3D String Mapping and Textured Animation: An Exploration of Subway Networks in Toronto and Athens

BY: SARAH DELIMA

SA8905 – Geovis Project, MSA Fall 2024

INTRODUCTION:

Greetings everyone! For my geo-visualization project, I wanted to combine my creative skills of Do It Yourself (DIY) crafting with the technological applications utilized today. This project was an opportunity to be creative using resources I had from home as well as utilizing the awesome applications and features of Microsoft Excel, ArcGIS Online, ArcGIS Pro, and Clipchamp.

In this blog, I’ll be sharing my process for creating a 3D physical string map model. To mirror my physical model, I’ll be creating a textured animated series of maps. My models display the subway networks of two cities. The first being the City of Toronto, followed by the metropolitan area of Athens, Greece.

Follow along this tutorial to learn how I completed this project!

PROJECT BACKGROUND:

For some background, I am more familiar with Toronto’s subway network. Fortunately enough, I was able to visit Athens and explore the city by relying on their subway network. As of now, both of these cities have three subway lines, and are both undergoing construction of additional lines. My physical model displays the present subway networks to date for both cities, as the anticipated subway lines won’t be opening until 2030. Despite the hands-on creativity of the physical model, it cannot be modified or updated as easily as a virtual map. This is where I was inspired to add to my concept through a video animated map, as it visualizes the anticipated changes to both subway networks!

PHYSICAL MODEL:

Materials Used:

Paper (used for map tracing)
Pine wood slab
Hellman ½ inch nails
Small hammer
Assorted colour cotton string
Tweezers
Krazy glue

Methods and Process:

For the physical model, I wanted to rely on materials I had at home. I also required a blank piece of paper for a tracing the boundary and subway network for both cities. This was done by acquiring open data and inputting it into ArcGIS Pro. The precise data sets used are discussed further in my virtual model making. Once the tracings were created, I taped it to a wooden base. Fortunately, I had a perfect base which was pine wood. I opted for hellman 1/2 inch nails as the wood was not too thick and these nails wouldn’t split the wood. Using a hammer, each nail was carefully placed onto the the tracing outline of the cities and subway networks .

I did have to purchase thread so that I could display each subway line to their corresponding colour. The process of placing the thread around the nails did require some patience. I cut the thread into smaller pieces to avoid knots. I then used tweezers to hold the thread to wrap around the nails. When a new thread was added, I knotted it tightly around a nail and applied krazy glue to ensure it was tightly secured. This same method was applied when securing the end of a string.

Images of threading process:

City of Toronto Map Boundary with Tracing

After threading the city boundary and subway network, the paper tracing was removed. I could then begin filling in the space of the boundary. I opted to use black thread for the boundary and fill, to contrast both the base and colours of the subway lines. The City of Toronto thread map was completed prior to the Athens thread map. The same steps were followed. Each city is on opposite sides of the wood base for convenience and to minimize the use of an additional wood base.

Of course, every map needs a title , legend, north star, projection, and scale. Once both of the 3D string maps were complete, the required titles and text were printed and laminated and added to the wood base for both 3D string maps. I once again used the nails and hammer with the threads to create both legends. Below is an image of the final physical products of my maps!

FINAL PHYSICAL MODELS:

City of Toronto Subway Network Model:

Athens Metropolitan Area Metro Network Model:

VIRTUAL MODEL:

To create the virtual model, I used ArcGIS Pro software to create my two maps and apply picture fill symbology to create a thread like texture. I’ll begin by discussing the open data acquired for the City of Toronto, followed by the Census Metropolitan Area of Athens to achieve these models.

The City of Toronto:

Data Acquisition:

For Toronto, I relied on the City of Toronto open data portal to retrieve the Toronto Municipal Boundary as well as TTC Subway Network dataset. The most recent dataset still includes Line 3, but was kept for the purpose of the time series map. As for the anticipated Eglinton line and Ontario line, I could not find open data for these networks. However, Metrolinx created interactive maps displaying the Ontario Line and Eglinton Crosstown (Line 5) stations and names. To note, the Eglinton Crosstown is identified as a light rail transit line, but is considered as part of the TTC subway network.

To compile the coordinates for each station for both subway routes, I utilized Microsoft Excel to create 2 sheets, one for the Eglinton line and one for the Ontario line. To determine the location of each subway station, I used google maps to drop a pin in the correct location by referencing the map visual published by Metrolinx.

Ontario Line Excel Table :

Using ArcGIS Pro, I used the XY Table to Point tool to insert the coordinates from each separate excel sheet, to establish points on the map. After successfully completing this, I had to connect each point to create a continuous line. For this, I used the Point to Line tool also in ArcGIS Pro.

XY Table to Point tool and Points to Line tool used to add coordinates to map as points and connect points into a continuous line to represent the subway route:

After achieving this, I did have to adjust the subway routes to be clipped within the boundary for The City of Toronto as well as Athens Metropolitan Area. I used the Pairwise Clip in the Geoprocessing pane to achieve this.

Geoprocessing pairwise clip tool parameters used. Note: The input features were the subway lines withe the city boundary as the clip features.

Athens Metropolitan Area:

Data Acquisition:

For retrieving data for Athens, I was able to access open data from Athens GeoNode I imported the following layers to ArcGIS Online; Athens Metropolitan Area, Athens Subway Network, and proposed Athens Line 4 Network which I added as accessible layers to ArcGIS online. I did have to make minor adjustments to the data, as the Athens metropolitan area data displays the neighbourhood boundaries as well. For the purpose of this project, only the outer boundaries were necessary. To overcome this, I used the merge modify feature to merge all the individual polygons within the metropolitan area boundary into one. I also had to use the pairwise clipping tool once again as the line 4 network exceeds the metropolitan boundary, thus being beyond the area of study for this project.

Adding Texture Symbology:

ArcGIS has a variety of tools and features that can enhance a map’s creativity and visualization. For this project , I was inspired by an Esri Yarn Map Tutorial. Given the physical model used thread, I wanted to create a textured map with thread. To achieve this, I utilized the public folder provided with the tutorial. This included portable network graphics (.png) cutouts of several fabrics as well as pen and pencil textures. To best mirror my physical model, I utilized a thread .png.

