This document presents a spatial analysis of Starbucks locations in the Toronto and Mississauga regions, including the creation of interactive leaflet maps and visualization of Starbucks locations using mapview.
# Load necessary libraries
library(sf)
library(leaflet)
library(leafem)
library(dplyr)
library(here)
library(mapview)The data preparation process involved loading the Starbucks locations dataset and filtering it to include only stores located in Toronto and Mississauga. This filtering ensured that the analysis focused solely on these two regions of interest. Additionally, the dataset was converted into a spatial object for visualization purposes, enabling the creation of maps to display the distribution of Starbucks stores effectively.
# Load the dataset
starbucks_data <- read.csv(here("r_data", "directory.csv"))
head(starbucks_data)## Brand Store.Number Store.Name Ownership.Type
## 1 Starbucks 47370-257954 Meritxell, 96 Licensed
## 2 Starbucks 22331-212325 Ajman Drive Thru Licensed
## 3 Starbucks 47089-256771 Dana Mall Licensed
## 4 Starbucks 22126-218024 Twofour 54 Licensed
## 5 Starbucks 17127-178586 Al Ain Tower Licensed
## 6 Starbucks 17688-182164 Dalma Mall, Ground Floor Licensed
## Street.Address City State.Province Country
## 1 Av. Meritxell, 96 Andorra la Vella 7 AD
## 2 1 Street 69, Al Jarf Ajman AJ AE
## 3 Sheikh Khalifa Bin Zayed St. Ajman AJ AE
## 4 Al Salam Street Abu Dhabi AZ AE
## 5 Khaldiya Area, Abu Dhabi Island Abu Dhabi AZ AE
## 6 Dalma Mall, Mussafah Abu Dhabi AZ AE
## Postcode Phone.Number Timezone Longitude Latitude
## 1 AD500 376818720 GMT+1:00 Europe/Andorra 1.53 42.51
## 2 GMT+04:00 Asia/Dubai 55.47 25.42
## 3 GMT+04:00 Asia/Dubai 55.47 25.39
## 4 GMT+04:00 Asia/Dubai 54.38 24.48
## 5 GMT+04:00 Asia/Dubai 54.54 24.51
## 6 GMT+04:00 Asia/Dubai 54.49 24.40# Filter data for a region of interest (e.g., Toronto and Mississauga)
region_data <- starbucks_data %>%
filter(City %in% c("Toronto", "Mississauga"))
head(region_data)## Brand Store.Number Store.Name Ownership.Type
## 1 Starbucks 75482-115327 Compass @ University of Toronto Mis Licensed
## 2 Starbucks 75924-102701 YYZ Toronto Int'l Term Pier F Upper Licensed
## 3 Starbucks 23283-228465 1016 Eglinton Ave E Company Owned
## 4 Starbucks 16013-164904 Compass @ Sheridan College, Mississ Licensed
## 5 Starbucks 20593-206884 5029 Hurontario St Company Owned
## 6 Starbucks 4665-100880 3035 Argentia Rd Company Owned
## Street.Address City
## 1 3359 Mississauga Rd N Mississauga
## 2 6301 Silver Dart Drive, Terminal 1 Mississauga
## 3 1016 Eglinton Avenue East Mississauga
## 4 4180 Duke of York Blvd Mississauga
## 5 5029 Hurontario Street, Unit 1 Mississauga
## 6 3035 Argentia Road, unIT 7, Square One Shopping Centre Mississauga
## State.Province Country Postcode Phone.Number Timezone
## 1 ON CA L5L 1C6 GMT-05:00 America/Toronto
## 2 ON CA L5P 1B2 416-776-3100 GMT-05:00 America/Toronto
## 3 ON CA L4W 1K3 (905) 624-9088 GMT-05:00 America/Toronto
## 4 ON CA L5B 0G5 GMT-05:00 America/Toronto
## 5 ON CA L4Z 3X7 9055070561 GMT-05:00 America/Toronto
## 6 ON CA L5N 8P7 905-785-7667 GMT-05:00 America/Toronto
## Longitude Latitude
## 1 -79.66 43.55
## 2 -79.62 43.68
## 3 -79.63 43.63
## 4 -79.64 43.59
## 5 -79.65 43.61
## 6 -79.79 43.60In this section, we utilize the Leaflet package to create an interactive map illustrating the Starbucks locations in Toronto and Mississauga. The map highlights the geographical distribution of stores in the region and provides additional details about each outlet upon clicking.
