Hands-on Exercise 7a

Author

Victoria Neo

Published

February 24, 2024

Modified

March 22, 2024

Work done	Hands-on Exercise 7a, 7b, 7c
Hours taken	⏱️⏱️⏱️⏱️⏱️ ( Two sick kids)
Questions	0
How do I feel?	😴
What do I think?	Wow… it is so cool that maps can be used to visualise data. I found the Analytical Mapping very interesting - I didn’t know maps can be used in such a way to help visualise.

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.

2 Getting Started

2.1 Installing and loading the required libraries

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.

code block

pacman::p_load(sf, tmap, tidyverse)

2.2 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.

2.2.1 Importing Data

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 block

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

Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `C:\Users\victo\OneDrive - Singapore Management University\victorianeo\ISS608-VAA\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: SVY21

2.2.2 Examining data

code block

mpsz

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
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...

2.2.3 Importing Attribute Data into R

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.

code block

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

2.2.4 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

2.2.4.1 Data Preparation

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 block

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`)

2.2.4.2 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 block

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.

code block

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

What did Prof Kam say?

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 block

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

3 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.

3.1 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 block

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

What did Prof Kam say?

Thing to learn from the 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)

3.2 **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 block

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.

3.2.1 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 block

tm_shape(mpsz_pop2020) +
  tm_polygons()

3.2.2 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 block

tm_shape(mpsz_pop2020)+
  tm_polygons("DEPENDENCY")

What did Prof Kam say?

Things to learn from the code chunk above

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.

3.2.3 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 block

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 block

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”.

3.3 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.

3.3.1 Plotting choropleth maps with built-in classification methods

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

code block

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.

code block

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 than the equal data classification method.

DIY 1: Using what you had learned, prepare choropleth maps by using different classification methods supported by tmap and compare their differences.

Note

Interestingly, not all data classification methods can be used.

‘cat’ is not accepted:
- Number of unique values of the variable “DEPENDENCY” is 227, which is more than max.categories (which is 30), so style = “cat” cannot be used. Please use numeric intervals instead, e.g. with style = “pretty”.

Fixed data classification
sd data classification

When using ‘quantile’, the distribution is the opposite skewed of ‘equal’ as it has wider bins on the right side.

code block

tm_shape(mpsz_pop2020)+
  tm_fill("DEPENDENCY",
          n = 5,
          style = "quantile") +
  tm_borders(alpha = 0.5)

The bins width do not seem to make sense as the lowest starts from -5 while the second level of 0.86 has a very wide range.

code block

tm_shape(mpsz_pop2020)+
  tm_fill("DEPENDENCY",
          n = 5,
          style = "sd") +
  tm_borders(alpha = 0.5)

DIY 2: 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?

Too skewed to the upper quantile - 2 classes is insufficient to capture the range of distribution.

code block

tm_shape(mpsz_pop2020)+
  tm_fill("DEPENDENCY",
          n = 2,
          style = "jenks") +
  tm_borders(alpha = 0.5)

4 number of classes is much better than 2 because of the increased granularity. However, some variability is lost within the first 3 levels.

code block

tm_shape(mpsz_pop2020)+
  tm_fill("DEPENDENCY",
          n = 4,
          style = "jenks") +
  tm_borders(alpha = 0.5)

There is improved granularity as we can tell other hotspots besides the area that is >= 1.50.

code block

tm_shape(mpsz_pop2020)+
  tm_fill("DEPENDENCY",
          n = 6,
          style = "jenks") +
  tm_borders(alpha = 0.5)

There is not much difference between 8 and 6 - the granularity does not really improve the data visualisation.

code block

tm_shape(mpsz_pop2020)+
  tm_fill("DEPENDENCY",
          n = 8,
          style = "jenks") +
  tm_borders(alpha = 0.5)

3.3.2 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.

code block

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.

code block

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)

3.4 Plotting choropleth map with custom break

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

3.4.1 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.

code block

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.

code block

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

3.5 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.

3.5.1 Map Legend

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

code block

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)

3.5.2 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 block

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

3.5.3 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 block

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"))

3.6 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().

3.6.1 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 block

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")

In this example, small multiple choropleth maps are created by assigning multiple values to at least one of the aesthetic arguments.

code block

tm_shape(mpsz_pop2020)+ 
  tm_polygons(c("DEPENDENCY","AGED"),
          style = c("equal", "quantile"), 
          palette = list("Blues","Greens")) +
  tm_layout(legend.position = c("right", "bottom"))

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

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

code block

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)

3.6.3 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 block

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)

3.7 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 block

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: "Lesson 8a: [Choropleth Mapping with R](https://r4va.netlify.app/chap21)" author: "Victoria Neo" date: 02/24/2024 date-modified: last-modified format: html: code-fold: true code-summary: "code block" code-tools: true code-copy: true execute: eval: true echo: true freeze: true warning: false message: false --- ![*Taken from* [Tableau](https://www.tableau.com/learn/articles/interactive-map-and-data-visualization-examples)](images/clipboard-3952092897.png){fig-align="left" width="674"} | | | |-------------|-----------------------------------------------------------| | Work done | Hands-on Exercise 7a, 7b, 7c | | Hours taken | ⏱️⏱️⏱️⏱️⏱️ ( Two sick kids) | | Questions | 0 | | How do I feel? | 😴 | | What do I think? | Wow... it is so cool that maps can be used to visualise data. I found the Analytical Mapping very interesting - I didn't know maps can be used in such a way to help visualise. | # 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://cran.r-project.org/web/packages/tmap/) package. # 2 **Getting Started** ## 2.1 Installing and loading the required libraries 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/) 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. ```{r} pacman::p_load(sf, tmap, tidyverse) ``` ## 2.2 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. ### 2.2.1 **Importing Data** 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") ``` ### 2.2.2 Examining data ```{r} mpsz ``` ### 2.2.3 **Importing Attribute Data into R** 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. ```{r} popdata <- read_csv("data/aspatial/respopagesextod2011to2020.csv") ``` ### 2.2.4 **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 #### 2.2.4.1 **Data Preparation** 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`) ``` #### 2.2.4.2 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_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. ```{r} mpsz_pop2020 <- left_join(mpsz, popdata2020, by = c("SUBZONE_N" = "SZ")) ``` ::: {.kambox .kam data-latex="kam"} #### What did Prof Kam say? **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") ``` # 3 **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. ## 3.1 **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") ``` ::: {.kambox .kam data-latex="kam"} #### What did Prof Kam say? **Thing to learn from the 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) ::: ## 3.2 **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_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. ### 3.2.1 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() ``` ### 3.2.2 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") ``` ::: {.kambox .kam data-latex="kam"} #### What did Prof Kam say? **Things to learn from the code chunk above** - 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. ::: ### 3.2.3 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_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”. ## 3.3 **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. ### 3.3.1 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_fill("DEPENDENCY", n = 5, style = "jenks") + tm_borders(alpha = 0.5) ``` In the code chunk below, *equal* data classification method is used. ```{r} 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 than the equal data classification method. #### DIY 1: Using what you had learned, prepare choropleth maps by using different classification methods supported by tmap and compare their differences. ::: callout-note Interestingly, not all data classification methods can be used. - 'cat' is not accepted: - Number of unique values of the variable "DEPENDENCY" is 227, which is more than max.categories (which is 30), so style = "cat" cannot be used. Please use numeric intervals instead, e.g. with style = "pretty". ::: ::: panel-tabset ## Fixed data classification When using 'quantile', the distribution is the opposite skewed of 'equal' as it has wider bins on the right side. ```{r} tm_shape(mpsz_pop2020)+ tm_fill("DEPENDENCY", n = 5, style = "quantile") + tm_borders(alpha = 0.5) ``` ## sd data classification The bins width do not seem to make sense as the lowest starts from -5 while the second level of 0.86 has a very wide range. ```{r} tm_shape(mpsz_pop2020)+ tm_fill("DEPENDENCY", n = 5, style = "sd") + tm_borders(alpha = 0.5) ``` ::: #### DIY 2: 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? ::: panel-tabset ## 2 classes Too skewed to the upper quantile - 2 classes is insufficient to capture the range of distribution. ```{r} tm_shape(mpsz_pop2020)+ tm_fill("DEPENDENCY", n = 2, style = "jenks") + tm_borders(alpha = 0.5) ``` ## 4 classes 4 number of classes is much better than 2 because of the increased granularity. However, some variability is lost within the first 3 levels. ```{r} tm_shape(mpsz_pop2020)+ tm_fill("DEPENDENCY", n = 4, style = "jenks") + tm_borders(alpha = 0.5) ``` ## 6 classes There is improved granularity as we can tell other hotspots besides the area that is \>= 1.50. ```{r} tm_shape(mpsz_pop2020)+ tm_fill("DEPENDENCY", n = 6, style = "jenks") + tm_borders(alpha = 0.5) ``` ## 8 classes There is not much difference between 8 and 6 - the granularity does not really improve the data visualisation. ```{r} tm_shape(mpsz_pop2020)+ tm_fill("DEPENDENCY", n = 8, style = "jenks") + tm_borders(alpha = 0.5) ``` ::: ### 3.3.2 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. ```{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_fill("DEPENDENCY", breaks = c(0, 0.60, 0.70, 0.80, 0.90, 1.00)) + tm_borders(alpha = 0.5) ``` ## 3.4 Plotting choropleth map with custom break **tmap** supports colour ramps either defined by the user or a set of predefined colour ramps from the **RColorBrewer** package. ### 3.4.1 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. ```{r} 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. ```{r} tm_shape(mpsz_pop2020)+ tm_fill("DEPENDENCY", style = "quantile", palette = "-Greens") + tm_borders(alpha = 0.5) ``` ## 3.5 **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. ### 3.5.1 Map Legend In **tmap**, several *legend* options are provided to change the placement, format and appearance of the legend. ```{r} 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) ``` ### 3.5.2 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") ``` ### 3.5.3 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_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")) ``` ## 3.6 **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()*. ### 3.6.1 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") ``` In this example, small multiple choropleth maps are created by assigning multiple values to at least one of the aesthetic arguments. ```{r} tm_shape(mpsz_pop2020)+ tm_polygons(c("DEPENDENCY","AGED"), style = c("equal", "quantile"), palette = list("Blues","Greens")) + tm_layout(legend.position = c("right", "bottom")) ``` ### 3.6.2 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, drop.shapes=FALSE) + tm_layout(legend.show = FALSE, title.position = c("center", "center"), title.size = 20) + tm_borders(alpha = 0.5) ``` ### 3.6.3 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) ``` ## 3.7 **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) ```

1 Overview

2 Getting Started

2.1 Installing and loading the required libraries

2.2 Data

2.2.1 Importing Data

2.2.2 Examining data

2.2.3 Importing Attribute Data into R

2.2.4 Data Preparation

2.2.4.1 Data Preparation

2.2.4.2 Joining the attribute data and geospatial data

What did Prof Kam say?

3 Choropleth Mapping Geospatial Data Using tmap

3.1 Plotting a choropleth map quickly by using qtm()

What did Prof Kam say?

3.2 Creating a choropleth map by using tmap’s elements

3.2.1 Drawing a base map

3.2.2 Drawing a choropleth map using tm_polygons()

What did Prof Kam say?

3.2.3 Drawing a choropleth map using tm_fill() and tm_border()

3.3 Data classification methods of tmap

3.3.1 Plotting choropleth maps with built-in classification methods

DIY 1: Using what you had learned, prepare choropleth maps by using different classification methods supported by tmap and compare their differences.

DIY 2: 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?

3.3.2 Plotting choropleth map with custom break

3.4 Plotting choropleth map with custom break

3.4.1 Using ColourBrewer palette

3.5 Map Layouts

3.5.1 Map Legend

3.5.2 Map style

3.5.3 Cartographic Furniture

3.6 Drawing Small Multiple Choropleth Maps

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

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

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

3.7 Mappping Spatial Object Meeting a Selection Criterion

3.2 **Creating a choropleth map by using tmap’s elements**