pacman::p_load(sf, tmap, tidyverse)Hands-on Exercise 8 Part III - Analytical Mapping
Learning Objectives:
Importing geospatial data in rds format into R environment.
Creating cartographic quality choropleth maps by using appropriate tmap functions.
Creating rate map
Creating percentile map
Creating boxmap
Getting Started
Installing and loading the required libraries
The following R packages will be used:
Tidyverse:
sf for handling geospatial data
tmap for plotting choropleth maps
Code chunk below will be used to check if these packages have been installed and also will load them into the working R environment.
Importing data
A data set called NGA_wp.rds will be used. The data set is a polygon feature data.frame providing information on water point of Nigeria at the LGA level. You can find the data set in the rds sub-direct of the hands-on data folder.
The code chunk below uses read_rds() function of readr package to import NGA_wp.rds into R as a tibble data frame called NGA_wp.
NGA_wp <- read_rds("data/rds/NGA_wp.rds")Basic Choropleth Mapping
Visualising distribution of non-functional water point
p1 <- tm_shape(NGA_wp) +
tm_fill("wp_functional",
n = 10,
style = "equal",
palette = "Blues") +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Distribution of functional water point by LGAs",
legend.outside = FALSE)
p2 <- tm_shape(NGA_wp) +
tm_fill("total_wp",
n = 10,
style = "equal",
palette = "Blues") +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Distribution of total water point by LGAs",
legend.outside = FALSE)
tmap_arrange(p2, p1, nrow = 1)Choropleth Map for Rates
As water points are not equally distributed in space, map rates are more important than the count. If the location of the water points are not considered, total water point size will be mapped instead of the topic of interest.
Deriving Proportion of Functional Water Points and Non-Functional Water Points
In the following code chunk, mutate() from dplyr package is used to derive two fields, namely pct_functional and pct_nonfunctional.
NGA_wp <- NGA_wp %>%
mutate(pct_functional = wp_functional/total_wp) %>%
mutate(pct_nonfunctional = wp_nonfunctional/total_wp)Plotting map of rate
Choropleth map showing the distribution of percentage functional water point by LGA
tm_shape(NGA_wp) +
tm_fill("pct_functional",
n = 10,
style = "equal",
palette = "Blues",
legend.hist = TRUE) +
tm_borders(lwd = 0.1,
alpha = 1) +
tm_layout(main.title = "Rate map of functional water point by LGAs",
legend.outside = TRUE)Extreme Value Maps
Extreme value maps are variations of common choropleth maps where the classification is designed to highlight extreme values at the lower and upper end of the scale, with the goal of identifying outliers. These maps were developed in the spirit of spatializing EDA, i.e., adding spatial features to commonly used approaches in non-spatial EDA (Anselin 1994).
Percentile Map
The percentile map is a special type of quantile map with six specific categories: 0-1%,1-10%, 10-50%,50-90%,90-99%, and 99-100%. The corresponding breakpoints can be derived by means of the base R quantile command, passing an explicit vector of cumulative probabilities as c(0,.01,.1,.5,.9,.99,1). Note: the begin and endpoint need to be included.
Data Preparation
Step 1: Exclude records with NA by using the code chunk below.
NGA_wp <- NGA_wp %>%
drop_na()Step 2: Creating customised classification and extracting values
percent <- c(0,.01,.1,.5,.9,.99,1)
var <- NGA_wp["pct_functional"] %>%
st_set_geometry(NULL)
quantile(var[,1], percent) 0% 1% 10% 50% 90% 99% 100%
0.0000000 0.0000000 0.2169811 0.4791667 0.8611111 1.0000000 1.0000000
Note
When variables are extracted from an sf data.frame, the geometry is also extracted. For mapping and spatial manipulation, many base R functions cannot deal with the geometry. e.g., quantile() gives an error. Thus, st_set_geomtry(NULL) is used to drop geomtry field.
