Package {spatialrisk}

Example addresses in Groningen

Description

A sample of addresses in Groningen with point coordinates in EPSG:4326 and an example numeric amount column.

Usage

Groningen

Format

A data frame with 25,000 rows and 9 variables:

street

Street name.

number

House number.

letter

House letter.

suffix

House number suffix.

postal_code

Postal code.

city

City name.

lon

Longitude.

lat

Latitude.

amount

Example numeric amount.

Details

The amount column is an example value used in package examples and tests.

Source

BAG, the Dutch registry for addresses and buildings (Basisregistratie Adressen en Gebouwen), adapted for package examples.

Identify the focal cells exceeding the threshold

Description

Generate a data.frame containing the cell indices of the focal cells surpassing the specified threshold. Additionally, include columns for the coordinates (xy) corresponding to the center of each cell.

Usage

cells_above_threshold(focal, threshold)

Arguments

Author(s)

Martin Haringa

Create a choropleth map of polygon-level values

Description

Creates a choropleth map from an 'sf' object containing polygon-level reporting values, for example one produced by summarise_points_by_polygon(). Polygons are shaded according to values in a specified column, with clustering based on the Fisher–Jenks algorithm.

Usage

choropleth(
  data,
  value = "output",
  id = NULL,
  mode = c("plot", "view"),
  n = 7,
  legend_title = "Value",
  palette = "viridis",
  id_name = NULL,
  ...
)

Arguments

Details

The function uses the Fisher–Jenks algorithm (style = "fisher") to classify values into n groups.

Value

A tmap object (static or interactive, depending on mode).

Author(s)

Martin Haringa

Examples

test <- summarise_points_by_polygon(nl_provincie, insurance, "amount")
choropleth(test, value = "amount_sum")
choropleth(test, value = "amount_sum", id = "areaname", mode = "view")

Choropleth map of an sf object with ggplot2

Description

Deprecated. Use [choropleth()] instead.

Usage

choropleth_ggplot2(
  sf_object,
  value = output,
  n = 7,
  dig.lab = 2,
  legend_title = "Class",
  option = "D",
  direction = 1
)

Arguments

Details

'choropleth_ggplot2()' is deprecated. Use [choropleth()] instead.

Value

A ggplot object containing the choropleth map.

Deprecated alias for radius_sum()

Description

'concentration()' is deprecated. Use [radius_sum()] instead.

Usage

concentration(
  sub,
  full,
  value,
  lon_sub = lon,
  lat_sub = lat,
  lon_full = lon,
  lat_full = lat,
  radius = 200,
  display_progress = TRUE,
  result_col = "radius_sum"
)

Arguments

Value

See [radius_sum()].

Find fixed-radius concentration hotspots

Description

Finds fixed-radius concentration hotspots in weighted point-level data. This is a computational building block for weighted circle-placement problems: given point locations and a fixed radius, find a centre whose surrounding circle contains a large aggregated value. In insurance applications, the weights may represent insured values or another exposure measure. This function is a wrapper around the decomposed workflow prepare_spatialrisk, select_candidates, and optimize_hotspot.

Usage

concentration_hotspot(
  data,
  value,
  n_hotspots = 1,
  radius = 200,
  cell_size = 100,
  grid_spacing = 1,
  max_refinement_points = 1500,
  lon = "lon",
  lat = "lat",
  crs_metric = 3035,
  progress = TRUE,
  method = c("continuous", "observed", "grid"),
  top_n = lifecycle::deprecated(),
  grid_precision = lifecycle::deprecated()
)

