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Spatial joins allow to augment one spatial dataset with information from another spatial dataset by linking overlapping features. In this post I will provide an example showing how to augment a dataset containing school locations with socioeconomic data of their surrounding statistical region using R and the package sf (Pebesma 2018). This approach has the drawback that the surrounding statistical region doesn’t reflect the actual catchment area of the school. I will present an alternative approach where the overlaps of the schools’ catchment areas with the statistical regions allow to calculate the weighted average of the socioeconomic statistics. If we have no data about the actual catchment areas of the schools, we may resort to approximating these areas as circular regions or as Voronoi regions around schools.

## Data

For this example, I’d like to compare the percentage of children whose parents obtain social welfare in the neighborhood regions around public and private primary schools in Berlin. This blog post concentrates on how to join the point samples (the schools) with the surrounding statistical regions and calculate a spatially weighted average the welfare rate, so I will present only a few descriptive results in the end.

We will work with several datasets: The first spatial dataset contains the shape of the statistical regions in Berlin, the second dataset contains the socioeconomic data for these regions, the third and fourth datasets contain the locations and other attributes of public and private primary schools in Berlin, respectively.

All data and the code are available in a GitHub repository. We will use the sf package for working with spatial data in R, dplyr for data management and ggplot2 for a few more advanced visualizations, i.e. when base plot() is not sufficient.

library(sf)
library(dplyr)
library(ggplot2)

### Socioeconomic data for statistical regions

We will at first load a dataset with the most granular official statistical regions for Berlin, called Planungsräume (planning areas). We select the area ID and name as spatial attributes. The result is a spatial dataframe (a simple feature (sf) collection).

bln_plan <- read_sf('data/berlin_plr.shp') %>%
mutate(areaid = as.integer(SCHLUESSEL)) %>%
select(areaid, name = PLR_NAME)
Simple feature collection with 6 features and 2 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
areaid    name                                                      geometry

1 1011101 Stülerstr.      (((387256.6 5818552, 387323.1 5818572, 387418.9 5…
2 1011102 Großer Tiergar… (((386767.5 5819393, 386768.3 5819389, 386769.6 5…
3 1011103 Lützowstr.      (((387952.6 5818275, 387986.7 5818313, 387994.6 5…
4 1011104 Körnerstr.      (((388847.1 5817875, 388855.5 5817899, 388865.1 5…
5 1011105 Nördlicher Lan… (((388129.5 5819015, 388157.1 5819017, 388170.8 5…
6 1011201 Wilhelmstr.     (((389845.7 5819286, 389840.9 5819311, 389846.1 5…

When printing this dataframe, the header reveals another important information: The coordinate reference system (CRS) of this dataset is ETRS89 / UTM zone 33N. We will later need to make sure that the coordinates of the school locations and the coordinates of the planning areas use the same coordinate system.

This data can be joined with socioeconomic information provided from official sources. Luckily, Helbig/Salomo 2021 compiled these information for some cities in Germany (available for download) among which is data for Berlin from 2020. I’ve created an excerpt with percentages of residents receiving social welfare (welfare) and percentage of children under 15 years whose parents receive social welfare (welfare_chld):

bln_welfare <- read.csv('data/berlin_welfare.csv', stringsAsFactors = FALSE)
areaid                 areaname welfare welfare_chld
1 1011101             Stülerstraße   10.09        15.44
2 1011102        Großer Tiergarten    4.76         0.00
3 1011103             Lützowstraße   22.21        36.80
4 1011104             Körnerstraße   24.81        42.14
5 1011105 Nördlicher Landwehrkanal    2.82         3.53
6 1011201            Wilhelmstraße   12.13        19.03

We can use the area ID for augmenting the planning areas with the welfare statistics. We’re joining a spatial with an ordinary dataframe, so we can use dplyr’s inner_join. Before that we can check that for each planning area we have welfare statistics information and vice versa:[1]Note that when joining spatial and ordinary dataframes, the order of arguments in the join function matters. If you have a spatial dataframe on the “left side” (x argument), the result … Continue reading

setequal(bln_plan$areaid, bln_welfare$areaid)
[1] TRUE
bln <- inner_join(bln_plan, bln_welfare, by = 'areaid') %>%
select(-name)
Simple feature collection with 6 features and 4 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
areaid                           geometry areaname   welfare welfare_chld
1 1011101 (((387256.6 5818552, 387323.1 581… Stülerstr…   10.1         15.4
2 1011102 (((386767.5 5819393, 386768.3 581… Großer Ti…    4.76         0
3 1011103 (((387952.6 5818275, 387986.7 581… Lützowstr…   22.2         36.8
4 1011104 (((388847.1 5817875, 388855.5 581… Körnerstr…   24.8         42.1
5 1011105 (((388129.5 5819015, 388157.1 581… Nördliche…    2.82         3.53
6 1011201 (((389845.7 5819286, 389840.9 581… Wilhelmst…   12.1         19.0

