Tag: R Studio

Parks and its association to Average Total Alcohol Expenditure (Alcohol in Parks Program in Toronto, ON)

Welcome to my Geovisualization Assignment!

Author: Gabyrel Calayan

Geovisualization Project Assignment

TMU Geography

SA8905 – FALL 2025

Today, we are going to be looking at Parks and Recreation Facilities and its possible association to average alcohol expenditure in census tracts (due to the Alcohol in Parks Program in the City of Toronto) using data acquired from the City of Toronto and Environics Analytics (City of Toronto, n.d.).

Context

Using R Studio’s expansive tool set for map creation and Quarto documentation, we are going to be creating a thematic and an interactive map for parks and its association with Average Total Alcohol Expenditure in Toronto. The idea behind topic was really out of the blue. I was just thinking of a fun, simple topic that I wanted to do that I haven’t done yet for my other assignments! And so I landed on this because of data availability while learning some new skills at R Studio and try out the Quarto documentation process.

Data

  • Environics Analytics – Average Alcohol Expenditure (Shapefile for census tracts and in CAD $) (Environics Analytics, 2025
  • City of Toronto – Parks and Recreation Facilities (Point data and filtered down to 40 parks that participate in the program) (City of Toronto, 2011).

Methodology

  • Using R Studio to map out my Average Alcohol Expenditure and the 55 Parks that are a part of the Alcohol in Parks Program by the City of Toronto
  • Utilize tmap functions to create both a static thematic and interactive maps
  • Utilize Quarto documentation to create a readme file of my assignment
  • Showcasing the mapping capabilities and potential of R Studio as a mapping tool

Example tmap code for viewing maps

This tmap code for initializing what kind of view you want (there are only two kinds of views)

  • Static thematic map

## This is for viewing as a static map

## tmap_mode("plot") + tm_shape(Alcohol_Expenditure)

  • Interactive map

## This for viewing as a interactive map

## tmap_mode("view") + tm_shape(Alcohol_Expenditure)

Visualization process

Step 1: Installing and loading the necessary packages so that R Studio can recognize our inputs

  • These inputs are kind of like puzzle pieces! Where you need the right puzzle piece (package) so that you can put the entire puzzle together.
  • So we would need a bunch of packages to visualize our project:
    • sf
    • tmap
    • dplyr
  • These two packages are important because “sf” lets us read the shapefiles into R Studio. While “tmap” lets us actually create the maps. And “dplyr” lets us filter our shapefiles and the data inside it.
  • Also, its very likely that the latest R Studio version has the necessary packages already. In that case, you can just do the library() function to call the packages that you would need. But, I like installing them again in case I forgot.

## Code for installing the packages

## install.packages("sf")

## install.packages("tmap")

## Loading the packages

## library(tmap)

## library(sf)

We can see in our console that it says “package ‘sf’ successfully unpacked and MD5 sums checked. That basically means its done installing.

  • In addition, these warning messages in this console output indicates that we have these packages already in the latest R Studio software.

After installing and loading these packages, then we can begin with loading and filtering the dataset so that we can move on to visualizing the data itself. The results of installing these packages can be seen in our “Console” section at the bottom left hand side of R Studio (it may depend on the user but I have seen people move the “Console” section to the top right hand side of R Studio interface.

Step 2: Loading and filtering our data

  • We must first set the working directory of where our data is and where our outputs are going to go

## Setting work directory

## setwd()

  • This code basically points to where your files are going to be outputted to in your computer
  • Now that we set our working directory, we can load in the data and filter it

## Code for naming our variables in R Studio and loading it in the software

## Alcohol_Parks <- read_sf("Parks and Recreation Facilities - 4326.shp")

## Alcohol_Expenditure <- read_sf("SimplyAnalytics_Shapefiles_5efb411128da3727b8755e5533129cb52f4a027fc441d8b031fbfc517c24b975.shp")

  • As we can see in the code snippets above, we are using one of the functions that belong to the sf package. The read_sf basically loads in the data that we have to be recognized as a shapefile.
  • It will appear on the right as part of the “Environment” section. This means it has read all the columns that are part of the dataset

Now we can see our data in the Environments Section. And there’s quite a lot. But no worries we only need to filter the Parks data!