Arguments

Details

The default method = "continuous" first uses terra rasterisation and focal sums to identify candidate areas above an automatically estimated lower bound. It then refines all retained areas using observed local points and the circle centres implied by local point pairs. Local refinement subsets are retrieved from the raster cells that can affect each candidate area, using a conservative margin based on radius and cell_size. If more than max_refinement_points local points are involved, it falls back to the grid refinement used by method = "grid". In that fallback case, grid_spacing controls the local refinement grid; otherwise the pair-intersection step does not use grid_spacing. The focal window includes the radius plus a raster-cell diagonal. For non-negative values this makes the focal sum an upper bound for exact centres in that cell. A second screening bound sums only active points within the radius of the centre-cell rectangle, including numerical boundary tolerance. Feasible trial centres in the most promising surviving cells can improve the lower bound before further pruning. With the default automatic lower bound, exact evaluation is consequently restricted to observed or pair-intersection centres whose own raster cell passed screening; the two centres generated by a point pair are tested separately. For a single hotspot, Rcpp processes these centres as a streaming angular sweep and maintains their exact totals over the complete active portfolio. It therefore avoids materialising all centres or running a separate radius query for each one. A retained centre is never scored only against the points used to generate it. This centre-level pruning is disabled for a user-supplied threshold or negative values. Under non-negative weights, the default automatic threshold, complete pair-intersection refinement, and exact scoring with no grid fallback, this screening is optimality-preserving for the single-disk problem: a cell containing a strictly improving centre cannot be removed. The "observed" method is fast and deterministic, but can miss a larger hotspot when the optimal centre lies between observed points. The "grid" method uses a grid-based search with local refinement; smaller grid_spacing values generally increase search resolution and computation time. Use prepare_spatialrisk, select_candidates, and optimize_hotspot when these steps need to be run or inspected separately. The high-level function always uses the screened continuous search.

The underlying continuous hotspot problem can be viewed as a fixed-radius weighted circle placement problem. For point observations with non-negative values in a projected metric coordinate system, candidate centres formed by observed point locations and by intersections of radius-'r' circles around pairs of observations are sufficient to characterise the single-disk optimum. The practical method = "continuous" implementation retains that optimum under the screening conditions stated above. This guarantee does not apply to grid fallback, a user-supplied threshold, negative values, or joint optimisation of multiple circles.

Calling optimize_hotspot() directly on an object returned by prepare_spatialrisk() instead performs the complete geometric candidate search over the full active portfolio. Calling it after select_candidates() restricts candidate generation to the screened search state. In both routes, every retained centre is scored against the complete active portfolio. The direct full route does not use grid fallback and is principally intended for small validation or benchmark problems.

For n_hotspots > 1, hotspots are selected greedily: after each hotspot is found, the covered observations are removed before the next hotspot is computed. The resulting sequence is not necessarily globally optimal as a joint multi-circle problem.

Value

An object of class hotspot. The main components are hotspots, containing the selected centre coordinates and summed values, and contributing_points, containing the points inside the selected hotspot radii. The summed value column is named from value; for example, value = "amount" creates an amount_sum column. In contributing_points, data_row gives the row number of the contributing point in the original input data.

Author(s)

Martin Haringa

References

Chazelle, B. M. and Lee, D. T. (1986). On a circle placement problem. Computing, 36(1–2), 1–16. doi:10.1007/BF02238188.

Examples

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

hotspot <- concentration_hotspot(
  portfolio,
  value = "amount",
  radius = 200,
  n_hotspots = 2,
  cell_size = 100,
  progress = FALSE
)

hotspot$hotspots
head(hotspot$contributing_points)

observed_hotspot <- concentration_hotspot(
  portfolio,
  value = "amount",
  radius = 200,
  method = "observed",
  progress = FALSE
)

rbind(
  continuous = hotspot$hotspots[1, ],
  observed = observed_hotspot$hotspots
)

Determine the concentrations within the highest focal cells for the current iteration

Description

Determine the concentrations within the highest focal cells for the current iteration.

Usage

concentration_per_candidate_cell(
  candidate_cells,
  dff,
  value,
  size,
  points,
  cache,
  radius,
  crs_metric,
  lon,
  lat
)

Arguments

Author(s)

Martin Haringa

Convert Coordinate Reference System (CRS)

Description

Convert Coordinate Reference System (CRS) of a data.frame from one CRS to another.

Usage

convert_crs_df(
  df,
  crs_from = 3035,
  crs_to = 4326,
  lon_from = "x",
  lat_from = "y",
  lon_to = "lon",
  lat_to = "lat"
)

Arguments

Value

data.frame with converted coordinates

Author(s)

Martin Haringa

Convert data.frame to simple features (sf) object

Description

This function converts a data.frame to a simple features (sf) object.

