Selecting the ideal location for a retail store is a critical factor in ensuring its success. Location analysis involves evaluating multiple aspects such as demographic trends, population density, competitor presence, and ease of accessibility. This helps businesses identify the most strategic sites for maximizing customer reach and profitability. In this tutorial, we will explore how to conduct retail store location analysis using the R. By combining real-world data with analytical techniques, this guide offers a practical approach to making data-driven decisions in retail planning. The example is based on resources from Geo-computation with R and a few other technical articles.

In this tutorial, we analyze the retail expansion strategy for In-N-Out Burger using R. This guide aims to:

If you never used R before, please check out this introduction here: https://www.geeksforgeeks.org/r-programming-language-introduction/ .

In this tutorial, we evaluate retail store locations using real-world city data. The analysis includes median household income, population, and competitor count.

Step 1: Set Up the Environment

First, load the required libraries.

## install.packages(c("ggplot2", "dplyr"))
library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Step 2: Create the Dataset for the In-N-Out Stores in several metropolitan cities.

The dataset includes data from authoritative sources like the Census Bureau and World Population Review and the In-N-Out website.

retail_data <- data.frame(
  City = c("San Diego", "Phoenix", "Dallas", "Los Angeles", "Las Vegas"),
  Median_Income = c(85000, 64000, 69000, 75000, 62000),  # Median household income in USD
  Population = c(1423851, 1690000, 1343000, 3898747, 653725),  # City population
  Competitor_Count = c(24, 25, 24, 79, 19)  # Hypothetical competitor counts
)
# View the dataset
print(retail_data)
##          City Median_Income Population Competitor_Count
## 1   San Diego         85000    1423851               24
## 2     Phoenix         64000    1690000               25
## 3      Dallas         69000    1343000               24
## 4 Los Angeles         75000    3898747               79
## 5   Las Vegas         62000     653725               19

Step 3: Data Analysis

We now calculate a popularity score for each city based on income, population, and competition. Please note that this is a heuristic index designed for analytical purposes and may not accurately reflect real-world conditions.

retail_data <- retail_data %>%
  mutate(
    Score = Median_Income / Competitor_Count * log(Population)
  ) %>%
  arrange(desc(Score))  # Sort by score

print(retail_data)
##          City Median_Income Population Competitor_Count    Score
## 1   San Diego         85000    1423851               24 50181.43
## 2   Las Vegas         62000     653725               19 43695.13
## 3      Dallas         69000    1343000               24 40567.45
## 4     Phoenix         64000    1690000               25 36711.01
## 5 Los Angeles         75000    3898747               79 14407.75

Step 4: Visualization

Median Income vs. Competitor Count

ggplot(retail_data, aes(x = reorder(City, -Median_Income), y = Median_Income, fill = Competitor_Count)) +
  geom_bar(stat = "identity") +
  labs(title = "Median Income vs Competitor Count by City",
       x = "City",
       y = "Median Income",
       fill = "In-N-Out Stores") +
  theme_minimal()

Let’s explore the relationship between Location Score and Median Income

ggplot(retail_data, aes(x = Median_Income, y = Score, color = City)) +
  geom_point(size = 4) +
  labs(title = "Location Score vs Median Income",
       x = "Median Income",
       y = "Score") +
  theme_minimal()

Let’s create a map that showcases the median income and store counts in each city.

# Load libraries
library(ggplot2)
library(sf)
## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1; sf_use_s2() is TRUE
library(maps)

# Let's redefine our dataset by adding the GIS features to the original data, Retail data
retail_data <- data.frame(
  City = c("San Diego", "Phoenix", "Dallas", "Los Angeles", "Las Vegas"),
  Median_Income = c(85000, 64000, 69000, 75000, 62000),
  Population = c(1423851, 1690000, 1343000, 3898747, 653725),
  Competitor_Count = c(24, 25, 24, 79, 19),
  Longitude = c(-117.1611, -112.0740, -96.7970, -118.2437, -115.1398),
  Latitude = c(32.7157, 33.4484, 32.7767, 34.0522, 36.1699)
)

# Load USA map data
usa <- map_data("state")

# Create the map plot
ggplot() +
  # Draw the USA map
  geom_polygon(data = usa, aes(x = long, y = lat, group = group),
               fill = "lightgray", color = "white") +
  # Add city points
  geom_point(data = retail_data, aes(x = Longitude, y = Latitude,
                                     size = Competitor_Count, color = Median_Income),
             alpha = 0.7) +
  # Add city labels
  geom_text(data = retail_data, aes(x = Longitude, y = Latitude, label = City),
            nudge_y = 1, size = 3, color = "black") +
  # Customize scales
  scale_size_continuous(name = "In-N-Out Burger", range = c(2, 8)) +
  scale_color_gradient(name = "Median Income", low = "blue", high = "red") +
  # Add labels and theme
  labs(title = "Retail Store Location Analysis",
       subtitle = "Mapping Median Income and In-N-Out Burger for Selected Cities",
       x = "Longitude", y = "Latitude") +
  theme_minimal()

Step 6: Save the Results

Export the analyzed data to a CSV file for further use.

write.csv(retail_data, "real_retail_analysis_results.csv", row.names = FALSE)

Reference:

Davis, P. (2006). Spatial competition in retail markets: movie theaters. The RAND Journal of Economics, 37(4), 964-982.

Geocomputation with R

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