Creating the get.var function
Firstly, an R function as is written to extract a variable (i.e. wp_nonfunctional) as a vector out of an sf data.frame.
arguments:
vname: variable name (as character, in quotes)
df: name of sf data frame
returns:
- v: vector with values (without a column name)
get.var <- function(vname,df) {
v <- df[vname] %>%
st_set_geometry(NULL)
v <- unname(v[,1])
return(v)
}A percentile mapping function
Next, a percentile mapping function is written by using the code chunk below.
percentmap <- function(vnam, df, legtitle=NA, mtitle="Percentile Map"){
percent <- c(0,.01,.1,.5,.9,.99,1)
var <- get.var(vnam, df)
bperc <- quantile(var, percent)
tm_shape(df) +
tm_polygons() +
tm_shape(df) +
tm_fill(vnam,
title=legtitle,
breaks=bperc,
palette="Blues",
labels=c("< 1%", "1% - 10%", "10% - 50%", "50% - 90%", "90% - 99%", "> 99%")) +
tm_borders() +
tm_layout(main.title = mtitle,
title.position = c("right","bottom"))
}Test drive the percentile mapping function
percentmap("total_wp", NGA_wp)
Note
Additional arguments e.g., title, legend positioning can be passed to customise various features of the map.
Box map
A box map is an augmented quartile map, with an additional lower and upper category. When there are lower outliers, then the starting point for the breaks is the minimum value, and the second break is the lower fence.
When there are no lower outliers, then the starting point for the breaks will be the lower fence, and the second break is the minimum value (there will be no observations that fall in the interval between the lower fence and the minimum value).
ggplot(data = NGA_wp,
aes(x = "",
y = wp_nonfunctional)) +
geom_boxplot()Displaying summary statistics on a choropleth map by using the basic principles of boxplot.
To create a box map, a custom breaks specification will be used. However, there is a complication. The break points for the box map vary depending on whether lower or upper outliers are present.
Creating the boxbreaks function
The code chunk below is an R function that creating break points for a box map.
arguments:
v: vector with observations
mult: multiplier for IQR (default 1.5)
returns:
- bb: vector with 7 break points compute quartile and fences
boxbreaks <- function(v,mult=1.5) {
qv <- unname(quantile(v))
iqr <- qv[4] - qv[2]
upfence <- qv[4] + mult * iqr
lofence <- qv[2] - mult * iqr
# initialize break points vector
bb <- vector(mode="numeric",length=7)
# logic for lower and upper fences
if (lofence < qv[1]) { # no lower outliers
bb[1] <- lofence
bb[2] <- floor(qv[1])
} else {
bb[2] <- lofence
bb[1] <- qv[1]
}
if (upfence > qv[5]) { # no upper outliers
bb[7] <- upfence
bb[6] <- ceiling(qv[5])
} else {
bb[6] <- upfence
bb[7] <- qv[5]
}
bb[3:5] <- qv[2:4]
return(bb)
}Creating the get.var function
The code chunk below is an R function to extract a variable as a vector out of an sf data frame.
arguments:
vname: variable name (as character, in quotes)
df: name of sf data frame
returns:
- v: vector with values (without a column name)
get.var <- function(vname,df) {
v <- df[vname] %>% st_set_geometry(NULL)
v <- unname(v[,1])
return(v)
}Test drive the newly created function
var <- get.var("wp_nonfunctional", NGA_wp)
boxbreaks(var)[1] -56.5 0.0 14.0 34.0 61.0 131.5 278.0Boxmap function
The code chunk below is an R function to create a box map. Arguments include:
vnam: variable name (as character, in quotes)
df: simple features polygon layer
legtitle: legend title
mtitle: map title
mult: multiplier for IQR
returns: a tmap-element (plots a map)
boxmap <- function(vnam, df,
legtitle=NA,
mtitle="Box Map",
mult=1.5){
var <- get.var(vnam,df)
bb <- boxbreaks(var)
tm_shape(df) +
tm_polygons() +
tm_shape(df) +
tm_fill(vnam,title=legtitle,
breaks=bb,
palette="Blues",
labels = c("lower outlier",
"< 25%",
"25% - 50%",
"50% - 75%",
"> 75%",
"upper outlier")) +
tm_borders() +
tm_layout(main.title = mtitle,
title.position = c("left",
"top"))
}
tmap_mode("plot")
boxmap("wp_nonfunctional", NGA_wp)