pacman::p_load(sf, tmap, tidyverse)Hands-on Exercise 1B: Chloropeth Mapping with R
What is Choropleth Mapping?
Choropleth mapping is used to symbolise countries/provinces/states/countries using area patterns/graduated colours. E.g. portray the spatial distribution of aged population of Singapore by Master Plan 2014 Subzone Boundary.
In this chapter, we will learn to plot choropleth maps using tmap package.
Getting Started
Beside tmap package, four other R packages will be used. They are:
readr for importing delimited text file,
tidyr for tidying data,
dplyr for wrangling data and
sf for handling geospatial data.
Since readr, tidyr, and dplyr are part of tidyverse package, we only need to install tidyverse instead of installing them individually.
Importing Data into R
The Data
These data sets will be used to create the choropleth map:
Master Plan 2014 Subzone Boundary (Web) (i.e.
MP14_SUBZONE_WEB_PL) in ESRI shapefile format. This is geospatial data, consisting of the geographical boundary of Singapore at the planning subzone level.Singapore Residents by Planning Area / Subzone, Age Group, Sex and Type of Dwelling, June 2011-2020 in csv format (i.e.
respopagesextod2011to2020.csv). This is an aspatial data file, it doesn’t contain any coordinate values but it’s PA and SZ values can be used as unique identifiers to geocode toMP14_SUBZONE_WEB_PLshapefile.
Importing Geospatial Data into R
We use the st_read() function of sf package to import MP14_SUBZONE_WEB_PL shapefile into R as a simple feature data frame called mpsz.
mpsz <- st_read(dsn = "data/geospatial",
layer = "MP14_SUBZONE_WEB_PL")Reading layer `MP14_SUBZONE_WEB_PL' from data source
`C:\PeiShan0502\ISSS624\Hands-on_Ex1\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21We examine the content of mpsz:
mpszSimple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21
First 10 features:
OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
1 1 1 MARINA SOUTH MSSZ01 Y MARINA SOUTH
2 2 1 PEARL'S HILL OTSZ01 Y OUTRAM
3 3 3 BOAT QUAY SRSZ03 Y SINGAPORE RIVER
4 4 8 HENDERSON HILL BMSZ08 N BUKIT MERAH
5 5 3 REDHILL BMSZ03 N BUKIT MERAH
6 6 7 ALEXANDRA HILL BMSZ07 N BUKIT MERAH
7 7 9 BUKIT HO SWEE BMSZ09 N BUKIT MERAH
8 8 2 CLARKE QUAY SRSZ02 Y SINGAPORE RIVER
9 9 13 PASIR PANJANG 1 QTSZ13 N QUEENSTOWN
10 10 7 QUEENSWAY QTSZ07 N QUEENSTOWN
PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
1 MS CENTRAL REGION CR 5ED7EB253F99252E 2014-12-05 31595.84
2 OT CENTRAL REGION CR 8C7149B9EB32EEFC 2014-12-05 28679.06
3 SR CENTRAL REGION CR C35FEFF02B13E0E5 2014-12-05 29654.96
4 BM CENTRAL REGION CR 3775D82C5DDBEFBD 2014-12-05 26782.83
5 BM CENTRAL REGION CR 85D9ABEF0A40678F 2014-12-05 26201.96
6 BM CENTRAL REGION CR 9D286521EF5E3B59 2014-12-05 25358.82
7 BM CENTRAL REGION CR 7839A8577144EFE2 2014-12-05 27680.06
8 SR CENTRAL REGION CR 48661DC0FBA09F7A 2014-12-05 29253.21
9 QT CENTRAL REGION CR 1F721290C421BFAB 2014-12-05 22077.34
10 QT CENTRAL REGION CR 3580D2AFFBEE914C 2014-12-05 24168.31
Y_ADDR SHAPE_Leng SHAPE_Area geometry
1 29220.19 5267.381 1630379.3 MULTIPOLYGON (((31495.56 30...
2 29782.05 3506.107 559816.2 MULTIPOLYGON (((29092.28 30...
3 29974.66 1740.926 160807.5 MULTIPOLYGON (((29932.33 29...
4 29933.77 3313.625 595428.9 MULTIPOLYGON (((27131.28 30...
