Introduction

This package contains a C++ implementation of the RecMap algorithm [@recmap] to draw maps according to given statistical values. These so-called cartograms or value-by-area-maps may be used to visualize any geospatial-related data, e.g., political, economic or public health data. The input consists of a map represented by overlapping rectangles. This map is defined by the following parameters for each map region:

• a tuple of (x, y) values corresponding to the (longitude, latitude) position
• a tuple of (dx, dy) of expansion along (longitude, latitude)
• and a statistical value z.

The (x, y) coordinates represent the center of the minimal bounding boxes (MBB), The coordinates of the MBB are derived by adding or subtracting the (dx, dy) tuple accordingly. The tuple (dx, dy) also defines the ratio of the map region. The statistical values define the desired area of each map region.

The output is a rectangular cartogram where the map regions are:

• Non-overlapping,
• connected,
• ratio and area of each rectangle correspond to the desired areas,
• rectangles are placed parallel to the axes.

The construction heuristic places the rectangles in a way that important spatial constraints, in particular

• the topology of the pseudo dual graph,
• the relative position of MBB centers.

are tried to be preserved.

The ratios are preserved, and the area of each region corresponds to the as input given statistical value z.

An example of a rectangular cartogram drawing is shown below.

The Usage

Input using the U.S. state Facts and Figures Dataset

usa <- data.frame(x=state.center$x, 
    y = state.center$y, 
    # make the rectangles overlapping by correcting lines of longitude distance
    dx = sqrt(state.area) / 2 / (0.8 * 60 * cos(state.center$y*pi/180)), 
    dy = sqrt(state.area) / 2 / (0.8 * 60) , 
    z = sqrt(state.area),
    name = state.name)

Compute Pseudo Dual Graph (PD)

The rectangles have to overlap to compute the dual graph. This enables to generate valid input having only the (x, y) coordinates of the map region.

op <- par(mfrow=c(1,1), mar=c(0,0,0,0))
library(recmap)
plot.recmap(M <- usa[!usa$name %in% c("Hawaii", "Alaska"), ],  
            col.text = 'black', lwd=2)

Metaheuristic

The index order of the input map has an impact to the resulting cartogram. This algorithmic property is caused due to the computation of the dual graph. In [@recmap] a genetic algorithm was applied as metaheuristic. Due to the limited compute resources on the CRAN package build process no metaheuristic will be executed to build this vignette file.

Study the examples of the reference manual ?recmapGA on how the GA package can be used.

Objective Functions

The topology error is an indicator of the deviation of the neighborhood relationships. The error is computed by counting the differences between dual graphs or adjacency graphs of map and cartogram

The relative positions error measures the angle difference between all region centers.

Output

The output is a data.frame object.

Cartogram <- recmap(Map <- usa[!usa$name %in% c("Hawaii", "Alaska"), ])
head(Cartogram)

##            x        y       dx       dy         z        name dfs.num
## 1  -86.12445 24.40204 3.201992 2.697824 227.17614     Alabama      31
## 2 -123.13242 29.61981 3.939586 3.257613 337.50407     Arizona      38
## 3  -85.30009 39.65352 3.265364 2.683509 230.44305    Arkansas      33
## 4 -131.42392 31.53933 4.341915 3.488740 398.36290  California      37
## 5 -122.00152 23.21422 3.965671 3.095895 322.87304    Colorado      47
## 6  -53.13517 41.31657 1.896954 1.418697  70.77429 Connecticut      19
##   topology.error relpos.error relposnh.error
## 1              4    0.2287945      0.6128110
## 2              4    0.2110718      0.7496132
## 3              6    0.7596130      2.8277677
## 4              3    0.1369841      0.1614465
## 5              8    0.6025215      1.3705189
## 6              3    0.1954060      0.4808290

Application

Rectangular Map Approximation

smp <- c(29, 22, 30, 3, 17, 8, 9, 41, 18, 15, 38, 35, 21, 23, 19, 6, 31, 32, 20, 
        28, 48, 4, 13, 14, 42, 37, 5, 16, 36 , 43, 25, 33, 12, 7, 39, 44, 2, 47,
        45, 46, 24, 10, 1,11 ,40 ,26 ,27 ,34)

plot(Cartogram.Population <- recmap(M[smp, ]), 
            col.text = 'black', lwd=2)

## Warning in recmap_(V, E): Arizona could not be placed on the first attempt;

summary.recmap(M)

##                             values
## number of map regions     48.00000
## area error                 0.13000
## topology error                  NA
## relative position error         NA
## screen filling [in %]    113.77072
## xmin                    -124.93741
## xmax                     -66.26565
## ymin                      25.35365
## ymax                      50.81868

summary(Cartogram.Population)

