Dissolving Maps Polygon: A Step-by-Step Guide with R

Dissolving Maps Polygon: A Step-by-Step Guide

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Dissolving a polygon in a map can be a challenging task, especially when dealing with complex regions and county boundaries. In this article, we will explore the process of dissolving a polygon using the maptools and sp packages in R, along with some practical examples.

Introduction

In the context of geographic information systems (GIS), polygons are used to represent various features such as countries, states, counties, and administrative boundaries. When dealing with complex regions, it can be challenging to visualize the individual components, especially when they overlap or intersect. This is where dissolving a polygon comes in handy.

Prerequisites

Before we dive into the code, make sure you have the necessary packages installed:

tidyverse
mapdata
maps
stringr
viridis
maptools
sp

You can install these packages using the following command:

install.packages(c("tidyverse", "mapdata", "maps", "stringr", "viridis",
                   "maptools", "sp"))

Getting Started with Dissolving a Polygon

In this section, we will explore how to dissolve a polygon using the maptools and sp packages.

Step 1: Load the Necessary Packages

library(tidyverse)
library(mapdata)
library(maps)
library(stringr)
library(viridis)
library(maptools)
library(sp)

Step 2: Get the Map Object

To start dissolving a polygon, we need to get the map object. In this example, we will use the map function from the maps package to get the California county map.

county_map <- map("county", regions = "california",
                  fill = T, plot = FALSE)

Step 3: Convert to SpatialPolygonsDataFrame Object

Next, we need to convert the map object to a spatial polygons dataframe object using the maptools and sp packages.

county_map_match <- data.frame(name = county_map$names) %>%
  separate(name, c("region", "subregion"), sep = ",", remove = FALSE) %>%
  mutate(central = subregion %in% c("alpine", "kings", "tulare",
                                    "fresno", "inyo", "kern", "madera")) %>%
  column_to_rownames("name")
county_map <- maptools::map2SpatialPolygons(county_map, ID = county_map$names)
county_map <- sp::SpatialPolygonsDataFrame(county_map, county_map_match)
rm(county_map_match)

Step 4: Remove Invalid Polygons

Before dissolving the polygon, we need to remove any invalid polygons using the rgeos package.

rgeos::gIsValid(county_map) # check
county_map <- rgeos::gBuffer(county_map, byid = TRUE, width = 0)
rgeos::gIsValid(county_map) # check again (invalidities removed)

Step 5: Dissolve the Polygon

Finally, we can dissolve the polygon using the unionSpatialPolygons function from the maptools package.

county_map <- maptools::unionSpatialPolygons(county_map, IDs = county_map$central)
county_map <- fortify(county_map)
county_map <- county_map %>% filter(group == "TRUE.1")

Visualizing the Dissolved Polygon

After dissolving the polygon, we need to visualize it using a map plot.

ggplot() +
  geom_polygon(data = county_map,
               aes(x = long, y = lat, group = group),
               fill = "white", colour = "black") +
  coord_map()

Conclusion

In this article, we have explored the process of dissolving a polygon in a map using the maptools and sp packages in R. We covered the necessary steps, including getting the map object, converting to spatial polygons dataframe, removing invalid polygons, and dissolving the polygon.

By following these steps, you can dissolve complex polygons and visualize the individual components, making it easier to understand and work with geographic data.

Additional Resources

Note: The code in this article is for illustrative purposes only and may need to be modified to suit your specific use case.

Last modified on 2025-02-12