Prepare fixed-radius concentration hotspot analysis — prepare

`prepare_spatialrisk()`, `select_candidates()`, and `optimize_hotspot()` expose the main steps used by concentration_hotspot. They are useful when the intermediate search state needs to be inspected or when the same prepared portfolio is used in more than one hotspot search strategy.

Usage

prepare_spatialrisk(
  data,
  value,
  radius = 200,
  cell_size = 100,
  lon = "lon",
  lat = "lat",
  crs_metric = 3035
)

select_candidates(
  x,
  grid_precision = 1,
  max_refinement_points = 1000,
  method = c("continuous", "grid", "observed"),
  threshold = NULL,
  progress = TRUE
)

optimize_hotspot(x, top_n = 1, progress = TRUE)

# S3 method for class 'spatialrisk_hotspot_workflow'
plot(x, type = c("auto", "raster", "candidates"), ...)

Arguments

data: A data.frame containing point-level exposures. Must include longitude, latitude, and the value of interest.
value: A string giving the numeric column in `data` to aggregate within each radius.
radius: Numeric. Radius of the circle in meters.
cell_size: Numeric. Size of the raster cells used for the initial screening raster.
lon: A string giving the longitude column in `data`.
lat: A string giving the latitude column in `data`.
crs_metric: Numeric. EPSG code for a projected CRS with meter units. The default `3035` is ETRS89 / LAEA Europe.
x: A prepared spatial-risk workflow object returned by `prepare_spatialrisk()` or `select_candidates()`.
grid_precision: Numeric. Approximate spacing in meters used for grid-based refinement.
max_refinement_points: Positive integer. Maximum number of local points used for pair-intersection refinement before falling back to grid refinement.
method: Hotspot search strategy. `"continuous"` is the default and searches for centres that may lie between observed points. `"observed"` searches only observed point locations. `"grid"` uses the grid-refinement workflow.
threshold: Optional numeric lower bound for candidate focal cells. If `NULL`, the lower bound is estimated using the same preliminary refinement step as `concentration_hotspot()`.
progress: Logical. Whether to print progress messages.
top_n: Positive integer. Number of non-overlapping hotspots to return.
type: Plot type. `"auto"` shows the prepared raster before candidate selection and selected focal candidate cells afterwards.
...: Additional arguments passed to `mapview::mapview()`.

Value

`prepare_spatialrisk()` and `select_candidates()` return an object of class `spatialrisk_hotspot_workflow`. `optimize_hotspot()` returns the same `hotspot` object structure as concentration_hotspot.

Details

The three-step interface decomposes the hotspot workflow without replacing `concentration_hotspot()`. The wrapper remains the simplest public function for normal use, while the decomposed functions make the intermediate candidate selection visible.

In `select_candidates()`, `threshold = NULL` estimates a lower bound by taking the highest focal raster cells, refining those cells on a small local grid, and using the best refined value as the candidate-cell threshold. The selected candidates are focal cells whose moving-window sum is at least this lower bound. These candidates describe the current search state. When `optimize_hotspot(top_n > 1)` or `concentration_hotspot(top_n > 1)` is used, the points in the selected hotspot are removed and the candidate-selection logic is run again for the next hotspot. Therefore the number of candidate cells shown by `select_candidates()` for the first iteration does not limit the number of hotspots returned by `top_n`.

Author

Martin Haringa

Examples

portfolio <- Groningen[1:200, c("lon", "lat", "amount")]

model <- prepare_spatialrisk(portfolio, value = "amount", radius = 200,
                             cell_size = 100)
model <- select_candidates(model, progress = FALSE)
hotspot <- optimize_hotspot(model, top_n = 1, progress = FALSE)

hotspot$hotspots
#>   id      lon      lat amount_sum
#> 1  1 6.554816 53.19424       1315