1.0 Overview

1.1 Survival Tip

2.0 Getting Started

3.0 Importing Data into R

3.1 The Data
3.2 Importing Geospatial Data into R
3.3 Importing Attribute Data into R
3.4 Data Preparation
3.4.1 Data wrangling
3.4.2 Joining the attribute data and geospatial data

4.0 Choropleth Mapping Geospatial Data Using tmap

4.1 Plotting a choropleth map quickly by using qtm()
4.2 Creating a choropleth map by using tmap’s elements

4.2.1 Drawing a base map
4.2.2 Drawing a choropleth map using tm_polygons()
4.2.3 Drawing a choropleth map using tm_fill() and *tm_border()**

4.3 Data classification methods of tmap

4.3.1 Plotting choropleth maps with built-in classification methods
4.3.2 Plotting choropleth map with custome break

4.4 Colour Scheme

4.4.1 Using ColourBrewer palette

4.5 Map Layouts

4.5.1 Map Legend
4.5.2 Map style
4.5.3 Cartographic Furniture

4.6 Drawing Small Multiple Choropleth Maps

4.6.1 By assigning multiple values to at least one of the aesthetic arguments
4.6.2 By defining a group-by variable in tm_facets()
4.6.3 By creating multiple stand-alone maps with tmap_arrange()

4.7 Mappping Spatial Object Meeting a Selection Criterion

Reference

All about tmap package
Geospatial data wrangling
Data wrangling

1.0 Overview

Choropleth mapping involves the symbolization 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 hands-on exercise, you will learn how to perform choropleth mapping using tmap package in R.

1.1 Survival Tip

It is advisible for you to read the functional description of each function before using them.

2.0 Getting Started

Beside tmap package, four other R packages will be used, they are: readr, tidyr, dplyr and sf. 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.

packages = c('sf', 'tmap', 'tidyverse')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

Notice that, we only need to install tidyverse instead of readr, tidyr and dplyr individually.

3.0 Importing Data into R

3.1 The Data

Two data set will be used to create the choropleth map, they are:

URA Master Plan subzone boundary in shapefile format (i.e. MP14_SUBZONE_WEB_PL). 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 and Sex, June 2000 - 2018 in csv format (i.e. respopagsex2000to2018.csv). This is an aspatial data fie. Although it does not contain any coordinates values, but it’s PA and SZ fields can be used as unique identifiers to georeference to MP14_SUBZONE_WEB_PL shapefile.

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 shapfile 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 `D:\IS415-AY2020-21T1\03-Hands-on Exercises\Hands-on_Ex03\data\geospatial' using driver `ESRI Shapefile'
## Simple feature collection with 323 features and 15 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
## projected CRS:  SVY21

You can examine the content of mpsz by simply typing the dataframe name in RStudio console.

mpsz

## Simple feature collection with 323 features and 15 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           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...

Notice that only the first ten records will be displayed. Do you know why?

3.3 Importing Attribute Data into R

Next, we will import respopagsex2000to2018.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.

popagsex <- read_csv("data/aspatial/respopagsex2000to2018.csv")

3.4 Data Preparation

Before a thematic map can be prepared, you need to preform the following data preparation.

Extracting 2018 records only.
Extracting Males records only.
Deriving three new variables, namely: Young, Economic Active and Aged.

3.4.1 Data wrangling

The following data wrangling and transformation functions will be used:

spread() of tidyr package, and
mutate(), filter(), and select() of dplyr package

popagsex2018_male <- popagsex %>%
  filter(Sex == "Males") %>%
  filter(Time == 2018) %>%
  spread(AG, Pop) %>%
  mutate(YOUNG = `0_to_4`+`5_to_9`+`10_to_14`+
`15_to_19`+`20_to_24`) %>%
mutate(`ECONOMY ACTIVE` = rowSums(.[9:13])+
rowSums(.[15:17]))%>%
mutate(`AGED`=rowSums(.[18:22])) %>%
mutate(`TOTAL`=rowSums(.[5:22])) %>%  
mutate(`DEPENDENCY` = (`YOUNG` + `AGED`)
/`ECONOMY ACTIVE`) %>%
mutate_at(.vars = vars(PA, SZ), .funs = funs(toupper)) %>%
select(`PA`, `SZ`, `YOUNG`, `ECONOMY ACTIVE`, `AGED`, 
       `TOTAL`, `DEPENDENCY`) %>%
  filter(`ECONOMY ACTIVE` > 0)

3.4.2 Joining the attribute data and geospatial data

Next, left_join() of dplyr is used to join the geographical data and attribute table using planning subzone name e.g. SUBZONE_N and SZ as the common identifier.

mpsz_agemale2018 <- left_join(mpsz, popagsex2018_male, 
                              by = c("SUBZONE_N" = "SZ"))

4.0 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.