##                             values
## number of map regions     48.00000
## area error                 0.00000
## topology error           156.00000
## relative position error    0.13000
## screen filling [in %]     39.74965
## xmin                    -131.88541
## xmax                     -26.73219
## ymin                      17.21867
## ymax                      57.88620

state.x77[, 'Population']

op <- par(mfrow=c(1,1), mar=c(0,0,0,0))
usa$z <- state.x77[, 'Population']
M <- usa[!usa$name %in% c("Hawaii", "Alaska"), ]
plot(Cartogram.Population <- recmap(M[order(M$x),]), 
            col.text = 'black', lwd=2)

op <- par(mfrow=c(1,1), mar=c(0,0,0,0))
# index order

smp <- c(20,47,4,40,9,6,32,33,3,10,34,22,2,28,15,12,39,7,42,45,19,13,43,30,24,
         25,11,17,37,41,26,29,21,35,8,36,14,16,31,48,46,38,23,18,1,5,44,27)

plot(Cartogram.Population <- recmap(M[smp,]), col.text = 'black', lwd = 2)

state.x77[, 'Income']

usa$z <- state.x77[, 'Income']
M <- usa[!usa$name %in% c("Hawaii", "Alaska"), ]
plot(Cartogram.Income <- recmap(M[order(M$x),]), col.text = 'black', lwd = 2)

state.x77[, 'Frost']

usa$z <- state.x77[, 'Frost'] 
M <- usa[!usa$name %in% c("Hawaii", "Alaska"), ]
plot(Cartogram.Income <- recmap(M[order(M$x),]), 
            col.text = 'black', lwd = 2)

More examples using state.x77 data and shiny application are available through https://recmap.shinyapps.io/state_x77/ or by running the following code snipped.

library(shiny)
recmap_state.x77 <- system.file("shiny-examples", "state.x77", package = "recmap")
shiny::runApp(recmap_state.x77, display.mode = "normal")

Synthetic checkerboard

Checkerboards provide examples of sets of map regions which do not have ideal cartogram solutions according to Definition 1 [@cartodraw].

op <- par(mar = c(0, 0, 0, 0), mfrow = c(1, 3), bg = 'white')

plot(checkerboard8x8 <- checkerboard(8),
            col=c('white','white','white','black')[checkerboard8x8$z])

# found by a greedy randomized search
index.greedy <- c(8, 56, 18, 5, 13, 57, 3, 37, 62, 58, 7, 16, 40, 59, 17, 34,
                  29, 41, 46, 27, 54, 43, 2, 21, 38, 52, 31, 20, 28, 48, 1, 22,
                  55, 11, 25, 19, 50, 10, 24, 53, 47, 30, 45, 44, 32, 35, 51,
                  15, 64, 12, 14, 39, 26, 6, 42, 33, 4, 36, 63, 49, 60, 61, 9,
                  23)

plot(Cartogram.checkerboard8x8.greedy <- recmap(checkerboard8x8[index.greedy,]),
            col = c('white','white','white','black')[Cartogram.checkerboard8x8.greedy$z])

# found by a genetic algorithm
index.ga <- c(52, 10, 27, 63, 7, 20, 32, 18, 47, 28, 6, 55, 11, 61, 38, 50, 5,
              21, 36, 34, 2, 22, 3, 1, 29, 57, 43, 4, 51, 58, 31, 49, 44, 25,
              59, 33, 17, 40, 8, 41, 26, 37, 19, 56, 45, 35, 62, 53, 24, 64,
              30, 15, 39, 12, 60, 48, 16, 23, 46, 42, 13, 54, 14, 9)

plot(Cartogram.checkerboard8x8.ga <- recmap(checkerboard8x8[index.ga,]),
            col = c('white','white','white','black')[Cartogram.checkerboard8x8.ga$z])

History

The work on RecMap was initiated by understanding the limits of contiguous cartogram drawing [@cartodraw] and after studying the visualizations drawn by Erwin Raisz [@ErwinRaisz]. The purpose of the first implementation [@recmap] was a feasibility check on how computer-generated rectangular cartograms with zero area error could look like. The recmap R package on CRAN provides a rectangular cartogram algorithm to be used by any R user. Now, it is easy to generate input (e.g., no complex polygon mesh), the code is maintainable (less than 500 lines of C++-11 code), and the algorithm is made robust to the price of not having all features implemented (simplified local placement; no empty space error; no MP1 variant yet). Recent research publication on rectangular cartograms drawing include [@Speckmann2004], [@Speckmann2007], [@Speckmann2012], [@Buchin:2016]. However, according to recent publication [@TheStateoftheArtInCartograms], recmap remains the only rectangular cartogram algorithm that 'maintain zero cartographic error'.

Session Info

sessionInfo()

## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux buster/sid
## 
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] recmap_0.5.26   sp_1.2-3        Rcpp_0.12.13    GA_3.0.2       
## [5] iterators_1.0.8 foreach_1.4.3  
## 
## loaded via a namespace (and not attached):
##  [1] compiler_3.4.3   magrittr_1.5     tools_3.4.3      codetools_0.2-15
##  [5] stringi_1.1.2    highr_0.6        grid_3.4.3       knitr_1.15.1    
##  [9] stringr_1.2.0    lattice_0.20-35  evaluate_0.10

#References