Mapping Child Friendly City Initiatives in Canada and in the World using ArcGIS StoryMaps

Anastasiia Smirnova
SA8905 Geovis project, Fall 2022


Through this project I wanted to gain and advance my skills in both storytelling and visualizing spatial data. Here you can learn more about my attempt of using ArcGIS StoryMaps to highlight the importance of including children in the urban planning agenda and to show the World- and Canada-wide spatial patterns of urban areas’ commitment to creating inclusive urban environments with children in mind.

I did it by mapping municipalities that are participating in UNICEF’s Child Friendly Cities Initiatives (CFCI), which aim to promote cities where the “ voices, needs, priorities and rights of children are an integral part of public policies, programs and decisions.”


I used ESRI’s ArcGIS Pro, Online Map Viewer and StoryMaps for my project. First, I used the desktop app (ArcGIS Pro) to import my data and create my initial maps. After that I uploaded the layers that I wanted to use as web layers to my ArcGIS account, and then I finalized them using ArcGIS online applications. I used the online map viewer to adjust symbology as necessary as was trying to figure out what worked better for each part of my story. It was easy to go back and forth between the Map Viewer and StoryMaps – to make the necessary changes, then to see how the updated maps work with the story, and then repeat these steps as needed. The Map viewer generally had the functionality I needed to change my map symbology and I did not have to go back to ArcGIS Pro too often to make modifications after I uploaded my layers online.

I liked the functionality of StoryMaps. I used the sidecar option to introduce my story, and for showing most of my maps. I find that this block type provides some of the most immersive experience while scrolling, so I used it for the parts of the story that I wanted to keep the reader’s attention on.

I found that the swipe option worked well for showing comparisons. In a regular map, it is often difficult to show all information you want without cluttering the map with too many layers and making the map unreadable. The swipe option can help solve this problem. As such, I used this function to show how many children did (not) live within the municipalities that were part of CFCI and therefore could (not) benefit from the initiative.

the map shows distribution of children and youth residences (on the left, yellow and red) and municipalities involved in CFCI (on the right, blue)

For inserting your maps to any blocks of StoryMaps, you can choose to either use your maps uploaded as images or insert the actual interactive online maps. While the image option has some benefits, such as more flexibility in styling the map and faster loading, the main benefit of inserting the actual online maps is interactivity. You can zoom in and out, search for a specific location, show/hide legend, learn more about each unit on the map and so on (as the creator of the story, you can edit and set restrictions of what readers can and cannot do with your online maps).

Since I wanted to keep my maps as simple visually as possible, I went with the second option. This way, if the reader wanted to learn more about my maps and the information they displayed, they could do so by using the interactive map functions.

Interesting findings

In addition to the main message of the project (the need to promote child friendly cities), the maps showed how the choice of data, scale and mapping methodology can influence the results and representation. On the CFCI website, the main map was showing all countries that were involved in the CFCI. The map did not consider how many municipalities in each country were actually involved in the initiatives.

The main map from the UNICEF CFCI website – CFCI countries

This way of displaying data may be misleading, since the level involvement of each country varied greatly. In some countries, most of the territory was part of CFCI, but some other countries only had a couple municipalities each with UNICEF’s child friendly initiatives.

For this story, in addition to the world CFCI country map similar to the one from the website, a proportional symbol map was created to show how many municipalities from each country were actually involved in the CFCI and I put these two maps in one sidecar block so that the reader could swipe back and forth to see how the distribution of CFCI changed with the change of the variable, and what the actual level of involvement if each country was.

A map from my StoryMap – Municipalities involved in CFCI

When zoomed in, even more information about the unevenness and clustering in the spatial distribution of the CFCI municipalities can be discovered.

The sidecar block (I used the float side by side option for my maps), and the smooth transitions it provided, worked well for showing the differences between the maps, as well as for zooming in into a smaller scale map.


Some of the main challenges for me were associated with updating the maps if I wanted to change something. It took some time for me to figure out what could be done at which step of the process (with different apps) and how far back I had to go to modify something. As such I had trouble updating and modifying the legends for the maps.

Unfortunately, the options for adjusting the legends using the ArcStory editor or the online map viewer were limited. For instance, it was impossible to hide or edit the name of the column which contained data used in the map while using the online apps. Since I was creating my original layers in ArcGIS Pro, then uploading them as web layers, and then adjusting my maps further in the online map viewer, it was difficult to go back to change the original data in the end, just to modify one little line on the map legend. Only some parts of the legend could be modified using the online apps. So, one of the lessons I took from this experience is that you need to make sure all the column names are appropriate before making all the edits online if you are using a similar process as I did. It is also helpful to think about the legends right from the start.

Conclusion and results

In general, I am satisfied with the ArcGIS StoryMap platform. It was easy to use, and it did a good job of assisting me in creating a map-based story that looks clean and flows smoothly. I am planning on further exploring the StoryMap functionality in the future.

