In mpjashby/crimemapping: Crime Mapping with R
library(learnr)
tutorial_options(exercise.timelimit = 120)
knitr::opts_chunk$set(echo = FALSE, fig.align='center')
# Load packages
library(ggrepel)
library(ggspatial)
library(patchwork)
library(sf)
library(tidyverse)
# Copy files
if (!dir.exists("css")) dir.create("css")
walk(
dir("../css/"),
~ file.copy(str_glue("../css/{.}"), str_glue("css/{.}"), overwrite = TRUE)
)
# Load data --------------------------------------------------------------------
## Hungerford data ----
hungerford_shootings <- tribble(
~latitude, ~longitude, ~victims,
51.4078, -1.6683, "Susan GODFREY†",
51.4094, -1.5779, "Kakaub DEAN",
51.4116, -1.5147, "Roland MASON†\nSheila MASON†\nMarjorie JACKSON\nLisa MILDENHALL",
51.4112, -1.5119, "Kenneth CLEMENTS†",
51.4115, -1.5140, "Roger BRERETON†\nLinda CHAPMAN\nAlison CHAPMAN",
51.4116, -1.5151, "Abdur KHAN†\nAlan LEPETIT\nHazel HASLETT\nGeorge WHITE†\nDorothy RYAN†\nIvor JACKSON",
51.4103, -1.5132, "Betty TOLLADAY",
51.4081, -1.5132, "Francis BUTLER†",
51.4079, -1.5144, "Marcus BARNARD†",
51.4080, -1.5150, "Ann HONEYBONE",
51.4098, -1.5156, "John STORMS",
51.4102, -1.5156, "Douglas WAINWRIGHT†\nKathleen WAINWRIGHT\nKevin LANCE\nEric VARDY†",
51.4088, -1.5173, "Sandra HILL†",
51.4076, -1.5174, "Victor GIBBS†\nMyrtle GIBBS†\nMichael JENNINGS\nMyra GEATER",
51.4065, -1.5175, "Ian PLAYLE†",
51.4062, -1.5152, "George NOON"
) |>
mutate(
coords = xy_transform(
longitude,
latitude,
from = "EPSG:4326",
to = "EPSG:27700"
),
order = row_number()
) |>
unnest(cols = "coords") |>
mutate(across(c(x, y), round)) |>
select(victims, order, easting = x, northing = y)
hungerford_lines <- hungerford_shootings |>
rename(x_end = easting, y_end = northing) |>
mutate(x_start = lag(x_end), y_start = lag(y_end)) |>
remove_missing(vars = c("x_start", "y_start"), na.rm = TRUE)
hungerford_shootings_sf <- hungerford_shootings |>
st_as_sf(coords = c("easting", "northing"), crs = "EPSG:27700")
# Create stored maps -----------------------------------------------------------
hungerford_map_overall <- ggplot() +
# Plot base map
annotation_map_tile(zoomin = 1, progress = "none") +
# Plot lines between points
geom_curve(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = hungerford_lines,
arrow = arrow(length = unit(3, "mm"), type = "closed"),
curvature = -0.2,
colour = "orange3"
) +
# Plot points
geom_sf(
data = hungerford_shootings_sf,
shape = 21,
colour = "white",
fill = "orange3",
size = 3
) +
geom_label_repel(
aes(x = easting, y = northing, label = order),
data = filter(hungerford_shootings, order %in% 1:2),
colour = "white",
fill = "orange3",
fontface = "bold",
label.size = NA
) +
annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") +
scale_y_continuous(expand = expansion(2)) +
labs(title = " Shootings in Wiltshire") +
coord_sf(crs = "EPSG:27700") +
theme_void() +
theme(
panel.border = element_rect(colour = "grey20", fill = NA),
plot.title = element_text(margin = margin(b = -18))
)
hungerford_map_town <- ggplot() +
annotation_map_tile(zoomin = 1, progress = "none") +
# Plot lines between points
geom_curve(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = slice(hungerford_lines, 3:n()),
arrow = arrow(length = unit(3, "mm"), type = "closed"),
curvature = -0.2,
colour = "orange3"
) +
# Plot points
geom_sf(
data = slice(hungerford_shootings_sf, 3:n()),
shape = 21,
colour = "white",
fill = "orange3",
size = 3
) +
geom_label_repel(
aes(x = easting, y = northing, label = order),
data = slice(hungerford_shootings, 3:n()),
colour = "white",
fill = "orange3",
fontface = "bold",
label.size = NA
) +
annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") +
scale_x_continuous(expand = expansion(0.3)) +
scale_y_continuous(expand = expansion(0.3)) +
coord_sf(crs = "EPSG:27700") +
labs(
title = "Shootings in Hungerford town"
) +
theme_void() +
theme(
panel.border = element_rect(colour = "grey20", fill = NA),
plot.margin = margin(t = 12)
)
Introduction
It's common in crime mapping to treat each crime as a separate event. But a
large proportion of crimes are committed by a relatively small proportion of all
the people who commit crime. These persistent or repeat offenders commit crimes
frequently, maybe several times a day in the case of some people. One type of
repeat offending is the crime series, in which an offender or group of
offenders commits the same or related crimes in different places over time.
Case linkage
One of the difficulties in studying serial offending is in identifying which
offences are committed by the same offender. Case linkage is the process of
identifying crime series by looking for similarities in the method used by an
offender (e.g. entering a house by breaking a lock on a side door out of sight
from the street) or by evidence left at the scene (such as fingerprints or DNA).
Case linkage is typically an imperfect process -- investigators might believe
that two offences were committed by the same person, but in most cases they are
unlikely to know for sure unless the offender has been caught (and sometimes not
even then).
Watch this video to learn a bit more about crime linkage.
Once crimes have been linked together as being part of the same series, it can
be useful to map them to better understand how the series has progressed. In
this tutorial we will learn how to map linked cases.