Usage

convert_df_to_sf(df, lon = "lon", lat = "lat", crs_from = 4326, crs_to = 3035)

Arguments

Value

Returns an sf object with the specified coordinate reference system.

Author(s)

Martin Haringa

Deprecated alias for concentration_hotspot()

Description

'find_highest_concentration()' is deprecated. Use [concentration_hotspot()] instead.

Usage

find_highest_concentration(
  df,
  value,
  top_n = 1,
  radius = 200,
  cell_size = 100,
  grid_precision = 1,
  lon = "lon",
  lat = "lat",
  crs_metric = 3035,
  print_progress = TRUE
)

Arguments

Value

See [concentration_hotspot()].

Haversine great-circle distance

Description

Calculates the shortest distance between two points on the Earth's surface using the Haversine formula, also known as the great-circle distance or "as the crow flies".

Usage

haversine(lat_from, lon_from, lat_to, lon_to, r = 6378137)

Arguments

Details

This function is vectorized: if multiple coordinates are supplied, it returns one distance for each corresponding pair of points.

Value

A numeric vector with distances in the same unit as 'r' (default: meters).

References

Sinnott, R.W, 1984. Virtues of the Haversine. Sky and Telescope 68(2): 159.

Examples

haversine(53.24007, 6.520386, 53.24054, 6.520386)

lat_from <- c(53.24, 52.37)
lon_from <- c(6.52, 4.90)
lat_to   <- c(48.85, 51.92)
lon_to   <- c(2.35, 4.48)
haversine(lat_from, lon_from, lat_to, lon_to)

Deprecated geohash hotspot search

Description

'highest_concentration()' is deprecated. Use concentration_hotspot for fixed-radius hotspot detection in new analyses.

Usage

highest_concentration(
  df,
  value,
  lon = lon,
  lat = lat,
  lowerbound = NULL,
  radius = 200,
  grid_distance = 25,
  gh_precision = 6,
  display_progress = TRUE
)

Arguments

Details

This legacy function used a geohash-based screening workflow. It is retained only for backward compatibility. The current high-level interface is concentration_hotspot, which documents the supported hotspot search methods and returns the current hotspot object structure.

Value

A legacy data frame with candidate hotspot coordinates and concentration values.

Author(s)

Martin Haringa

Find the highest concentration for the current iteration

Description

Find the highest concentration for the current iteration.

Usage

highest_concentration_candidate(new_candidates, candidate_cells, cache)

Arguments

Author(s)

Martin Haringa

Example insurance portfolio

Description

A sample insurance portfolio with postal codes, insured amounts, population at 4-digit postcode level, and point coordinates in EPSG:4326.

Usage

insurance

Format

A data frame with 29,990 rows and 5 variables:

postcode

6-digit postal code.

population_pc4

Population for the corresponding 4-digit postcode area.

amount

Insured amount.

lon

Longitude of the corresponding 6-digit postal code.

lat

Latitude of the corresponding 6-digit postal code.

Details

This dataset is intended for examples and tests of spatial concentration workflows.

Author(s)

Martin Haringa

Interpolate values using spherical splines

Description

Deprecated. Spline interpolation and smoothing on the sphere. This function is outside the main scope of spatialrisk and will be removed in a future release.

Usage

interpolate_spline(
  observations,
  targets,
  value,
  lon_obs = lon,
  lat_obs = lat,
  lon_targets = lon,
  lat_targets = lat,
  k = 50
)

Arguments

Value

Object equal to targets with an extra prediction column.

References

Splines on the sphere

Retrieve historic weather data for the Netherlands

Description

This function retrieves historic hourly weather data collected by official KNMI weather stations. See knmi_stations for the station metadata included in this package.

Usage

knmi_historic_data(
  startyear,
  endyear,
  stations = NULL,
  progress = interactive()
)

Arguments

Format

The returned data frame contains the following columns:

• station = ID of measurement station;

• date = Date;

• FH = Hourly mean wind speed (in 0.1 m/s);

• FX = Maximum wind gust (in 0.1 m/s) during the hourly division;

• DR = Precipitation duration (in 0.1 hour) during the hourly division;

• RH = Hourly precipitation amount (in 0.1 mm) (-1 for <0.05 mm);

• city = City where the measurement station is located;

• lon = Longitude of station (EPSG:4326);

• lat = Latitude of station (EPSG:4326).