If you are interested in learning more about child friendly cities and seeing my StoryMap result, you can follow this link:

Canadian cities and towns for happy children ( Mapping Child Friendly City Initiatives in Canada and in the World using ArcGIS StoryMaps

Locations of Music Videos from Reggaeton Artist Daddy Yankee

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2021

By: Katrina Chandler

For my GeoVisualization Project, I chose to map locations of music videos by the Reggaeton artist, Daddy Yankee, using ArcGIS Story Map. Daddy Yankee has been producing music and making music videos for more than 20 years. I got the idea for this project when watching his music video ‘Limbo’.

Data Aggregation

Official music videos were selected from Daddy Yankee’s YouTube channel. Behind the scenes videos on Daddy Yankee’s YouTube channel and articles from various sources were used to locate cities where these videos were filmed. Out of the 56 official videos, excluding remixes and extended versions, I was able find the locations for 27 of the Daddy Yankee’s music videos. It should be noted that this project has minimal information about Daddy Yankee as the focus of it was the locations where the music videos were filmed.

Making the Story Map

To display my project, I decided to use story map tour as it allows multimedia content and text to be displayed side by side with a map. I started by logging into ArcGIS story map, selected new story then selected guided map tour.

I entered a title for my project then looked into changing the base map. I also wanted to change the zoom to a level appropriate for the music video locations. To do this I selected map options (in the top right corner), changed my base map into imagery hybrid and changed my initial zoom level to city. I chose imagery hybrid as it will help me locate the cities better and I prefer the look of it.

I added my multimedia content, i.e. YouTube links, by selecting ‘add image or video’. I selected ‘link’ and pasted the video link in to the appropriate box. I added text stating where the video was filmed, when it was released (uploaded) on Daddy Yankee’s YouTube channel and any additional information I found.

After entering the multimedia content and text, I added the location on the map that corresponds with the slide. To do this, I selected add location, zoomed into the city and then clicked to drop the location point. Another way to add a location point to the map is to ‘search by location’.

While dropping location points on the map, I did not get all as precise as I would have liked the points to be so I edited them. I selected ‘edit location’ then either clicked and dragged the point or deleted it completely and dropped a new point. In the figure below, there are red edges around the 22nd point. This signifies that the point has been selected and can be dragged to its new location. It can also be deleted by clicking on garbage bin icon (at the bottom centre of the picture). If deleted a new point was reselected.

Dependent on what the user wants, the level of zoom can be different on each slide. To change the zoom level, simply zoom in or out of the current map then select ‘use current zoom level’. This worked well for me when I wanted to show exact locations of where a video was filmed. Slides 6, 11, 14, 18, 19, 22 and 26 in the story map show pin point locations of the following respectively: the Faena Hotel, Hôtel de Glace, Comprehensive Cancer Centre of Puerto Rico, Escuela Dr. Antonio S. Pedrerira, Puerto Rico Memorial Cemetery, Centro, Ceremonial Otomi and La Bombonera Stadium. Pinpoint locations were compared to google maps to ensure the correct placement of the location point. These pinpoint locations are where the music videos were partially or fully filmed.

To change the design of my story map, I clicked ‘design’ at the top of the page and selected the Obsidian theme. To change the colour of my text, I highlighted it, clicked the colour palette and selected the colour I wanted.

There is an option to add multiple media to one slide. To do this, click the ‘+’ icon at the top of slide and upload a file or add a link. To play the music video, select play (like how it is on YouTube) and select full screen if you like. To open the YouTube link in a new window, click the title of the music video. If the user wants to reorder the multimedia content, they have to click the icon with three horizontal lines and a new window will open. There the user can reorder the content by dragging it to the where they like it to be seen. In order to see the multimedia content in one slide, the user clicks the right (and left) arrow as seen below. To see the credited information, hover over the information icon (i) at the top left of the page.

To add a slide, select ‘+’ at the bottom right of the story map. To change the layout, select the ‘…’ at the bottom left of the story map and customize. The first option is Guided where you can select if you want the story to be map focused or media focused. The second option is Explorer where you can select if you want the slides to be listed or in a grid format. To rearrange a slide, select it and drag to the new position.

Although this project is based on media content, I decided to use guided map focus as it is best suited for this GeoVisualization project. The order of this project was based on the dates the music videos were released on Daddy Yankee’s YouTube channel. It is in chronological order starting with the newest upload to the oldest upload. Below is a picture to visualize the locations of the music videos from this project.


A few of the music videos were filmed in multiple locations. I was only able to add one location point per slide so I select the point based on interest or where the majority of the video was filmed. The song Con Calma had 2 filming locations, however Daddy Yankee filmed his part in Los Angeles so Los Angeles was selected for the location point. Another issue was that eight of the music videos were filmed in Miami, Florida and no precise locations were found for these videos. To allow the viewer to read the name of the city clearly, at the selected zoom level, point locations were placed around the name of the city instead of directly on top of it. This was taken into consideration for all locations. Unfortunately, one of the precise locations (Puerto Rico Memorial Cemetery – slide 19) had a fair amount of cloud cover so the full location could not be seen clearly. I also had an issue changing the story map title and slide titles text colour. Data collection was the most difficult part of this project. The sources of this data (articles) are not scholarly peer reviewed and can be considered a limitation as the accuracy of their data is unknown.