The case used in this tutorial
Due to the uncertainty of case linkage, there is little publicly available data
on the locations of serial crimes. The available data tends to focus on serial
murders. To minimise the likelihood of anyone taking this course having been
directly affected by -- or know anyone affected by -- the cases we use in this
tutorial, we will use a historical example.
hungerford_murder_count <- hungerford_shootings |>
mutate(murder_count = str_count(victims, "†")) |>
summarise(murder_count = sum(murder_count)) |>
pull(murder_count)
The Hungerford massacre
occurred in southern England in August 1987, when a marauding attacker (also
known as a spree offender) killed r hungerford_murder_count people and shot
many others in the space of about 90 minutes. We will use a dataset called
hungerford_shootings that contains the names of each victim and the
approximate location at which they were shot (recorded as eastings and northings
in the British National Grid). This data is taken from the
official report into the massacre.
The order column shows the order in which the victims were shot. These are the
first few rows of the dataset (the dagger symbol † shows that the victim was
killed):
hungerford_shootings |>
mutate(victims = str_replace_all(victims, "\\n", ", ")) |>
head(5) |>
knitr::kable()
In this tutorial we will learn how to create this composite map of the locations
of the shootings in this crime series.
hungerford_map <- (hungerford_map_overall / hungerford_map_town) +
plot_annotation(
title = "Shootings during the Hungerford massacre",
caption = "data from the official report into the shootings",
theme = theme(
plot.caption = element_text(colour = "grey40", hjust = 0),
plot.title = element_text(colour = "grey50", face = "bold", size = 14)
)
)
ggsave(
here::here("inst/tutorials/13_crime_series/images/hungerford_map.jpg"),
hungerford_map,
width = 900 / 150,
height = 900 / 150,
dpi = 150
)
Mapping linked cases
Mapping crime series can be useful for several reasons. For example, a map of a
crime series might help the jurors in a criminal trial to better understand the
sequence of events that the suspect is accused of. This can be especially
helpful if the sequence is complicated or especially long.
At the moment, the hungerford_shootings dataset contains the point location
of each event and the order in which they occurred. To show a sequence of events
on a map, we need to link each event with those that happened immediately before
and after it.
hungerford_shootings |>
mutate(victims = str_replace_all(victims, "\\n", ", ")) |>
head(5) |>
knitr::kable()
One way to do this is to draw lines on the map connecting each incident in turn.
To do this, we will create a dataset where each row contains two pairs of
co-ordinates: one representing the location of a particular shooting and the
other representing the location of the previous shooting. To do that, we will:
- Take the
hungerford_shootings dataset and rename the existing columns
holding the co-ordinates so that it is clear those co-ordinates are for the
end of each line between points. To do this we will use the rename()
function from the dplyr package.
- Add two new columns to each row that show the co-ordinates of the row in
the dataset immediately above the current row (i.e. the row that represents
the location before the current location. We will do this with the
lag()
function, also from dplyr.
- Use these two pairs of co-ordinates to plot lines on a map.
hungerford_lines <- hungerford_shootings |>
rename(x_end = easting, y_end = northing) |>
mutate(x_start = lag(x_end), y_start = lag(y_end)) |>
# Remove the first line, which contains missing values and which we don't need
remove_missing(vars = c("x_start", "y_start"), na.rm = TRUE)
head(hungerford_lines)
Why does this code include `remove_missing()`?
You might notice that the values in the first row of the `x_start` and `x_end`
columns in the `hungerford_lines` object are `NA` values. This is because these
columns have been created using the `lag()` function, which gets the value from
the same column in the row immediately above. The first row doesn't have a row
immediately above it, so `lag()` returns `NA`. Since the first row does not
represent a link between shootings in any case, we can remove it with
`remove_missing()`.
This method for creating lines between points is one of several ways we could
achieve the same result. For example, we could create lines connecting the
points and store them as SF objects. However, the code to do that is more
complicated and gives us slightly less control over the appearance of the lines,
so we will not use that method. Using the method shown in the box above means
the lines on our map are not SF objects, so we cannot use geom_sf() to add
them to the map. Instead, we will create a map using the geom_segment()
function from the ggplot2 package.
# Convert the `hungerford_shootings` object to SF
hungerford_shootings_sf <- st_as_sf(
hungerford_shootings,
coords = c("easting", "northing"),
crs = "EPSG:27700"
)
# Plot a basic map
ggplot() +
# Plot base map
annotation_map_tile(zoomin = 0, progress = "none") +
# Plot lines between points
geom_segment(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = hungerford_lines
) +
# Plot points
geom_sf(data = hungerford_shootings_sf) +
# Since one of our layers is not an SF object, tell `ggplot()` what
# co-ordinate system to use
coord_sf(crs = "EPSG:27700") +
theme_void()
This map is not particularly useful -- we will return to that in a minute.
If you look at this code, you'll see that we've included the function
coord_sf() in our ggplot() stack. In all the previous maps that we have
made, we have used geom_sf() to plot geographic data on our maps. geom_sf()
understands how to translate co-ordinates specified using different co-ordinate
systems to locations on the surface of each map. Other functions in the
geom_*() family of functions do not know how to do this automatically, so we
must specify the co-ordinate system by adding the coord_sf() function to the
stack. We only need to use one argument to coord_sf(): the crs argument to
specify the co-ordinate system that our data uses. In this case, we know that
the co-ordinates in the hungerford_lines object are specified using the
British National Grid, which has the EPSG code 27700.
The map we have made above shows the sequence of events, but there are at least
four ways that we can make it better.
- It would be useful to be able to see a larger area around the shooting
locations, to see more of the town surrounding the area in which the crimes
occurred.
- We don't know which order to move through the sequence of lines. We can
deal with this by adding arrows to show the direction of travel.
- The straight lines make it look like the offender travelled across fields
between locations, which is probably not true. Since we do not have data on
what routes the offender took, we can replace the straight lines with
curves to indicate that the exact route is unknown.
- Because the first two shootings occurred outside the town, the map has to
cover a large area and this makes it harder to see the sequence of events
in the town itself.