A quick plot confirms that it is similar to the figure from the dashboard.[2]I prefer using the base plot function for quick exploration of spatial data and usually only turn to ggplot2 for more advanced or “publication ready” plots. The help page for plot.sf … Continue reading

plot(bln['welfare_chld'])

The median percentage of children whose parents receive social welfare is ~20% with an interquartile range of about 29%. The following shows the distribution of this welfare rate:

### Public and private primary schools

The Berlin geodata catalog “FIS Broker” provides the locations of public schools in Berlin.[3]The catalog is a bit clumsy to use, but actually works quite well: You search for the data, get an URL to the WFS endpoint from the data’s metainformation panel and use that URL to obtain the … Continue reading I obtained the data and converted it to GeoJSON, which we can now load. We’ll only retain primary schools and add a variable denoting that these are public schools. We also see that the CRS of the school locations matches the CRS of the Berlin statistical regions data.

pubschools <- read_sf('data/berlin_pubschools.geojson') %>%
filter(SCHULART == 'Grundschule') %>%
select(name = NAME) %>%
mutate(ownership = 'pub', .before = 1)
Simple feature collection with 6 features and 2 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
ownership name                                       geometry

1 pub       Grundschule am Arkonaplatz       (391497.3 5821994)
2 pub       Papageno-Grundschule             (390876.3 5821514)
3 pub       Kastanienbaum-Grundschule        (391579.6 5820819)
4 pub       Grundschule Neues Tor            (390139.2 5820930)
5 pub       GutsMuths-Grundschule            (393086.4 5819617)
6 pub       Grundschule am Brandenburger Tor   (390255 5819341)

Now to the private schools’ locations. Marcel Helbig, Rita Nikolai and me collected data on school locations in East Germany from 1992 to 2015 in order to analyze the development of the network of schools in East Germany and which role private schools play in it (Helbig/Konrad/Nikolai 2018). Besides creating an interactive map, we also published the data and are planning an update with newer data (until 2020) from which will we now use an excerpt. This dataset provides school locations from 2019 as longitude/latitude WGS84 coordinates which we can load and convert to a spatial dataset using st_as_sf. We also transform these locations to the ETRS89 CRS used in all prior spatial datasets.

privschools <- read.csv('data/grundschulen_berlin_2019.csv',
stringsAsFactors = FALSE) %>%
filter(traeger == 'priv') %>%
select(ownership = traeger, name, lng, lat) %>%
st_as_sf(coords = c('lng', 'lat'), crs = 4326) %>%  # EPSG 4326 is WGS84 lat/long
st_transform(crs = st_crs(pubschools))  # transform to same CRS as publ. schools
Simple feature collection with 6 features and 2 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
ownership                                          name                 geometry
1      priv              Freie Waldorfschule Berlin Mitte POINT (391783.5 5820737)
2      priv                 Freie Waldorfschule Kreuzberg   POINT (391544 5818222)
3      priv        Freie Waldorfschule am Prenzlauer Berg POINT (395177.8 5822721)
4      priv                           Annie-Heuser-Schule POINT (384902.8 5816817)
5      priv Freie Waldorfschule Havelhöhe - Eugen Kolisko POINT (374724.8 5813820)
6      priv                  Rudolf-Steiner-Schule Berlin POINT (382728.5 5813179)

The variable ownership encodes whether a given facility is a public (“pub”) or private (“priv”) primary school. We can now append the public and private primary schools datasets to form a single schools dataset. The public school data comes from 2020 and the private school data from 2019, but this shouldn’t be an issue because the number of public and private schools has been quite stable in recent years.

schools <- bind_rows(pubschools, privschools) %>%
mutate(schoolid = 1:nrow(.), .before = 1)
Simple feature collection with 6 features and 3 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
schoolid ownership name                                       geometry
1        1 pub       Grundschule am Arkonaplatz       (391497.3 5821994)
2        2 pub       Papageno-Grundschule             (390876.3 5821514)
3        3 pub       Kastanienbaum-Grundschule        (391579.6 5820819)
4        4 pub       Grundschule Neues Tor            (390139.2 5820930)
5        5 pub       GutsMuths-Grundschule            (393086.4 5819617)
6        6 pub       Grundschule am Brandenburger Tor   (390255 5819341)

In our dataset we now have 361 public and 71 private primary schools in Berlin.