Toronto Maple Leafs Game-Day Guide

Author: Olivia Kariunas

Geovisualization Project Assignment @RyersonGeo, SA8905, Fall 2021

Project Link:


The inspiration behind creating this geovisualization project stems from my own curiosity about Toronto’s tourism industry and love of the hometown hockey team. There have been numerous instances where I found myself stressed and anxious about planning a stay within Toronto due to the overwhelming number of options for every element of my stay. I wanted to create content in an interactive manner that would reduce the scope of options in terms of accommodations, restaurants, and other attractions in a user-friendly way. With a focus on attending a Toronto Maple Leafs game, I have created an interactive map that presents readers with hotels, restaurants and other attractions that are highly reviewed, along with additional descriptions that may provide useful to those going to these places for the first time. Each of these locations are located under 1 kilometer from the Scotiabank Arena to ensure that patrons will not require extensive transportation and can walk from venue to venue. Also, the intent behind the interactive map is to increase fan engagement by helping fans find a sense of community within the selected places and ease potential stressors of planning their stay. For a Toronto Maple Leafs fan, the fan experience starts before the game even begins.

Why Story Map?

Esri’s Story Map was chosen to conduct this project because it is a free user-friendly method that allows anyone with an Esri Online account to create beautiful stories to share with the world. By creating a free platform, any individual or business can harness the benefits of content creation for their own personal pleasure or for their small business. Furthermore, the Shortlist layout was chosen to include images and descriptions about multiple locations for the Story Map to give readers visual cues of the locations being suggested. The major goal behind using this technology is to ensure that individuals in any capacity can access and utilize this platform by making it accessible and easy to understand.


To obtain the data for the specific locations of the hotels, restaurants, and other attractions, I inspected various travel websites for their top 10 recommendations. From these recommendations, I selected commonalities among the sites and included other highly recommended venues to incorporate diversity among the selection. For the selected hotels, I attempted to include various category levels to accommodate different budgets of those attending the Leafs game. Additionally, all attractions chosen do require an additional purchase of tickets or admission, but vary in price point as well.

Creating Your Story Map

Start the Story Map Shortlist Builder using a free ArcGIS public account on ArcGIS Online.

Create a title for your interactive map under the “What do you want to call your Shortlist?”. Try to be as creative, but concise, as possible!

The main screen will now appear. You can now see your title on the top left, as well as a subtitle and tabs below. To the right, there is a map that you can alter as you like. To add a place, click the “Add” button within the tab frame. This will allow you to create new places that you want to further describe.

Story Map Project Main Screen

A panel will appear where you can enter the name of the chosen destination, provide a picture, include text, and specify its location. You can include multiple images per tab using the “Import” feature. Once the location has been specified using the venue’s address, a marker will appear on the map. You are able to click and drag this marker to any destination that you choose. The colour of the marker correlates to the colour of the tab. Additionally, you can include links within the description area to redirect readers to the respective venue’s website.

Completed location post with title, image, and description.

Click the “+” button on the top right hand corner of the left side panel to add more destinations. The places that you add will show as thumbnails on the left side of the screen. Click the “Organize” button underneath the tab to reorder the places. You can order these in any way that seems logical for your project. Click “Done” when satisfied.

To create multiple tabs, click the “Add Tab” button. To edit a tab, click the “Edit Tab” button. This will allow you to change the colour of the tab and its title.

The Edit, Add, and Organize Tabs can be found to the right of the other tabs and above the map.

To save your work, press the “Save” button occasionally, so all of your hard work is preserved.

There are also optional elements that you can include as well. You can change the behaviour and appearance of your Shortlist by clicking the “Settings” button. You are able to change the various functions people can utilize on the map. This includes implementing a “Location Button” and “Feature Finder” where readers can see their own location on the map and find specific locations on the map, respectively. You are also able change the colour scheme and header information by clicking on their tab options. Hit “Apply” when satisfied.

Settings options tab

To share your Shortlist click the “Save” button and then click the “Share” button. You can share publicly or just within your organization. Additionally, you can share using a url link or even embed the Story Map within a website.

Final output of content

Limitations & Future Work

The main limitation of this project was selecting what venues to include. Toronto is a lively city with an overwhelming amount of options for visitors to choose from, resulting in many places being overlooked or unaccounted for. Overall, the businesses chosen represent a standard set of places for those who are unfamiliar with the city. To include a more diverse set of offerings, an addition to the current project, or an entirely new project, can be created to include places that provide more niche products/services. Furthermore, a large portion of the venues chosen were selected from travel/tourism advisory websites where the businesses on the sites may pay a fee to be included, thus limiting the amount of exposure other businesses may have.