Extending the map area
By default, ggplot() works out the limits of the area shown on the map based
on the area covered by the data we add to each stack with functions from the
geom_*() family of functions. Usually this works well, but in this case the
distribution of the shootings means that the map shows only a small slice of the
town of Hungerford and the surrounding area. It would be useful for readers to
be able to see a larger area, to be able to better identify where the shootings
occurred.
To expand the area shown on the map, we can use scale_y_continuous() function
from the ggplot2 package. We have used the scale_fill_distiller()
and scale_fill_gradient() functions in previous tutorials to control how
columns in the data are represented as colours on the map. The
scale_y_continuous() function is similar, in that it controls the vertical
(Y) axis on the map. We don't need to use most of the capabilities of
scale_y_continuous() because ggplot() sets reasonable default values. The
only argument we need to set is the expand argument, which controls how far
the map should extend around the area covered by the data.
The easiest way to set the correct values for the expand function is to use
the expansion() helper function. We can use this to specify how much extra
area around the data we want to include on the map. For example, expansion(2)
means that the Y axis of the map should cover the area covered by the data and
twice that distance either side of the data.
# Plot a basic map
ggplot() +
# Plot base map
annotation_map_tile(zoomin = 0, progress = "none") +
# Plot lines between points
geom_segment(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = hungerford_lines
) +
# Plot points
geom_sf(data = hungerford_shootings_sf) +
scale_y_continuous(expand = expansion(2)) +
coord_sf(crs = "EPSG:27700") +
theme_void()
::: {.box .notewell}
The scale_y_continuous() function specifically controls the vertical (Y) axis
of the map, so it only adds space above and below the data on the map. If we
wanted to add space to the left and right of the data, we would need to use the
scale_x_continuous() function. But in this case, since the map is already very
wide relative to its height, we will not make the map any wider.
:::
Adding arrows and curves
To add an arrow to the end of each line segment we can use the arrow()
helper function from the ggplot2 package to specify the arrow argument of
the geom_segment() function in our ggplot() stack. Here, we make the arrow
head smaller than the default by setting length = unit(3, "mm") and choose the
style of the arrow head with type = "closed".
To prevent the arrow heads from obscuring the points, we will give the points a
thin white border by specifying shape = 21 (a circle with a separate border)
and colour = "white", as well as making the points slightly bigger with
size = 2.
ggplot() +
# Plot base map
annotation_map_tile(zoomin = 0, progress = "none") +
# Plot lines between points
geom_segment(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = hungerford_lines,
arrow = arrow(length = unit(3, "mm"), type = "closed"),
colour = "orange3"
) +
# Plot points
geom_sf(
data = hungerford_shootings_sf,
shape = 21,
colour = "white",
fill = "orange3",
size = 3
) +
scale_y_continuous(expand = expansion(2)) +
coord_sf(crs = "EPSG:27700") +
theme_void()
This makes it easier to see that the shootings started at the point on the left
of the map, but makes the problem of understanding the sequence of events in the
town itself even worse. We will deal with this in the next section.
In the map above, we used the geom_segment() function to add the lines to our
map. If we change this to geom_curve() the lines will become curved rather
than straight. By specifying curvature = -0.2 we get a slightly straighter
line than the default, and a left-hand curve because the value of curvature is
negative.
The other change we can make at this point is to add a layer of labels showing
the order in which the shootings occurred. We don't want the labels to overlap
the points, so we will use geom_label_repel() from the ggrepel package to
create labels that are automatically offset from the points they relate to.
For now, we will just label the first two locations.
At this point we can also make some minor changes to the map -- adding a title
and scale bar, putting a neat line around the map -- the purpose of which will
become clear in the next section.
library(ggrepel)
hungerford_map_overall <- ggplot() +
# Plot base map
annotation_map_tile(zoomin = 0, progress = "none") +
# Plot lines between points
geom_curve(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = hungerford_lines,
arrow = arrow(length = unit(3, "mm"), type = "closed"),
curvature = -0.2,
colour = "orange3"
) +
# Plot points
geom_sf(
data = hungerford_shootings_sf,
shape = 21,
colour = "white",
fill = "orange3",
size = 3
) +
geom_label_repel(
aes(x = easting, y = northing, label = order),
data = filter(hungerford_shootings, order %in% 1:2),
colour = "white",
fill = "orange3",
fontface = "bold",
label.size = NA
) +
annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") +
scale_y_continuous(expand = expansion(2)) +
labs(title = " Shootings in Wiltshire") +
coord_sf(crs = "EPSG:27700") +
theme_void() +
theme(
panel.border = element_rect(colour = "grey20", fill = NA),
plot.title = element_text(margin = margin(b = -18))
)
hungerford_map_overall
Now we have added arrow heads and curved lines, in the next section we will
learn how to solve the problem of the events in the town itself being unclear on
the map.
Multiple maps
Our existing map is difficult to understand because we have a mix of some events
very close together (including several on the same street) and some that
occurred further away. This means the closer events overlap on the map,
especially now that we have made the points larger to distinguish them from the
lines linking each event.
To deal with this problem we can display two maps: one showing the whole area
covered by the points and a second (sometimes called an inset map) showing
only the events in the town of Hungerford itself. We will do this using the
patchwork package to combine multiple
maps made using ggplot().
We have already saved the first map as hungerford_map_overall. We can create a
map showing only the shootings in Hungerford itself by removing the first two
points from hungerford_points_sf (since they represent shootings outside the
town itself). We can do this for all the existing layers using slice().