Step 3: Filtering the data

  • Since we only need to filter the data for the parks in Toronto, we only need to grab the data that are a part of the 55 parks in the Alcohol in Parks Program
  • This follows a two – step approach:
    • Name your variable to match its filtered state
    • Then the actual filtering comes into play

## Code for running the filtering process

## Alcohol_Parks_Filtered <- filter(Alcohol_Parks, ASSET_NAME == "ASHTONBEE RESERVOIR PARK" | ASSET_NAME == "BERT ROBINSON PARK"| ASSET_NAME == "BOND PARK" | ASSET_NAME == "BOTANY HILL PARK" | ASSET_NAME == "BYNG PARK"

  • As we can see in the code above, before the filtering process we name the new variable to match its filtered state as “Alcohol_Parks_Filtered”
    • In addition, we are matching the column name that we type out in the code to the park names that are found in the Park data set!
    • For example: The filtering wouldn’t work if it was “Bond Park”. It must be all caps “BOND PARK”
  • Then we used the filter() function to filter the shapefile by ASSET_NAME to pick out the 40 parks
  • We can see in our filtered dataset that we have filtered it down to 53 parks with all the original columns attached. Most important being the geometry column so we can conduct visualizations!
  • Once we completed that, we can test out the tmap function to see how the data looks before we map it out.

Step 4: Do some testing visualizations to see if there is any issues

  • Now, we can actually use some tmap functions to see if our data work
  • tm_shape is the function for recognizing what shapefile we are using to visualize the variable
  • tm_polygons and tm_dots is for visualizing the variables as either a polygon or dot shapefile
  • For tm_polygons, fill and style is basically what columns are you visualizing the variable on and what data classification method you would like to use

## Code for testing our visualizations

## tm_shape(Alcohol_Expenditure) + tm_polygons(fill = "VALUE0", style = "jenks")

## tm_shape(Alcohol_Parks_Filtered) + tm_dots()

Now, we can see that it actually works! We can see that the map above is our shapefile and the one on the bottom is our parks!

Step 5: Using tmap and its extensive functions to build our map

  • We can now fully visualize our map and add all the cartographic elements necessary to flesh it out and make it as professional as possible

## Building our thematic map

##``tmap_mode("plot") + tm_shape(Alcohol_Expenditure) +

tm_polygons(fill = "VALUE0", fill.legend = tm_legend ("Average Alcohol Expenditure ($ CAD)"), fill.scale = tm_scale_intervals(style = "jenks", values = "Greens")) +

tm_shape(Alcohol_Parks_Filtered) + tm_bubbles(fill = "TYPE", fill.legend = tm_legend("The 40 Parks in Alcohol in Parks Program"), size = 0.5, fill.scale = tm_scale_categorical(values = "black")) +

tm_borders(lwd = 1.25, lty = "solid") +

tm_layout(frame = TRUE, frame.lwd = 2, text.fontfamily = "serif", text.fontface = "bold", color_saturation = 0.5, component.autoscale = FALSE) +

tm_title(text = "Greenspaces and its association with Alcohol Expenditure in Toronto, CA", fontfamily = "serif", fontface = "bold", size = 1.5) +

tm_legend(text.size = 1.5, title.size = 1.2, frame = TRUE, frame.lwd = 1) +

tm_compass(position = c ("top", "left"), size = 4) +

tm_scalebar(text.size = 1, frame = TRUE, frame.lwd = 1) +

tm_credits("Source: Environics Analytics\nProjection: NAD83", frame = TRUE, frame.lwd = 1, size = 0.75)

  • Quite a lot of code!
  • Now this is where the puzzle piece analogy comes into play as well
    • First, we add our tmap_plot function to specify that we want it as a static map first
    • We add both our variables together because we want to see our point data and how it lies on top of our alcohol expenditure shapefile
    • Utilizing tm_polygons, tm_shape, and tm_bubbles to draw both our variables as polygons and as point data
      • tm_bubbles is dots and tm_polygons draws the polygons of our alcohol expenditure shapefile
    • The code that is in our brackets for those functions are additional details that we would like to have in our map
    • For example: fill.legend = tm_legend ("Average Alcohol Expenditure ($ CAD)")
      • This code snippet makes it so that our legend title is “Average Alcohol Expenditure ($ CAD) for our polygon shapefile
      • The same applies for our point data for our parks
    • Basically, we can divide our code into two sections:
      • The tm_polygons all the way to tm_bubbles is essentially drawing our shapefiles
      • The tm_borders all the way to the tm_credits are what goes on outside our shapefiles
        • For example:
    • tm_title() and the code inside it is basically all the details that can be modified for our map. component.autoscale = FALSE is turning off the automatic rescaling of our map so that I can have more control over modifying the title part of the map to my liking

Now we have made our static thematic map! On to the next part which is the interactive visualization!