## Public / private primary schools and poverty by statistical region

Both datasets use the same coordinate system now, so we can plot the school locations on top of the planning areas. I will use ggplot2 this time to make a choropleth map of the welfare_chld variable and overlay that with the public and private primary school locations.

ggplot() +
geom_sf(aes(fill = welfare_chld), color = 'black', data = bln) +
geom_sf(aes(color = ownership), size = 1, alpha = 0.75, data = schools) +
scale_fill_binned(type = 'viridis', guide = guide_bins(title = '% Welfare')) +
scale_color_manual(values = c('pub' = '#c767cb',  'priv' = '#cdc566'),
labels = c('public school', 'private school'),
guide = guide_legend(title = '')) +
coord_sf(datum = NA) +  # disable graticule
labs(title = "Public / private primary schools and poverty",
subtitle = "Choropleth map of percentage of children whose parents obtain social welfare.\nDots represent primary schools.") +
theme_minimal()

From the figure alone, it’s probably hard to assess whether there’s a pattern in the distribution of private and public schools regarding areas with higher welfare rate in the city. In order to compare the social welfare statistics of regions around private schools with those around public schools, we can join the schools’ data with the socioeconomic information of the planning areas they’re located in. This can be done with a spatial join using st_join. By default, this function joins the spatial features of the first argument with features of the second argument when they intersect – in our case this means a school is linked with the planning area it’s located in. Note that the order of arguments matters here and that the spatial geometry of the first argument is retained in the resulting dataset.

schools_plan <- st_join(schools, bln)
Simple feature collection with 6 features and 7 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
schoolid ownership name                  geometry  areaid areaname   welfare welfare_chld
1        1 pub       Grundsc...  (391497.3 5821994) 1011402 Arkonap... 4.46            3.53
2        2 pub       Papagen...  (390876.3 5821514) 1011401 Invalid... 6.29            7.7
3        3 pub       Kastani...  (391579.6 5820819) 1011302 Oranien... 8.16           10.4
4        4 pub       Grundsc...  (390139.2 5820930) 1011301 Charité... 3.68            3.92
5        5 pub       GutsMut...  (393086.4 5819617) 1011304 Karl-Ma... 23.5           36.3
6        6 pub       Grundsc...    (390255 5819341) 1011201 Wilhelm... 12.1           19.0 

We can see that the schools’ data was linked with the data from the planning areas. We should also check whether there’s a school that was not located in any planning area (this may for example happen when a school is very close to the Berlin-Brandenburg border):