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.

qtm(mpsz_agemale2018, fill = "DEPENDENCY")

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.

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_shpae() is used to define the input data (i.e mpsz_agmale2018) and tm_polygons() is used draw the planning subzone polygons

tm_shape(mpsz_agemale2018) +
  tm_polygons()

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().

tm_shape(mpsz_agemale2018)+
  tm_polygons("DEPENDENCY")

Things to learn from tm_polygons():

The default interval binning used to draw the choropleth map is called “pretty”. A detail disucssion of the data classification methods supported by tmap will be discussed in sub-section 4.3.
The default colour scheme used is “YlOrRd” of ColorBrewer. You will learn more about the color pallete in sub-section 4.4.
By default, Missing value will be shaded in grey.

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 draw a choropleth map by using tm_fill() alone.

tm_shape(mpsz_agemale2018)+
  tm_fill("DEPENDENCY")

Notice that the planning subzones are shared according to the respective dependecy values

To add the boundary of the planning subzones, tm_borders will be used as shown in the code chunk below.

tm_shape(mpsz_agemale2018)+
  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”.

4.3 Data classification methods of tmap

Most choropleth maps employ some method 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.

4.3.1 Plotting choropleth maps with built-in classification methods

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

tm_shape(mpsz_agemale2018)+
  tm_fill("DEPENDENCY",
          n = 8,
          style = "jenks") +
  tm_borders(alpha = 0.5)

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

tm_shape(mpsz_agemale2018)+
  tm_fill("DEPENDENCY",
          n = 5,
          style = "equal") +
  tm_borders(alpha = 0.5)

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

4.3.2 Plotting choropleth map with custome break

For all the built-in styles, the category breaks are computed internally. In order to overide these defaults, the breakpoints can be set explicitly by means of the breaks argument to the tm_fill(). It is important to note that, in tmap the breaks include a minimum and maximum. As a result, in order to end up with n categories, n+1 elements must be specified in the breaks option (the values must be in increasing order).

Before we get started, it is always a good practice to get some descriptive statistics on the variable before setting the break points. Code chunk below will be used to compute and display the descriptive statistics of DEPENDENCY field.

summary(mpsz_agemale2018$DEPENDENCY)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.6480  0.6847  0.6708  0.7393  0.9559      90

With reference to the results above, we set break point at 0.60, 0.70, 0.80, and 0.90. In additional, we also need to include a minimum and maximum, which we set at 0 and 100. Our breaks vector is thus c(0, 0.60, 0.70, 0.80, 0.90, 1.00)

Now, we will plot the choropleth map by using the code chunk below.

tm_shape(mpsz_agemale2018)+
  tm_fill("DEPENDENCY",
          breaks = c(0, 0.60, 0.70, 0.80, 0.90, 1.00)) +
  tm_borders(alpha = 0.5)

4.4 Colour Scheme

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

4.4.1 Using ColourBrewer palette

To change the colour, we assign the prefered colour to palette argument of tm_fill() as shown in the code chunk below.

tm_shape(mpsz_agemale2018)+
  tm_fill("DEPENDENCY",
          n = 6,
          style = "quantile",
          palette = "Blues") +
  tm_borders(alpha = 0.5)

Notice that the choropleth map is shaded in green.

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

tm_shape(mpsz_agemale2018)+
  tm_fill("DEPENDENCY",
          style = "quantile",
          palette = "-Greens") +
  tm_borders(alpha = 0.5)

Notice that the colour scheme has been reversed.

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, while the colour settings and data classification methods covered in the previous section relate to the palette and break-points used to affect how the map looks.

4.5.1 Map Legend

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

tm_shape(mpsz_agemale2018)+
  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)

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.

tm_shape(mpsz_agemale2018)+
  tm_fill("DEPENDENCY", 
          style = "quantile", 
          palette = "-Greens") +
  tm_borders(alpha = 0.5) +
  tmap_style("classic")

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.

tm_shape(mpsz_agemale2018)+
  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, the code chunk use the code chunk below.

tmap_style("white")

4.6 Drawing Small Multiple Choropleth Maps

Small multiple maps, also referred to 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().

