Package 'spatialrisk'

Title: Calculating Spatial Risk
Description: Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m. See Church (1974) <doi:10.1007/BF01942293> for a description of the problem.
Authors: Martin Haringa [aut, cre]
Maintainer: Martin Haringa <[email protected]>
License: GPL (>= 2)
Version: 0.7.1.9000
Built: 2025-02-19 05:16:30 UTC
Source: https://github.com/mharinga/spatialrisk

Help Index

Create choropleth map

Description

Takes an object produced by points_to_polygon(), and creates the corresponding choropleth map. The given clustering is according to the Fisher-Jenks algorithm. This commonly used method for choropleths seeks to reduce the variance within classes and maximize the variance between classes.

Usage

choropleth(
  sf_object,
  value = "output",
  id_name = "areaname",
  mode = "plot",
  n = 7,
  legend_title = "Clustering",
  palette = "viridis"
)

Arguments

sf_object

object of class sf
value

column name to shade the polygons
id_name

column name of ids to plot
mode

choose between static ('plot' is default) and interactive map ('view')
n

number of clusters (default is 7)
legend_title

title of legend
palette

palette name or a vector of colors. See tmaptools::palette_explorer() for the named palettes. Use a - as prefix to reverse the palette. The default palette is "viridis".

Value

tmap

Author(s)

Martin Haringa

Examples

test <- points_to_polygon(nl_provincie, insurance, sum(amount, na.rm = TRUE))
choropleth(test)
choropleth(test, id_name = "areaname", mode = "view")

Map object of class sf using ggplot2

Description

Takes an object produced by choropleth_sf(), and creates the correspoding choropleth map.

Usage

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

Arguments

sf_object

object of class sf
value

column to shade the polygons
n

number of clusters (default is 7)
dig.lab

number of digits in legend (default is 2)
legend_title

title of legend
option

a character string indicating the colormap option to use. Four options are available: "magma" (or "A"), "inferno" (or "B"), "plasma" (or "C"), "viridis" (or "D", the default option) and "cividis" (or "E").
direction

Sets the order of colors in the scale. If 1, the default, colors are ordered from darkest to lightest. If -1, the order of colors is reversed.

Value

ggplot map

Author(s)

Martin Haringa

Examples

test <- points_to_polygon(nl_postcode2, insurance, sum(amount, na.rm = TRUE))
choropleth_ggplot2(test)

Concentration calculation

Description

Calculates the concentration, which is the sum of all observations within a circle of a certain radius.

Usage

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

Arguments

sub

data.frame of target points to calculate concentration risk for, including at least columns for longitude and latitude.
full

data.frame containing reference points, where the function finds locations within a radius from the target points. Should include at least columns for longitude, latitude, and the value of interest to summarize.
value

column name with value of interest to summarize in full.
lon_sub

column name in sub for longitude (default is lon).
lat_sub

column name in sub for latitude (default is lat).
lon_full

column name in full for longitude in full (default is lon).
lat_full

column name in full for latitude in full (default is lat).
radius

numeric. Radius of the circle in meters (default is 200).
display_progress

boolean indicating whether to show progress bar (TRUE/FALSE). Defaults to TRUE.

Value

A data.frame equal to sub including an additional column concentration.

Author(s)

Martin Haringa

Examples

df <- data.frame(location = c("p1", "p2"), lon = c(6.561561, 6.561398),
 lat = c(53.21369, 53.21326))
concentration(df, Groningen, value = amount, radius = 100)

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

df

data.frame to be converted.
crs_from

CRS code of the original coordinate system (default: 3035).
crs_to

CRS code of the target coordinate system (default: 4326).
lon_from

column name of longitude values in df (default: "x").
lat_from

column name of latitude values in df (default: "y").
lon_to

column name for longitude values in the converted data frame (default: "lon").
lat_to

column name for latitude values in the converted data frame (default: "lat").

Value

data.frame with converted coordinates

Author(s)

Martin Haringa

Find highest concentration

Description

Determines the central coordinates of a circle with a constant radius that maximizes the coverage of demand points.

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

df

data.frame. Should include at least columns for longitude, latitude, and the value of interest to summarize.
value

column name with value of interest to summarize in df.
top_n

positive integer value greater or equal to 1 (default is 1).
radius

numeric. Radius of the circle in meters (default is 200).
cell_size

numeric. Size of cell in meters (default is 100).
grid_precision

numeric. Precision of grid in meters (default is 1).
lon

column name in df with longitude (default is "lon"). Should be in EPSG:4326.
lat

column name in df with latitude (default is "lat"). Should be in EPSG:4326.
crs_metric

numeric. The metric Coordinate Reference System (CRS) is used solely in the background calculations. For European coordinates, EPSG:3035 (default) is recommended. For the United States, EPSG:6317 can be utilized. For Asia and the Pacific regions, EPSG:8859 is recommended.
print_progress

print progress iteration steps.

