772536.2 3876586, 772536.2 3876786, 772736.2 3876786, 772736.…
#>  5     1 1.00   ((772736.2 3876586, 772736.2 3876786, 772936.2 3876786, 772936.…
#>  6     0 0.997  ((772936.2 3876586, 772936.2 3876786, 773136.2 3876786, 773136.…
#>  7     0 0.821  ((773136.2 3876586, 773136.2 3876786, 773336.2 3876786, 773336.…
#>  8     0 0.518  ((773336.2 3876586, 773336.2 3876786, 773536.2 3876786, 773536.…
#>  9     0 0.208  ((773536.2 3876586, 773536.2 3876786, 773736.2 3876786, 773736.…
#> 10     0 0.183  ((771136.2 3876786, 771136.2 3876986, 771336.2 3876986, 771336.…
#> # ℹ 16,123 more rows
# }