pacman::p_load(sf, tmap, tidyverse)Hands on Exercise 7.1
Choropleth Mapping with R
Overview
Choropleth mapping involves the symbolisation of enumeration units, such as countries, provinces, states, counties or census units, using area patterns or graduated colors. For example, a social scientist may need to use a choropleth map to portray the spatial distribution of aged population of Singapore by Master Plan 2014 Subzone Boundary.
In this chapter, you will learn how to plot functional and truthful choropleth maps by using an R package called tmap package.
Tip
It is advisable for you to read the functional description of each function before using them.
Getting Started
In this hands-on exercise, the key R package use is tmap package in R. 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.
Among the four packages, readr, tidyr and dplyr are part of tidyverse package.
The code chunk below will be used to install and load these packages in RStudio.
Importing the Data
Two data set will be used to create the choropleth map. They are:
Master Plan 2014 Subzone Boundary (Web) (i.e.
MP14_SUBZONE_WEB_PL) in ESRI shapefile format. It can be downloaded at data.gov.sg This is a geospatial data. It consists of the geographical boundary of Singapore at the planning subzone level. The data is based on URA Master Plan 2014.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 fie. It can be downloaded at Department of Statistics, Singapore Although it does not contain any coordinates values, but it’s PA and SZ fields can be used as unique identifiers to geocode toMP14_SUBZONE_WEB_PLshapefile.
The code chunk below uses 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:\ZacharymwMITB\ISSS608-VAA\Hands-on_Ex\Hands-on_Ex07\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: SVY21You can examine the content of mpsz by using the code chunk below.
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
Next, we will import respopagsex2011to2020.csv file into RStudio and save the file into an R dataframe called popagsex.
The task will be performed by using read_csv() function of readr package as shown in the code chunk below.
popdata <- read_csv("data/aspatial/respopagesextod2011to2020.csv")Data Prep
Before a thematic map can be prepared, you are required to prepare a data table with year 2020 values. The data table should include the variables PA, SZ, YOUNG, ECONOMY ACTIVE, AGED, TOTAL, DEPENDENCY.
YOUNG: age group 0 to 4 until age groyup 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
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`)
Joining Attribute and Geospatial Data
Before we can perform the georelational join, one extra step is required to convert the values in PA and SZ fields to uppercase. This is because the values of PA and SZ fields are made up of upper- and lowercase. On the other, hand the SUBZONE_N and PLN_AREA_N are in uppercase.
popdata2020 <- popdata2020 %>%
mutate_at(.vars = vars(PA, SZ),
.funs = funs(toupper)) %>%
filter(`ECONOMY ACTIVE` > 0)Next, 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"))
Things to Learn from Code Chunk
- left_join() of dplyr package is used with
mpszsimple feature data frame as the left data table is 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
Two approaches can be used to prepare thematic map using tmap, they are:
Plotting a thematic map quickly by using qtm().
Plotting highly customisable thematic map by using tmap elements.
Plotting a Choropleth Map Quickly by Using qtm()
The easiest and quickest to draw a choropleth map using tmap is using qtm(). It is concise and provides a good default visualisation in many cases.
The code chunk below will draw a cartographic standard choropleth map as shown below.
tmap_mode("plot")
qtm(mpsz_pop2020,
fill = "DEPENDENCY")
Things to Learn from Code Chunk above
tmap_mode() with “plot” option is used to produce a static map. For interactive mode, “view” option should be used.
fill argument is used to map the attribute (i.e. DEPENDENCY)
Creating a Choropleth Map by using tmap’s elements
Despite its usefulness of drawing a choropleth map quickly and easily, the disadvantge of qtm() is that it makes aesthetics of individual layers harder to control. To draw a high quality cartographic choropleth map as shown in the figure below, tmap’s drawing elements should be used.
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"))
In the following sub-section, we will share with you tmap functions that used to plot these elements.
Drawing a Base Map
The basic building block of tmap is tm_shape() followed by one or more layer elemments 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 Chloropleth Map using tm_polygons()
To draw a choropleth map showing the geographical distribution of a selected variable by planning subzone, we just need to assign the target variable such as Dependency to tm_polygons().
tm_shape(mpsz_pop2020)+
tm_polygons("DEPENDENCY")
Things to learn from tm_polygons()
The default interval binning used to draw the choropleth map is called “pretty”. A detailed discussion of the data classification methods supported by tmap will be provided in sub-section 4.3.
The default colour scheme used is
YlOrRdof ColorBrewer. You will learn more about the color scheme in sub-section 4.4.By default, Missing value will be shaded in grey.
Drawing a Choropleth Map using tm_fill() and tm_border()
Actually, tm_polygons() is a wraper of tm_fill() and tm_border(). tm_fill() shades the polygons by using the default colour scheme and tm_borders() adds the borders of the shapefile onto the choropleth map.
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 dependecy 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 Eethods 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
The code chunk below shows a 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 method is used.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 5,
style = "equal") +
tm_borders(alpha = 0.5)
Notice that the distribution of quantile data classification method are more evenly distributed then equal data classification method.
Maps Lie!
DIY: Using what you had learned, prepare choropleth maps by using different classification methods supported by tmap and compare their differences.
DIY: Preparing choropleth maps by using similar classification method but with different numbers of classes (i.e. 2, 6, 10, 20). Compare the output maps, what observation can you draw?
DIY
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 10,
style = "equal") +
tm_borders(alpha = 0.5)
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 20,
style = "equal") +
tm_borders(alpha = 0.5)
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 40,
style = "equal") +
tm_borders(alpha = 0.5)
Plotting Choropleth Map with Custom Break
For all the built-in styles, the category breaks are computed internally. In order to override these defaults, the breakpoints can be set explicitly by means of the breaks argument to the tm_fill(). It is important to note that, in tmap the breaks include a minimum and maximum. As a result, in order to end up with n categories, n+1 elements must be specified in the breaks option (the values must be in increasing order).
Before we get started, it is always a good practice to get some descriptive statistics on the variable before setting the break points. Code chunk below will be used to compute and display the descriptive statistics of DEPENDENCY field.
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 With reference to the results above, we set break point at 0.60, 0.70, 0.80, and 0.90. In addition, we also need to include a minimum and maximum, which we set at 0 and 100. Our breaks vector is thus c(0, 0.60, 0.70, 0.80, 0.90, 1.00)
Now, we will plot the choropleth map by using the code chunk below.
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)
Colour Schemes
tmap supports colour ramps either defined by the user or a set of predefined colour ramps from the RColorBrewer package.
Using ColourBrewer Palette
To change the colour, we assign the preferred colour to palette argument of tm_fill() as shown in the code chunk below.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 6,
style = "quantile",
palette = "Blues") +
tm_borders(alpha = 0.5)
Notice that the choropleth map is shaded in green.
To reverse the colour shading, add a “-” prefix.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "-Greens") +
tm_borders(alpha = 0.5)
Notice that the colour scheme has been reversed.
Map Layouts
Map layout refers to the combination of all map elements into a cohensive map. Map elements include among others the objects to be mapped, the title, the scale bar, the compass, margins and aspects ratios. Colour settings and data classification methods covered in the previous section relate to the palette and break-points are used to affect how the map looks.