Details

The data is downloaded from KNMI when the function is called. This requires an internet connection and may take some time when many stations or years are requested.

Value

Data frame containing weather data and metadata for weather station locations.

Author(s)

Martin Haringa

Examples

## Not run: 
knmi_historic_data(2015, 2019, stations = c(260, 280))

## End(Not run)

KNMI weather stations

Description

A data frame containing station IDs and location metadata for official KNMI weather stations in the Netherlands.

Usage

knmi_stations

Format

A data frame with 50 rows and 7 variables:

station

Station ID.

city

City where the station is located.

lon

Longitude of the station in EPSG:4326.

lat

Latitude of the station in EPSG:4326.

altitude

Altitude of the station in meters.

X

Projected X coordinate of the station in EPSG:32631.

Y

Projected Y coordinate of the station in EPSG:32631.

Author(s)

Martin Haringa

Source

Royal Netherlands Meteorological Institute (KNMI), adapted for package examples.

Create interactive point map

Description

Creates an interactive map for a data.frame containing point coordinates, optionally colored by a selected variable.

Usage

map_points(
  data,
  value = NULL,
  lon = "lon",
  lat = "lat",
  crs = 4326,
  at = NULL,
  layer_name = NULL,
  ...
)

Arguments

Value

An interactive mapview object.

Examples

## Not run: 
map_points(Groningen, value = "amount")

## End(Not run)

Map point coordinates to cell indices

Description

Map point coordinates to cell indices.

Usage

map_points_to_cells(pts, focal, lon, lat, crs_from, crs_to, r = NULL)

Arguments

Author(s)

Martin Haringa

Create focal ("moving window") weight matrix

Description

Create a focal ("moving window") weight matrix for use in terra::focal().

Usage

mw_create(r, radius)

Arguments

Details

The returned mask includes raster-cell centres whose Euclidean distance from the focal-cell centre is no greater than 'radius' plus the full raster-cell diagonal. This conservative expansion accounts for the possible displacement of both a disk centre and a contributing point from their respective cell centres.

Author(s)

Martin Haringa

Deprecated geohash neighbourhood refinement

Description

'neighborhood_gh_search()' is deprecated. Use concentration_hotspot for fixed-radius hotspot detection in new analyses.

Usage

neighborhood_gh_search(
  hc,
  highest_geohash = 1,
  max.call = 1000,
  verbose = TRUE,
  seed = 1
)

Arguments

Value

A legacy data frame with refined hotspot coordinates.

Author(s)

Martin Haringa

COROP regions in the Netherlands

Description

An sf object with COROP region geometries for the Netherlands. Centroid coordinates are included in EPSG:4326.

Usage

nl_corop

Format

A simple feature object with 40 rows and 5 variables:

corop_nr

COROP number.

areaname

COROP region name.

geometry

COROP region geometry.

lon

Longitude of the COROP centroid.

lat

Latitude of the COROP centroid.

Details

COROP regions are regional areas used for analytical purposes by, among others, Statistics Netherlands.

Author(s)

Martin Haringa

Source

Statistics Netherlands (CBS), adapted for package examples.

Municipalities in the Netherlands

Description

An sf object with municipal geometries for the Netherlands in 2021. Centroid coordinates are included in EPSG:4326.

Usage

nl_gemeente

Format

A simple feature object with 380 rows and 6 variables:

id

Municipality identifier.

code

Municipality code.

areaname

Municipality name.

lon

Longitude of the municipality centroid.

lat

Latitude of the municipality centroid.

geometry

Municipality geometry.

Author(s)

Martin Haringa

Source

Statistics Netherlands (CBS), adapted for package examples.

Two-digit postcode regions in the Netherlands

Description

An sf object with 2-digit postcode region geometries for the Netherlands. Centroid coordinates are included in EPSG:4326.