Since we built our puzzle parts for the thematic map, we just need to switch it over to the interactive map using tmap_mode(“view”)

This code chunk describes the process to create the interactive map

library(tmap)
library(sf)
library(dplyr)


##Loading in the data to check if it works
Alcohol_Parks <- read_sf("Parks and Recreation Facilities - 4326.shp")
Alcohol_Expenditure <- read_sf("SimplyAnalytics_Shapefiles_5efb411128da3727b8755e5533129cb52f4a027fc441d8b031fbfc517c24b975.shp")

#Filtering test_sf_point to show only parks where you can drink alcohol
Alcohol_Parks_Filtered <- 
  filter(Alcohol_Parks, ASSET_NAME == "ASHTONBEE RESERVOIR PARK" | ASSET_NAME == "BERT ROBINSON PARK"
                                 | ASSET_NAME == "BOND PARK" | ASSET_NAME == "BOTANY HILL PARK" | ASSET_NAME == "BYNG PARK"
                                 | ASSET_NAME == "CAMPBELL AVENUE PLAYGROUND AND PARK" | ASSET_NAME == "CEDARVALE PARK" 
                                 | ASSET_NAME == "CHRISTIE PITS PARK" | ASSET_NAME == "CLOVERDALE PARK" | ASSET_NAME == "CONFEDERATION PARK"
                                 | ASSET_NAME == "CORKTOWN COMMON" | ASSET_NAME == "DIEPPE PARK" | ASSET_NAME == "DOVERCOURT PARK"
                                 | ASSET_NAME == "DUFFERIN GROVE PARK" | ASSET_NAME == "EARLSCOURT PARK" | ASSET_NAME == "EAST LYNN PARK"
                                 | ASSET_NAME == "EAST TORONTO ATHLETIC FIELD" | ASSET_NAME == "EDWARDS GARDENS" | ASSET_NAME == "EGLINTON PARK"
                                 | ASSET_NAME == "ETOBICOKE VALLEY PARK" | ASSET_NAME == "FAIRFIELD PARK" | ASSET_NAME == "GRAND AVENUE PARK"
                                 | ASSET_NAME == "GORD AND IRENE RISK PARK" | ASSET_NAME == "GREENWOOD PARK" | ASSET_NAME == "G. ROSS LORD PARK"
                                 | ASSET_NAME == "HILLCREST PARK" | ASSET_NAME == "HOME SMITH PARK" | ASSET_NAME == "HUMBERLINE PARK" | ASSET_NAME == "JUNE ROWLANDS PARK"
                                 | ASSET_NAME == "LA ROSE PARK" | ASSET_NAME == "LEE LIFESON ART PARK" | ASSET_NAME == "MCCLEARY PARK" | ASSET_NAME == "MCCORMICK PARK" 
                                 | ASSET_NAME == "MILLIKEN PARK" | ASSET_NAME == "MONARCH PARK" | ASSET_NAME == "MORNINGSIDE PARK" | ASSET_NAME == "NEILSON PARK - SCARBOROUGH"
                                 | ASSET_NAME == "NORTH BENDALE PARK" | ASSET_NAME == "NORTH KEELESDALE PARK" | ASSET_NAME == "ORIOLE PARK - TORONTO" | ASSET_NAME == "QUEEN'S PARK"
                                 | ASSET_NAME == "RIVERDALE PARK EAST" | ASSET_NAME == "RIVERDALE PARK WEST" | ASSET_NAME == "ROUNDHOUSE PARK" | ASSET_NAME == "SCARBOROUGH VILLAGE PARK"
                                 | ASSET_NAME == "SCARDEN PARK" | ASSET_NAME == "SIR WINSTON CHURCHILL PARK" | ASSET_NAME == "SKYMARK PARK" | ASSET_NAME == "SORAREN AVENUE PARK"
                                 | ASSET_NAME == "STAN WADLOW PARK" | ASSET_NAME == "THOMSON MEMORIAL PARK" | ASSET_NAME == "TRINITY BELLWOODS PARK" | ASSET_NAME == "UNDERPASS PARK"
                                 | ASSET_NAME == "WALLACE EMERSON PARK" |  ASSET_NAME == "WITHROW PARK")  