5 30005.70 2825.594 387429.4 MULTIPOLYGON (((26451.03 30...
6 29991.38 4428.913 1030378.8 MULTIPOLYGON (((25899.7 297...
7 30230.86 3275.312 551732.0 MULTIPOLYGON (((27746.95 30...
8 30222.86 2208.619 290184.7 MULTIPOLYGON (((29351.26 29...
9 29893.78 6571.323 1084792.3 MULTIPOLYGON (((20996.49 30...
10 30104.18 3454.239 631644.3 MULTIPOLYGON (((24472.11 29...Importing Attribute Data into R
Next, we will import respopagsex2011to2020.csv file into RStudio and save the file into an R dataframe called popdata.
popdata <- read_csv("data/aspatial/respopagesextod2011to2020.csv")Rows: 984656 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): PA, SZ, AG, Sex, TOD
dbl (2): Pop, Time
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Data Preparation
We need to first prepare a data table with year 2020 values. The data table should include the following variables:
YOUNG: age group 0 to 4 until age group 20 to 24,
ECONOMY ACTIVE: age group 25-29 until age group 60-64,
AGED: age group 65 and above,
TOTAL: all age group, and
DEPENDENCY: the ratio between young and aged against economy active group
PA
SZ
Data wrangling
The following data wrangling and transformation functions will be used:
pivot_wider() of tidyr package, and
mutate(), filter(), group_by() and select() of dplyr package
popdata2020 <- popdata %>%
filter(Time == 2020) %>%
group_by(PA, SZ, AG) %>%
summarise(`POP` = sum(`Pop`)) %>%
ungroup()%>%
pivot_wider(names_from=AG,
values_from=POP) %>%
mutate(YOUNG = rowSums(.[3:6])
+rowSums(.[12])) %>%
mutate(`ECONOMY ACTIVE` = rowSums(.[7:11])+
rowSums(.[13:15]))%>%
mutate(`AGED`=rowSums(.[16:21])) %>%
mutate(`TOTAL`=rowSums(.[3:21])) %>%
mutate(`DEPENDENCY` = (`YOUNG` + `AGED`)
/`ECONOMY ACTIVE`) %>%
select(`PA`, `SZ`, `YOUNG`,
`ECONOMY ACTIVE`, `AGED`,
`TOTAL`, `DEPENDENCY`)`summarise()` has grouped output by 'PA', 'SZ'. You can override using the
`.groups` argument.Joining the attribute data and geospatial data
We need to convert the values in PA and SZ fields to uppercase, because the SUBZONE_N and PLN_AREA_N fields are in uppercase:
popdata2020 <- popdata2020 %>%
mutate_at(.vars = vars(PA, SZ),
.funs = list(toupper)) %>%
filter(`ECONOMY ACTIVE` > 0)Then, left_join() of dplyr is used to join the geographical data and attribute table using planning subzone name e.g. SUBZONE_N and SZ as the common identifier.
mpsz_pop2020 <- left_join(mpsz, popdata2020,
by = c("SUBZONE_N" = "SZ"))In the above code chunk, left_join() of dplyr package is used with mpsz simple feature data frame as the left data table to ensure that the output will be a simple features data frame.
write_rds(mpsz_pop2020, "data/rds/mpszpop2020.rds")Choropleth Mapping Geospatial Data using tmap
Plotting a choropleth map quickly by using qtm()
The code chunk below will draw a standard choropleth map:
tmap_mode("plot")tmap mode set to plottingqtm(mpsz_pop2020,
fill = "DEPENDENCY")
Creating a choropleth map by using tmap’s elements
Although qtm() is very useful for drawing a choropleth map quickly and easily, the disadvantage is that it is harder to customise the aesthetics of individual layers. To do so, we use tmap’s drawing elements:
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "Blues",
title = "Dependency ratio") +
tm_layout(main.title = "Distribution of Dependency Ratio by planning subzone",
main.title.position = "center",
main.title.size = 1.2,
legend.height = 0.45,
legend.width = 0.35,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_compass(type="8star", size = 2) +
tm_scale_bar() +
tm_grid(alpha =0.2) +
tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and Population data from Department of Statistics DOS",
position = c("left", "bottom"))
Next, we will learn about tmap functions used to plot these elements.