Overall Thoughts

Story Map was simple to understand and the platform was aesthetically pleasing. My only reservations about this program is the limited amount of stylization control in terms of the text and other design elements. I would most likely use this platform again, but may attempt to find a technology that allows for more control over the overall appearance and settings of the geovisualization.

Thank you for reading my post. Have fun creating!

Building digitization using Artificial Intelligence – an Open Source approach

By Nikita Markevich

Geovisualization Project Assignment, SA8905, Fall 2021


With the development of automation and machine learning, a new approach in raw data acquisition has been opened for people to try. QGIS is a popular open-source GIS software that allows the creation of custom plugins for all sorts of geoprocessing. One such plugin is called Mapflow, developed by Russian-based company GEOAlert. Mapflow is an easy-to-use plugin to retrieve ground data from satellite imagery such as buildings, roads, construction zones, and forest canopies. This blog will introduce how to use Mapflow through a browser environment. To learn how to use the plugin, please refer to the Esri Story Maps tutorial through this link:

The difference between the use of Mapflow in the browser and through the plugin is that browser only allows detection from web-based satellite services such as Mapbox, or custom imagery through URL, while in the plugin, custom satellite imagery can be processed straight from the user’s device. The major advantage of the browser approach is that the process is using remote servers which is faster than the plugin process.

Mapflow website project page


Mapflow online service uses free to try system by giving 500 free credits when opening an account. Each process requires credits based on the size of the data area that the user wishes to process. If the user runs out of credits, it is possible to top up the balance in the top right corner for the price of 100 CAD per 1000 points.

Let’s explore the project page. The project is organized in steps where the user can choose the data source, the type of AI Model that the user wishes to run, and post-processing operation for additional data gathering. AI Models that are available in the browser copy the models which are available in the QGIS plugin. AI can provide digitization for buildings, high-density housing, forests, roads, construction, and agricultural fields.

User Interface of the Data source tab in Mapflow. Mapbox API is used to display geographic data.

In the data source tab, a user can either use the embedded draw tool to choose the area for processing or upload polygon data in GEOJSON format. The draw rectangle tool is very intuitive in its use and as soon as it’s drawn, the website provides the area’s size in squared kilometers. This number is used by the website to determine how many credits are required to process the area. The larger the area, the more credits it costs to process.


The area of interest for this example would be focused on the same area as was used in the plugin tutorial in Esri Story Maps: the city of Ciego De Avilo in Cuba. The drawn rectangle over the city and closest suburbs estimated the area to be 45.31 squared kilometers. Originally the area was raised to my attention when I was doing some research project for the company I work for to explore the possibility of constructing fiber service in the Caribbean region. While searching for building and road data through open sources such as OpenStreetMap, I realized that some Caribbean countries and especially Cuba is missing geographic data that is required to create a fiber map model. After exploring several options, the plugin Mapflow proved to be most useful to generate geodata from available free commercial satellite imageries.

Selected Area of the City of Ciego De Avila chosen through draw rectangle tool in the data source page of Mapflow


The Chosen area is now inputted in our project. The next steps would be to choose the model and post-processing data. We will choose a buildings model to test the speed of the browser process and compare it with the plugin process. The big perk of the browser tool is the post-processing options. One such option is automatic polygon simplification, which would simplify the results of the model. In the plugin version, the results of the model outputted some building polygons in broken shapes or fuzzy polygons. That would create additional work post-processing polygons manually. The browser tool offers that option for free.

Project window of Mapflow right before the beginning of the process.

The area of interest costs 227 credits to be processed, which means that every 100 squared kilometers processed costs 500 points.

As soon as the Run processing button is pressed, the final step is to wait for the process to finish and download the processed data. The process finished in 32 minutes. That is 15 minutes faster than in the plugin process, which was 47 minutes.

After the process is finished, the user can view the results in the browser and download the file in the GEOJSON format.

Data results in the browser window

The process assigns id numbers to each shape as well as shape types, such as rectangle, grid snap, or l-shape. This information can help with further post-processing and solve any automation mistakes.


The most important limitation of this tool is its cost, however, if the user decides to process an area larger than 100 square kilometers, one can create multiple accounts and use free credits each time. Secondly, the processed results sometimes output shapes that are very questionable in their nature. Some polygons merged multiple buildings into ones, others detected buildings partially, in other cases the orientations of polygons are off. This can be fixed in the manual post-processing by GIS professionals.

In the future, this tool can be potentially be used to populate the OpenStreetMap dataset with the building polygons and roads data. Open Source data is very important for many gis users, and AI automation is the perfect companion that makes the work of GIS enthusiasts much easier by streamlining the most tedious processes in geographic analysis.