##Now as a interactive map
tmap_mode("view") + tm_shape(Alcohol_Expenditure) + 
  
  tm_polygons(fill = "VALUE0", fill.legend = tm_legend ("Average Alcohol Expenditure ($ CAD)"), fill.scale = tm_scale_intervals(style = "jenks", values = "Greens")) +
  
  tm_shape(Alcohol_Parks_Filtered) + tm_bubbles(fill = "TYPE", fill.legend = tm_legend("The 55 Parks in Alcohol in Parks Program"), size = 0.5, fill.scale = tm_scale_categorical(values = "black")) + 
  
  tm_borders(lwd = 1.25, lty = "solid",) + 
  
  tm_layout(frame = TRUE, frame.lwd = 2, text.fontfamily = "serif", text.fontface = "bold", color_saturation = 0.5, component.autoscale = FALSE) +
 
   tm_title(text = "Greenspaces and its association with Alcohol Expenditure in Toronto, CA", fontfamily = "serif", fontface = "bold", size = 1.5) +
  tm_legend(text.size = 1.5, title.size = 1.2, frame = TRUE, frame.lwd = 1) +
  
  tm_compass(position = c("top", "right"), size = 2.5) + 
  
  tm_scalebar(text.size = 1, frame = TRUE, frame.lwd = 1, position = c("bottom", "left")) +
  
  tm_credits("Source: Environics Analytics\nProjection: NAD83", frame = TRUE, frame.lwd = 1, size = 0.75)

Link to viewing the interactive map: https://rpubs.com/Gab_Cal/Geovis_Project

  • The only differences that can be gleaned from this code chunk is that the tmap_mode() is not “plot” but instead set as “view”
    • For example: tmap_mode(“view”)

The map is now complete!

Results (Based on our interactive map)

  • Just based on the default settings for the interactive map, tmap includes a wide range of elements that make the map dynamic!
    • We have the zoom in and layer selection/basemap selection function on the top left
    • The compass that we created is shown in the top right
    • And the legend that we made is locked in at the bottom right
    • Our scalebar is also dynamic which changes scales when we zoom in and out
    • And our credits and projection section is also seen in the bottom right of our interactive map
    • We can also click on our layers to see the columns attached to the shapefiles
  • For example, we can click on the point data to see the id, LocationID, AssetID, Asset_Name, Type, Amenities, Address, Phone, and URL. While for our polygon shapefile we can see the spatial_id, name of the CT, and the alcohol spending value in that CT
  • As we can see in our interactive map, the areas that have the highest “Average Alcohol Expediture” lie near the upper part of the downtown core of Toronto
    • For example: The neighbourhoods that are dark green are Bridle Path-Sunnybrook-York Mills, Forest Hill North and South and Rosedale to name a few
  • However, only a few parks that are a park of the program reside in these high spending regions on alcohol
  • Most parks reside in census tracts where the alcohol expenditure is either the $500 to $3000 range
  • While there doesn’t seems to be much of an association, there is definitely more factors into play as to where people buy their alcohol or where they decide to consume it
  • Based on just visual findings:
    • For example: It’s possible that people simply do not drink in these parks even though its allowed. They probably find the comfort of their home a better place to consume alcohol
    • Or people don’t want to drink at a park when they could be doing more active group – like activities

References

GeoVis: Mapdeck Package in R

Gregory Huang
Geovisualization Project, @RyersonGeo, Fall 2019

Introduction

This project is a demonstration of the abilities of the mapdeck package in R, including its shiny interactive app compatibility.

Mapdeck is an R package created by David Cooley. Essentially, it integrates some of mapbox’s functionality into the R environment. Mapbox is a popular web-based mapping service that is community-driven and provides some great geovisualization functionalities. Strava’s global heat map is one example.

