A while back on StackOverflow, I answered a question on how to produce a map which showed how many polygons away a location was from another. This blog provides an update on how this can be used, with some slight tweaks to the answer to expand on the original. A lot of this content is based around the original from Roger Bivand here.

# Setup

In order to calculate neighbouring polygons, we need to form a network of nodes for the data. This is done using the **igraph** (file. 2020) **spdep** (Bivand 2020) and **spatialreg** (Bivand and Piras 2019) packages. We also use the **raster** (Hijmans 2020) package to download some example data, and **ggplot2** (Wickham et al. 2020) for the final visualisation.

```
# Loading example data
library(raster) # loads shapefile
# Data Analysis
library(igraph) # build network
library(spdep) # builds network
library(spatialreg)
# Visualisation
library(RColorBrewer) # for plot colour palette
library(ggplot2) # plots results
library(tmap)
```

First downloading the data as follows. The input data is shown in Figure 1. You could easily provide your own shapefile here instead or run the code for a different country by changing the three-digit code. For my example, I have selected Hungary:

```
# Load Data
boundaries <- raster::getData(name = "GADM", country = "HUN", level = 2)
# Show data
tm_shape(boundaries) +
tm_polygons()
```

## Extracting neigbours

Firstly the `poly2nb`

function is used to calculate neighbouring regions, based on contiguous boundaries, that is sharing one or more boundary point. The resulting mesh and coordinates is shown in Figure 2

```
# Find neighbouring areas
nb_q <- poly2nb(boundaries)
# Plot original results
coords <- coordinates(boundaries)
# Show the results
plot(boundaries)
plot(nb_q, coords, col="grey", add = TRUE)
```

With this mesh, we can then calculate the shortest path between two locations. The follow function returns a list for each and every pair of polygons.

```
# Sparse matrix
nb_B <- nb2listw(nb_q, style="B", zero.policy=TRUE)
B <- as(nb_B, "symmetricMatrix")
# Calculate shortest distance
g1 <- graph.adjacency(B, mode="undirected")
sp_mat <- shortest.paths(g1)
```

Having made the calculations, the data can now be formatted to get into plotting format, so the shortest path matrix is merged with the spatial dataframe.

```
# Name used to identify data
referenceCol <- boundaries$GID_2
# Rename spatial matrix
sp_mat2 <- as.data.frame(sp_mat)
sp_mat2$id <- rownames(boundaries@data)
names(sp_mat2) <- paste0(referenceCol)
# Add distance to shapefile data
boundaries@data <- cbind(boundaries@data, sp_mat2)
boundaries@data$id <- rownames(boundaries@data)
```

The data is now in a suitable format to display. Using the basic function `spplot`

we can get a graph quite quickly:

```
tm_shape(boundaries) +
tm_polygons("HUN.15.5_1", n = 15)
```

# Plotting in ggplot2

I prefer ggplot for plotting more complex graphs as you can control the styling easier. Fortunately, **ggplot2** now directly supports spatial data through `geom_sf`

format. I have customised it a bit by removing elements which arenâ€™t required and added a background. The final map is shown below in Figure 3.

```
# Convert data to sf format
boundaries_sf <- sf::st_as_sf(boundaries)
ggplot(boundaries_sf) +
geom_sf(aes(fill = HUN.15.5_1)) +
geom_sf(fill = "grey", alpha = ifelse(boundaries_sf$GID_2 == "HUN.15.5_1", 1, 0)) +
scale_fill_gradient2(low = "#d73027", mid = "#fee08b", high = "#1a9850", midpoint = 10) +
labs(fill = "Distance from \nselected zone") +
theme(
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
plot.background = element_rect(fill = "#f5f5f2", color = NA),
panel.background = element_rect(fill = "#f5f5f2", color = NA),
legend.background = element_rect(fill = "#f5f5f2", color = NA),
panel.border = element_blank())
```

# References

Bivand, Roger. 2020. *Spdep: Spatial Dependence: Weighting Schemes, Statistics*. https://CRAN.R-project.org/package=spdep.

Bivand, Roger, and Gianfranco Piras. 2019. *Spatialreg: Spatial Regression Analysis*. https://CRAN.R-project.org/package=spatialreg.

file., See AUTHORS. 2020. *Igraph: Network Analysis and Visualization*. https://CRAN.R-project.org/package=igraph.

Hijmans, Robert J. 2020. *Raster: Geographic Data Analysis and Modeling*. https://CRAN.R-project.org/package=raster.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2020. *Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics*. https://CRAN.R-project.org/package=ggplot2.