Toronto’s Waterfront Parking Lot Transformation

Author Name: Vera Usherovich

StoryMap Project link:

SA 8905 Fall 2020


During one of my study breaks, I was looking at aerial photographs of Toronto’s Waterfront. One thing in particular caught my attention; the parking lots. I did not grow up in Toronto and had no idea how drastically different the waterfront area looked like. I kept on opening up images from various years and comparing the changes. The Waterfront area was different; at first the roundhouses disappeared and followed by parking lots and industrial warehouses. This is the short answer to what inspired this StoryMap. I wanted to see how the surface of our city changed over time, specifically the role of parking lots.

Key Findings

  1. There has been a 32 % reduction in surfaces dedicated to parking lots between 2003 and 2019.
  2. Even though there are fewer parking lots, there is a similar proportion of parking lot size surfaced between 2003-2019.
  3. Many of the parking lots in the entertainment district turned into condos.

About the StoryMap


For this project, I used areal photographs from the City of Toronto, works and Emergency Services. I chose 2003 and 2019 as my years to compare.

Platform and Method

The digitization process was done through Esri’s software, ArcMap. I then exported the layers into gis online and made a map. this map was embedded into the StoryMap with adjustment to the layers. Additionally, I cross-referenced information with google maps, to identify what has replaced the parking lots (broke into 4 categories: residential, commercial, public, and other).


Note: The data showcased in this story and maps is based on manual aerial photograph digitization. Some features might have been inadvertently missed or incorrectly categorized.

Future Work

This can be done for a wider range of years. Also, a more comprehensive classification of what is no longer a parking lot could be described in greater detail.

Toronto Theft: A Neighbourhood Investigation

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019

By: Julia DiMartella-Orsi


ESRI’s creation of the Story Map changed the way we could visualize data. Not only did it allow for a broader audience to interact and create their own maps due to its easy to use design, it also contained many new amazing functions, templates, and themes. Users can personalize their story by adding in their own images, text, videos, and map layers by creating their own free ArcGIS Online account. Popular templates include Map Series, Tour, Journal, and Cascade.

Get started making your own Story Map here:

Creating Your Story Map:

Once you have selected the template you want to use the choice is up to you. By clicking the “+” symbol you can choose to include text, media sources such as a videos, a new title page, or immersive content such as a web map.

ESRI also designed Story Maps to link to outside content and various social media sites such as Flickr and Unsplash. ‘Link to Content’ is also extremely useful as it allows users to add photos and videos found on the internet directly to their story map by copying and pasting their link.

To add interactive web maps into your story map users can link map layers from their ArcGIS Online account. Layers can be created in ArcGIS Online, but also in ArcMap where layers are exported as a zip file and imported onto your ArcGIS Online base map. Map layers can also be found online using the ‘add layer from the web’ or ‘search for layers’ options.  The layers that appear are based on the type of ArcGIS Online account you have created. Enterprise accounts contain additional layers provided by your organization, however ESRI also has free downloadable layers available for users without an organization.

Users also have the option to make their story maps public by clicking the globe icon, or private for their own personal use by clicking the lock icon. To save your story map select the floppy disk icon. Your saved map will appear under ‘My Content’ in your ArcGIS Online account.

My Story and Creating Web Maps:

Over the last few years, theft in Toronto has been increasing at a rapid rate. According to the Toronto Police Service, Toronto experienced a total of 5430 thefts between 2014-2018. However, these are only those that have been reported and documented by police. In order to analyze the distribution of theft across the city, the Toronto Police created a point dataset that summarized when and where each theft took place. Additional datasets were also created for prominent types of theft such as bicycle and auto theft.

To compare the number and types of theft in each Toronto neighbourhood I decided to create a story map using the Cascade template. This created a scrolling narrative that would allow viewers to observe the data in a clear, unique way. The reason why I chose to use a story map was due to the number of layers I wanted to compare, as well as use the ‘swipe tool’ to easily compare each neighbourhood. Therefore, I created a series of choropleth maps based on the 2014-2018 theft/crime data from the Toronto Police Open Data Portal.

The following steps were used to create each web map used in my Story Map:

Step 1: Download the point data and add the layer into ArcMap.

Step 2: Use the ‘spatial join’ analysis tool and select your neighbourhood boundary file as the target layer and the theft point data as the join feature. Make sure to select ‘join one to one’. This will produce a new layer with a ‘count’ field that counts the number of thefts in each neighbourhood – each neighbourhood is given a count.

Step 3: In order to produce accurate results, you must normalize your data. To do so add a new field into your attribute table (same layer with the count field) titled ‘Area’, and right click to select ‘calculate geometry’. Change the property to ‘area’ and choose the units you wish to use. Click ‘ok’ and the results will populate your new field.

Step 5: Export the layer and save it as a compressed zip folder. Import the data into ArcGIS Online by clicking the “Add” tab.