ESRI yarn map tutorial public folder:

I added the thread .png images by replacing the solid fill of the boundaries and subway networks with a picture fill. This symbology works best with a .png image for lines as it seamlessly blends with the base and surrounding features of the map. The thread .png image uploaded as a white colour, which I was able to modify its colour according to the boundary or particular subway line without distorting the texture it provides.

For both the Toronto and Athens maps, the picture fill for each subway line and boundary was set to a thread .png with its corresponding colour. The boundaries for both maps were set to black as in the physical model, where the subway lines also mirror the physical model which is inspired by the existing/future colours used for subway routes. Below displays the picture symbology with the thread .png selected and tint applied for the subway lines.

City of Toronto subway Networks with picture fill of thread symbology applied:

The base map for the map was also altered, as the physical model is placed on a wood base. To mirror that, I extracted a Global Background layer from ArcGIS online, which I modified using the picture fill to upload a high resolution image of pine wood to be the base map for this model. For the city boundaries for both maps, the thread .png imagery was also applied with a black tint.

PUTTING IT ALL TOGETHER:

After creating both maps for Toronto and Athens, it was time to put it into an animation! The goal of the animation was to display each route, and their opening year(s) to visually display the evolution of the subway system, as my physical model merely captures the current subway networks.

I did have to play around with the layers to individually capture each subway line. The current subway network data for both Toronto and Athens contain all 3 of their routes in one layer, in which I had to isolate each for the purpose of the time lapse in which each route had to be added in accordance to their initial opening date and year of most recent expansion. To achieve this, I set a Definition Query for each current subway route I was mapping whilst creating the animation.

Definition query tool accessed under layer properties:

Once I added each keyframe in order of the evolution of each subway route, I created a map layout for each map to add in the required text and titles as I did with the physical model. The layouts were then exported into Microsoft Clipchamp to create the video animation. I imported each map layout in .png format. From there, I added transitions between my maps, as well as sound effects !

CITY OF TORONTO SUBWAY NETWORK TIMELNE:

Geovis Project, TMU Geography, SA8905 Sarah Delima
— (@s1delima.bsky.social) 2024-11-19T15:05:37.007Z

ATHENS METROPOLITAN AREA METRO TIMELINE:

Geovis Project, TMU Geography, SA8905 Sarah Delima
— (@s1delima.bsky.social) 2024-11-19T15:12:18.523Z

LIMITATIONS:

While this project allowed me to be creative both with my physical and virtual models, it did present certain limitations. A notable limitation to this geovisualization for the physical model is that it is meant to be a mere visual representation of the subway networks.

As for the virtual map, although open data was accessible for some of the subway routes, I did have to manually enter XY coordinates for future subway networks. I did reference reputable maps of the anticipated future subway routes to ensure accuracy. Furthermore, given my limited timeline, I was unable to map the proposed extensions of current subway routes. Rather, I focused on routes currently under construction with an anticipated completion date.

CONCLUSION:

Although I grew up applying my creativity through creating homemade crafts, technology and applications such as ArcGIS allow for creativity to be expressed on a virtual level. Overall, the concept behind this project is an ode to the evolution of mapping, from physical carvings to the virtual cartographic and geo-visualization applications utilized today.

Evolution of Residential Real Estate in Toronto – 2014 to 2022

Shashank Prabhu, Geovis Project Assignment, TMU Geography, SA8905, Fall 2024

Introduction
Toronto’s residential real estate market has experienced one of the most rapid price increases among major global cities. This surge has led to a significant affordability crisis, impacting the quality of life for residents. My goal with this project was to explore the key factors behind this rapid increase, while also analyzing the monetary and fiscal policies implemented to address housing affordability.

The Approach: Mapping Median House Prices
To ensure a more accurate depiction of the market, I used the median house price rather than the average. The median better accounts for outliers and provides a clearer view of housing trends. This analysis focused on all home types (detached, semi-detached, townhouses, and condos) between 2014 and 2022.

Although data for all years were analyzed, only pivotal years (2014, 2017, 2020, and 2022) were mapped to emphasize the factors driving significant changes during the period.

Data Source
The Toronto Regional Real Estate Board (TRREB) was the primary data source, offering comprehensive market watch reports. These reports provided median price data for Central Toronto, East Toronto, and West Toronto—TRREB’s three primary regions. These regions are distinct from the municipal wards used by the city.

Creating the Maps

Step 1: Data Preparation
The Year-to-Date (YTD) December figures were used to capture an accurate snapshot of annual performance. The median price data for each of the years across the different regions was organized in an Excel sheet, joined with TRREB’s boundary file (obtained through consultation with the Library’s GIS department), and imported into ArcGIS Pro. WGS 1984 Web Mercator projection was used for the maps.

Step 2: Visualization with 3D Extrusions
3D extrusions were used to represent price increases, with the height of each bar corresponding to the median price. A green gradient was selected for visual clarity, symbolizing growth and price.

Step 3: Overcoming Challenges

After creating the 3D extrusion maps for the respective years (2014, 2017, 2020, 2022), the next step was to export those maps to ArcOnline and then to Story Maps, the easiest way of doing so was to export it as a Web Scene, from which it would show up under the Content section on ArcOnline.

Flattened 3D Shapes: Exporting directly as a Web Scene to add onto Story Maps caused extrusions to lose their 3D properties. This was resolved using the “Layer 3D to Feature Class” tool.

Lost Legends: However, after using the aforementioned tool, the Legends were erased during export. To address this, static images of the legends were added below each map in Story Maps.

Step 4: Finalizing the Story Map
After resolving these issues, the maps were successfully exported using the Export Web Scene option. They were then embedded into Story Maps alongside text to provide context and analysis for each year.

Key Insights
The project explored housing market dynamics primarily through an economic lens.

Interest Rates: The Bank of Canada’s overnight lending rate played a pivotal role, with historic lows (0.25%) during the COVID-19 pandemic fueling a housing boom, and sharp increases (up to 5% by 2023) leading to market cooling.
Immigration: Record-breaking immigration inflows also contributed to increased demand, exacerbating the affordability crisis.