Details

A recent regulation by the European Commission mandates insurance companies to report the maximum value of insured fire risk policies for all buildings partially or fully situated within a circle with a radius of 200 meters (see Article 132 - fire risk sub-module - of the Delegated Regulation). This article captures the risk of catastrophic fire or explosion, including as a result of terrorist attacks. The sub-module is based on the scenario that the insurance or reinsurance undertaking incurs a loss equal to the capital insured for each building located partly or fully within a radius of 200 meters.

This problem resembles a Maximal Covering Location Problem (MCLP) with a fixed radius, belonging to the category of facility location problems. The main aim is to select the best locations for a predetermined number of facilities to achieve maximum coverage of demand points within a specified radius of each facility. In essence, the objective is to identify optimal facility locations to cover as many demand points as feasible, while ensuring that each demand point falls within the designated distance (radius) of at least one facility.

Value

A list with two elements:

  1. A data.frame containing the top_n concentrations as specified by top_n.

  2. A data.frame containing the rows from df that correspond to the top_n concentrations.

Author(s)

Martin Haringa

References

Commission Delegated Regulation (EU) (2015). Solvency II Delegated Act 2015/35. Official Journal of the European Union, 58:124.

Examples

x <- find_highest_concentration(Groningen, "amount")
plot(x)

y <- find_highest_concentration(
    Groningen, "amount", top_n = 2, cell_size = 50
)
plot(y)

Coordinates of houses in Groningen

Description

A dataset of postal codes and the corresponding spatial locations in terms of a latitude and a longitude.

Usage

Groningen

Format

A data frame with 25000 rows and 8 variables:

street

Name of street

number

Number of house

letter

Letter of house

suffix

Suffix to number of house

postal_code

Postal code of house

city

The name of the city

lon

Longitude (in degrees)

lat

Latitude (in degrees)

amount

Random value

Source

The BAG is the Dutch registry for Buildings and adresses (Basisregistratie adressen en gebouwen).

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

lat_from

Latitude of the starting point.
lon_from

Longitude of the starting point.
lat_to

Latitude of the destination point.
lon_to

Longitude of the destination point.
r

Radius of the Earth in meters (default = 6378137).

Details

The Haversine ('half-versed-sine') formula was published by R.W. Sinnott in 1984, although it has been known for much longer.

Value

Vector of distances in the same unit as r (default in meters).

Author(s)

Martin Haringa

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)

Sum insured per postal code in the Netherlands

Description

A dataset of postal codes with their sum insured, population and the corresponding spatial locations in terms of a latitude and a longitude.

Usage

insurance

Format

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

postcode

6-digit postal code

population_pc4

Population per 4-digit postal code

amount

Sum insured

lon

Longitude (in degrees) of the corresponding 6-digit postal code

lat

Latitude (in degrees) of the corresponding 6-digit postal code

Author(s)

Martin Haringa

Splines on the sphere

Description

Spline interpolation and smoothing on the sphere.

Usage

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

Arguments

observations

data.frame of observations.
targets

data.frame of locations to calculate the interpolated and smoothed values for (target points).
value

Column with values in observations.
lon_obs

Column in observations with longitude (lon is default).
lat_obs

Column in observations with latitude (lat is default).
lon_targets

Column in targets with longitude (lon is default).
lat_targets

Column in targets with latitude (lat is default).
k

(default 50) is the basis dimension. For small data sets reduce k manually rather than using default.

Details

observations should include at least columns for longitude and latitude.

targets should include at least columns for longitude, latitude and value of interest to interpolate and smooth.

A smooth of the general type discussed in Duchon (1977) is used: the sphere is embedded in a 3D Euclidean space, but smoothing employs a penalty based on second derivatives (so that locally as the smoothing parameter tends to zero we recover a "normal" thin plate spline on the tangent space). This is an unpublished suggestion of Jean Duchon.

Value

Object equal to object targets including an extra column with predicted values.