sum(is.na(schools_plan$areaid)) [1] 0 All schools were linked with their planning area, so we can now compare the percentage of children whose parents obtain social welfare between public and private primary schools: ggplot(schools_plan) + geom_violin(aes(x = ownership, y = welfare_chld), draw_quantiles = c(0.5)) + geom_jitter(aes(x = ownership, y = welfare_chld), alpha = 0.25) + scale_x_discrete(labels = c('pub' = 'public primary schools', 'priv' = 'private primary schools')) + labs(title = 'Percentage of children whose parents obtain social welfare', x = '', y = '% welfare') Our descriptive results indicate that the median percentage of children whose parents obtain social welfare is around six percent higher in the statistical regions around public schools than around private schools: [4]I’m using st_drop_geometry here, because otherwise a spatial aggregation would be performed which takes much longer to compute and is not necessary here. st_drop_geometry(schools_plan) %>% group_by(ownership) %>% summarise(median_welfare_chld = median(welfare_chld)) ownership median_welfare_chld 1 priv 16.8 2 pub 22.8 This is an interesting descriptive result and we may continue with our spatial analysis from here. However, our current approach doesn’t consider the catchment area of a school correctly: Children from nearby planning areas will most likely visit a school, but at the moment we only consider the one planning area in which a school is located. As an example, let’s zoom to school #388 “Evangelische Schule Berlin Buch” in the north of Berlin. As you can see, only considering the planning area in which this school is located omits the higher welfare rates in nearby areas: ggplot(bln) + geom_sf(aes(fill = welfare_chld), color = 'black') + geom_sf(data = filter(schools, schoolid == 388), size = 3, color = 'red') + scale_fill_binned(type = 'viridis', guide = guide_bins(title = '% Welfare')) + coord_sf(datum = st_crs(bln), xlim = c(395e3, 401e3), ylim = c(583e4, 5837e3)) + labs(title = 'School #388 "Evangelische Schule Berlin Buch" and\nsurrounding planning areas') + theme_minimal() ## Spatial weighting with official school catchment areas In Berlin, parents can send their children to a primary school that is within the official school catchment area of their home address (“Grundschuleinzugsbereich”).[5]There’s much debate about this and parents can try to register their children in a primary school outside their home catchment area but this comes with juristic obstacles, so we can assume for … Continue reading Luckily, there’s spatial data for these catchment areas again available in the Berlin geodata catalog. I again converted the obtained data from the Berlin geodata catalog to GeoJSON, which we can load now. I also generate a catchment area ID catchid which however has nothing to do with the school ID from the schools dataset. schoolareas <- read_sf('data/berlin_ezb.geojson') %>% select(-BSN, -BEREICH) %>% mutate(catchid = 1:nrow(.), .before = 1) head(schoolareas) Simple feature collection with 6 features and 1 field [...] Projected CRS: ETRS89 / UTM zone 33N catchid geometry 1 1 (((384834.8 5823436, 384868.2 5823434, 385030 5823424, 385075.1 582… 2 2 (((388073.3 5824827, 388062.1 5824807, 387940.3 5824687, 387718.8 5… 3 3 (((389274.1 5824382, 389277.2 5824350, 389278.2 5824340, 389282.7 5… 4 4 (((387025.5 5822534, 387032.1 5822532, 387032.7 5822532, 387049.5 5… 5 5 (((388654.7 5823154, 388734.6 5823076, 388917.1 5822903, 388926 582… 6 6 (((388843.8 5822105, 388843.3 5822105, 388770.6 5822038, 388728.5 5… Let’s generate a plot that overlays the planning areas, catchment areas and school locations. We can see that the catchment areas differ from the planning areas: plot(bln$geometry)
plot(schoolareas$geometry, col = '#80000055', border = 'white', add = TRUE) plot(schools$geometry, col = '#0000AA77', cex = 0.5, pch = 19, add = TRUE)

The goal is now to calculate the weighted average of the welfare rate for a given school by taking into account all planning areas that the school’s catchment area intersects with. The weights will be determined by the intersection area between the catchment area and the planning areas. I will first do this with a single school only to illustrate how it works. This school will be #269 “Müggelheimer Schule” located in the south east of Berlin:

exampleschool <- schools[schools$schoolid == 269,] plot(bln$geometry)
plot(schoolareas$geometry, col = '#80000055', border = 'white', add = TRUE) plot(exampleschool$geometry, col = 'red', pch = 19, add = TRUE)

First, we need the catchment area of that school. We can again apply st_join for this in order to get the catchment area that intersects with the school. Note that the catchment areas should be the first argument in the st_join function since we want to retain the catchment areas’ geometries in the resulting dataset. We also use an inner join instead of a left join by setting left = FALSE so that the result set only contains the single catchment area that intersects with the school.

(example_catchment_area <- st_join(schoolareas, exampleschool, left = FALSE))
Simple feature collection with 1 feature and 4 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
catchid                                 geometry schoolid ownership name
1     254 (((405754.5 5807399, 405773.6 5807400, …      269 pub       Müggelheime… 

The next step is to get the intersections between the planning areas and catchment areas, i.e. to clip the planning areas according to the school’s catchment area. We do this with the help of st_intersection, which calculates the intersection between spatial objects. The result is a spatial dataframe of seven planning regions that overlap with the school’s catchment area:

(example_plr <- st_intersection(bln, example_catchment_area))
Simple feature collection with 7 features and 8 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
areaid areaname     welfare welfare_chld catchid schoolid ownership name
1 9031101 Grünau          8.8         10.4      254      269 pub       Müggelheim…
2 9031201 Karolinenhof    2.01         1.73     254      269 pub       Müggelheim…
3 9031202 Schmöckwitz…    6.25         9.47     254      269 pub       Müggelheim…
4 9041301 Kietzer Fel…   12.8         16.8      254      269 pub       Müggelheim…
5 9041601 Müggelheim      3.68         4.54     254      269 pub       Müggelheim…
6 9051702 Bölschestra…    7.63         7.58     254      269 pub       Müggelheim…
7 9051801 Rahnsdorf/H…    5.89         5.67     254      269 pub       Müggelheim… 

We can put that a little bit into perspective again and display it on the Berlin planning areas map overlayed with the schools’ catchment areas. Here we can see that our example school’s catchment area mainly intersects with only two planning areas. The other five intersections listed above are only tiny overlaps from surrounding planning areas, as we can also confirm next by computing their surface areas.

ggplot() +
geom_sf(color = 'black', data = bln) +
geom_sf(fill = NA, color = 'red', linetype = 'dotted', data = schoolareas) +
geom_sf(aes(fill = welfare_chld), color = 'black', data = example_plr) +
geom_sf(fill = NA, color = 'red', data = example_catchment_area) +
geom_sf(color = 'red', size = 3, data = exampleschool) +
scale_fill_continuous(type = 'viridis', guide = guide_colorbar(title = '% Welfare')) +
coord_sf(datum = NA) +
labs(title = "Berlin statistical regions and school catchment areas",
subtitle = "Highlighted school #270 with surrounding catchment area and planning areas intersection.") +
theme_minimal()

All that is left now for our example school is to take the weighted average of the welfare rate. The weights are the area of the planning area intersections so that planning areas with larger overlap in the catchment area have a higher influence on the overall average. The following shows the planning area intersections along with their area as calculated via st_area. We can see that the welfare rate of ~5% in Müggelheim will have the largest weight, followed by the ~17% rate in Kietzer Feld/Nachtheide:

cbind(example_plr[c('areaname', 'welfare_chld')], area = st_area(example_plr)) %>%
mutate(weight = as.numeric(area / sum(area))) %>%
arrange(desc(weight))
Simple feature collection with 7 features and 4 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
areaname welfare_chld               area       weight geometry
1                   Müggelheim         4.54 2.217476e+07 [m^2] 8.013399e-01 POLYGON…
2      Kietzer Feld/Nachtheide        16.75 5.479226e+06 [m^2] 1.980054e-01 MULTIPO…
3       Rahnsdorf/Hessenwinkel         5.67 1.683612e+04 [m^2] 6.084149e-04 MULTIPO…
4 Schmöckwitz/Rauchfangswerder         9.47 1.179133e+03 [m^2] 4.261089e-05 MULTIPO…
5                 Karolinenhof         1.73 7.181086e+01 [m^2] 2.595063e-06 MULTIPO…
6                       Grünau        10.39 2.955579e+01 [m^2] 1.068072e-06 MULTIPO…
7                Bölschestraße         7.58 1.774700e-02 [m^2] 6.413317e-10 POLYGON…

We pass these area measurements to weighted.mean (stripping the m² unit via as.numeric since weighted.mean can’t handle it) and obtain a weighted average welfare rate of ~7% which is quite a bit higher than the ~4.5% we get when using the former approach (linking the school with its planning area “Müggelheim”):

