Unlocking Spatial Insights: Creating Point Maps with R

Overview

A point map displays spatial data by plotting discrete occurrences or features directly onto a geographic base. Each point signifies the presence or occurrence of a particular event, object, or attribute at a precise location. These maps are used to show where things happen, rather than how much or how often - though additional data (like size or color of the point) can be layered to convey more information.

Example of a Point Map

Replicate the visualization: Link to code

Step-by-Step Guide

Loading the required libraries

load libraries

library(tidyverse)

library(maps)

Loading data (in this case political conflict data in Africa)

load data

conflict_data<-read.csv("https://raw.githubusercontent.com/bernardkilonzo-rigor/geoinsights/refs/heads/main/data/Conflict%20Fatalities.csv")

Grouping the fatalities into bins to be used when applying color to the point map.

grouping fatalities cases into bins

conflict_data<-conflict_data%>%mutate(bin = case_when(

  Fatalities<=0 ~"0",

  Fatalities<=10 ~"1-10",

  Fatalities<=20 ~"11-20",

  Fatalities<=30 ~"21-30",

  Fatalities<=50 ~"31-50",

  Fatalities<=100 ~"51-100",

  Fatalities>100 ~"101+"

))

Ordering the bins using a factor.

ordering the bins

conflict_data$bin<-factor(conflict_data$bin,

                          levels = c("0","1-10","11-20","21-30",

                                     "31-50","51-100","101+"))

Loading the world map data

load world map data

world_map<-map_data("world")

Extracting the Africa map data from world map data through filtering – this will be used as a base map in our plot.

extracting the base map of Africa (by filtering regions)

africa_map <- world_map %>%filter(region %in% c("Algeria","Angola","Benin","Botswana","Burkina Faso","Burundi","Cabo Verde","Cameroon","Central African Republic","Chad","Comoros","Republic of Congo","Democratic Republic of the Congo","Djibouti","Egypt","Equatorial Guinea","Eritrea","Eswatini","Ethiopia","Gabon","Gambia","Ghana","Guinea","Guinea-Bissau","Ivory Coast","Kenya","Lesotho","Liberia","Libya","Madagascar","Malawi","Mali","Mauritania","Mauritius","Morocco","Mozambique","Namibia","Niger","Nigeria","Rwanda","Sao Tome and Principe","Senegal","Seychelles","Sierra Leone","Somalia","South Africa","South Sudan","Sudan","Tanzania","Togo","Tunisia","Uganda","Western Sahara","Zambia","Zimbabwe"))

Creating a simple point map in R

creating point distribution map

ggplot()+

  geom_polygon(data = africa_map, aes(x =long, y = lat, group = group),

               fill ="white", color = "gray35", linewidth =0.1)+

  geom_point(data = conflict_data, aes(x = Longitude, y = Latitude, size = Fatalities, color = bin), alpha = 0.4)+

  scale_color_manual(values = c("#2c5c8a","#5f81af","#77acd3","#d9d5c9",

                                "#f69035","#d45b21","#9e3d22"))+

  coord_quickmap()

Executing the above code generates the view below.

Formatting the point map using the additional line of code.

creating point distribution map + formatting

ggplot()+

  geom_polygon(data = africa_map, aes(x =long, y = lat, group = group),

               fill ="white", color = "gray35", linewidth =0.1)+

  geom_point(data = conflict_data, aes(x = Longitude, y = Latitude, size = Fatalities, color = bin), alpha = 0.4)+

  scale_color_manual(values = c("#2c5c8a","#5f81af","#77acd3","#d9d5c9",

                                "#f69035","#d45b21","#9e3d22"))+

  coord_quickmap()+

labs(title = "Political Conflict in Africa (1997 - 2017)",

       subtitle = "Reported Fatalities by Location",

       caption = "Viz by: Bernard Kilonzo", color = "Fatalities")+

  theme(panel.background = element_blank(),

        panel.grid = element_blank(),

        axis.title = element_blank(),

        axis.text = element_blank(),

        axis.ticks = element_blank(),

        plot.background = element_rect(fill = "gray95",color = "gray95"),

        legend.background = element_rect(fill = "gray95"),

        legend.title = element_text(family = "serif", size = 10, color = "gray25"),

        legend.text = element_text(family = "serif", size = 9, color = "gray25"),

        plot.title = element_text(family = "serif", face = "bold", size = 12, color = "gray20"),

        plot.subtitle = element_text(family = "serif", size = 10, color = "gray20"),

        plot.caption = element_text(family = "serif", face = "italic", size = 9, color = "gray35"))

Executing the above code generates the view below.

Conclusion

Creating point maps in R is a straightforward yet powerful way to visualize spatial data and uncover geographic patterns. With packages like ggplot2, sf, and leaflet, users can plot coordinates as individual points on a map, enriching them with color, shape, or size to reflect additional variables. These maps help identify trends, clusters, and anomalies across regions. Whether you're visualizing store locations, wildlife sightings, or public service access, R gives you the tools to bring location-based insights to life with clarity and flexibility.

If you like the work we do and would like to work with us, drop us an email on our contacts page and we’ll reach out!

Thank you for reading!