We will use the plot.margin argument to the theme() function to add a small
amount of space at the top of this map, which will help separate it from the
first map when we put them together. We will also get the map to cover a
slightly larger area than it would by default by using the
scale_x_continuous() and scale_y_continuous() functions.
hungerford_map_town <- ggplot() +
annotation_map_tile(zoomin = 0, progress = "none") +
# Plot lines between points
geom_curve(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = slice(hungerford_lines, 3:n()),
arrow = arrow(length = unit(3, "mm"), type = "closed"),
curvature = -0.2,
colour = "orange3"
) +
# Plot points
geom_sf(
data = slice(hungerford_shootings_sf, 3:n()),
shape = 21,
colour = "white",
fill = "orange3",
size = 3
) +
geom_label_repel(
aes(x = easting, y = northing, label = order),
data = slice(hungerford_shootings, 3:n()),
colour = "white",
fill = "orange3",
fontface = "bold",
label.size = NA
) +
annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") +
scale_x_continuous(expand = expansion(0.3)) +
scale_y_continuous(expand = expansion(0.3)) +
coord_sf(crs = "EPSG:27700") +
labs(
title = "Shootings in Hungerford town"
) +
theme_void() +
theme(
panel.border = element_rect(colour = "grey20", fill = NA),
plot.margin = margin(t = 12)
)
hungerford_map_town
We can now combine the two maps using the patchwork package. To place two
plots next to each other using patchwork you just add one map object to the
other using the | operator. To place one plot on top of the other, you use the
/ operator (you can combine plots in more-complicated ways by combining these
operators, together with parentheses).
library(patchwork)
(hungerford_map_overall / hungerford_map_town)
When we created the hungerford_map_overall object in the previous section, we
gave the map title a negative border using the plot.title argument to the
theme() function. This had the effect of moving the title downwards onto the
map to save space, since the top-left corner of the map was empty. We also added
a scale bar, since when you present two related maps of different scales next to
one another, it is useful to show the scales of both maps to help readers relate
them to one another.
We can add shared titles and captions to combined maps created with patchwork
using the plot_annotation() function. This function has a theme argument,
which we can use to change the appearance of the shared title and caption just
as we use theme() to change the appearance of elements on single maps.
(hungerford_map_overall / hungerford_map_town) +
plot_annotation(
title = "Shootings during the Hungerford massacre",
caption = "data from the official report into the shootings",
theme = theme(
plot.caption = element_text(colour = "grey40", hjust = 0),
plot.title = element_text(colour = "grey50", face = "bold", size = 14)
)
)
There are various other improvements that we could make to this map based on the
mapping skills we have already learned during this course. For example, we could
add the locations of buildings or particular facilities using data from
OpenStreetMap. What information we choose to present on the map will depend on
what information we think it is important to communicate to our audience.
Putting it all together
::: {.box}
In this tutorial we have learned how to map crime series, focusing on displaying
a sequence of events on a map. We have also learned how to combine maps using
the patchwork package.
:::
names_killed <- hungerford_shootings |>
pull("victims") |>
str_split("\n") |>
unlist() |>
str_subset("†") |>
str_remove("†") |>
as_tibble() |>
separate(value, into = c("first", "last")) |>
arrange(last) |>
mutate(name = str_glue("{first} {last}")) |>
pull("name") |>
str_to_title() |>
knitr::combine_words(oxford_comma = FALSE)
When we are mapping crime data, it is important to remember that the rows in the
data we are using often represent traumatic events that have happened to people.
In this tutorial, every row in the data represents one or more people who were
killed or injured during the massacre. The people killed during the tragedy were
r names_killed.
You can put the code from this tutorial together to see everything that is
needed to make a composite map of the Hungerford massacre from the original data
file called hungerford_shootings.
library(ggrepel)
library(ggspatial)
library(patchwork)
library(sf)
library(tidyverse)
# Prepare data -----------------------------------------------------------------
# Create lines showing progression between points
hungerford_lines <- hungerford_shootings |>
rename(x_end = easting, y_end = northing) |>
mutate(x_start = lag(x_end), y_start = lag(y_end)) |>
# Remove the first line, which contains missing values and which we don't need
remove_missing(vars = c("x_start", "y_start"), na.rm = TRUE)
# Convert the `hungerford_shootings` object to SF
hungerford_shootings_sf <- st_as_sf(
hungerford_shootings,
coords = c("easting", "northing"),
crs = "EPSG:27700"
)
# Make component maps ----------------------------------------------------------
hungerford_map_overall <- ggplot() +
# Plot base map
annotation_map_tile(zoomin = 0, progress = "none") +
# Plot lines between points
geom_curve(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = hungerford_lines,
arrow = arrow(length = unit(3, "mm"), type = "closed"),
curvature = -0.2,
colour = "orange3"
) +
# Plot points
geom_sf(
data = hungerford_shootings_sf,
shape = 21,
colour = "white",
fill = "orange3",
size = 3
) +
geom_label_repel(
aes(x = easting, y = northing, label = order),
data = filter(hungerford_shootings, order %in% 1:2),
colour = "white",
fill = "orange3",
fontface = "bold",
label.size = NA
) +
annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") +
scale_y_continuous(expand = expansion(2)) +
labs(title = " Shootings in Wiltshire") +
coord_sf(crs = "EPSG:27700") +
theme_void() +
theme(
panel.border = element_rect(colour = "grey20", fill = NA),
plot.title = element_text(margin = margin(b = -18))
)
hungerford_map_town <- ggplot() +
annotation_map_tile(zoomin = 0, progress = "none") +
# Plot lines between points
geom_curve(
aes(x = x_start, y = y_start, xend = x_end, yend = y_end),
data = slice(hungerford_lines, 3:n()),
arrow = arrow(length = unit(3, "mm"), type = "closed"),
curvature = -0.2,
colour = "orange3"
) +
# Plot points
geom_sf(
data = slice(hungerford_shootings_sf, 3:n()),
shape = 21,
colour = "white",
fill = "orange3",
size = 3
) +
geom_label_repel(
aes(x = easting, y = northing, label = order),
data = slice(hungerford_shootings, 3:n()),
colour = "white",
fill = "orange3",
fontface = "bold",
label.size = NA
) +
annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") +
scale_x_continuous(expand = expansion(0.3)) +
scale_y_continuous(expand = expansion(0.3)) +
coord_sf(crs = "EPSG:27700") +
labs(
title = "Shootings in Hungerford town"
) +
theme_void() +
theme(
panel.border = element_rect(colour = "grey20", fill = NA),
plot.margin = margin(t = 12)
)
# Combine maps and add titles --------------------------------------------------
(hungerford_map_overall / hungerford_map_town) +
plot_annotation(
title = "Shootings during the Hungerford massacre",
caption = "data from the official report into the shootings",
theme = theme(
plot.caption = element_text(colour = "grey40", hjust = 0),
plot.title = element_text(colour = "grey50", face = "bold", size = 14)
)
)
Photograph of PC Roger Brereton's funeral from Berkshire Live
mpjashby/crimemapping documentation built on Jan. 9, 2025, 7:18 p.m.