While earlier periods like 2008 were critical in shaping the market, boundary changes in TRREB’s data made them difficult to include.

Conclusion
Analyzing real estate trends over nearly a decade and visualizing them through 3D extrusions offers a profound insight into the rapid rise of residential real estate prices in Toronto. This approach underscores the magnitude of the housing surge and highlights how policy measures, while impactful, have not fully addressed the affordability crisis.

The persistent rise in prices, even amidst various interventions, emphasizes the critical need for increased housing supply. Initiatives aimed at boosting the number of housing units in the city remain essential to alleviate the pressures of affordability and meet the demands of a growing population.

Link to Story Map (You will need to sign in through your TMU account to view it): https://arcg.is/WCSXG

Visualizing the Influence of Afghanistan’s Geography on Its History and Culture Using 3D Animation in ArcGIS Pro

Geovis Project Assignment, TMU Geography, SA8905, Fall 2024 by Khadija Safi

Hello everyone! I’m excited to share my tutorial on how to use the animation capabilities in ArcGIS Pro to visualize 3D data and create an animated video.

My inspiration for this project was learning more about my ancestral homeland, Afghanistan, whose history and culture are known to have been heavily influenced by its location and topography.

Since I also wanted to gain experience working with the 3D layers and animation tools available in ArcGIS Pro, I decided to create a 3D animation of how geography has influenced Afghanistan’s history and culture.

My end product was an educational video that I narrated and posted on Youtube.

Geovisualization Product

The GIS software I used in this project was ArcGIS Pro 3.3.1. I also used the Voice Memos app to record my narration, and iMovie to compile the audio recordings and the exported ArcGIS Pro movie into one video.

For my data sources, I derived the historical information presented in the animation from a textbook by Jalali (2021), the political administrative boundary of Afghanistan from geoBoundaries (Runfola et al., 2020), and the World Elevation 3D/Terrain 3D and World Imagery basemap layers from ArcGIS Pro (Esri et al., 2024; Maxar et al., 2024).

For this tutorial, I will only be providing a broad overview of the steps I took to create my end product. For additional details on how to use the animation capabilities in ArcGIS Pro, please refer to Esri’s free online Help documentation.

Now, without further ado, let’s get started!

Key Learning Objective

To design and create a geographic-based animation involving 3D data using ArcGIS Pro.

Note

The following convention was used to represent the process of navigating the ArcGIS Pro ribbon: Tab (Group) > Command

Step 1: Come Up With the Storyline

Since I wanted to create a narrated video as my end product, I first had to research my topic and decide what kind of story I wanted to tell by writing the script that would go along with each keyframe.

Step 2: Record the Narration Using Voice Memos

The next step was to record the narration using the script I wrote so that I could have a reference point for my keyframe transitions.

This process was as simple as hitting record on Voice Memos, then uploading each audio file to a new iMovie project.

The audio files were trimmed and aligned until a seamless transition between each clip was achieved.

Step 3: Create the Animation in ArcGIS Pro

To create the animation, the following steps were taken:

(3.1) Open a new “Local Scene” project in ArcGIS Pro

(3.2) Load and prepare the data

In my case, the Terrain 3D layer was automatically loaded as the elevation surface. To load the World Imagery layer, I had to navigate to Map (Layer) > Basemap and select “Imagery”.

I then added and symbolized the political administrative boundary shapefile I downloaded for Afghanistan.

To mark the locations of the three cities I included in some of the keyframes, I also created my own point geometry using the graphical notation layer available through Insert (Layer Templates) > Point Map Notes. The Create tool under Edit (Features) was used to digitize the points.

Finally, I downloaded two PNG images to insert into the animation at a later time (Anonymous, 2014; Khan, 2010).

GeoVis

(3.3) Create bookmarks for the animation keyframes

Although an animation can be created regardless, bookmarking the view you intend to use for each keyframe is a good way of planning out your animation. The Scene’s view can be adjusted and updated at a later time, but this allows you to have an initial framework to start with.

ArcGIS Pro also allows you to import your bookmarks to automatically create keyframes using preconfigured playback styles.

Creating a Bookmark

To open the Bookmarks pane, click on “Manage Bookmarks” under Map (Navigate) > Bookmarks. Zoom to your desired keyframe location and create a bookmark using the New Bookmark subcommand.

Tip

The Locate command under Map (Inquiry) can be used to quickly search for and zoom to any geocoded location on the Earth’s surface.

Adjusting the View

To change the camera angle of your current view, use the on-screen navigator in the lower left corner of the Scene window. Click on the chevron to access full control.

By clicking and holding down on the bottom of the arrow on the outer ring of the on-screen navigator, you can rotate the view around the current target by 360^o.

Clicking and holding down on the outer ring only will allow you to pan the Scene towards the selected heading.

To change the pitch of the camera angle or rotate the view around the current target, click and hold down on the inner ring around the globe, then drag your mouse in the desired direction.

Finally, clicking and holding down on the globe allows you to change your current target.

(3.4) Activate the Animation tab

If your current Scene has never been initialized for an animation, the Animation tab can be activated through View (Animation) > Add.

(3.5) Set up the resolution of the exported animation

To ensure you design the animation to fit the resolution you intend to export to, click on Animation (Export) > Movie.

In the Export Movie pane, under “Advanced Movie Export Settings”, select your desired “Resolution”. You could also use one of the “Movie Export Presets” if desired. I chose “1080p HD Letterbox (1920 x 1080)” to produce a good quality video.

Note

This step is very important, as the view of your keyframes and the placement of any overlays you add are directly affected by the aspect ratio of your export, which is directly tied to your selected resolution.

GeoVis

(3.6) Create the animation

Start off by opening the Animation Timeline pane through Animation (Playback) > Timeline.

In the Bookmarks pane, click on your first bookmark. With your view set, click “Create first keyframe” in the Animation Timeline pane to add a keyframe.

Repeat this process until all of your keyframes are added.

Alternatively, as mentioned before, the Import command in Animation (Create) can be used to automatically load all of the bookmarks in your project as keyframes using a preconfigured playback style.