Drawing a base map
The basic building block of tmap is tm_shape() followed by one or more layer elements such as tm_fill() and tm_polygons().
In the code chunk below, tm_shape() is used to define the input data (i.e., mpsz_pop2020) and tm_polygons() is used to draw the planning subzone polygons:
tm_shape(mpsz_pop2020) +
tm_polygons()
Drawing a choropleth map using tm_polygons()
tm_shape(mpsz_pop2020)+
tm_polygons("DEPENDENCY")
Note: by default, missing value will be shaded in grey.
Drawing a choropleth map using tm_fill() and tm_border()
The code chunk below draws a choropleth map by using tm_fill() alone.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY")
Notice that the planning subzones are shared according to the respective dependency values.
To add the boundary of the planning subzones, tm_borders will be used as shown in the code chunk below.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY") +
tm_borders(lwd = 0.1, alpha = 1)
Notice that light-gray border lines have been added on the choropleth map.
The alpha argument is used to define transparency number between 0 (totally transparent) and 1 (not transparent). By default, the alpha value of the col is used (normally 1).
Beside alpha argument, there are three other arguments for tm_borders(), they are:
col = border colour,
lwd = border line width. The default is 1, and
lty = border line type. The default is “solid”.
Data classification methods of tmap
Most choropleth maps employ some methods of data classification. The point of classification is to take a large number of observations and group them into data ranges or classes.
tmap provides a total ten data classification methods, namely: fixed, sd, equal, pretty (default), quantile, kmeans, hclust, bclust, fisher, and jenks.
To define a data classification method, the style argument of tm_fill() or tm_polygons() will be used.
Plotting choropleth maps with built-in classification methods
This is quantile data classification that used 5 classes:
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 5,
style = "jenks") +
tm_borders(alpha = 0.5)
In the code chunk below, equal data classification is used:
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 5,
style = "equal") +
tm_borders(alpha = 0.5)
You may notice that the distribution of quantile data classification method is more evenly distributed than equal data classification method.
Plotting choropleth map with custom break
To override default category breaks. The breakpoints can be set explicitly using the breaks argument in tm_fill(). The breaks include a minimum and maximum, so to end up with n categories, n+1 elements must be specified in the breaks option (values must be in ascending order).
let’s get some descriptive statistics of the DEPENDENCY field first before setting the break points:
summary(mpsz_pop2020$DEPENDENCY) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.1111 0.7147 0.7866 0.8585 0.8763 19.0000 92 Based on the descriptive stats, let’s set break point at 0.60, 0.70, 0.80, and 0.90. also let’s set minimum and maximum to be 0 and 1.00 respectively. Our breaks vector is thus c(0, 0.60, 0.70, 0.80, 0.90, 1.00)
Now we plot the choropleth map accordingly:
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
breaks = c(0, 0.60, 0.70, 0.80, 0.90, 1.00)) +
tm_borders(alpha = 0.5)Warning: Values have found that are higher than the highest break
Colour Scheme
Using ColourBrewer palette
Assign preferred colour to palette argument of tm_fill():
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 6,
style = "quantile",
palette = "Blues") +
tm_borders(alpha = 0.5)
To reverse the colour shading, add a “-” prefix:
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "-Greens") +
tm_borders(alpha = 0.5)
Map Layouts
Refers to combination of all map elements into a cohesive map
Map elements: e.g. objects to be mapped, title, scale bar, compass, margins, aspects ratios
Colour settings and data classification methods covered in previous section relate to the palette and break-points are used to affect how the map used.
Map Legend
In tmap, several legend options are provided to change the placement, format and appearance of the legend.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "jenks",
palette = "Blues",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_layout(main.title = "Distribution of Dependency Ratio by planning subzone \n(Jenks classification)",
main.title.position = "center",
main.title.size = 1,
legend.height = 0.45,
legend.width = 0.35,
legend.outside = FALSE,
legend.position = c("right", "bottom"),
frame = FALSE) +
tm_borders(alpha = 0.5)
Map Style
tmap allows a wide variety of layout settings to be changed. They can be called by using tmap_style().
