Identify change in hotspots over time

Identify change in the number of points (typically representing events) between two periods (before and after a specified date) or in two groups (e.g. on weekdays or at weekends).

Usage

hotspot_change(
  data,
  time = NULL,
  boundary = NULL,
  groups = NULL,
  cell_size = NULL,
  grid_type = "rect",
  grid = NULL,
  quiet = FALSE
)

Arguments

data: sf data frame containing points.
time: Name of the column in data containing Date or POSIXt values representing the date associated with each point. Ignored if groups is not NULL. If this argument is NULL and data contains a single column of Date or POSIXt values, that column will be used automatically.
boundary: A single Date or POSIXt value representing the point after which points should be treated as having occurred in the second time period. See 'Details'.
groups: Name of a column in data containing exactly two unique non-missing values, which will be used to identify whether each row should be counted in the first (before) or second (after) groups. Which groups to use will be determined by calling sort(unique(groups)). If groups is not a factor, a message will be printed confirming which value has been used for which group. See 'Details'.
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.
grid: sf data frame containing points containing polygons, which will be used as the grid for which counts are made.
quiet: if set to TRUE, messages reporting the values of any parameters set automatically will be suppressed. The default is FALSE.

Value

An sf tibble of regular grid cells with corresponding hot-spot classifications 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 difference between the number of points in each grid cell:

before and after a set point in time, if boundary is specified,
between two groups of points, if a column of grouping values is specified with groups,
before and after the mid-point of the dates/times present in the data, if both boundary and groups are NULL (the default).

If both boundary and groups are not NULL, the value of boundary will be ignored.

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 zero counts 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.

Examples


# Compare counts from the first half of the period covered by the data to
# counts from the second half
# \donttest{
hotspot_change(memphis_robberies)
#> Boundary point set as 09:30 hours on 02 July 2019 automatically
#> Cell size set to 0.00524 degrees automatically
#> Simple feature collection with 2926 features and 3 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -90.1261 ymin: 34.99475 xmax: -89.72786 ymax: 35.26199
#> Geodetic CRS:  WGS 84
#> # A tibble: 2,926 × 4
#>    n_before n_after change                                              geometry
#>  *    <dbl>   <dbl>  <dbl>                                         <POLYGON [°]>
#>  1        0       0      0 ((-90.08418 34.99475, -90.07894 34.99475, -90.07894 …
#>  2        0       0      0 ((-90.07894 34.99475, -90.0737 34.99475, -90.0737 34…
#>  3        0       0      0 ((-90.0737 34.99475, -90.06846 34.99475, -90.06846 3…
#>  4        0       0      0 ((-90.06846 34.99475, -90.06322 34.99475, -90.06322 …
#>  5        0       0      0 ((-90.06322 34.99475, -90.05798 34.99475, -90.05798 …
#>  6        0       0      0 ((-90.05798 34.99475, -90.05274 34.99475, -90.05274 …
#>  7        0       0      0 ((-90.05274 34.99475, -90.0475 34.99475, -90.0475 34…
#>  8        0       0      0 ((-90.0475 34.99475, -90.04226 34.99475, -90.04226 3…
#>  9        0       0      0 ((-90.04226 34.99475, -90.03702 34.99475, -90.03702 …
#> 10        0       0      0 ((-90.03702 34.99475, -90.03178 34.99475, -90.03178 …
#> # ℹ 2,916 more rows
# }

# Create a grouping variable, then compare counts across values of that
# variable
# \donttest{
memphis_robberies$weekend <-
  weekdays(memphis_robberies$date) %in% c("Saturday", "Sunday")
hotspot_change(memphis_robberies, groups = weekend)
#> Comparing periods based on values of `weekend`:
#> • Rows with `weekend == FALSE` used as the 'before' period
#> • Rows with `weekend == TRUE` used as the 'after' period
#> Cell size set to 0.00524 degrees automatically
#> Simple feature collection with 2926 features and 3 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -90.1261 ymin: 34.99475 xmax: -89.72786 ymax: 35.26199
#> Geodetic CRS:  WGS 84
#> # A tibble: 2,926 × 4
#>    n_before n_after change                                              geometry
#>  *    <dbl>   <dbl>  <dbl>                                         <POLYGON [°]>
#>  1        0       0      0 ((-90.08418 34.99475, -90.07894 34.99475, -90.07894 …
#>  2        0       0      0 ((-90.07894 34.99475, -90.0737 34.99475, -90.0737 34…
#>  3        0       0      0 ((-90.0737 34.99475, -90.06846 34.99475, -90.06846 3…
#>  4        0       0      0 ((-90.06846 34.99475, -90.06322 34.99475, -90.06322 …
#>  5        0       0      0 ((-90.06322 34.99475, -90.05798 34.99475, -90.05798 …
#>  6        0       0      0 ((-90.05798 34.99475, -90.05274 34.99475, -90.05274 …
#>  7        0       0      0 ((-90.05274 34.99475, -90.0475 34.99475, -90.0475 34…
#>  8        0       0      0 ((-90.0475 34.99475, -90.04226 34.99475, -90.04226 3…
#>  9        0       0      0 ((-90.04226 34.99475, -90.03702 34.99475, -90.03702 …
#> 10        0       0      0 ((-90.03702 34.99475, -90.03178 34.99475, -90.03178 …
#> # ℹ 2,916 more rows
# }