# Check if the filtered dataset contains valid values
print(unique(region_data$City))## [1] "Mississauga" "Toronto"# Create a color palette and scheme according to the variable mapped
# Assuming there's a variable representing the number of stores in each city
color_palette <- colorFactor(c("blue", "red"), domain = c("Toronto", "Mississauga"))
# Create the leaflet map
toronto_map <- leaflet(data = region_data) %>%
# Add light tiles for a clearer map view
addProviderTiles("CartoDB.Positron") %>%
# Add circle markers for Starbucks locations with color ramp based on City
addCircleMarkers(
lng = ~Longitude,
lat = ~Latitude,
color = ~color_palette(City),
fillOpacity = 0.5,
radius = 5,
popup = ~paste("<b>Store Name:</b>", `Store.Name`, "<br>",
"<b>Address:</b>", `Street.Address`, "<br>",
"<b>City:</b>", City)
) %>%
# Add legend for the marker color ramp
addLegend(
position = "topright",
pal = color_palette,
values = ~City,
title = "City"
) %>%
# Add Leaflet Map Caption
addControl(
html = "<div class='leaflet-control leaflet-control-custom'>This interactive Leaflet map displays the distribution of Starbucks locations in Toronto and Mississauga. Each marker represents a Starbucks store, with colors distinguishing between Toronto (blue) and Mississauga (red). Clicking on a marker reveals additional information such as store name, address, and city.</div>",
position = "bottomleft"
)
# Show the map
toronto_mapThis interactive map visualizes the Starbucks locations in Toronto and Mississauga. Each circle marker represents a store, with the color indicating the respective city. Clicking on a marker reveals details such as the store name and address.
In this section, we employ the mapview package to create a spatial map displaying Starbucks locations in Toronto and Mississauga. The map offers a different perspective on the distribution of stores and allows for easy comparison between the two cities.
# Convert region_data to a spatial object
GTA_Region <- st_as_sf(region_data, coords = c("Longitude", "Latitude"), crs = 4326)
# Define colors for each city
colors <- c("blue", "red") # Blue for Toronto, Red for Mississauga
# Define city names for the labels
city_labels <- c("Toronto", "Mississauga")
# Assign colors to cities
GTA_Region$city_color <- factor(GTA_Region$City, levels = city_labels)
# Display the map using mapview with colors for each city
map <- mapview(GTA_Region, zcol = "city_color", col.regions = colors, legend.title = "Cities")
# Display the map with caption
mapThis map provides a spatial representation of Starbucks locations in Toronto and Mississauga. Each point corresponds to a store, with colors indicating the respective city. The map offers insights into the geographical spread of Starbucks outlets across the two regions.
The spatial analysis of Starbucks locations in Toronto and Mississauga reveals interesting insights into the distribution patterns of the popular coffee chain within these regions. Through the visualization of store locations using Leaflet and mapview, we observed the following:
Geographical Distribution: Both maps showcase the geographical spread of Starbucks stores, highlighting areas of high concentration as well as areas with fewer outlets.
City-wise Comparison: The maps allow for a comparison between Toronto and Mississauga, revealing differences in the density and distribution of Starbucks stores between the two cities.
Accessibility: Analyzing the spatial distribution of Starbucks outlets can provide insights into accessibility for residents and visitors, as well as potential market saturation in certain areas.