weighted.mean(example_plr$welfare_chld, as.numeric(st_area(example_plr))) [1] 6.958543 # former approach: linking the school with its planning area schools_plan[schools_plan$schoolid == 269, ]$welfare_chld [1] 4.54 We’ll next perform these calculations for all schools. First, we link each school with its catchment area using st_join as before: schools_catch <- st_join(schoolareas, schools, left = FALSE) head(schools_catch) Simple feature collection with 6 features and 4 fields [...] Projected CRS: ETRS89 / UTM zone 33N catchid geometry schoolid ownership name 1 1 (((384834.8 5823436, 384868.2 5823434, 38… 25 pub Möwensee-Gr… 2 1 (((384834.8 5823436, 384868.2 5823434, 38… 27 pub Anna-Lindh-… 3 2 (((388073.3 5824827, 388062.1 5824807, 38… 13 pub Gottfried-R… 4 2 (((388073.3 5824827, 388062.1 5824807, 38… 26 pub Erika-Mann-… 5 3 (((389274.1 5824382, 389277.2 5824350, 38… 17 pub Wilhelm-Hau… 6 3 (((389274.1 5824382, 389277.2 5824350, 38… 19 pub Carl-Kraeme… We confirm that there can be several schools in the same catchment area: st_drop_geometry(schools_catch) %>% count(catchid) %>% pull(n) %>% hist(main = 'Number of schools per catchment area', breaks = 1:6 - 0.5, xlab = '') Next we calculate the planning area intersections, their areas and weighted average of the welfare rate for each school’s catchment area using sapply. We define a function spat_weighted_mean for this, which we can later reuse. This computation takes some seconds to complete and in the end adds the weighted average of the welfare rate as welfare_chld variable to the schools’ catchment area dataset: spat_weighted_mean <- function(catch) { # the catchment area polygon "catch" loses the CRS during sapply # -> set it here again catch <- st_sfc(catch, crs = st_crs(bln)) areas <- st_intersection(bln, catch) weighted.mean(areas$welfare_chld, as.numeric(st_area(areas)))
}

schools_catch$welfare_chld <- sapply(schools_catch$geometry, spat_weighted_mean)

select(schools_catch, catchid, schoolid, ownership, name, welfare_chld) %>%
Simple feature collection with 6 features and 5 fields
[...]
Projected CRS: ETRS89 / UTM zone 33N
catchid schoolid ownership name      welfare_chld                         geometry
1       1       25 pub       Möwensee…         47.9 (((384834.8 5823436, 384868.2 5…
2       1       27 pub       Anna-Lin…         47.9 (((384834.8 5823436, 384868.2 5…
3       2       13 pub       Gottfrie…         50.1 (((388073.3 5824827, 388062.1 5…
4       2       26 pub       Erika-Ma…         50.1 (((388073.3 5824827, 388062.1 5…
5       3       17 pub       Wilhelm-…         65.2 (((389274.1 5824382, 389277.2 5…
6       3       19 pub       Carl-Kra…         65.2 (((389274.1 5824382, 389277.2 5…
plot(distinct(schools_catch['welfare_chld']),
main = 'Weighted average of percentage of children\nwhose parents receive social welfare per school catchment area',
cex.main = 0.75)

Note that blank areas in the above figure represent catchment areas in which no primary school was located — this may be a flaw in the official data (12 such areas in total).

We again compare public and private schools, this time with our revised calculations:

ggplot(schools_catch) +
geom_violin(aes(x = ownership, y = welfare_chld), draw_quantiles = c(0.5)) +
geom_jitter(aes(x = ownership, y = welfare_chld), alpha = 0.25) +
scale_x_discrete(labels = c('pub' = 'public primary schools',
'priv' = 'private primary schools')) +
labs(title = 'Percentage of children whose parents obtain social welfare', x = '', y = '% welfare')

The median percentage of children whose parents obtain social welfare is still higher for public schools, but the difference is now five instead of six percent.

st_drop_geometry(schools_catch) %>%
group_by(ownership) %>%
summarise(median_welfare_chld = median(welfare_chld))
ownership median_welfare_chld
1 priv                     17.1
2 pub                      22.0

Our updated approach led to a difference that is only a bit smaller. The difference is not so large because of the very small catchment areas for the many schools in the inner city that result in a weighted average of the welfare rate that is very close to the rate of the schools’ planning areas. For other data, where catchment areas are bigger than the statistical regions (like in the example school in the south east of Berlin), you can expect a larger difference between the two approaches.

## Approximating catchment areas as circular regions or Voronoi regions around schools

So far, we’ve assumed that private primary schools have the same catchment area as their nearby public schools, since there are no official catchment areas for private primary schools and parents can choose more freely which school they send their children to when they prefer a private school. So if we have no spatial data about the catchment areas of private primary schools, what can we do?

One possibility would be to construct a circle around each private school which represents the catchment area for a certain radius. This can be done via st_buffer. However, it’s hard to justify a certain value for that radius and the radius for such a catchment area should probably vary depending on where the school is located (smaller catchment areas in inner city schools than for schools in the outskirts).

Another approach relies on Voronoi regions. They partition the space between given points so that the Voronoi region around each point covers an area of minimal distance to that origin point. In other words: the Voronoi region around a school is the area in which all households are located that are closest to that school. It is reasonable to assume that most parents choose among the closest private schools to their home. This means approximating the catchment area of private primary schools as Voronoi regions may be a good option, while still using the official public primary school catchment areas only for the public schools.