Step 6: Once you import your layer you are given a variety of styles to choose from. Select the one you like best (ex: choropleth) as well as the field you wish to map – in this case select ‘count’. To normalize ‘count’ select the ‘divided by’ dropdown and choose your ‘Area’ field. Change the colour of your map to your preference by clicking ‘symbols’.

Step 7: Save your layer to and select the tags that relate to your topic. The layer will now appear in ‘My Content’ where it can be added to your Story Map.

Step 8: To compare each layer add both layers you wish to compare to your story map by using the “+” symbol. Once you have done so, choose the transition type (ex: horizontal swipe) you want to use by clicking on the arrow below. The transition will take place as the user scrolls through your story map.

My Story Map titled “Toronto Theft: A Neighbourhood Investigation” can be viewed here:

Actually Snapchat Follows You…

by Lindsay Ginou
Geovis Class Project @RyersonGeo, SA8905, Fall 2017


This Story Map is part of a lesson plan developed for Grade 9 Issues in Canadian Geography in the Department of Geography and Environmental Studies at Ryerson University.

This screen shot is the introduction of Snap Maps and background on Snap Inc.

Story Map is a template for publishing text, interactive maps, and other media as a package. It is provided by ESRI and is only available on ArcGIS Online. You can apply for limited ArcGIS Online account for free, or, if you are apart of a larger organisation that can afford it, a full account at a cost.

This lesson is intended to teach students about how major social media platforms (specifically Snapchat) can geo-code their uploaded photos allowing others to follow the student’s location and learn details about a student’s life that maybe they didn’t indent the viewer to know.

This Story Map

This style of Story Map is called “Short List.” As you can see, it shows places of interest organized into a set of tabs. It would be particularly useful for travel diaries or trip guides. It is very easy to create. Just upload the pictures you want to use and either type in an address that you want them associated with, or click and drag the marker to the appropriate location on the map. A description can be added to every picture. Viewers can either select the picture under the tab they are interested in and its marker will be highlighted, or select the marker and the picture will be highlighted.

What It Looks Like
The first tab is a warm up section providing basic information on Snapchat, Instagram, Facebook, Twitter, and Niantic (Pokemon Go) and it allows the students to make some spacial observations about the locations of these companies headquarters.

This screenshot is of the first tab where students learn about different social media platforms that make use of geo-coding.

The second tab shows a Snapmap-like account for a fake student named, Lily. Using the worksheets provided with the lesson plan, students piece together details about Lily’s life and whereabouts, and reflect on what they are telling people with their own social media accounts.

Locations in Toronto that Lily has Snap-mapped and unintentionally given valuable information about herself.


This picture indirectly tells people where Lily lives and where you can find her almost every evening.

Check It Out!

Check out my Story Map here!

Lessons & Questions

However, this blog is missing an important component of this project, the lesson plan. It is really only applicable for teachers, but I will include some of the questions that that students would be required to answer while investigating these maps.

You (and the students) may feel uncomfortable at the idea of asking some of these questions about Lily’s life, but it is be a good opportunity for a teachable moment discussion. Many people hearing about this lesson plan felt it was important, but ‘creepy”. Here we want to encourage students to express their feelings and reflect on the idea that people could be doing the same thing to them through their social media accounts. This type of spatial analysis can be used for unsavory goals and it’s important that this reality be acknowledged.

1. What school does Lily go to?

2. How do you think Lily gets to school each day?

3. What year is she in?

4.What programme is she in?

5. Name two of Lily’s friends. Do they go to same school as her?

6. Where does Lily live?

7. Do you think Lily has a lot of money? Why or why not? Bonus: What bank does      she use?

8. Imagine that after looking at Lily’s Snap Map that you would like to become friends with her. Create a plan for how you would befriend her. Be sure to include where and when you can find her during the week and what you would talk to her about to get her interested in hanging out with you. Be sure to give evidence of why your ideas will work. (8)

9. Do you use Snap Map, or post pictures on any Social Media platform even if it’s not on a map? Discuss what you have learned in this activity about at least 3 drawbacks of posting information about yourself on the internet and how GIS can be used against you. (6)

I hope this post gave you some helpful and interesting ideas. Thanks for reading!

Movies and Television shows filmed in Toronto but based elsewhere…

by Alexander Pardy
Geovis Class Project @RyersonGeo, SA8905, Fall 2017

Data and Data Cleaning:

To obtain my data I used and selected Toronto  the website displays a map that shows locations in the Greater Toronto Area  where movies and television shows were filmed. The point locations are overlaid on top of Google Maps imagery.

If you use the inspect element tool in internet explorer, you can find a single line of JavaScript code within the map section of the webpage that contains the latitude and longitude of every single point.