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library(learnr) tutorial_options(exercise.timelimit = 120) knitr::opts_chunk$set(echo = FALSE, fig.align='center') # Load packages library(ggrepel) library(ggspatial) library(patchwork) library(sf) library(tidyverse) # Copy files if (!dir.exists("css")) dir.create("css") walk( dir("../css/"), ~ file.copy(str_glue("../css/{.}"), str_glue("css/{.}"), overwrite = TRUE) ) # Load data -------------------------------------------------------------------- ## Hungerford data ---- hungerford_shootings <- tribble( ~latitude, ~longitude, ~victims, 51.4078, -1.6683, "Susan GODFREY†", 51.4094, -1.5779, "Kakaub DEAN", 51.4116, -1.5147, "Roland MASON†\nSheila MASON†\nMarjorie JACKSON\nLisa MILDENHALL", 51.4112, -1.5119, "Kenneth CLEMENTS†", 51.4115, -1.5140, "Roger BRERETON†\nLinda CHAPMAN\nAlison CHAPMAN", 51.4116, -1.5151, "Abdur KHAN†\nAlan LEPETIT\nHazel HASLETT\nGeorge WHITE†\nDorothy RYAN†\nIvor JACKSON", 51.4103, -1.5132, "Betty TOLLADAY", 51.4081, -1.5132, "Francis BUTLER†", 51.4079, -1.5144, "Marcus BARNARD†", 51.4080, -1.5150, "Ann HONEYBONE", 51.4098, -1.5156, "John STORMS", 51.4102, -1.5156, "Douglas WAINWRIGHT†\nKathleen WAINWRIGHT\nKevin LANCE\nEric VARDY†", 51.4088, -1.5173, "Sandra HILL†", 51.4076, -1.5174, "Victor GIBBS†\nMyrtle GIBBS†\nMichael JENNINGS\nMyra GEATER", 51.4065, -1.5175, "Ian PLAYLE†", 51.4062, -1.5152, "George NOON" ) |> mutate( coords = xy_transform( longitude, latitude, from = "EPSG:4326", to = "EPSG:27700" ), order = row_number() ) |> unnest(cols = "coords") |> mutate(across(c(x, y), round)) |> select(victims, order, easting = x, northing = y) hungerford_lines <- hungerford_shootings |> rename(x_end = easting, y_end = northing) |> mutate(x_start = lag(x_end), y_start = lag(y_end)) |> remove_missing(vars = c("x_start", "y_start"), na.rm = TRUE) hungerford_shootings_sf <- hungerford_shootings |> st_as_sf(coords = c("easting", "northing"), crs = "EPSG:27700") # Create stored maps ----------------------------------------------------------- hungerford_map_overall <- ggplot() + # Plot base map annotation_map_tile(zoomin = 1, progress = "none") + # Plot lines between points geom_curve( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = hungerford_lines, arrow = arrow(length = unit(3, "mm"), type = "closed"), curvature = -0.2, colour = "orange3" ) + # Plot points geom_sf( data = hungerford_shootings_sf, shape = 21, colour = "white", fill = "orange3", size = 3 ) + geom_label_repel( aes(x = easting, y = northing, label = order), data = filter(hungerford_shootings, order %in% 1:2), colour = "white", fill = "orange3", fontface = "bold", label.size = NA ) + annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") + scale_y_continuous(expand = expansion(2)) + labs(title = " Shootings in Wiltshire") + coord_sf(crs = "EPSG:27700") + theme_void() + theme( panel.border = element_rect(colour = "grey20", fill = NA), plot.title = element_text(margin = margin(b = -18)) ) hungerford_map_town <- ggplot() + annotation_map_tile(zoomin = 1, progress = "none") + # Plot lines between points geom_curve( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = slice(hungerford_lines, 3:n()), arrow = arrow(length = unit(3, "mm"), type = "closed"), curvature = -0.2, colour = "orange3" ) + # Plot points geom_sf( data = slice(hungerford_shootings_sf, 3:n()), shape = 21, colour = "white", fill = "orange3", size = 3 ) + geom_label_repel( aes(x = easting, y = northing, label = order), data = slice(hungerford_shootings, 3:n()), colour = "white", fill = "orange3", fontface = "bold", label.size = NA ) + annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") + scale_x_continuous(expand = expansion(0.3)) + scale_y_continuous(expand = expansion(0.3)) + coord_sf(crs = "EPSG:27700") + labs( title = "Shootings in Hungerford town" ) + theme_void() + theme( panel.border = element_rect(colour = "grey20", fill = NA), plot.margin = margin(t = 12) )
Introduction
It's common in crime mapping to treat each crime as a separate event. But a large proportion of crimes are committed by a relatively small proportion of all the people who commit crime. These persistent or repeat offenders commit crimes frequently, maybe several times a day in the case of some people. One type of repeat offending is the crime series, in which an offender or group of offenders commits the same or related crimes in different places over time.
Case linkage
One of the difficulties in studying serial offending is in identifying which offences are committed by the same offender. Case linkage is the process of identifying crime series by looking for similarities in the method used by an offender (e.g. entering a house by breaking a lock on a side door out of sight from the street) or by evidence left at the scene (such as fingerprints or DNA). Case linkage is typically an imperfect process -- investigators might believe that two offences were committed by the same person, but in most cases they are unlikely to know for sure unless the offender has been caught (and sometimes not even then).
Watch this video to learn a bit more about crime linkage.
Once crimes have been linked together as being part of the same series, it can be useful to map them to better understand how the series has progressed. In this tutorial we will learn how to map linked cases.
The case used in this tutorial
Due to the uncertainty of case linkage, there is little publicly available data on the locations of serial crimes. The available data tends to focus on serial murders. To minimise the likelihood of anyone taking this course having been directly affected by -- or know anyone affected by -- the cases we use in this tutorial, we will use a historical example.
hungerford_murder_count <- hungerford_shootings |> mutate(murder_count = str_count(victims, "†")) |> summarise(murder_count = sum(murder_count)) |> pull(murder_count)
The Hungerford massacre
occurred in southern England in August 1987, when a marauding attacker (also
known as a spree offender) killed r hungerford_murder_count people and shot
many others in the space of about 90 minutes. We will use a dataset called
hungerford_shootings that contains the names of each victim and the
approximate location at which they were shot (recorded as eastings and northings
in the British National Grid). This data is taken from the
official report into the massacre.
The order column shows the order in which the victims were shot. These are the
first few rows of the dataset (the dagger symbol † shows that the victim was
killed):
hungerford_shootings |> mutate(victims = str_replace_all(victims, "\\n", ", ")) |> head(5) |> knitr::kable()
In this tutorial we will learn how to create this composite map of the locations of the shootings in this crime series.
hungerford_map <- (hungerford_map_overall / hungerford_map_town) + plot_annotation( title = "Shootings during the Hungerford massacre", caption = "data from the official report into the shootings", theme = theme( plot.caption = element_text(colour = "grey40", hjust = 0), plot.title = element_text(colour = "grey50", face = "bold", size = 14) ) ) ggsave( here::here("inst/tutorials/13_crime_series/images/hungerford_map.jpg"), hungerford_map, width = 900 / 150, height = 900 / 150, dpi = 150 )
Mapping linked cases
Mapping crime series can be useful for several reasons. For example, a map of a crime series might help the jurors in a criminal trial to better understand the sequence of events that the suspect is accused of. This can be especially helpful if the sequence is complicated or especially long.
At the moment, the hungerford_shootings dataset contains the point location
of each event and the order in which they occurred. To show a sequence of events
on a map, we need to link each event with those that happened immediately before
and after it.
hungerford_shootings |> mutate(victims = str_replace_all(victims, "\\n", ", ")) |> head(5) |> knitr::kable()
One way to do this is to draw lines on the map connecting each incident in turn. To do this, we will create a dataset where each row contains two pairs of co-ordinates: one representing the location of a particular shooting and the other representing the location of the previous shooting. To do that, we will:
- Take the
hungerford_shootingsdataset and rename the existing columns holding the co-ordinates so that it is clear those co-ordinates are for the end of each line between points. To do this we will use therename()function from thedplyrpackage. - Add two new columns to each row that show the co-ordinates of the row in
the dataset immediately above the current row (i.e. the row that represents
the location before the current location. We will do this with the
lag()function, also fromdplyr. - Use these two pairs of co-ordinates to plot lines on a map.
hungerford_lines <- hungerford_shootings |> rename(x_end = easting, y_end = northing) |> mutate(x_start = lag(x_end), y_start = lag(y_end)) |> # Remove the first line, which contains missing values and which we don't need remove_missing(vars = c("x_start", "y_start"), na.rm = TRUE) head(hungerford_lines)
Why does this code include `remove_missing()`?
You might notice that the values in the first row of the `x_start` and `x_end`
columns in the `hungerford_lines` object are `NA` values. This is because these
columns have been created using the `lag()` function, which gets the value from
the same column in the row immediately above. The first row doesn't have a row
immediately above it, so `lag()` returns `NA`. Since the first row does not
represent a link between shootings in any case, we can remove it with
`remove_missing()`.
This method for creating lines between points is one of several ways we could
achieve the same result. For example, we could create lines connecting the
points and store them as SF objects. However, the code to do that is more
complicated and gives us slightly less control over the appearance of the lines,
so we will not use that method. Using the method shown in the box above means
the lines on our map are not SF objects, so we cannot use geom_sf() to add
them to the map. Instead, we will create a map using the geom_segment()
function from the ggplot2 package.
# Convert the `hungerford_shootings` object to SF hungerford_shootings_sf <- st_as_sf( hungerford_shootings, coords = c("easting", "northing"), crs = "EPSG:27700" ) # Plot a basic map ggplot() + # Plot base map annotation_map_tile(zoomin = 0, progress = "none") + # Plot lines between points geom_segment( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = hungerford_lines ) + # Plot points geom_sf(data = hungerford_shootings_sf) + # Since one of our layers is not an SF object, tell `ggplot()` what # co-ordinate system to use coord_sf(crs = "EPSG:27700") + theme_void()
This map is not particularly useful -- we will return to that in a minute.
If you look at this code, you'll see that we've included the function
coord_sf() in our ggplot() stack. In all the previous maps that we have
made, we have used geom_sf() to plot geographic data on our maps. geom_sf()
understands how to translate co-ordinates specified using different co-ordinate
systems to locations on the surface of each map. Other functions in the
geom_*() family of functions do not know how to do this automatically, so we
must specify the co-ordinate system by adding the coord_sf() function to the
stack. We only need to use one argument to coord_sf(): the crs argument to
specify the co-ordinate system that our data uses. In this case, we know that
the co-ordinates in the hungerford_lines object are specified using the
British National Grid, which has the EPSG code 27700.
The map we have made above shows the sequence of events, but there are at least four ways that we can make it better.
- It would be useful to be able to see a larger area around the shooting locations, to see more of the town surrounding the area in which the crimes occurred.
- We don't know which order to move through the sequence of lines. We can deal with this by adding arrows to show the direction of travel.
- The straight lines make it look like the offender travelled across fields between locations, which is probably not true. Since we do not have data on what routes the offender took, we can replace the straight lines with curves to indicate that the exact route is unknown.
- Because the first two shootings occurred outside the town, the map has to cover a large area and this makes it harder to see the sequence of events in the town itself.