Voronoi regions can be generated with st_voronoi, which accepts the points as MULTIPOINT geometry object. The second argument is an envelope polygon for which we’ll use the the Berlin borders. The resulting object is a GEOMETRYCOLLECTION geometry object which we pass on to st_collection_extract and st_sfc in order to transform this to a geometry set object that has the same CRS as our other spatial data (ETRS89).

Let’s generate the Voronoi regions around all private primary schools:

bln_outline <- st_union(bln$geometry) # Berlin borders privschools <- filter(schools, ownership == 'priv') pubschools <- filter(schools, ownership == 'pub') (priv_voro <- st_coordinates(privschools$geometry) %>%
st_multipoint() %>%
st_voronoi(bln_outline) %>%
st_collection_extract() %>%
st_sfc(crs = st_crs(bln)))
Geometry set for 71 features
[...]
Projected CRS: ETRS89 / UTM zone 33N
First 5 geometries:
POLYGON ((375671.8 5822022, 378042.6 5829508, 3…
POLYGON ((343176.8 5818841, 343176.8 5864683, 3…
POLYGON ((343176.8 5808476, 343176.8 5818841, 3…
POLYGON ((377498.9 5816477, 369373 5818823, 375…
POLYGON ((382822.9 5777090, 370640.2 5777090, 3…

We can now plot the generated regions along with the school locations:

plot(bln_outline)
plot(privschools$geometry, col = 'blue', pch = 19, add = TRUE) plot(priv_voro, border = 'red', col = NA, add = TRUE) We can see that the Voronoi regions extend beyond the borders of Berlin so we should take the intersection between the Voronoi regions and the Berlin border in order to clip these regions: priv_voro <- st_intersection(priv_voro, bln_outline) plot(bln_outline) plot(privschools$geometry, col = 'blue', pch = 19, add = TRUE)
plot(priv_voro, border = 'red', col = NA, add = TRUE)

Let’s overlay the planning areas with the private schools’ Voronoi regions to see how they differ.

plot(bln$geometry) plot(priv_voro, col = '#80000077', border = 'white', add = TRUE) plot(privschools$geometry, col = 'blue', pch = 19, cex = 0.5, add = TRUE)

Once we have circular regions or Voronoi regions around the private schools, the rest of the calculations are similar to those with the official catchment areas. We link the private schools with their approximated catchment areas and apply the spat_weighted_mean function that we’ve defined before. I’ve done this in the R notebook for this blog article.

## Conclusion

The descriptive results suggest that private schools in Berlin may tend to be located in areas with lower rates of children whose parents obtain social welfare as compared to public schools. Further spatial analysis could be done to test this hypothesis.

We have seen how we can calculate a weighted average for some variable of interest for a catchment area around sample points, when this variable of interest was measured for regions that overlap with that catchment area. In the best case scenario, you know the geometry of the catchment areas. Otherwise you may need to approximate them, for example as circular regions around the points or as Voronoi regions. Additionally, you may consider nearest-feature-joins or travel time isochrones. Which option is more appropriate depends on your use-case.

## References

Footnotes

↑1 Note that when joining spatial and ordinary dataframes, the order of arguments in the join function matters. If you have a spatial dataframe on the “left side” (x argument), the result will be a spatial dataframe. If you have an ordinary dataframe on the left side, the result will be an ordinary dataframe, i.e. the merged dataset loses its “spatial nature” and spatial operations won’t work with it any more (unless you convert it back to a spatial dataframe again with st_as_sf). I prefer using the base plot function for quick exploration of spatial data and usually only turn to ggplot2 for more advanced or “publication ready” plots. The help page for plot.sf provides some information about the arguments of this plotting function used for sf objects. The catalog is a bit clumsy to use, but actually works quite well: You search for the data, get an URL to the WFS endpoint from the data’s metainformation panel and use that URL to obtain the data e.g. via a WFS layer in QGIS. I’m using st_drop_geometry here, because otherwise a spatial aggregation would be performed which takes much longer to compute and is not necessary here. There’s much debate about this and parents can try to register their children in a primary school outside their home catchment area but this comes with juristic obstacles, so we can assume for now that most will stay within their area.