The data is in a similar format to python code. The entire line of JavaScript code was inputted into a Python script.  The python script writes the data into a CSV file that can then easily be opened in Microsoft Excel. Once the file was opened in Excel, Google was used to search for the setting of each and every single movie or television show, using the results of various different websites such as fan websites, IMDB, or Wikipedia. Some locations take place in fictional towns and cities, in this case locations were approximated using best judgement to find a similar location to the setting. All the information was than saved into a CSV file. Python was then used to delete out any duplicates in the CSV file and was used to give a count of each unique location value. This gives the total number of movies and television shows filmed at each different geographical location. The file was than saved out of python back into a CSV file. The latitude and longitude coordinates for each location was than obtained from Google and inputted into the CSV file.  An example is shown below.

Geospatial Work:

The CSV file was inputted into QGIS as a delimited text layer with the coordinate system WGS 84. The points were than symbolized using a graduated class method based on a classified count of the number of movies or television shows filmed in Toronto. A world country administrative shape file was obtained from the Database of Global Administrative Areas (GADM). There was a slight issue with this shapefile,  the shapefile had too much data and every little island on the planet was represented in this shapefile. Since we are working at a global scale the shapefile contained too much detail for the scope of this project.

Using WGS 84 the coordinate system positions the middle of the map at the prime meridian and the equator. Since a majority of the films and television shows are based in North America,  a custom world projection was created. This was accomplished in QGIS by going into Settings, Custom CRS, and selecting World Robinson projection. The parameters of this projection was then changed to change the longitude instead of being the prime meridian at 0 degrees, it was changed to -75 degrees to better center North America in the middle of the map. An issue came up after completing this is that a shapefile cannot be wrapped around a projection in QGIS.

After researching how to fix this, it was found that it can be accomplished by deleting out the area where the wrap around occurs. This can be accomplished by deleting the endpoints of where the occurrence happens. This is done by creating a text file that says:

This text box defines the corners of a polygon we wish to create in QGIS.  A layer  can now be created from the delimited text file, using custom delimiters set to semi colon and well-known text. It creates a polygon on our map, which is a very small polygon that looks like a line. Then by going into Vector, Geoprocessing Tools, Difference and selecting the input layer as the countries layer and the difference layer as the polygon that was created. Once done it gives a new country layer with a very thin part of the map deleted out (this is where the wrap around occurred). Now the map wraps around fine and is not stretched out. There is still a slight problem in Antarctica so it was selected and taken out of the map.


The shapefile background was made grey with white hairlines to separate the countries. The count and size of the locations was kept the same. The locations were made 60% transparent. Since there was not a lot of  different cities the  symbols were classified to be in 62 classes, therefore each time the number increased, the size of the point would increase.  The map is now complete. A second map was added in the print composer section to show a zoomed in section of North America. Labels and lines were then added into the map using Illustrator.

Story Map:

I felt that after the map was made a visualization should also be created to help covey the map that was created by being able to tell a story of the different settings of films and television shows that were filmed in Toronto.  I created a ESRI story map that can be found Here .

The Story Map shows 45 points on a world map, these are all based on the setting of television shows and movies that were filmed in the City of Toronto. The points on the map are colour coded. Red point locations had 4-63 movie and television shows set around the points. Blue point locations had 2-3 movie and television shows set around the points. Green point locations had 1 movie or television show set around the point. When you click on a point it brings you to a closer view of the city the point is located in. It also brings up a description that tells you the name of the place you are viewing and the number of movies and television shows whose settings takes place in that location. You also have the option to play a selected movie or television show trailer from YouTube in the story map to give you an idea of what was filmed in Toronto but is conveyed by the media industry to be somewhere else.

Exploring Street Art in Central Toronto – A Story Map

by Daniel LeBlanc
GeoVisualization Project Assignment @RyersonGeo, SA8905, Fall 2017

For my GeoVis project I wanted to do something that focused on the confluence of art and cartography. After some research, I settled on the use of story maps because they are a great way of bringing many different layers of content together and setting them in a geographical context. They allow for the ability to supplement a map with pictures, music, and video in an engaging way that is on the forefront of how people interact with maps and GIS applications. I also knew that I wanted to do something related to the Toronto street art scene, with graffiti being its most prevalent manifestation, because it has always been something that has interested me. I love turning around a corner in the city and being confronted with a colourful mural, or finding a back alley with some amazing hidden art work.

Though there are many story mapping platforms out there now, ESRI offers a great range of templates easily available on their website (you can create a free account and login). It is an engaging type of project, and can be picked up by just about anyone. ESRI’s templates range in style and format, with the type of content you want to present determining the best choice (or choices) for you. I chose to work with the Map Journal format as the main framing tool, and inserted many smaller Cascade stories to provide a smooth viewing experience for the photographs I took.

The Map Journal template revolves around a scrolling sidebar or ‘side panel’ that controls content on the ‘main stage’. Side panel content usually involves text or pictures that lays out the narrative while the main stage highlights content with maps, pictures, videos, or other story maps. I chose this template because I knew I wanted to include as many different forms of media as possible, and the Map Journal provides an easy and logical way to bring them all together and connect them to specific points on a map. Other formats include the Map Tour, Swipe, Spyglass and Crowdsource. Because ESRI is seeking to promote this type of format for map interaction, there is a wide range of support resources available including tutorials, message boards, blogs, and galleries of examples. The galleries gave me some great ideas of what was possible and what wasn’t and the blogs were very helpful when troubleshooting.