The code chunk below shows the classic style is used.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "-Greens") +
tm_borders(alpha = 0.5) +
tmap_style("classic")tmap style set to "classic"other available styles are: "white", "gray", "natural", "cobalt", "col_blind", "albatross", "beaver", "bw", "watercolor" 
Cartographic Furniture
tmap also also provides arguments to draw other map furniture such as compass, scale bar and grid lines.
In the code chunk below, tm_compass(), tm_scale_bar() and tm_grid() are used to add compass, scale bar and grid lines onto the choropleth map.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "Blues",
title = "No. of persons") +
tm_layout(main.title = "Distribution of Dependency Ratio \nby planning subzone",
main.title.position = "center",
main.title.size = 1.2,
legend.height = 0.45,
legend.width = 0.35,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_compass(type="8star", size = 2) +
tm_scale_bar(width = 0.15) +
tm_grid(lwd = 0.1, alpha = 0.2) +
tm_credits("Source: Planning Sub-zone boundary from Urban Redevelopment Authorithy (URA)\n and Population data from Department of Statistics DOS",
position = c("left", "bottom"))
To reset the default style:
tmap_style("white")tmap style set to "white"other available styles are: "gray", "natural", "cobalt", "col_blind", "albatross", "beaver", "bw", "classic", "watercolor" Drawing Small Multiple Choropleth Maps
Many maps arranged side-by-side, sometimes stacked vertically.
Enable visualisation of how spatial relationships change with respect to another variable, such as time.
In tmap, small multiple maps can be plotted in three ways:
by assigning multiple values to at least one of the aesthetic arguments,
by defining a group-by variable in tm_facets(), and
by creating multiple stand-alone maps with tmap_arrange().
By assigning multiple values to at least one of the aesthetic arguments
In this example, small multiple choropleth maps are created by defining ncols in tm_fill()
tm_shape(mpsz_pop2020)+
tm_fill(c("YOUNG", "AGED"),
style = "equal",
palette = "Blues") +
tm_layout(legend.position = c("right", "bottom")) +
tm_borders(alpha = 0.5) +
tmap_style("white")tmap style set to "white"other available styles are: "gray", "natural", "cobalt", "col_blind", "albatross", "beaver", "bw", "classic", "watercolor" 
In this example, small multiple choropleth maps are created by assigning multiple values to at least one of the aesthetic arguments
tm_shape(mpsz_pop2020)+
tm_polygons(c("DEPENDENCY","AGED"),
style = c("equal", "quantile"),
palette = list("Blues","Greens")) +
tm_layout(legend.position = c("right", "bottom"))
By defining a group-by variable in tm_facets()
tm_shape(mpsz_pop2020) +
tm_fill("DEPENDENCY",
style = "quantile",
palette = "Blues",
thres.poly = 0) +
tm_facets(by="REGION_N",
free.coords=TRUE,
drop.shapes=TRUE) +
tm_layout(legend.show = FALSE,
title.position = c("center", "center"),
title.size = 20) +
tm_borders(alpha = 0.5)Warning: The argument drop.shapes has been renamed to drop.units, and is
therefore deprecated
By creating multiple stand-alone maps with tmap_arrange()
youngmap <- tm_shape(mpsz_pop2020)+
tm_polygons("YOUNG",
style = "quantile",
palette = "Blues")
agedmap <- tm_shape(mpsz_pop2020)+
tm_polygons("AGED",
style = "quantile",
palette = "Blues")
tmap_arrange(youngmap, agedmap, asp=1, ncol=2)
Mapping Spatial Object Meeting a Selection Criterion
can also use selection function to map spatial objects meeting the selection criterion:
tm_shape(mpsz_pop2020[mpsz_pop2020$REGION_N=="CENTRAL REGION", ])+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "Blues",
legend.hist = TRUE,
legend.is.portrait = TRUE,
legend.hist.z = 0.1) +
tm_layout(legend.outside = TRUE,
legend.height = 0.45,
legend.width = 5.0,
legend.position = c("right", "bottom"),
frame = FALSE) +
tm_borders(alpha = 0.5)Warning in pre_process_gt(x, interactive = interactive, orig_crs =
gm$shape.orig_crs): legend.width controls the width of the legend within a map.
Please use legend.outside.size to control the width of the outside legend
Thank you for reading! :)