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()

tm_shape(mpsz_agemale2018)+
  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

tm_shape(mpsz_agemale2018)+ 
  tm_polygons(c("DEPENDENCY","AGED"),
          style = c("equal", "quantile"), 
          palette = list("Blues","Greens")) +
  tm_layout(legend.position = c("right", "bottom"))

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().

tm_shape(mpsz_agemale2018) +
  tm_fill("DEPENDENCY",
          style = "quantile",
          palette = "Blues",
          thres.poly = 0) + 
  tm_facets(by="REGION_N", 
            free.coords=TRUE, 
            drop.shapes=TRUE) +
  tm_layout(legend.show = FALSE,
            title.position = c("center", "center"), 
            title.size = 20) +
  tm_borders(alpha = 0.5)

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().

youngmap <- tm_shape(mpsz_agemale2018)+ 
  tm_polygons("YOUNG", 
              style = "quantile", 
              palette = "Blues")

agedmap <- tm_shape(mpsz_agemale2018)+ 
  tm_polygons("AGED", 
              style = "quantile", 
              palette = "Blues")

tmap_arrange(youngmap, agedmap, asp=1, ncol=2)

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.

tm_shape(mpsz_agemale2018[mpsz_agemale2018$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)

IS415 Hands-on Exercise 3:

Choropleth Mapping with R

Dr. Kam Tin Seong
Assoc. Professor of Information Systems (Practice)

2019-04-25 (updated: 2020-08-29)

1.0 Overview

1.1 Survival Tip

2.0 Getting Started

3.0 Importing Data into R

3.1 The Data

3.2 Importing Geospatial Data into R

3.3 Importing Attribute Data into R

3.4 Data Preparation

3.4.1 Data wrangling

3.4.2 Joining the attribute data and geospatial data

4.0 Choropleth Mapping Geospatial Data Using tmap

4.1 Plotting a choropleth map quickly by using qtm()

4.2 Creating a choropleth map by using tmap’s elements

4.2.1 Drawing a base map

4.2.2 Drawing a choropleth map using tm_polygons()

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

4.3 Data classification methods of tmap

4.3.1 Plotting choropleth maps with built-in classification methods

4.3.2 Plotting choropleth map with custome break

4.4 Colour Scheme

4.4.1 Using ColourBrewer palette

4.5 Map Layouts

4.5.1 Map Legend

4.5.2 Map style

4.5.3 Cartographic Furniture

4.6 Drawing Small Multiple Choropleth Maps

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

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

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

4.7 Mappping Spatial Object Meeting a Selection Criterion

Reference

All about tmap package

Geospatial data wrangling

Data wrangling

IS415 Hands-on Exercise 3:

Choropleth Mapping with R

Dr. Kam Tin SeongAssoc. Professor of Information Systems (Practice)

2019-04-25 (updated: 2020-08-29)

1.0 Overview

1.1 Survival Tip

2.0 Getting Started

3.0 Importing Data into R

3.1 The Data

3.2 Importing Geospatial Data into R

3.3 Importing Attribute Data into R

3.4 Data Preparation

3.4.1 Data wrangling

3.4.2 Joining the attribute data and geospatial data

4.0 Choropleth Mapping Geospatial Data Using tmap

4.1 Plotting a choropleth map quickly by using qtm()

4.2 Creating a choropleth map by using tmap’s elements

4.2.1 Drawing a base map

4.2.2 Drawing a choropleth map using tm_polygons()

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

4.3 Data classification methods of tmap

4.3.1 Plotting choropleth maps with built-in classification methods

4.3.2 Plotting choropleth map with custome break

4.4 Colour Scheme

4.4.1 Using ColourBrewer palette

4.5 Map Layouts

4.5.1 Map Legend

4.5.2 Map style

4.5.3 Cartographic Furniture

4.6 Drawing Small Multiple Choropleth Maps

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

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

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

4.7 Mappping Spatial Object Meeting a Selection Criterion

Reference

All about tmap package

Geospatial data wrangling

Data wrangling

Dr. Kam Tin Seong
Assoc. Professor of Information Systems (Practice)