GeoVis

Tip

If you need to adjust the view of a keyframe, adjust your current view in the Scene window, then select the keyframe in the Animation Timeline pane and hit Update in Animation (Edit).

(3.7) Fine-tune the transition and timing between each keyframe

To configure the transition, time, and layer visibility of each keyframe, open the Animation Properties pane through Animation (Edit) > Properties and click on the Keyframe tab in this pane.

Choose one of the five transition types to animate the camera path: “Fixed”, “Adjustable”, “Linear”, “Hop”, or “Stepped”.

To create a tour animation that pans between geographic locations, a combination of “Hold” and “Hop” can be used. “Fixed” can be used to create a fly-through that navigates along a topographic feature.

Hit the play button in the Animation Timeline pane to view your animation and adjust accordingly.

Note

Although the Terrain 3D and World Imagery layers may not draw well in ArcGIS Pro due to their sheer size, they should appear fine in the exported video.

(3.8) Add text, images, and other graphics to annotate and communicate key information

Text, images, and other graphics can be added using the commands available in Animation (Overlay). Acceptable image file formats are JPG, TIFF, PNG, and BMP.

The position and timing of an overlay can be adjusted in the Overlays tab in the Animation Properties pane.

GeoVis

(3.9) Export the animation

Once you’re satisfied with your animation, you can export by clicking on Animation (Export) > Movie again.

Name the file and select your desired “Media Format” and “Frames Per Second” settings.

Your resolution should already be set, but you can adjust the “Quality” to determine the size of your file.

Hit “Export” once you’re ready. Depending on the size of your animation, it can take several hours for the video to export. Mine took over 10 hours.

Tip

You can also export a subsection of your animation by specifying a “Start Time” and “End Time”. This can be useful to preview the end result of your animation bit by bit without having to export the entire video, which can take a lot of time.

Step 4: Combine the Video and Audio Files Using iMovie

With my animation exported, I added the video to my project in iMovie. Since I timed the animation according to my narration, the two files aligned perfectly at the zero mark and no further editing had to be done.

To export the final video, I used File > Share > Youtube & Facebook and made sure to match the resolution to the one I selected in ArcGIS Pro (1920 x 1080). iMovie will notify you once the .mov file is exported.

Step 5: Upload the Animation on Youtube

The final step was uploading the video on Youtube.

Create and/or log in to your Youtube account. On the Youtube homepage, click on You > Your videos > Content > Create > Upload videos to add the .mov file. A wizard will pop up.

Under the Details tab, fill out the “Title” and provide a “Description” for your video. Timestamps marking different chapters in the video can also be added here.

Select a thumbnail and fill out the remaining fields, including those under “Show more”, such as “Video language”. Selecting a “Video language” is necessary to add subtitles, which can be done through the Video elements tab.

Once your video is set up, hit “Publish”. Youtube will supply you with the link to your published video.

Congratulations!

You just visualized 3D data and created a geographic-based animation using ArcGIS Pro!

References

Anonymous. (2014, September 18). Ahmad Shah Durrani [Artwork]. https://history-of-pashtuns.blogspot.com/2014/09/ahmed-shah-durrani.html

Esri, Maxar, Earthstar Geographics, & GIS User Community. (2024, November 19). World Imagery (November 26, 2024) [Tile layer]. Esri. https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer

Jalali, A. A. (2021). Afghanistan: A Military History From the Ancient Empires to the Great Game. University Press of Kansas.

Khan, M. (2010, December 11). Horse [Artwork]. https://www.foundmyself.com/Momin+khan/art/horse/66007

Maxar, Airbus DS, USGS, NGA, NASA, CGIAR, GEBCO, N Robinson, NCEAS, NLS, OS, NMA, Geodatastyrelsen, & GIS User Community. (2024, June 12). World Elevation 3D/Terrain 3D (November 26, 2024) [Image service layer]. Esri. https://services.arcgisonline.com/arcgis/rest/services/WorldElevation3D/Terrain3D/ImageServer

Runfola, D., Anderson, A., Baier, H., Crittenden, M., Dowker, E., Fuhrig, S., Goodman, S., Grimsley, G., Layko, R., Melville, G., Mulder, M., Oberman, R., Panganiban, J., Peck, A., Seitz, L., Shea, S., Slevin, H., Youngerman, R., & Hobbs, L. (2020). GeoBoundaries: A Global Database of Political Administrative Boundaries (September 21, 2024) [Shapefile]. GeoBoundaries. https://www.geoboundaries.org

Toronto’s Rapid Transit System Throughout the Years, 1954 to 2030: Creating an Animated Map on ArcGIS Pro

Johnson Lumague

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2022

Background

Toronto’s rapid transit system has been constantly growing throughout the decades. This transit system is managed by the Toronto Transit Commission (TTC) which has been operating since the 1920s. Since then, the TTC has reached several milestones in rapid transit development such as the creation of Toronto’s heavy rail subway system. Today, the TTC continues to grow through several new transit projects such as the planned extension of one of their existing subway lines as well as by partnering with Metrolinx for the implementation of two new light rail systems. With this addition, Toronto’s rapid transit system will have a wider network that spans all across the city.

**Timeline of the development of Toronto’s rapid transit system**

Based on this, a geovisualization product will be created which will animate the history of Toronto’s rapid transit system and its development throughout the years. This post will provide a step-by-step tutorial on how the product was created as well as showing the final result at the end.

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

Anastasiia Smirnova
SA8905 Geovis project, Fall 2022

Introduction

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.”

Technology

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.

Challenges

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 (arcgis.com)

Drone Package Deployment Tutorial / Animation

Anugraha Udas

SA8905 – Cartography & Visualization
@RyersonGeo

Introduction

Automation’s prevalence in society is becoming normalized as corporations have begun noticing its benefits and are now utilizing artificial intelligence to streamline everyday processes. Previously, this may have included something as basic as organizing customer and product information, however, in the last decade, the automation of delivery and transportation has exponentially grown, and a utopian future of drone deliveries may soon become a reality. The purpose of this visualization project is to convey what automated drone deliveries may resemble in a small city and what types of obstacles they may face as a result of their deployment. A step-by-step process will also be provided so that users can learn how to create a 3D visualization of cities, import 3D objects into ArcGIS Pro, convert point data into 3D visualizations, and finally animate a drone flying through a city. This is extremely useful as 3D visualization provides a different perspective that allows GIS users to perceive study areas from the ground level instead of the conventional birds-eye view.