Extending the map area
By default, ggplot() works out the limits of the area shown on the map based
on the area covered by the data we add to each stack with functions from the
geom_*() family of functions. Usually this works well, but in this case the
distribution of the shootings means that the map shows only a small slice of the
town of Hungerford and the surrounding area. It would be useful for readers to
be able to see a larger area, to be able to better identify where the shootings
occurred.
To expand the area shown on the map, we can use scale_y_continuous() function
from the ggplot2 package. We have used the scale_fill_distiller()
and scale_fill_gradient() functions in previous tutorials to control how
columns in the data are represented as colours on the map. The
scale_y_continuous() function is similar, in that it controls the vertical
(Y) axis on the map. We don't need to use most of the capabilities of
scale_y_continuous() because ggplot() sets reasonable default values. The
only argument we need to set is the expand argument, which controls how far
the map should extend around the area covered by the data.
The easiest way to set the correct values for the expand function is to use
the expansion() helper function. We can use this to specify how much extra
area around the data we want to include on the map. For example, expansion(2)
means that the Y axis of the map should cover the area covered by the data and
twice that distance either side of the data.
# Plot a basic map ggplot() + # Plot base map annotation_map_tile(zoomin = 0, progress = "none") + # Plot lines between points geom_segment( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = hungerford_lines ) + # Plot points geom_sf(data = hungerford_shootings_sf) + scale_y_continuous(expand = expansion(2)) + coord_sf(crs = "EPSG:27700") + theme_void()
::: {.box .notewell}
The scale_y_continuous() function specifically controls the vertical (Y) axis
of the map, so it only adds space above and below the data on the map. If we
wanted to add space to the left and right of the data, we would need to use the
scale_x_continuous() function. But in this case, since the map is already very
wide relative to its height, we will not make the map any wider.
:::
Adding arrows and curves
To add an arrow to the end of each line segment we can use the arrow()
helper function from the ggplot2 package to specify the arrow argument of
the geom_segment() function in our ggplot() stack. Here, we make the arrow
head smaller than the default by setting length = unit(3, "mm") and choose the
style of the arrow head with type = "closed".
To prevent the arrow heads from obscuring the points, we will give the points a
thin white border by specifying shape = 21 (a circle with a separate border)
and colour = "white", as well as making the points slightly bigger with
size = 2.
ggplot() + # Plot base map annotation_map_tile(zoomin = 0, progress = "none") + # Plot lines between points geom_segment( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = hungerford_lines, arrow = arrow(length = unit(3, "mm"), type = "closed"), colour = "orange3" ) + # Plot points geom_sf( data = hungerford_shootings_sf, shape = 21, colour = "white", fill = "orange3", size = 3 ) + scale_y_continuous(expand = expansion(2)) + coord_sf(crs = "EPSG:27700") + theme_void()
This makes it easier to see that the shootings started at the point on the left of the map, but makes the problem of understanding the sequence of events in the town itself even worse. We will deal with this in the next section.
In the map above, we used the geom_segment() function to add the lines to our
map. If we change this to geom_curve() the lines will become curved rather
than straight. By specifying curvature = -0.2 we get a slightly straighter
line than the default, and a left-hand curve because the value of curvature is
negative.
The other change we can make at this point is to add a layer of labels showing
the order in which the shootings occurred. We don't want the labels to overlap
the points, so we will use geom_label_repel() from the ggrepel package to
create labels that are automatically offset from the points they relate to.
For now, we will just label the first two locations.
At this point we can also make some minor changes to the map -- adding a title and scale bar, putting a neat line around the map -- the purpose of which will become clear in the next section.
library(ggrepel) hungerford_map_overall <- ggplot() + # Plot base map annotation_map_tile(zoomin = 0, progress = "none") + # Plot lines between points geom_curve( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = hungerford_lines, arrow = arrow(length = unit(3, "mm"), type = "closed"), curvature = -0.2, colour = "orange3" ) + # Plot points geom_sf( data = hungerford_shootings_sf, shape = 21, colour = "white", fill = "orange3", size = 3 ) + geom_label_repel( aes(x = easting, y = northing, label = order), data = filter(hungerford_shootings, order %in% 1:2), colour = "white", fill = "orange3", fontface = "bold", label.size = NA ) + annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") + scale_y_continuous(expand = expansion(2)) + labs(title = " Shootings in Wiltshire") + coord_sf(crs = "EPSG:27700") + theme_void() + theme( panel.border = element_rect(colour = "grey20", fill = NA), plot.title = element_text(margin = margin(b = -18)) ) hungerford_map_overall
Now we have added arrow heads and curved lines, in the next section we will learn how to solve the problem of the events in the town itself being unclear on the map.
Multiple maps
Our existing map is difficult to understand because we have a mix of some events very close together (including several on the same street) and some that occurred further away. This means the closer events overlap on the map, especially now that we have made the points larger to distinguish them from the lines linking each event.
To deal with this problem we can display two maps: one showing the whole area
covered by the points and a second (sometimes called an inset map) showing
only the events in the town of Hungerford itself. We will do this using the
patchwork package to combine multiple
maps made using ggplot().
We have already saved the first map as hungerford_map_overall. We can create a
map showing only the shootings in Hungerford itself by removing the first two
points from hungerford_points_sf (since they represent shootings outside the
town itself). We can do this for all the existing layers using slice().