Author(s)

Martin Haringa

References

Splines on the sphere

Examples

## Not run: 
target <- sf::st_drop_geometry(nl_postcode3)
obs <- dplyr::sample_n(insurance, 1000)
pop_df <- interpolate_spline(obs, target, population_pc4, k = 20)
pop_sf <- dplyr::left_join(nl_postcode3, pop_df)
choropleth(pop_sf, value = "population_pc4_pred", n = 13)

## End(Not run)

Retrieve historic weather data for the Netherlands

Description

This function retrieves historic weather data collected by the official KNMI weather stations. See spatialrisk::knmi_stations for a list of the official KNMI weather stations.

Usage

knmi_historic_data(startyear, endyear)

Arguments

startyear

start year for historic weather data.
endyear

end year for historic weather data.

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 (crs = 4326);

  • lat = Latitude of station (crs = 4326).

Value

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

Author(s)

Martin Haringa

Examples

## Not run: 
knmi_historic_data(2015, 2019)

## End(Not run)

KNMI stations

Description

A data frame containing the IDs and meta-data on the official KNMI weather stations.

Usage

knmi_stations

Format

A data frame with 50 rows and 7 variables:

station

ID of the station (209-391)

city

City where the station is located

lon

Longitude of station (crs = 4326)

lat

Latitude of the station (crs = 4326)

altitude

Altitude of the station (in meters)

X

X coordinate of the station (crs = 32631)

Y

Y coordinate of the station (crs = 32631)

Author(s)

Martin Haringa

Object of class sf for COROP regions in the Netherlands

Description

An object of class sf (simple feature) for COROP regions in the Netherlands.

Usage

nl_corop

Format

A simple feature object with 40 rows and 5 variables:

corop_nr

corop number

areaname

corop name

geometry

geometry object of COROP region

lon

longitude of the corop centroid

lat

latitude of the corop centroid

Details

A COROP region is a regional area within the Netherlands. These regions are used for analytical purposes by, among others, Statistics Netherlands. The Dutch abbreviation stands for Coordinatiecommissie Regionaal Onderzoeksprogramma, literally the Coordination Commission Regional Research Programme.

Author(s)

Martin Haringa

Object of class sf for municipalities in the Netherlands

Description

An object of class sf (simple feature) for municipalities (Dutch: gemeentes) in the Netherlands in the year 2021.

Usage

nl_gemeente

Format

A simple feature object with 380 rows and 6 variables:

id

id of gemeente

code

code of gemeente

areaname

name of gemeente

lon

longitude of the gemeente centroid

lat

latitude of the gemeente centroid

geometry

geometry object of gemeente

Author(s)

Martin Haringa

Object of class sf for 2-digit postcode regions in the Netherlands

Description

An object of class sf (simple feature) for 2-digit postal codes (Dutch: postcode) regions in the Netherlands.

Usage

nl_postcode2

Format

A simple feature object with 90 rows and 4 variables:

areaname

2-digit postal code

geometry

geometry object of postal code

lon

longitude of the 2-digit postal code centroid

lat

latitude of the 2-digit postal code centroid

Details

Postal codes in the Netherlands, known as postcodes, are alphanumeric, consisting of four digits followed by two uppercase letters. The first two digits indicate a city and a region, the second two digits and the two letters indicate a range of house numbers, usually on the same street.

Author(s)

Martin Haringa

Object of class sf for 3-digit postcode regions in the Netherlands

Description

An object of class sf (simple feature) for 3-digit postal codes (Dutch: postcode) regions in the Netherlands.

Usage

nl_postcode3

Format

A simple feature object with 799 rows and 3 variables:

areaname

3-digit postal code

geometry

geometry object of postal code

lon

longitude of the 3-digit postal code centroid

lat

latitude of the 3-digit postal code centroid

Details

Postal codes in the Netherlands, known as postcodes, are alphanumeric, consisting of four digits followed by two uppercase letters. The first two digits indicate a city and a region, the second two digits and the two letters indicate a range of house numbers, usually on the same street.

Author(s)

Martin Haringa

Object of class sf for 4-digit postcode regions in the Netherlands

Description

An object of class sf (simple feature) for 4-digit postal codes (Dutch: postcode) regions in the Netherlands.

Usage

nl_postcode4

Format

A simple feature object with 4053 rows and 7 variables:

pc4

4-digit postal code

areaname

name of corresponding 4-digit postal code

city

name of city

biggest_20cities

pc4 is in one of the following twenty (biggest) cities in the Netherlands: Amsterdam, Rotterdam, 's-Gravenhage, Utrecht, Eindhoven, Tilburg, Groningen, Almere, Breda, Nijmegen, Enschede, Apeldoorn, Haarlem, Amersfoort, Arnhem, 's-Hertogenbosch, Zoetermeer, Zwolle, Maastricht, Leiden.

geometry

geometry object of postal code

lon

longitude of the 4-digit postal code centroid

lat

latitude of the 4-digit postal code centroid

Details

Postal codes in the Netherlands, known as postcodes, are alphanumeric, consisting of four digits followed by two uppercase letters. The first two digits indicate a city and a region, the second two digits and the two letters indicate a range of house numbers, usually on the same street.