Usage

nl_postcode2

Format

A simple feature object with 90 rows and 4 variables:

areaname

2-digit postcode area.

geometry

Postcode region geometry.

lon

Longitude of the 2-digit postcode centroid.

lat

Latitude of the 2-digit postcode centroid.

Details

Postal codes in the Netherlands are alphanumeric and consist of four digits followed by two uppercase letters. This object aggregates those codes to their first two digits.

Author(s)

Martin Haringa

Source

Adapted from Dutch postcode boundary data for package examples.

Three-digit postcode regions in the Netherlands

Description

An sf object with 3-digit postcode region geometries for the Netherlands. Centroid coordinates are included in EPSG:4326.

Usage

nl_postcode3

Format

A simple feature object with 799 rows and 4 variables:

areaname

3-digit postcode area.

geometry

Postcode region geometry.

lon

Longitude of the 3-digit postcode centroid.

lat

Latitude of the 3-digit postcode centroid.

Details

Postal codes in the Netherlands are alphanumeric and consist of four digits followed by two uppercase letters. This object aggregates those codes to their first three digits.

Author(s)

Martin Haringa

Source

Adapted from Dutch postcode boundary data for package examples.

Four-digit postcode regions in the Netherlands

Description

An sf object with 4-digit postcode region geometries for the Netherlands. Centroid coordinates are included in EPSG:4326.

Usage

nl_postcode4

Format

A simple feature object with 4053 rows and 7 variables:

pc4

4-digit postcode.

areaname

Name of corresponding 4-digit postcode area.

city

City name.

biggest_20cities

Whether the area is in one of the twenty largest cities in the Netherlands.

geometry

Postcode region geometry.

lon

Longitude of the 4-digit postcode centroid.

lat

Latitude of the 4-digit postcode centroid.

Details

Postal codes in the Netherlands are alphanumeric and consist of four digits followed by two uppercase letters. This object aggregates those codes to their first four digits.

Author(s)

Martin Haringa

Source

Adapted from Dutch postcode boundary data for package examples.

Provinces in the Netherlands

Description

An sf object with province geometries for the Netherlands. Centroid coordinates are included in EPSG:4326.

Usage

nl_provincie

Format

A simple feature object with 12 rows and 4 variables:

areaname

Province name.

geometry

Province geometry.

lon

Longitude of the province centroid.

lat

Latitude of the province centroid.

Author(s)

Martin Haringa

Source

Statistics Netherlands (CBS), adapted for package examples.

Plot deprecated geohash hotspot results

Description

Deprecated plotting method for objects produced by highest_concentration(). For current hotspot results, use plot() on the object returned by concentration_hotspot.

Usage

## S3 method for class 'conc'
plot(
  x,
  grid_points = TRUE,
  legend_title = NULL,
  palette = "viridis",
  legend_position = "bottomleft",
  providers = c("CartoDB.Positron", "nlmaps.luchtfoto"),
  ...
)

Arguments

Value

An interactive map.

Author(s)

Martin Haringa

Plot concentration hotspot results

Description

Visualise objects returned by concentration_hotspot(). The default plot shows hotspot centres, fixed-radius buffers, and the contributing points. For terra-based results, diagnostic raster layers can also be plotted.

Usage

## S3 method for class 'hotspot'
plot(
  x,
  type = c("concentration", "focal", "rasterized", "updated_focal"),
  color1 = NULL,
  max.rad = 20,
  ...
)

Arguments

Details

The observed-points hotspot method does not create terra raster or focal objects. For observed-points results, use type = "concentration".

Value

A mapview object.

Plot deprecated geohash neighbourhood results

Description

Deprecated plotting method for objects produced by neighborhood_gh_search(). For current hotspot results, use plot() on the object returned by concentration_hotspot.

Usage

## S3 method for class 'neighborhood'
plot(
  x,
  buffer = 0,
  legend_title = NULL,
  palette = "viridis",
  legend_position = "bottomleft",
  palette_circle = "YlOrRd",
  legend_position_circle = "bottomright",
  legend_title_circle = "Highest concentration",
  providers = c("CartoDB.Positron", "nlmaps.luchtfoto"),
  ...
)

Arguments

Value

An interactive map.