Area of Study

The focus area for this pilot study is Niagara Falls in Ontario, Canada. The city of Niagara Falls was chosen due to its characteristics of being a smaller city but nonetheless still containing buildings over 120 meters in height. These buildings sizes provide a perfect obstruction for simulating drone flights as Transport Canada has set a maximum altitude limit of 120 meters for safety reasons. Niagara Falls also contains a good distribution of Canada Post locations that will be used as potential drone deployment centres for the package deliveries. Additionally, another hypothetical scenario where all drones deploy from one large building will be visualized. In this instance, London’s gherkin will be utilized as a potential drone-hive (hypothetically owned by Amazon) that drones can deploy from (See https://youtu.be/mzhvR4wm__M). Due to the nature of this project being a pilot study, this method be further expanded in the future to larger dense areas, however, a computer with over 16GB of RAM and a minimum of 8GB of video memory is highly recommended for video rendering purposes. In the video below, we can see the city of Niagara Falls rendered in ArcPro with the gherkin represented in a blue cone shape, similarly, the Canada Post buildings are also represented with a dark blue colour.

City of Niagara Falls (Rendered in ArcPro)

Data

The data for this project was derived from numerous sources as a variety of file types were required. Regarding data directly relating to the city of Niagara Falls – Cellular Towers, Street Lights, Roads, Property parcel lines, Building Footprints and the Niagara Falls Municipal Boundary Shapefiles were all obtained from Niagara Open data and imported into ArcPro. Similarly, the Canada Post Locations Shapefile was derived from Scholar’s Geoportal. In terms of the 3D objects – London’s Gherkin, was obtained from TurboSquid in and the helipad was obtained from CGTrader in the form of DAE files. The Gherkin was chosen because it serves as a hypothetic hive building that can be employed in cities by corporations such as Amazon. Regarding the helipad 3D model, it will be distributed in numerous neighbourhoods around Niagara Falls as a drop-off zones for the drones to deliver packages. In a hypothetical scenario, people would be alerted on their phones as to when their package is securely arriving, and they would visit the loading zone to pick up their package. It should be noted that all files were copyright-free and allowed for personal use.

Process (Step by step)

Importing Files

First, access the Niagara Open Data website and download all the aforementioned files in the search datasets box. Ensure that the files are downloaded in SHP format for recognition in ArcPro (Names are listed at the end of this blog). Next, go on TurboSquid and search for the Gherkin and make sure that the price drop down has a minimum and maximum value of $0 (Figure 1). Additionally, search for ‘Simple helipad free 3D model’ on CGtrader. Ensure that these files are downloaded in DAE format for recognition in ArcPro. Once all files are downloaded open ArcPro and import the Shape files (via Add Data) to first conduct some basic analysis.

Basic GIS Analysis

First, double click on the symbology box for each imported layer, and a symbology dialog should open on the right-hand side of the screen. Click on the symbol box and assign each layer with a distinct yet subtle colour. Once this is finished, select the Canada Post Locations layer, and go to the analysis tab and select the buffer icon to create a buffer around the Canada Post Locations. Input features – The Canada Post Locations. Provide a file location and name in the output feature class and enter a value of 5 kilometres for distance and dissolve the buffers (Figure 2). The reason why 5km was chosen is that regular consumer drones have a battery that can last up to ten kilometres (or 30 min flight time), thus traveling to the parcel destination and back would use up this allotted flight time.

Once this buffer is created the symbology is adjusted to a gradient fill within the layer tab of the symbol. This is to show the groupings of clusters and visualize furthering distance from the Canada Post Locations. In this project we are assuming that the Canada Post Locations are where the drones are deploying from, thus this buffer shows the extent of the drones from the location (Figure 3). As we can see, most residential areas are covered by the drone package service. Next, we are going to give the Canada post buildings a distinct colour from the other buildings. Go to ‘Select by Location’ in the ‘Map’ tab and click ‘Select by Location’. In this dialog box, an intersection relationship is created where the input features are the buildings, and the selecting features is the Canada Post location point data. Hit okay, and now create a new layer from the selection and name it Canada Post buildings. Assign a distinct colour to separate the Canada Post buildings from the rest of the buildings.

3D Visualization – Buildings

Now we are going to extrude our buildings in terms of their height in feet. Click on the View tab in ArcPro and click on the Convert to local scene tab. This process essentially creates a 3D visual of your current map. Next you will notice that all of the layers are under 2D view, once we adjust the settings of the layers, we will drag these layers to the 3D layers section. To extrude the buildings, click on the layer and the appearance tab should come up under the feature layer. Click on the Type diagram drop down and select ‘Max Height’. Thereafter, select the field and choose ‘SHAPE_leng’ as this is the vertical height of the buildings and select feet as the unit. Give ArcPro some time and it should automatically move your building’s layer from the 2D to 3D layers section. Perform this same process with the Canada Post Buildings layer.

Now you should have a 3D view of the city of Niagara Falls. Feel free to move around with the small circle on the bottom left of the display page (Figure 4). You can even click the up arrow to show full control and move around the city. Furthermore, can also add shadows to the buildings by right clicking the map 3D layers tab and selecting ‘Display shadows in 3D’ under Illumination.

Converting Point Data into 3D Objects

In this step, we are going to convert our point data into 3D objects to visualize obstructions such as lamp posts and cell phone towers. First click the Street Lights symbol under 2D layers and the symbology pane should open up on the right side of Arc Pro. Click the current symbol box beside Symbol and under the layer’s icon change the type from ‘Shape Marker’ to 3D model marker (Figure 5).

Next, click style, search for ‘street-light’, and choose the overhanging streetlight. Drag the Street Light layer from the 2D layer to the 3D layer. Finally, right-click on the layer and navigate to display under properties. Enable ‘Display 3D symbols in real-world units’ and now the streetlamp point data should be replaced by 3D overhanging streetlights. Repeat this same process for the cellphone tower locations but use a different model.

Importing 3D objects & Texturing

Finally, we are going to import the 3D DAE helipad and tower files, place them in our local scene and apply textures from JPG files. First, go on the view tab, click on Catalog Pane and a Catalog should show up on the right side of the viewer. Expand the Databases folder and your saved project should show up as a GDB. Right-click on the GDB and create a new feature class. Name it ‘Amazon Tower’ and change the type from polygon to 3D object and click finish. You should notice that under Drawing Order there should be a new 3D layer with the ‘Amazon Tower’ file name. Select the layer, go on the edit tab and click create to open up the ‘Create Features’ dialog on the right side of the display panel (Figure 6). Click on the Model File tab, click the blue arrow and finally, click the + button. Navigate to your DAE file location, select it and now your model should show up in the view pane and it will allow you to place it on a certain spot. For our purposes, we’ll reduce the height to 30 feet and adjust the Z position to -40 to get rid of the square base under the tower. Click on the location of where you want to place the tower, close the create feature box, apply the multi-patch tool and clear the selection. Finally, to texture the tower, select the tower 3D object, click on the edit tab and this time hit modify. Under the new modify features pane select multi patch features under reshape. Now go on to Google and find a glass building texture JPG file that you like. Click load texture, choose the file, check the ‘Apply to all’ box and click apply. Now the Amazon tower should have the texture applied on it (Figure 7).

Animation

Finally, now that all of the obstructions are created, we are going to animate a drone flying through the city. Navigate to the animation tab on the top pane and click on timeline. This is where individual keyframes will be combined for the purpose of creating a drone package delivery. Navigate your view so that it is resting on a Canada Post Building and you have your desired view. Click on ‘Create first key frame’ to create your first view, next click up on the ‘full control view’ so that the drone flies up in elevation, and click the + to designate this as a new keyframe. Ensure that the height does not exceed 120 meters as this is the maximum altitude for drones, provided by Transport Canada (Bottom left box). Next, click and drag the hand on the viewer to move forward and back and click + for a new keyframe. Repeat this process and navigate the proposed drone to a helipad (Figure 8). Finally, press the ‘Move down’ button to land the done on the helipad and create a new key frame. Congratulations, you have created your first animation in ArcPro!

Discussion

Through the process of extruding buildings, maintaining a height less than 120 meters, adding in proposed landing spaces, and turning point data into real-world 3D objects we can visualize many obstructions that drones may face if drone delivery were to be implemented in the city of Niagara Falls. Although this is a basic example, creating an animation of a drone flying through certain neighbourhoods will allow analysts to determine which areas are problematic for autonomous flying and which paths would provide a safer option. Regarding the animation portion, there are two possible scenarios that have been created. First, is a drone deployment from the aforementioned Canada Post Locations. This scenario envisions Niagara Falls as having drone package deployment set out directly from their locations. This option would cover a larger area of Niagara Falls as seen through the buffer, however, having multiple locations may be hard to get funding for. Also, people may not want to live close to a Canada Post due to the noise pollution that comes from drones.

Scenario 1. Canada Post Delivery

The second scenario is to utilize a central building that drones can pickup packages from. This is exemplified as the hive delivery building as seen below. In sharp contrast to option 1, a central location may not be able to reach rural areas of Niagara Falls due to the distance limitations of current drones. However, two major benefits are that all drone deliveries could come from a central location and less noise pollution would occur as a result of this.

Scenario 2. Single HIVE Building

Conclusions & Future Research

Overall, it is evident that drone package deliveries are completely possible within the city of Niagara Falls. Through 3D visualizations in ArcPro, we are able to place simple obstructions such as conventional street lights and cell phone towers within the roads. Through this analysis and animation it is evident that they may not pose an issue to package delivery drones when incorporating communal landing zones. For future studies, this research can be furthered by incorporating more obstructions into the map; such as electricity towers, wiring, and trees. Likewise, future studies can also incorporate the fundamentals of drone weight capacity in relation to how far they can travel and overall speed of deliveries. In doing so, the feasibility of drone package deployment can be better assessed and hopefully implemented in future smart cities.

References

https://www.dji.com/ca/phantom-4/info

https://youtu.be/mzhvR4wm__M

3D Files

Gerkin Model DAE File https://www.turbosquid.com/3d-models/free-30-st-mary-axe-3d-model/991165

Simple Helipad DAE File – https://cgtrader.com/items/212615/download-page

Shape Files

Postal Outlet Points (2020) – Scholar’s GeoPortal

Niagara Falls Building Footprints (2010) – Niagara Open Data

Road Segments (2021) – Niagara Open Data

Niagara Falls Cellular Tower Locations (2021) – Niagara Open Data

Street Lighting Pilot Project (2021) – Niagara Open Data

Niagara Falls Municipal Boundary (2021) – Niagara Open Data

Niagara Falls Property Parcels (2021) – Niagara Open Data

COVID-19 in Toronto: A Tale of Two Age Groups

By Meira Greenbaum

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2020

Story Map Link

Introduction

The COVID-19 pandemic has affected every age group in Toronto, but not equally (breakdown here). As of November 2020, the 20-29 age group accounts for nearly 20% of cases, which is the highest proportion compared to the other groups. The 70+ age group accounts for 15.4% of all cases. During the first wave, seniors were affected the most, as there were outbreaks in long-term care homes across the city. By the end of summer and early fall, the probability of a second wave was certain, and it was clear that an increasing number of cases were attributed to younger people, specifically those 20-29 years old. Data from after October 6th was not available at the time this project began, but since then Toronto has seen another outbreak in long-term care homes and an increasing number of cases each week. This story map will investigate the spatial distribution and patterns of COVID-19 cases in the city’s neighbourhoods using ArcGIS Pro and Tableau. Based on the findings, specific neighbourhoods with high rates can be analyzed further.

Why these age groups?

Although other age groups have seen spikes during the pandemic, the trends of those cases have been more even. Both the 20-29 and 70+ groups have seen significant increases and decreases between February and November. Seniors are more likely to develop extreme symptoms from COVID-19, which is why it is important to focus on identifying neighbourhoods with higher rates of seniors. 20-29 is an important age group to track because increases within that group are more unique to the second wave and there is a clear cluster of neighbourhoods with high rates.

Data and Methods

The COVID-19 data for Toronto was provided by the Geo-Health Research Group. Each sheet within the Excel file contained a different age group and the number of cases each neighbourhood had per week from January to early October. The format of the data had to be arranged differently for Tableau and ArcGIS Pro. I was able to table join the original excel sheet with the columns I needed (rates during the week of April 14th and October 6th for the specific age groups) to a Toronto neighbourhood shapefile in Pro and map the rates. The maps were then exported as individual web layers to ArcGIS Online, where the pop-ups were formatted. After this was done, the maps were added to the Story Map. This was a simple process because I was still working within the ArcGIS suite so the maps could be transported from Pro to Online seamlessly.

For animations with a time and date component, Tableau requires the data to be vertical (i.e. had to be transposed). This is an example of what the transformation looks like (not the actual values):

A time placeholder was added beside the date (T00:00:00Z) and the excel file was imported into Tableau. The TotalRated variable was numeric, and put in the “Columns” section. Neighbourhoods was a string column and dragged to the “Colour” and “Label” boxes so the names of each neighbourhood would show while playing the animation. The row column was more complicated because it required the calculated field as follows:

TotalRatedRanking is the new calculation name. This produced a new numeric variable which was placed in the “Rows” box.

If TotalRatedRanking is right clicked, various options will pop-up. To ensure the animation was formatted correctly, the “Discrete” option had to be chosen as well as “Compute Using —> Neighbourhoods.” The data looked like the screenshot below, with an option to play the animation in the bottom right corner. This process was repeated for the other two animations.

Unfortunately, this workbook could not be imported directly into Tableau Public (where there would be a link to embed in the Story Map) because I was using the full version of Tableau. To work around this issue, I had to re-create the visualization in Tableau Public (does not support animation), and then I could add the animation separately when the workbook was uploaded to my Tableau Public account. These animations had to be embedded into the Story Map, which does have an “Embed” option for external links. To do this, the “Share” button on Tableau Public had to be clicked and a link appeared. But when embedded in the Story Map, the animation is not shown because the link is not formatted correctly. To fix this, the link had to be altered manually (a quick Google search helped me solve it):

Limitations and Future Work

Creating an animation showing the rate of cases over time in each neighbourhood (for whichever age group or other category in the excel spreadsheet) may have been beneficial. An animation in ArcGIS Pro would have been cool (just not enough time to learn about how ArcGIS animation works), and this is an avenue that could be explored further. The compromise was to focus on certain age groups, although patterns between the start (April) and end (October) points are less obvious. It would also be interesting to explore other variables in the spreadsheet, such as community spread and hospitalizations per neighbourhood. I tried using kepler.gl, which is a powerful data visualization tool developed by Uber, to create an animation from January to October for all cases, and this worked for the most part (video at the end of the Story Map). The neighbourhoods were represented as dots (not polygons), which is not very intuitive for the viewer because the shape of the neighbourhood cannot be seen. Polygons can be imported into kepler.gl but only as a geojson and I am unfamiliar with that file format.

Visualizing Atlantic Tropical Storm Activity

by Christopher Rudolph

Hurricane Florence | NASA — *Fig 1. Hurricane Florence as recorded by NASA*

Tropical storms are a category of weather events that create wind and rainfall conditions of varying intensity. These conditions can have high destructive potential depending on intensity, with these storms being classified from tropical depression at the weakest, to hurricane at the most intense. They occur between 5- and 20-degrees latitude when low atmospheric pressure systems cross warm ocean surface temperatures. Depending on conditions, winds can develop from as low as 23 mph to over 157 mph. When these storms meet land, they will often cause property damage and threaten lives due to flooding and wind force before dissipating.

The most dangerous of these storms are classified as hurricanes, which are characterized by exceedingly high wind speeds. Hurricanes are famed across the south-eastern United States for the devastating effects they can have when they reach land such as 2005’s Hurricane Katrina with over $125 billion in damage and over 1800 deaths or 2012’s Hurricane Sandy with $70 Billion in damage and 233 deaths. Due to this, the study and prediction of tropical storm development has remained continually relevant.

Why track tropical storms?

Many of the processes surrounding hurricane development are poorly understood, such as ocean and atmospheric circulation. To better understand these events, efforts have been made to form detailed histories of past tropical storm conditions. The National Oceanic and Atmospheric Administration (NOAA) has created detailed records of tropical storms as far back as the mid 1800’s.

The atmosphere and ocean are 2 of the largest carbon and thermal sinks on Earth. With anthropogenic climate change changing the conditions of these two bodies, there is concern that tropical storm development will change with it, potentially with intensification of these destructive events. A search for periods analogous to forecasted future conditions has emerged in an attempt to predict how tropical storm conditions may change. Paleotempestology is a scientific field that has sought to extend tropical storm records past modern monitoring technology using geological proxies and historical documentary records.

This visualization will represent the frequency of tropical storm activity in the Atlantic as a heat map. Kernel Density values are assigned based on proximity to tropical storm path activity. The higher the value, the more tropical storm activity seen in proximity to the location. Kernel density will be visualized on a 10-year basis, helping to visualize how storm activity over time and the frequency at which these storms may impact coastal communities.

Visualization

Fig. 2 Visualization of tropical storm activity density in the west Atlantic.

Data and Platform

For this project, tropical storm data is visualized using the International Best Track Archive for Climate Stewardship (IBTrACS), a tropical cyclone best track data collection published by NOAA.

ARCGis was selected as the platform that would be used for the visualization. The software was familiar and effective for doing the project’s geoprocessing, and looked promising for the visualization product. ARCGis features robust geoprocessing tools for creating the visualization, and has an animation feature that can produce the video format and implement overlay features such as a timeline and text. As the project developed, the animation tool would be abandoned however in favor of Windows Video Editor for the video as discussed later.

Methods

With data available in shapefile form, importing NOAA’s data into ARCGis was simple. The data on display upon importation is overwhelming with over 120 thousand records displayed as travel paths. Performance is low and there is little to no context to what is being viewed.