The first stage of the project was to research the street art scene in Toronto and decide which pieces would be included in the project. Blogs focused on the topic, as well as newspaper reports and tour information were used to get an idea of what some of the most well known pieces or areas are in Toronto. A total of 12 art pieces or areas were selected, most of which were chosen through this review process and a few were from my personal knowledge. The addresses of the buildings they were painted on, or the closest reasonable address to where they were located were determined. This was tricky in some cases as some of the areas were over 100 meters long, or inaccessible by foot in the case of one area located along some train tracks. Google Maps was used for some initial spot checking and determining some of the addresses to confirm.

Once the addresses were decided, ArcGIS Desktop was used to extract them from the Address Points (Municipal) shapefile retrieved from the Toronto Open Data Catalogue. One of the main ideas was to style the maps with colours corresponding to each art piece. Each address, called an ‘Art Point’, was buffered three times (250, 500, 750 meters) using the Buffer tool. The Select by Location – Intersect tool was then used to select features from the Toronto CentreLine shapefile. This shapefile, also retrieved from the Toronto Open Data Catalogue, contains all the linear features in Toronto including roads, pathways, rivers etc. It was used because it created a complex visual effect and gave the illusion of each Art Point radiating outwards. Each selected Centreline layer was then saved and exported, providing three ‘halos’ of differing distances around each artwork. Figure 1 shows ArcGIS desktop and a few of the many layers of buffers and halos being created.

Figure 1 – ArcGIS desktop, creation of buffers and corresponding halos.

12 Art Points X 3 halos = 36 buffer-selection-exports, all of which were then compressed into zip files separately so they could be uploaded to the ArcOnline mapping tool. ArcOnline was used because of it’s webmapping capabilities and easy integration with the story map templates. A number of tools is also available through ArcOnline, including the ability to add layers from their Living Atlas. This will be discussed more later. A dark grey canvas basemap was selected in order to better show off the halos once added, configured, and coloured. Figure 2 shows the construction of the overview map with all 12 Art Points and their 750 meter associated halos.

Figure 2 – ArcOnline being used to construct an overview map with Art Points and halos.

In the meantime, I spent two long mornings driving around Toronto (or taking the TTC) in the sun and the rain, taking my own photos and videos of each area or artwork. Introduction and background sections in the side panel were created, along with 12 different sections for each Art Point. All the photos were then uploaded and an example of each Art Point was inserted into the side panel while the rest of the photos were arranged in a Cascade story map. The Cascade story map template is not used to it’s full extent here, but provided a convenient way of integrating the photos that was in line with the scrolling functionality of the rest of the project.  The twelve different halo sets were then coloured based on the example artwork and each point on the map was linked with the side panel so the map would jump to the appropriate section as the user scrolled down. The videos I took of selected Art Points were also uploaded to YouTube and joined with music from their free Audio Library. Figure 3 shows the Graffiti Alley Art Point and associated halos (once finished).

Figure 3 – Graffiti Alley, side panel content and main stage map.

Content including background on each Art Point and the artist (if applicable) was then added to each section of the side panel. If an artist was identified, their name was also hyperlinked to their own website, flikr, or instagram account if possible. As the user scrolls down through the side panel section then, each Art Point is shown including the background content, a link to the Cascade to view more photos, a link to the YouTube video (if applicable), and the main stage would jump to the associated location with the styled map halos. Figure 4 shows the Underpass Park section, with the Cascade story map inserted on the main stage showing a series of more detailed pictures about the place. Figure 5 shows the Reclamation Wall section, with the link to the created YouTube video open.

Figure 4 – Underpass Park, side panel content and Cascade photographs opened.

Figure 5 – Reclamation Wall, side panel content and link to YouTube video opened on main stage.

Each halo was also designed to correspond to a walking distance as laid out in the introduction side panel sections, meaning that by looking at the map, any halo corresponded to a 10 minutes or less walking distance to an Art Point. For improved navigation and map usability, public transportation layers were added in from ESRI’s Living Atlas (which is connected to ArcOnline), allowing users to click on all the TTC bus, streetcar, and subway routes shown faintly on the map to help them navigate to each Art Point.

In the end, two different maps (one overview and one specific Art Point), 36 halos, 4 YouTube videos, and over 150 photos were taken to tell a story about the street art in Toronto.

Have you a look yourself though, don’t they say a picture (or map) is worth 1000 words?


Open Data Toronto. (2017). Address Points and Toronto Centreline shapefiles. Retrieved from:

What Kind of Story Do You Want to Tell? (2017). ESRI Story Maps. Retrieved from: