spatialrisk is a R-package 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. The key functions in
spatialrisk are written in C++ (using Rcpp), and are therefore very fast.
spatialrisk from CRAN:
Or the development version from GitHub:
# install.packages("remotes") remotes::install_github("MHaringa/spatialrisk")
Find all observations in data.frame
Groningen that are located within circle of a radius of 100m from the point
(lon,lat) = (6.561561,53.21326):
library(spatialrisk) circle <- points_in_circle(Groningen, lon_center = 6.571561, lat_center = 53.21326, radius = 100) circle
## # A tibble: 14 x 10 ## street number letter suffix postal_code city lon lat amount distance_m ## <chr> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Heresi… 5 <NA> <NA> 9711EP Groni… 6.57 53.2 5 31.4 ## 2 Heresi… 11 <NA> <NA> 9711ER Groni… 6.57 53.2 11 47.8 ## 3 Zuider… 1003 <NA> <NA> 9724AK Groni… 6.57 53.2 1003 57.6 ## 4 Heresi… 13 <NA> <NA> 9711ER Groni… 6.57 53.2 13 68.1 ## 5 Herepl… 10 <NA> <NA> 9711GA Groni… 6.57 53.2 10 74.6 ## 6 Heresi… 16 <NA> <NA> 9711ES Groni… 6.57 53.2 16 84.1 ## 7 Heresi… 6 <NA> <NA> 9711ES Groni… 6.57 53.2 6 86.2 ## 8 Heresi… 6 a <NA> 9711ES Groni… 6.57 53.2 6 87.8 ## 9 Heresi… 6 b <NA> 9711ES Groni… 6.57 53.2 6 90.9 ## 10 Heresi… 20 <NA> <NA> 9711ET Groni… 6.57 53.2 20 91.5 ## 11 Heresi… 20 a <NA> 9711ET Groni… 6.57 53.2 20 93.0 ## 12 Heresi… 15 a <NA> 9711ER Groni… 6.57 53.2 15 95.1 ## 13 Zuider… 1007 <NA> <NA> 9724AK Groni… 6.57 53.2 1007 97.2 ## 14 Zuider… 25 a <NA> 9724AJ Groni… 6.57 53.2 25 97.8
The sum of all observations within a circle of a radius of 100m is equal to:
##  2163
The next example shows how to determine the sum of all observations within a circle with a certain radius for multiple points.
concentration() determines the sum of all observations within a circle of a certain radius for multiple points. Find for each row in
df the sum of all observations in
Groningen within a circle of a radius of 100m from the
df <- data.frame(location = c("p1", "p2", "p3"), lon = c(6.561561, 6.561398, 6.571561), lat = c(53.21369, 53.21326, 53.21326)) conc <- concentration(df, Groningen, value = amount, radius = 100) conc
## location lon lat concentration ## 1 p1 6.561561 53.21369 775 ## 2 p2 6.561398 53.21326 2271 ## 3 p3 6.571561 53.21326 2163
Show that result is indeed equal to the result from Example 1:
##  TRUE
Example 2 shows how to determine the sum of all observations within a circle of certain radius for multiple points.
highest_concentration() can be used to find the coordinates of the center of a circle for which the sum of the observations within the circle is the highest. This example gives an application to data set
highest_concentration() uses Gustavo Niemeyer’s wonderful and elegant geohash coordinate system. Niemeyer’s Geohash method encodes latitude and longitude as binary string where each binary value derived from a decision as to where the point lies in a bisected region of latitude or longitudinal space.
Note that all functions are written in C++, and are therefore very fast. It takes about 5-10 seconds to find the highest concentration for a portfolio with 500,000 objects.
Show all points in data set
plot_points(Groningen, value = amount)
Find the highest concentration:
hconc <- highest_concentration(Groningen, amount, radius = 200, grid_distance = 50)
For a portfolio of about 25,000 it takes about 0.5 second to find the highest concentration.
microbenchmark::microbenchmark( highest_concentration(Groningen, amount, radius = 200, grid_distance = 50), times = 10)
## Unit: milliseconds ## expr ## highest_concentration(Groningen, amount, radius = 200, grid_distance = 50) ## min lq mean median uq max neval ## 607.0512 609.9632 619.7995 611.0177 614.7176 696.1569 10
The two highest concentrations are found in geohash u1kwug:
## concentration lon lat geohash ## 1: 63485 6.547372 53.23650 u1kwug ## 2: 61075 6.547372 53.23695 u1kwug ## 3: 57121 6.523147 53.23101 u1kwu6 ## 4: 57009 6.589809 53.20534 u1kwtv ## 5: 56336 6.589809 53.20579 u1kwtv ## 6: 55631 6.523897 53.23145 u1kwu6
The following gives an illustration of this. The yellow parts show the areas with the highest concentrations.
highest_concentration() returns the highest concentration within a portfolio based on a grid. 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.
Look for higher concentrations in the geohash with the highest concentration found by
hconc_nghb <- neighborhood_gh_search(hconc, max.call = 7000)
The highest concentration is found in:
## highest_concentration lon lat geohash ## 1 64438 6.547329 53.23658 u1kwug
The concentration 64,438 is higher than the highest concentration of 63,485 on the grid points. This concentration is the highest in data set Groningen.
Show the highest concentration on a map (the highest concentration includes two apartment buildings with many objects):
Its also possible to show the coordinates for more than one concentration. To show the second and third highest concentration:
nb3 <- neighborhood_gh_search(hconc, max.call = 7000, highest_geohash = 3) nb3
Create interactive map:
Show objects in the highest geohash:
points_in_circle(Groningen, lon_center = nb3$lon, lat_center = nb3$lat, radius = 200)
## # A tibble: 208 x 10 ## street number letter suffix postal_code city lon lat amount distance_m ## <chr> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 Elzenla… 135 <NA> <NA> 9741ND Gron… 6.55 53.2 135 3.81 ## 2 Elzenla… 139 <NA> <NA> 9741ND Gron… 6.55 53.2 139 8.13 ## 3 Elzenla… 70 <NA> <NA> 9741NG Gron… 6.55 53.2 70 30.7 ## 4 Elzenla… 68 <NA> <NA> 9741NG Gron… 6.55 53.2 68 34.1 ## 5 Duindoo… 1 <NA> <NA> 9741NM Gron… 6.55 53.2 12 35.2 ## 6 Duindoo… 17 <NA> <NA> 9741NM Gron… 6.55 53.2 17 44.3 ## 7 Duindoo… 15 <NA> <NA> 9741NM Gron… 6.55 53.2 15 48.2 ## 8 Duindoo… 21 <NA> <NA> 9741NM Gron… 6.55 53.2 21 48.6 ## 9 Duindoo… 13 <NA> <NA> 9741NM Gron… 6.55 53.2 13 52.3 ## 10 Ranonke… 38 <NA> <NA> 9741LT Gron… 6.55 53.2 38 59.5 ## # … with 198 more rows
spatialrisk also contains functionality to create choropleths. Typically in R it is difficult to create choropleths.
points_to_polygon() attempts to elegantly solve this problem.
The common approach is to first aggregate the data on the level of the regions in the shapefile, and then merging the aggregated data with the shapefile. Despite it being common, it is problematic in case the names in the data and the names in the shapefile do not match. This is for example the case when there are differences in punctuation marks in the area names. Therefore,
points_to_polygon() uses the longitude and latitude of a point to map this point to a region. This approach makes it easy to create choropleth maps on different region levels.
This example shows how
points_to_polygon() is used to map the total sum insured on the municipality level in the Netherlands:
choropleth() creates a map based on the simple feature object obtained in the previous step. There are two options to create a choropleth map. When
mode is set to
plot a static map is created. The given clustering is according to the Fisher-Jenks algorithm. This commonly used classification method for choropleths seeks to reduce the variance within classes and maximize the variance between classes.
choropleth(gemeente_sf, mode = "plot", legend_title = "Sum insured (EUR)", n = 5)
mode is set to
view an interactive map is created:
choropleth(gemeente_sf, mode = "view", legend_title = "Sum insured (EUR)")
The following simple feature objects are available in