Author(s)

Martin Haringa

Object of class sf for provinces in the Netherlands

Description

An object of class sf (simple feature) for provinces (Dutch: provincies) in the Netherlands.

Usage

nl_provincie

Format

A simple feature object with 12 rows and 4 variables:

areaname

province name

geometry

geometry object of province

lon

longitude of the province centroid

lat

latitude of the province centroid

Author(s)

Martin Haringa

Create map with points

Description

Create map for a data.frame containing points.

Usage

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

Arguments

df

data.frame containing columns for longitude and latitude.
value

column in df to be visualized.
lon

column in df containing longitude values.
lat

column in df containing latitude values.
crs

crs code for the coordinate reference system (default is 4326).
at

the breakpoints used for visualisation.

Examples

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

## End(Not run)

Automatically create a plot for objects obtained from find_highest_concentration()

Description

Automatically create a plot for objects obtained from find_highest_concentration().

Usage

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

Arguments

x

x object of class concentration obtained from highest_concentration()
type

is one of "concentration" (default), "rasterized", "focal", "updated_focal". See details for more information.
color1

color when one concentration is plotted (default is "#4B0055").
max.rad

maximal radius for size of circles in plot (default is 20).
...

additional arguments.

Details

More info for type:

  1. "concentration": this is..

  2. "focal": this is..

  3. "rasterized": this is..

  4. "updated_focal": this is..

Author(s)

Martin Haringa

Examples

x <- find_highest_concentration(Groningen, "amount")
plot(x, "concentration")
plot(x, "rasterized")
plot(x, "focal")
plot(x, "updated_focal")

Filter observations within circle

Description

Filter all observations in a data.frame that fall within a circle of a specified radius drawn around a given latitude and longitude point.

Usage

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

Arguments

data

data.frame with at least columns for longitude and latitude.
lon_center

numeric. Representing the longitude of the circle's center.
lat_center

numeric. Representing the latitude of the circle's center.
lon

column name in data containing longitudes (default is lon).
lat

column name in data containing latitudes (default is lat).
radius

radius of the circle in meters (default is 200m).

Value

A subset of the input data.frame containing only the observations that fall within the specified circle.

Author(s)

Martin Haringa

Examples

points_in_circle(Groningen, lon_center = 6.571561, lat_center = 53.21326,
radius = 60)

Filter observations within circle (vectorized)

Description

Filter all observations in a data.frame that fall within a circle of a specified radius drawn around a given latitude and longitude point.

Usage

points_in_circle_vec(
  data,
  lon_center,
  lat_center,
  lon = lon,
  lat = lat,
  radius = 200
)

Arguments

data

data.frame with at least columns for longitude and latitude.
lon_center

numeric. Representing the longitude of the circle's center.
lat_center

numeric. Representing the latitude of the circle's center.
lon

column name in data containing longitudes (default is lon).
lat

column name in data containing latitudes (default is lat).
radius

radius of the circle in meters (default is 200m).

Value

A subset of the input data.frame containing only the observations that fall within the specified circle.

Author(s)

Martin Haringa

Examples

points_in_circle_vec(Groningen, lon_center = c(6.571561, 6.56561),
lat_center = c(53.21326, 53.20326), radius = 60)

Map points to polygons

Description

Join a data.frame containing coordinates (longitude and latitude) to polygon geometries. Arithmetic operations are then applied to the attributes of the joined coordinates to obtain aggregated values for each polygon.

Usage

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

Arguments

sf_map

object of class sf representing the polygon geometries.
df

data.frame containing coordinates (column names should be 'lon' and 'lat')
oper

arithmetic operation to be applied on the polygon level.
crs

coordinate reference system (default is 4326).
outside_print

logical indicating whether to print points that are not within a polygon (default is FALSE).

Value

An object of class sf

Author(s)

Martin Haringa

Examples

points_to_polygon(nl_postcode2, insurance, sum(amount, na.rm = TRUE))
## Not run: 
shp_read <- sf::st_read("~/path/to/file.shp")
points_to_polygon(shp_read, insurance, sum(amount, na.rm = TRUE))

## End(Not run)