Note: Please toggle between ‘Show’ and ‘Hide’ above based on your preference for viewing the R codes.

8.1 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.

8.2 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.

Code

pacman::p_load(sf, tmap, tidyverse)

8.3 Importing Data into R

8.3.1 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 to MP14_SUBZONE_WEB_PL shapefile.

8.3.2 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.

Code

mpsz <- st_read(dsn = "data/geospatial", 
                layer = "MP14_SUBZONE_WEB_PL")

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\Users\sandr\Documents\sndrjcb\ISSS608-VAA\HandsOn_Exercises\HandsOn_Ex08A\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: SVY21

8.3.3 Importing Attribute Data into R

Next, we will import the 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 as shown below.

Code

popdata <- read_csv("data/aspatial/respopagesextod2011to2020.csv")

8.3.4 Data Preparation

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

8.3.4.1 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

Code

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`)

8.3.4.2 Joining the attribute data 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.

Code

popdata2020 <- popdata2020 %>%
  mutate(across(c(PA, SZ), 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.

Code

mpsz_pop2020 <- left_join(mpsz, popdata2020,
                          by = c("SUBZONE_N" = "SZ"))

Thing to learn from the code chunk above:

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.

Code

write_rds(mpsz_pop2020, "data/rds/mpszpop2020.rds")

8.4 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.

8.4.1 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.

Code

tmap_mode("plot")
qtm(mpsz_pop2020, 
    fill = "DEPENDENCY")

Things to learn from the code 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)

8.4.2 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.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons(fill = "DEPENDENCY", 
              fill.scale = tm_scale_intervals(
                style = "quantile",
                n = 5,
                values = "brewer.blues"),
              fill.legend = tm_legend(
                title = "Dependency ratio")) +
  tm_title("Distribution of Dependency Ratio by planning subzone") +
  tm_layout(frame = TRUE) +
  tm_borders(fill_alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  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.

8.4.2.1 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

Code

tm_shape(mpsz_pop2020) +
  tm_polygons()

8.4.2.2 Drawing a choropleth 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().

Code

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 YlOrRd of ColorBrewer. You will learn more about the color scheme in sub-section 4.4.
By default, Missing value will be shaded in grey.

8.4.2.3 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.

Code

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.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons(fill = "DEPENDENCY") +
  tm_borders(lwd = 0.01,  
             fill_alpha = 0.1)

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”.

8.4.3 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.

8.4.3.1 Plotting choropleth maps with built-in classification methods

The code chunk below shows a quantile data classification that used 5 classes.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons("DEPENDENCY",
      fill.scale = tm_scale_intervals(
        style = "jenks",
        n = 5)) +
  tm_borders(fill_alpha = 0.5)

In the code chunk below, equal data classification method is used.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons("DEPENDENCY",
      fill.scale = tm_scale_intervals(
        style = "equal",
        n = 5)) +
  tm_borders(fill_alpha = 0.5)

Notice that the distribution of quantile data classification method are more evenly distributed then equal data classification method.

Warning: 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?

8.4.3.2 Plotting choropleth map with custome 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.

Code

summary(mpsz_pop2020$DEPENDENCY)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.1111  0.7147  0.7867  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.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons("DEPENDENCY",
          breaks = c(0, 0.60, 0.70, 0.80, 0.90, 1.00)) +
  tm_borders(fill_alpha = 0.5)

8.4.4 Colour Scheme

tmap supports colour ramps either defined by the user or a set of predefined colour ramps from the RColorBrewer package.

8.4.4.1 Using ColourBrewer palette

To change the colour, we assign the preferred colour to values argument of tm_scale_intervals() as shown in the code chunk below.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons("DEPENDENCY",
      fill.scale = tm_scale_intervals(
        style = "quantile",
        n = 5,
        values = "brewer.greens")) +
  tm_borders(fill_alpha = 0.5)

Notice that the choropleth map is shaded in green.

To reverse the colour shading, add a “-” prefix.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons("DEPENDENCY",
      fill.scale = tm_scale_intervals(
        style = "quantile",
        n = 5,
        values = "-brewer.greens")) +
  tm_borders(fill_alpha = 0.5)

8.4.5 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.

8.4.5.1 Map Legend

In tmap, several tm_legend() options are provided to change the placement, format and appearance of the legend.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons("DEPENDENCY",
      fill.scale = tm_scale_intervals(
        style = "jenks",
        n = 5,
        values = "brewer.greens"),
      fill.legend = tm_legend(
        title = "Dependency ratio")) +
  tm_borders(fill_alpha = 0.5) +
  tm_title("Distribution of Dependency Ratio by planning subzone \n(Jenks classification)")

8.4.5.2 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.

Code

tm_shape(mpsz_pop2020)+
  tm_fill("DEPENDENCY", 
          style = "quantile", 
          palette = "-Greens") +
  tm_borders(alpha = 0.5) +
  tmap_style("classic")

8.4.5.3 Cartographic Furniture

Beside map style, 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.

Code

tm_shape(mpsz_pop2020)+
  tm_polygons(fill = "DEPENDENCY", 
              fill.scale = tm_scale_intervals(
                style = "quantile",
                n = 5,
                values = "brewer.blues"),
              fill.legend = tm_legend(
                title = "Dependency ratio")) +
  tm_title("Distribution of Dependency Ratio by planning subzone") +
  tm_layout(frame = TRUE) +
  tm_borders(fill_alpha = 0.5) +
  tm_compass(type="8star", size = 2) +
  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"))

8.4.6 Drawing Small Multiple Choropleth Maps

Small multiple maps, also referred to as facet maps, are composed of many maps arrange side-by-side, and sometimes stacked vertically. Small multiple maps enable the 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 asthetic arguments,
by defining a group-by variable in tm_facets(), and
by creating multiple stand-alone maps with tmap_arrange().

8.4.6.1 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()

Code

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")

8.4.6.2 By defining a group-by variable in tm_facets()

In this example, multiple small choropleth maps are created by using tm_facets().

Code

tm_shape(mpsz_pop2020) +
  tm_fill("DEPENDENCY",
          style = "quantile",
          palette = "Blues",
          thres.poly = 0) + 
  tm_facets(by="REGION_N", 
            free.coords=TRUE) +
  tm_layout(legend.show = FALSE,
            title.position = c("center", "center"), 
            title.size = 20) +
  tm_borders(alpha = 0.5)

8.4.6.3 By creating multiple stand-alone maps with `tmap_arrange()`

In this example, multiple small choropleth maps are created by creating multiple stand-alone maps with tmap_arrange().

Code

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)

8.4.7 Mappping Spatial Object Meeting a Selection Criterion

Instead of creating small multiple choropleth map, you can also use selection funtion to map spatial objects meeting the selection criterion.

Code

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)

--- title: "Hands On Exercise 8A" subtitle: "Choropleth Mapping in R" format: html date: 06/09/2025 date-format: long date-modified: last-modified editor: visual execute: eval: true echo: true warning: false freeze: true --- [*Note: Please toggle between 'Show' and 'Hide' above based on your preference for viewing the R codes.*]{.underline} # 8.1 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**](https://r-tmap.github.io/tmap/) package. # 8.2 Getting Started In this hands-on exercise, the key R package use is [**tmap**](https://cran.r-project.org/web/packages/tmap/) package in R. Beside **tmap** package, four other R packages will be used. They are: - [**readr**](https://readr.tidyverse.org/) for importing delimited text file, - [**tidyr**](https://tidyr.tidyverse.org/) for tidying data, - [**dplyr**](https://dplyr.tidyverse.org/) for wrangling data and - [**sf**](https://cran.r-project.org/web/packages/sf/index.html) for handling geospatial data. Among the four packages, **readr**, **tidyr** and **dplyr** are part of **tidyverse** package. ```{r} pacman::p_load(sf, tmap, tidyverse) ``` # 8.3 Importing Data into R ## 8.3.1 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](https://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](https://www.singstat.gov.sg/) Although it does not contain any coordinates values, but it’s PA and SZ fields can be used as unique identifiers to geocode to `MP14_SUBZONE_WEB_PL` shapefile. ## 8.3.2 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`. ```{r} mpsz <- st_read(dsn = "data/geospatial", layer = "MP14_SUBZONE_WEB_PL") ``` ## 8.3.3 Importing Attribute Data into R Next, we will import the *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 as shown below. ```{r} popdata <- read_csv("data/aspatial/respopagesextod2011to2020.csv") ``` ## 8.3.4 Data Preparation 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 ### 8.3.4.1 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 ```{r} 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`) ``` ### 8.3.4.2 Joining the attribute data 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. ```{r} popdata2020 <- popdata2020 %>% mutate(across(c(PA, SZ), 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. ```{r} mpsz_pop2020 <- left_join(mpsz, popdata2020, by = c("SUBZONE_N" = "SZ")) ``` Thing to learn from the code chunk above: - *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. ```{r} write_rds(mpsz_pop2020, "data/rds/mpszpop2020.rds") ``` # 8.4 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. ## 8.4.1 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. ```{r} tmap_mode("plot") qtm(mpsz_pop2020, fill = "DEPENDENCY") ``` Things to learn from the code 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) ## 8.4.2 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. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons(fill = "DEPENDENCY", fill.scale = tm_scale_intervals( style = "quantile", n = 5, values = "brewer.blues"), fill.legend = tm_legend( title = "Dependency ratio")) + tm_title("Distribution of Dependency Ratio by planning subzone") + tm_layout(frame = TRUE) + tm_borders(fill_alpha = 0.5) + tm_compass(type="8star", size = 2) + 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. ### 8.4.2.1 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 ```{r} tm_shape(mpsz_pop2020) + tm_polygons() ``` ### 8.4.2.2 Drawing a choropleth 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()*. ```{r} 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 `YlOrRd` of ColorBrewer. You will learn more about the color scheme in sub-section 4.4. - By default, Missing value will be shaded in grey. ### 8.4.2.3 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. ```{r} 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. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons(fill = "DEPENDENCY") + tm_borders(lwd = 0.01, fill_alpha = 0.1) ``` 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”. ## 8.4.3 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. ### 8.4.3.1 Plotting choropleth maps with built-in classification methods The code chunk below shows a quantile data classification that used 5 classes. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons("DEPENDENCY", fill.scale = tm_scale_intervals( style = "jenks", n = 5)) + tm_borders(fill_alpha = 0.5) ``` In the code chunk below, *equal* data classification method is used. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons("DEPENDENCY", fill.scale = tm_scale_intervals( style = "equal", n = 5)) + tm_borders(fill_alpha = 0.5) ``` Notice that the distribution of quantile data classification method are more evenly distributed then equal data classification method. > **Warning: 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? ### 8.4.3.2 Plotting choropleth map with custome 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. ```{r} summary(mpsz_pop2020$DEPENDENCY) ``` 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. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons("DEPENDENCY", breaks = c(0, 0.60, 0.70, 0.80, 0.90, 1.00)) + tm_borders(fill_alpha = 0.5) ``` ## 8.4.4 Colour Scheme **tmap** supports colour ramps either defined by the user or a set of predefined colour ramps from the **RColorBrewer** package. #### 8.4.4.1 Using ColourBrewer palette To change the colour, we assign the preferred colour to *values* argument of *tm_scale_intervals()* as shown in the code chunk below. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons("DEPENDENCY", fill.scale = tm_scale_intervals( style = "quantile", n = 5, values = "brewer.greens")) + tm_borders(fill_alpha = 0.5) ``` Notice that the choropleth map is shaded in green. To reverse the colour shading, add a “-” prefix. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons("DEPENDENCY", fill.scale = tm_scale_intervals( style = "quantile", n = 5, values = "-brewer.greens")) + tm_borders(fill_alpha = 0.5) ``` ## 8.4.5 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. ### 8.4.5.1 Map Legend In **tmap**, several `tm_legend()` options are provided to change the placement, format and appearance of the legend. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons("DEPENDENCY", fill.scale = tm_scale_intervals( style = "jenks", n = 5, values = "brewer.greens"), fill.legend = tm_legend( title = "Dependency ratio")) + tm_borders(fill_alpha = 0.5) + tm_title("Distribution of Dependency Ratio by planning subzone \n(Jenks classification)") ``` ### 8.4.5.2 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. ```{r} tm_shape(mpsz_pop2020)+ tm_fill("DEPENDENCY", style = "quantile", palette = "-Greens") + tm_borders(alpha = 0.5) + tmap_style("classic") ``` ### 8.4.5.3 Cartographic Furniture Beside map style, **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. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons(fill = "DEPENDENCY", fill.scale = tm_scale_intervals( style = "quantile", n = 5, values = "brewer.blues"), fill.legend = tm_legend( title = "Dependency ratio")) + tm_title("Distribution of Dependency Ratio by planning subzone") + tm_layout(frame = TRUE) + tm_borders(fill_alpha = 0.5) + tm_compass(type="8star", size = 2) + 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")) ``` ## 8.4.6 Drawing Small Multiple Choropleth Maps **Small multiple maps**, also referred to as **facet maps**, are composed of many maps arrange side-by-side, and sometimes stacked vertically. Small multiple maps enable the 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 asthetic arguments, - by defining a group-by variable in *tm_facets()*, and - by creating multiple stand-alone maps with *tmap_arrange()*. ### 8.4.6.1 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()** ```{r} 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") ``` ### 8.4.6.2 By defining a group-by variable in *tm_facets()* In this example, multiple small choropleth maps are created by using `tm_facets()`. ```{r} tm_shape(mpsz_pop2020) + tm_fill("DEPENDENCY", style = "quantile", palette = "Blues", thres.poly = 0) + tm_facets(by="REGION_N", free.coords=TRUE) + tm_layout(legend.show = FALSE, title.position = c("center", "center"), title.size = 20) + tm_borders(alpha = 0.5) ``` ### 8.4.6.3 By creating multiple stand-alone maps with `tmap_arrange()` In this example, multiple small choropleth maps are created by creating multiple stand-alone maps with `tmap_arrange()`. ```{r} 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) ``` ## 8.4.7 Mappping Spatial Object Meeting a Selection Criterion Instead of creating small multiple choropleth map, you can also use selection funtion to map spatial objects meeting the selection criterion. ```{r} 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) ```

8.1 Overview

8.2 Getting Started

8.3 Importing Data into R

8.3.1 Data

8.3.2 Importing Geospatial Data into R

8.3.3 Importing Attribute Data into R

8.3.4 Data Preparation

8.3.4.1 Data wrangling

8.3.4.2 Joining the attribute data and geospatial data

8.4 Choropleth Mapping Geospatial Data Using tmap

8.4.1 Plotting a choropleth map quickly by using qtm()

8.4.2 Creating a choropleth map by using tmap’s elements

8.4.2.1 Drawing a base map

8.4.2.2 Drawing a choropleth map using tm_polygons()

8.4.2.3 Drawing a choropleth map using tm_fill() and *tm_border()**

8.4.3 Data classification methods of tmap

8.4.3.1 Plotting choropleth maps with built-in classification methods

8.4.3.2 Plotting choropleth map with custome break

8.4.4 Colour Scheme

8.4.4.1 Using ColourBrewer palette

8.4.5 Map Layouts

8.4.5.1 Map Legend

8.4.5.2 Map style

8.4.5.3 Cartographic Furniture

8.4.6 Drawing Small Multiple Choropleth Maps

8.4.6.1 By assigning multiple values to at least one of the aesthetic arguments

8.4.6.2 By defining a group-by variable in tm_facets()

8.4.6.3 By creating multiple stand-alone maps with tmap_arrange()

8.4.7 Mappping Spatial Object Meeting a Selection Criterion

8.4.6.3 By creating multiple stand-alone maps with `tmap_arrange()`