Author(s)

Martin Haringa

Deprecated alias for map_points()

Description

'plot_points()' is deprecated. Use [map_points()] instead.

Usage

plot_points(
  df,
  value = NULL,
  lon = "lon",
  lat = "lat",
  crs = 4326,
  at = NULL,
  layer_name = NULL,
  ...
)

Arguments

Value

See [map_points()].

Deprecated alias for points_within_radius()

Description

'points_in_circle()' is deprecated. Use [points_within_radius()] instead.

Usage

points_in_circle(
  data,
  lon_center,
  lat_center,
  lon = lon,
  lat = lat,
  radius = 200,
  sort = TRUE
)

Arguments

Value

See [points_within_radius()].

Deprecated alias for summarise_points_by_polygon()

Description

'points_to_polygon()' is deprecated. Use [summarise_points_by_polygon()] instead.

Usage

points_to_polygon(sf_map, df, oper, crs = 4326, outside_print = FALSE)

Arguments

Value

See [summarise_points_by_polygon()].

Find points within radius around one or more centre coordinates

Description

This function selects rows from a data frame whose longitude/latitude coordinates fall within a given radius (in meters) from one or more specified centre points. It also calculates the distance of each point to the centre.

Usage

points_within_radius(
  data,
  lon_center,
  lat_center,
  lon = "lon",
  lat = "lat",
  radius = 200,
  sort = TRUE
)

Arguments

Value

A data frame subset of 'data' with an extra column 'distance_m' and if multiple centres are provided, also a column 'center_index'.

Prepare fixed-radius concentration hotspot analysis

Description

'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_spacing = 1,
  max_refinement_points = 1500,
  method = c("continuous", "observed", "grid"),
  threshold = NULL,
  progress = TRUE,
  grid_precision = lifecycle::deprecated()
)

optimize_hotspot(
  x,
  n_hotspots = 1,
  progress = TRUE,
  top_n = lifecycle::deprecated()
)

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

Arguments

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.

'optimize_hotspot()' optimises over the candidate search state supplied to it. Called directly on the output of 'prepare_spatialrisk()', it performs a full geometric search: candidate centres are the active observed locations and the valid radius-circle intersections generated by every active point pair no farther than twice the radius apart. This route does not use raster screening or grid fallback and is intended mainly for small portfolios, diagnostics, validation, and methodological benchmarks. Pair generation can be computationally expensive.

In 'select_candidates()', 'threshold = NULL' estimates a lower bound by taking the highest focal raster cells, refining those cells on a small local grid, evaluating those centres in the projected metric coordinates, and using the best feasible value as the candidate-cell threshold. The focal cells first survive when their moving-window sum is at least this lower bound. For continuous search with non-negative values and an automatic threshold, a second bound sums only active points whose minimum distance to the closed centre-cell rectangle is within the radius, allowing for the scoring tolerance. It does not count the entire value of an extra raster cell. The five highest surviving point bounds also seed feasible trial centres; only their actual portfolio sums can raise the lower bound. Cells with a point bound strictly below that lower bound are removed. The focal window includes the requested radius plus a raster-cell diagonal. With non-negative values, its sum is therefore an upper bound for every exact centre located in that focal cell. Under the default automatic threshold, observed and pair-intersection centres are mapped back to the raster and exact evaluation is restricted to centres that lie in a selected cell. For a point pair, its two possible circle centres are screened separately. For a single hotspot, a streaming Rcpp angular sweep maintains the complete active-portfolio total at the retained pair-intersection events; this avoids materialising and separately querying every centre. With complete pair-intersection refinement and exact scoring, and provided no grid fallback occurs, this removes no cell that can contain a strictly improving centre and therefore preserves a global optimum of the single-disk problem. The guarantee does not apply to a user-supplied threshold, negative values, or grid fallback.