We will use the plot.margin argument to the theme() function to add a small
amount of space at the top of this map, which will help separate it from the
first map when we put them together. We will also get the map to cover a
slightly larger area than it would by default by using the
scale_x_continuous() and scale_y_continuous() functions.
hungerford_map_town <- ggplot() + annotation_map_tile(zoomin = 0, progress = "none") + # Plot lines between points geom_curve( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = slice(hungerford_lines, 3:n()), arrow = arrow(length = unit(3, "mm"), type = "closed"), curvature = -0.2, colour = "orange3" ) + # Plot points geom_sf( data = slice(hungerford_shootings_sf, 3:n()), shape = 21, colour = "white", fill = "orange3", size = 3 ) + geom_label_repel( aes(x = easting, y = northing, label = order), data = slice(hungerford_shootings, 3:n()), colour = "white", fill = "orange3", fontface = "bold", label.size = NA ) + annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") + scale_x_continuous(expand = expansion(0.3)) + scale_y_continuous(expand = expansion(0.3)) + coord_sf(crs = "EPSG:27700") + labs( title = "Shootings in Hungerford town" ) + theme_void() + theme( panel.border = element_rect(colour = "grey20", fill = NA), plot.margin = margin(t = 12) ) hungerford_map_town
We can now combine the two maps using the patchwork package. To place two
plots next to each other using patchwork you just add one map object to the
other using the | operator. To place one plot on top of the other, you use the
/ operator (you can combine plots in more-complicated ways by combining these
operators, together with parentheses).
library(patchwork) (hungerford_map_overall / hungerford_map_town)
When we created the hungerford_map_overall object in the previous section, we
gave the map title a negative border using the plot.title argument to the
theme() function. This had the effect of moving the title downwards onto the
map to save space, since the top-left corner of the map was empty. We also added
a scale bar, since when you present two related maps of different scales next to
one another, it is useful to show the scales of both maps to help readers relate
them to one another.
We can add shared titles and captions to combined maps created with patchwork
using the plot_annotation() function. This function has a theme argument,
which we can use to change the appearance of the shared title and caption just
as we use theme() to change the appearance of elements on single maps.
(hungerford_map_overall / hungerford_map_town) + plot_annotation( title = "Shootings during the Hungerford massacre", caption = "data from the official report into the shootings", theme = theme( plot.caption = element_text(colour = "grey40", hjust = 0), plot.title = element_text(colour = "grey50", face = "bold", size = 14) ) )
There are various other improvements that we could make to this map based on the mapping skills we have already learned during this course. For example, we could add the locations of buildings or particular facilities using data from OpenStreetMap. What information we choose to present on the map will depend on what information we think it is important to communicate to our audience.
Putting it all together
::: {.box}
In this tutorial we have learned how to map crime series, focusing on displaying
a sequence of events on a map. We have also learned how to combine maps using
the patchwork package.
:::
names_killed <- hungerford_shootings |> pull("victims") |> str_split("\n") |> unlist() |> str_subset("†") |> str_remove("†") |> as_tibble() |> separate(value, into = c("first", "last")) |> arrange(last) |> mutate(name = str_glue("{first} {last}")) |> pull("name") |> str_to_title() |> knitr::combine_words(oxford_comma = FALSE)
When we are mapping crime data, it is important to remember that the rows in the
data we are using often represent traumatic events that have happened to people.
In this tutorial, every row in the data represents one or more people who were
killed or injured during the massacre. The people killed during the tragedy were
r names_killed.
You can put the code from this tutorial together to see everything that is
needed to make a composite map of the Hungerford massacre from the original data
file called hungerford_shootings.
library(ggrepel) library(ggspatial) library(patchwork) library(sf) library(tidyverse) # Prepare data ----------------------------------------------------------------- # Create lines showing progression between points hungerford_lines <- hungerford_shootings |> rename(x_end = easting, y_end = northing) |> mutate(x_start = lag(x_end), y_start = lag(y_end)) |> # Remove the first line, which contains missing values and which we don't need remove_missing(vars = c("x_start", "y_start"), na.rm = TRUE) # Convert the `hungerford_shootings` object to SF hungerford_shootings_sf <- st_as_sf( hungerford_shootings, coords = c("easting", "northing"), crs = "EPSG:27700" ) # Make component maps ---------------------------------------------------------- hungerford_map_overall <- ggplot() + # Plot base map annotation_map_tile(zoomin = 0, progress = "none") + # Plot lines between points geom_curve( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = hungerford_lines, arrow = arrow(length = unit(3, "mm"), type = "closed"), curvature = -0.2, colour = "orange3" ) + # Plot points geom_sf( data = hungerford_shootings_sf, shape = 21, colour = "white", fill = "orange3", size = 3 ) + geom_label_repel( aes(x = easting, y = northing, label = order), data = filter(hungerford_shootings, order %in% 1:2), colour = "white", fill = "orange3", fontface = "bold", label.size = NA ) + annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") + scale_y_continuous(expand = expansion(2)) + labs(title = " Shootings in Wiltshire") + coord_sf(crs = "EPSG:27700") + theme_void() + theme( panel.border = element_rect(colour = "grey20", fill = NA), plot.title = element_text(margin = margin(b = -18)) ) hungerford_map_town <- ggplot() + annotation_map_tile(zoomin = 0, progress = "none") + # Plot lines between points geom_curve( aes(x = x_start, y = y_start, xend = x_end, yend = y_end), data = slice(hungerford_lines, 3:n()), arrow = arrow(length = unit(3, "mm"), type = "closed"), curvature = -0.2, colour = "orange3" ) + # Plot points geom_sf( data = slice(hungerford_shootings_sf, 3:n()), shape = 21, colour = "white", fill = "orange3", size = 3 ) + geom_label_repel( aes(x = easting, y = northing, label = order), data = slice(hungerford_shootings, 3:n()), colour = "white", fill = "orange3", fontface = "bold", label.size = NA ) + annotation_scale(style = "ticks", line_col = "grey40", text_col = "grey40") + scale_x_continuous(expand = expansion(0.3)) + scale_y_continuous(expand = expansion(0.3)) + coord_sf(crs = "EPSG:27700") + labs( title = "Shootings in Hungerford town" ) + theme_void() + theme( panel.border = element_rect(colour = "grey20", fill = NA), plot.margin = margin(t = 12) ) # Combine maps and add titles -------------------------------------------------- (hungerford_map_overall / hungerford_map_town) + plot_annotation( title = "Shootings during the Hungerford massacre", caption = "data from the official report into the shootings", theme = theme( plot.caption = element_text(colour = "grey40", hjust = 0), plot.title = element_text(colour = "grey50", face = "bold", size = 14) ) )
Photograph of PC Roger Brereton's funeral from Berkshire Live
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