Classify hot-spots — hotspot_classify • sfhotspot

Classify cells in a grid based on changes in the clustering of points (typically representing events) in a two-dimensional regular grid over time.

Usage

hotspot_classify(
  data,
  time = NULL,
  period = NULL,
  start = NULL,
  cell_size = NULL,
  grid_type = "rect",
  grid = NULL,
  collapse = FALSE,
  params = hotspot_classify_params(),
  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. If this argument is NULL and data contains a single column of Date or POSIXt values, that column will be used automatically.
period: A character value containing a number followed by a unit of time, e.g. for example, "12 months" or "3.5 days", where the unit of time is one of second, minute, hour, day, week, month, quarter or year (or their plural forms).
start: A Date or POSIXt value specifying when the first temporal period should start. If NULL (the default), the first period will start at the beginning of the earliest date found in the data (if period is specified in days, weeks, months, quarters or years) or at the earliest time found in the data otherwise.
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.
collapse: If the range of dates in the data is not a multiple of period, the final period will be shorter than the others. In that case, should this shorter period be collapsed into the penultimate period?
params: A list of optional parameters that can affect the output. The list can be produced most easily using the hotspot_classify_params helper function.
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.

Hot-spots are spatial areas that contain more points than would be expected by chance; cold-spots are areas that contain fewer points than would be expected. Whether an area is a hot-spot can vary over time. This function creates a space-time cube, determines whether an area is a hot-spot for each of several consecutive time periods and uses that to classify areas according to whether they are persistent, intermittent, emerging or former hot- or cold-spots.

Hot and cold spots

Hot- and cold-spots are identified by calculating the Getis-Ord G_i^* (gi-star) or G_i^* \(Z\)-score statistic for each cell in a regular grid for each time period. Cells are classified as follows, using the parameters provided in the params argument:

Persistent hot-/cold-spots are cells that have been hot-/cold-spots consistently over time. Formally: if the p-value is less than critical_p for at least persistent_prop proportion of time periods.
Emerging hot-/cold-spots are cells that have become hot-/cold-spots recently but were not previously. Formally: if the p-value is less than critical_p for at least hotspot_prop of time periods defined as recent by recent_prop but the p-value was not less than critical_p for at least hotspot_prop of time periods defined as non-recent by 1 - recent_prop.
Former hot-/cold-spots are cells that used to be hot-/cold-spots but have not been more recently. Formally: if the p-value was less than critical_p for at least hotspot_prop of time periods defined as non-recent by 1 - recent_prop but the p-value was not less than critical_p for for at least hotspot_prop of time periods defined as recent by recent_prop.
Intermittent hot-/cold-spots are cells that have been hot-/cold-spots, but not as frequently as persistent hotspots and not only during recent/non-recent periods. Formally: if the p-value is less than critical_p for at least hotspot_prop of time periods but the cell is not an emerging or former hotspot.
No pattern if none of the above categories apply.

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 G_i^* or G_i^* 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

Chainey, S. (2020). Understanding Crime: Analyzing the Geography of Crime. Redlands, CA: ESRI.