Candidate screening never defines the evaluation portfolio. Every retained centre is scored against all records in the complete active portfolio, so a point outside the candidate-generation subset still contributes when it lies within the radius. Point-to-raster-cell membership is stored during preparation and reused to retrieve nearby records; candidate regions within one hotspot iteration share the same active-portfolio evaluation state. When 'optimize_hotspot(n_hotspots > 1)' or 'concentration_hotspot(n_hotspots > 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 'n_hotspots'.

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.

Author(s)

Martin Haringa

Examples

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

model <- prepare_spatialrisk(portfolio, value = "amount", radius = 200,
                             cell_size = 100)

# Full geometric reference search
full <- optimize_hotspot(model, progress = FALSE)

# Screened continuous search
screened <- model |>
  select_candidates(progress = FALSE) |>
  optimize_hotspot(progress = FALSE)

full$hotspots
screened$hotspots

Sum values within a radius around target coordinates

Description

Calculates the sum of all observations from a reference data set that fall within a given radius (in meters) of each target point.

Usage

radius_sum(
  targets,
  reference,
  value,
  lon_targets = "lon",
  lat_targets = "lat",
  lon_reference = "lon",
  lat_reference = "lat",
  radius = 200,
  progress = TRUE,
  result_col = "radius_sum"
)

Arguments

Details

This function uses a C++ backend for efficient distance calculations (Haversine formula).

Value

A data.frame equal to 'targets' with an additional numeric column named by 'result_col' containing the summed values from 'reference'.

Author(s)

Martin Haringa

Examples

targets <- data.frame(location = c("p1", "p2"),
                      lon = c(6.561561, 6.561398),
                      lat = c(53.21369, 53.21326))

reference <- data.frame(lon = c(6.5614, 6.5620, 6.5630),
                        lat = c(53.2132, 53.2140, 53.2150),
                        amount = c(10, 20, 15))

radius_sum(targets, reference, value = "amount", radius = 100,
           progress = FALSE)

Summarise point exposures by reporting polygon

Description

Spatially joins point data to polygon geometries and summarises a numeric point exposure or value for each reporting area.

Usage

summarise_points_by_polygon(
  polygons,
  points,
  value,
  fun = sum,
  lon = "lon",
  lat = "lat",
  crs = 4326,
  output_col = NULL,
  na.rm = TRUE,
  outside = c("message", "warning", "ignore"),
  repair_geometry = TRUE
)

Arguments

Value

An sf object equal to polygons with an additional summary column.

Examples

summarise_points_by_polygon(
  polygons = nl_postcode2,
  points = insurance,
  value = "amount",
  fun = sum
)

Identify the focal indices with the highest values

Description

Generate a data.frame containing the cell indices of the focal cells with the n highest values. Additionally, include columns for the coordinates (xy) corresponding to the center of each cell.

Usage

top_n_focals(focal, n)

Arguments

Author(s)

Martin Haringa

Update current focal for the next iteration

Description

Update current focal for the next iteration.

Usage

update_focal(old_focal, new_rasterized, extent, mw)

Arguments

Details

Spatial extent refers to the geographic area covered by a spatial dataset. It defines the boundaries in terms of its geographic coordinates (north, east, south, west).

An focal is updated by the following steps:

1. The extent of the cells corresponding to the coordinates with the highest concentration (xmin, xmax, ymin, ymax) is determined.

2. A buffer of size two times the radius plus the cell size around this extent. All cells within this new extent will impact the focal values.

3. Subset (crop) the rasterized data to include only the cells within this new extent. Perform focal calculations only for these new cells.

4. As the focal values of the cells near the borders may be inflated, crop the result again to include only the cells within a radius of the original extent.

5. Merge this updated focal with the previous focal object.

Author(s)

Martin Haringa

Save highest concentrations per cell for subsequent iterations

Description

Save highest concentrations per cell for subsequent iterations.

Usage

update_hotspot_cache(new_candidates, cache, cells)

Arguments

Author(s)

Martin Haringa

Update current rasterize for the next iteration

Description

Update current rasterize for the next iteration.

Usage

update_rasterize(old_rasterized, extent, new_spatvector, col)

Arguments

Details

Spatial extent refers to the geographic area covered by a spatial dataset. It defines the boundaries in terms of its geographic coordinates (north, east, south, west).

Author(s)

Martin Haringa