I am interested in looking at flight routes across global hubs and see if there are destination overlaps for these routes. Since the arc layer provided by mapdeck has impressive visualization capabilities of the flight routes, I’ve chosen to use mapdeck to visualize some flight route data around the world.

Example of a map generated by mapdeck: arcs, text, lines, and scatterplots are all available. Perspective changes can be done by pressing down Ctrl and clicking. The base maps are customizable with a massive selection of both mapbox and user-generated maps. This map is one of the results from longest_flights.R, which uses the “decimal” basemap.
The Map has some level of built-in interactivity: Here is an example of using a “tooltip” where if a user hovers over an arc, the arc highlights and shows information about that particular route. Note that mapdeck doesn’t want to draw flight routes across the Pacific – so if accuracy is key, do keep this in mind.

Software Requirements

To replicate this project, you’ll need your own mapbox access token. It is free as long as you have a valid email address. Since the code is written in R, you’ll also need R and R Studio downloaded on your machine to run the code.

Tl;dr…

Here’s the Shiny App

The code I created and the data I used can also be found on my GitHub repository, Geovis. To run them on your personal machine, simply download the folder and follow the instructions on the README document at the bottom of the repository page.

Screenshot of the shiny app: The slide bar will tell the map which flights to show, based on the longitudes of the destinations. All flights depart out of YYZ/KEF/AMS/FRA/DXB.

Details: Code to Generate a Map

The code I’ve written contained 2 major parts, both utilizing flight route data. The first part is done with longest_flights.R, demonstrating the capabilities of the mapdeck package using data I curated for the longest flights in the world. The second part is done with yyz_fra.R and shinyApp.R to demonstrate the shiny app compatibility and show how the package handles larger datasets (hint – very well). The shinyApp uses flight route data from 5 airports: Toronto, Iceland-Keflavik, Amsterdam, Frankfurt, and Dubai, pulled from openflights.org.

For the flight route data for the 5 airports, in particular, the data needed cleaning to make the data frame useable to mapdeck. This involved removing empty rows, selecting only the relevant data, and merging the tables.

Code snippet for cleaning the data. After the for loop completes, the flight route data downloaded from openflights.org becomes available to be used for mapdeck.

Once the data were cleaned, I began using the mapdeck functions to map out the routes. The basic parts of the mapdeck() function are to first declare your key, give it a style, and assign it a pitch if needed. There are many more parameters you can customize, but I just changed the style and pitch. Once the mapdeck map is created, use the “pipe” notion (%>%) to add any sort of layers to your map. For example, add_arc() to add the arcs seen in this post. Of course, there are many parameters that you can set, but the most important are the first three: Where your data come from, and where the origin/destination x-y coordinates are.

An example creating an arc on a map. In addition to the previously mentioned parameters, tooltip generates the little chat boxes when you hover over a layer entry, and layer_id is important when there are multiple layers on the same map.

Additional details on creating all different types of layers, including heatmaps, can be found on the documentation page HERE.

Details: Code to make a “Shiny” app

On top of the regular interactive functionalities of mapdeck, incorporating a mapdeck map into shiny can add more layers of interactivity to the map. In this particular instance, I added a slider bar in Shiny where the user can indicate the longitudes of the destinations they want to see. For example, I can filter to see just the flights going to East Asia by using that slider bar. Additional functions of shiny include using drop-menus to select specific map layers, and checkboxes as well.

The shiny code can roughly be broken down into three parts: ui, server, and shinyApp(ui, server). The ui handles the user interface and receives data from the server, while the server decides what map to produce by the input given by the user in ui. shinyApp(ui,server) combines the two to generate a shiny app.

Mapdeck integrates into the shiny app environment by mapdeckOutput() in ui to specify the map to display, and by renderMapdeck() and mapdeck_update() in server to generate the map (rendeerMapdeck) and appropriate layers to display (mapdeck_update).

Below is the code used to run the shiny app demonstrated in this blog post. Note the ui and server portions of the code bode. To run the shiny app after that, simply run shinyApp(ui,server) to generate the app.

Creating the UI
Snippet of the Server creation section. Note that the code listens to what the UI says with reactive() and observeEvent().

This concludes my geovis blog post. If you have any questions, please feel free to email me at gregory.huang@ryerson.ca.

Here is the link to my GitHub repository again: https://github.com/greghuang8/Geovis