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).

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 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

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

Value

ggplot map

Author(s)

Martin Haringa

Examples

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

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

conc_per_cell_new(
  high_foc,
  dff,
  value,
  size,
  points,
  db,
  radius,
  crs_from,
  crs_to,
  lon,
  lat
)

Arguments

Author(s)

Martin Haringa

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

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

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

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

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)

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

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)

Find the highest concentration for the current iteration

Description

Find the highest concentration for the current iteration.

Usage

highest_conc(hf_conc_new, high_foc, db)

Arguments

Author(s)

Martin Haringa

Highest concentration risk

Description

Find the centre coordinates of a circle with a fixed radius that maximizes the coverage of total fire risk insured. 'highest_concentration()' returns the coordinates (lon/lat) and the corresponding concentration. The concentration is defined as the sum of all observations within a circle of a certain radius. See concentration for determining concentration for pre-defined coordinates.

Usage

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

Arguments

Details

A recently European Commission regulation requires insurance companies to determine the maximum value of insured fire risk policies of all buildings that are partly or fully located within circle of a radius of 200m (Commission Delegated Regulation (EU), 2015, Article 132). The problem can be stated as: "find the centre coordinates of a circle with a fixed radius that maximizes the coverage of total fire risk insured". This can be viewed as a particular instance of the Maximal Covering Location Problem (MCLP) with fixed radius. See Gomes (2018) for a solution to the maximum fire risk insured capital problem using a multi-start local search meta-heuristic. The computational performance of highest_concentration() is investigated to overcome the long times the MCLP algorithm is taking. highest_concentration() is written in C++, and for 500,000 buildings it needs about 5-10 seconds to determine the maximum value of insured fire risk policies that are partly or fully located within circle of a radius of 200m.

Value

data.frame with coordinates (lon/lat) with the highest 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.

Gomes M.I., Afonso L.B., Chibeles-Martins N., Fradinho J.M. (2018). Multi-start Local Search Procedure for the Maximum Fire Risk Insured Capital Problem. In: Lee J., Rinaldi G., Mahjoub A. (eds) Combinatorial Optimization. ISCO 2018. Lecture Notes in Computer Science, vol 10856. Springer, Cham. <doi:10.1007/978-3-319-96151-4_19>

Examples

 ## Not run: 
# Find highest concentration with a precision of a grid of 25 meters
hc1 <- highest_concentration(Groningen, amount, radius = 200,
 grid_distance = 25)

# Look for coordinates with even higher concentrations in the
# neighborhood of the coordinates with the highest concentration
hc1_nghb <- neighborhood_gh_search(hc1, max.call = 7000)
print(hc1_nghb)

# Create map with geohashes above the lowerbound
# The highest concentration lies in one of the geohashes
plot(hc1)

# Create map with highest concentration
plot(hc1_nghb)

## End(Not run)

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

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

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

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

mw_create() is a modified version of terra::focalMat(). While terra::focalMat() creates a matrix where the border is the distance from the center of the focal cell, mw_create() creates a matrix where the border of the moving window is the distance from the edge of the focal cell.

Author(s)

Martin Haringa

Search for coordinates with higher concentrations within geohash

Description

highest_concentration returns the highest concentration within a portfolio based on grid points. However, higher concentrations can be found within two grid points. 'neighborhood_gh_search()' looks for even higher concentrations in the neighborhood of the grid points with the highest concentrations. This optimization is done by means of Simulated Annealing.

Usage

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

Arguments

Value

data.frame

Author(s)

Martin Haringa

Examples

## Not run: 
# Find highest concentration with a precision of a grid of 25 meters
hc1 <- highest_concentration(Groningen, amount, radius = 200,
 grid_distance = 25)

# Increase the number of calls for more extensive search
hc1_nghb <- neighborhood_gh_search(hc1, max.call = 7000, highest_geohash = 1)
hc2_nghb <- neighborhood_gh_search(hc1, max.call = 7000, highest_geohash = 2)
plot(hc1_nghb)
plot(hc2_nghb)

## End(Not run)

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

Automatically create a plot for objects obtained from highest_concentration()

Description

Takes an object produced by 'highest_concentration()', and creates an interactive map.

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

Interactive view of geohashes with highest concentrations

Author(s)

Martin Haringa

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

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")

Automatically create a plot for objects obtained from neighborhood_gh_search()

Description

Takes an object produced by 'neighborhood_gh_search()', and creates an interactive map.

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

Interactive view of highest concentration on map

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

Examples

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

## End(Not run)

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

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

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

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)

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

Save highest concentrations per cell for subsequent iterations

Description

Save highest concentrations per cell for subsequent iterations.

Usage

update_db(hf_conc_new, db, cells)

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

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