pacman::p_load(sf, tmap, tidyverse)Hands-on Exercise 8 Part I - Choropleth Mapping with R
Learning Objectives:
- plot functional and truthful choropleth maps
Getting Started
Installing and loading the required libraries
The following R packages will be used:
Tidyverse:
sf for handling geospatial data
tmap for plotting choropleth maps
Code chunk below will be used to check if these packages have been installed and also will load them into the working R environment.
Importing Data into R
The Data
Two 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 downloaded from data.gov.sg This geospatial data 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 file downloaded from 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.
Importing Geospatial Data into R
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:\lnealicia\ISSS608\Hands-on_Ex\Hands-on_Ex08\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: SVY21Examining the data content
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
Use read_csv() function of readr package to import respopagsex2011to2020.csv file into RStudio and save the file into an R dataframe called popagsex.
popdata <- read_csv("data/aspatial/respopagesextod2011to2020.csv")Data Preparation
Before a thematic map can be prepared, a data table with year 2020 values needs to be prepped. 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
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 the attribute data and geospatial data
The values of PA and SZ fields are a mix of upper and lower case. However, the values of SUBZONE_N and PLN_AREA_N are in upper case. Thus, we convert those of PA and SZ to uppercase, before we proceed with the join.
popdata2020 <- popdata2020 %>%
mutate_at(.vars = vars(PA, SZ),
.funs = funs(toupper)) %>%
filter(`ECONOMY ACTIVE` > 0)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"))
Note
left_join() of dplyr package is used with mpsz simple feature data frame as the left data table is to ensure that the output will be a simple features data frame.
Save output into rds file
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:
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.
tmap_mode("plot")
qtm(mpsz_pop2020,
fill = "DEPENDENCY")
Note
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
To draw a high quality and highly customisable cartographic choropleth map, 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"))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 choropleth map using tm_polygons()
To draw a choropleth map showing the geographical distribution of a selected variable by planning subzone, the target variable such as Dependency needs to be assigned to tm_polygons().
tm_shape(mpsz_pop2020)+
tm_polygons("DEPENDENCY")
Note
The default interval binning used to draw the choropleth map is called “pretty”.
The default colour scheme used is
YlOrRdof ColorBrewer.By default, Missing value will be shaded in grey.
Drawing a choropleth map using tm_fill() and tm_border()
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. Note that the planning subzones are shared according to the respective dependecy values.
tm_borders will be used to add the boundary of the planning subzones.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY") +
tm_borders(lwd = 0.1, alpha = 1)
Note
Note that light-gray border lines have been added to 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 is 1.
Besides alpha argument, there are three other arguments for tm_borders():
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 to take a large number of observations and group them into data ranges or classes.
tmap provides a 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() is used.
Plotting choropleth maps with built-in classification methods
The code chunk below shows a quantile data classification with 5 classes.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 5,
style = "jenks") +
tm_borders(alpha = 0.5)In the code chunk below, the equal data classification method is used.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
n = 5,
style = "equal") +
tm_borders(alpha = 0.5)
Note
Tthe distribution of quantile data classification method are more evenly distributed then equal data classification method
Plotting choropleth map with custome break
For all built-in styles, category breaks are computed internally. To override these defaults, the breakpoints can be set explicitly using the breaks argument to the tm_fill().
Note: tmap breaks include a minimum and maximum. Thus, to end up with n categories, n+1 elements must be specified in the breaks option (the values must be in increasing order).
Code chunk below will be used to compute and display the descriptive statistics of DEPENDENCY field in order to get descriptive statistics on the variable to aid setting 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 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)
The choropleth map is plotted with the above values by 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 Scheme
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, assign the 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",
n = 6,
style = "quantile",
palette = "-Blues") +
tm_borders(alpha = 0.5)Map Layouts
Map layout refers to the combination of all map elements into a cohensive map. Map elements includes: objects to be mapped, title, scale bar, 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.
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 using tmap_style().
The code chunk below shows the classic style.
tm_shape(mpsz_pop2020)+
tm_fill("DEPENDENCY",
style = "quantile",
palette = "-Greens") +
tm_borders(alpha = 0.5) +
tmap_style("classic")Cartographic Furniture
Beside map style, tmap also also draws other map furniture e.g., 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")Drawing Small Multiple Choropleth Maps
Small multiple maps, aka facet maps, are composed of many maps arranged side-by-side, and/or stacked vertically. Small multiple maps enable the visualisation of how spatial relationships change with respect to another variable, e.g. time.
In tmap, small multiple maps can be plotted in three ways:
by assigning multiple values to at least one of the asthetic arguments
by defining a group-by variable in tm_facets()
by creating multiple stand-alone maps with tmap_arrange().
Assigning multiple values to at least one of the aesthetic arguments
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")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"))Defining a group-by variable in tm_facets()
multiple small choropleth maps are created by using 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=FALSE) +
tm_layout(legend.show = FALSE,
title.position = c("center", "center"),
title.size = 20) +
tm_borders(alpha = 0.5)Creating multiple stand-alone maps with tmap_arrange()
multiple small choropleth maps are created 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)Mappping Spatial Object Meeting a Selection Criterion
Use selection funtion 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)