Fig. 3 – A map of all tropical storm tracks recorded

Using density geoprocessing and the filtering of data range through time, this will be transformed into something interpretable.

Time

Time was the first filter implemented. In the properties of the layer, time was enabled. Each row has corresponding time fields. In this case, year was used. Implementing this introduced an adjustable filter to the map area in the top right. This slider could be adjusted to narrow down the range.

Fig. 4 – Layer Time properties and the resulting time range filter

While handy on the fly, more precise results for filtering time is found within the Map Time tab, with precision controls available there.

Creating the density view

For creating heatmaps typically the heatmap symbology option is used to create effective density views with time enabled filtering. For this visualization, this approach was not available as the approach was incompatible with the line datatype used. To create a density map, geoprocessing would need to be done using the density toolset. The kernel density toolset was selected. This tool uses a bivariate kernel function for form a weight range surrounding each point. These ranges are then summed to form cell density values for each raster grid point, resulting in a heatmap.

This approach carried some issues for implementation however. In the process of geoprocessing, the tool doesn’t take into account or assign any time data to the output. This meant that the processed layer couldn’t be effectively filtered for the visualization. To work around this, the data was broken into layers by desired year range, then processed, creating a layer for each time window. These layers could still be used to make keyframes and scenes for the animation, though this solution would have some added housekeeping in displaying certain details such as time and legend within the video

Format

As mentioned earlier, the ARCGis animation tools were planned for use as the delivery format. Working with the results generated so far would prove problematic however. The animation tool is focused on applications involving changes of view and time. Given the needs and constraints of the solutions taken for this project, neither of these would be active components of this visualization, and would complicate the creation of the animation. Issues with preview playback, overlays and exports further complicated this. Given the relatively simple needs, a different approach using other software was selected.

In researching this topic, much forum discussion was found surrounding similar projects. Consensus seemed to be that for a visualization using static views such as this, exporting to an external main-stream video-processing platform would be most effective. To do this, each time view would need to be honed and exported as images through a layout. These layouts would then be arranged into a video with windows video editor.

Elements such as legend, title and attribution that had been causing issues under the animation tool were added to a layout. They automatically updated relevant information as layers were swapped within the layout view. Each layer in turn were exported as layouts representing each year range. Once these images were created, they were imported into windows video editor where they were composed into a timeline. Each layout was given period of 3 seconds before it would transition to the next layout. The video was then exported in 1080p and published to Youtube. Once hosted on Youtube, it can be easily embedded into a site like above or shared via link.

Fig. 5 – Video editing in Windows Video Editor

Future Work

There are different factors and semi-regular phenomenon that have impacts on tropical storm development. Events such as El Nino and the Pacific Decadal Oscillation are recurring events that could enhance. Relating the timeline of these events as well as ocean surface temperature could help interpret trends within this visualization. Creating a methodology behind time ranges displayed also could have enhanced this visualization. For example, breaking this visualization into phases of El Nino-Southern Oscillation rather than even time windows may have presented a lot of value to this sort of visualization.

Toronto’s Waterfront Parking Lot Transformation

Author Name: Vera Usherovich

StoryMap Project link: https://arcg.is/004vSb

SA 8905 Fall 2020

Introduction:

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

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

About the StoryMap

Data

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).

Limitations

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.

Geovisualization of Crime in the City of Toronto Using Time-Series Animation Heat Map in ARCGIS PRO

Hetty Fu

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019

Background/Introduction

The City of Toronto Police Services have been keeping track of and stores historical crime information by location and time across the City of Toronto since 2014. This data is now downloadable in Excel and spatial shapefiles by the public and can be used to help forecast future crime locations and time. I have decided to use a set of data from the Police Services Data Portal to create a time series map to show crime density throughout the years 2014 to 2018. The data I have decided to work with are auto-theft, break and enter, robbery, theft and assault. The main idea of the video map I want to display is to show multiple heat density maps across month long intervals between 2014 to 2018 in the City of Toronto and focus on downtown Toronto as most crimes happen within the heart of Toronto.

The end result is an animation time-series map that shows density heat map snapshots during the 4-year period, 3-month interval at a time. Examples of my post are shown at the end of this blog post under Heat Map Videos.

Dataset

All datasets were downloaded through the Toronto Police Services Data Portal which is accessible to the public.

The data that was used to create my maps are:

Assault
Auto Theft
Robbery
Break and Enter
Theft

Process Required to Generate Time-Series Animation Heat Maps

Step 1: Create an additional field to store the date interval in ArcGis Pro.

Add the shapefile downloaded from the Toronto Police Services Portal intoArcGIS Pro.

First create a new field under View Table and then click on Add.

To get only the date, we use the Calculate Field in the Geoprocessing tools with the formula

date2=!occurrence![:10]

where Occurrence is the existing text field that contains the 10 digit date: YYYY-MM-DD. This removes the time of day which is unnecessary for our analysis.

Step 2: Create a layer using the new date field created.

Go into properties in the edited layer. Under the time tab, place in the new date field created from Step 1 and enter in the time extent of the dataset. In this case, it will be from 2014-01-01 to 2018-12-31 as the data is between 2014 to 2018.

Step 3: Create Symbology as Heat Map

Go into the Symbology properties for the edited layer and select heat map under the drop down menu. Select 80 as its radius which will show the size of the density concentration in a heat map. Choose a color scheme and set the method as Dynamic. The method used will show how each color in the scheme relates to a density value. In a Dynamic setting versus and constant, the density is recalculated each time the map scale or map extent changes to reflect only those features that are currently in view. The Dynamic method is useful to view the distribution of data in a particular area, but is not valid for comparing different areas across a map (ArcGIS Pro Help Online).

Step 4: Convert Map to 3D global scene.

Go to View tab on the top and select convert to global scene. This will allow the user to create a 3D map feature when showing their animated heat map.

Step 5: Creating the 3D look.

Once a 3D scene is set, press and hold the middle mouse button and drag it down or up to create a 3D effect.

Step 6: Setting the time-series map.

Under the Time tab, set the start time and end time to create the 3 month interval snapshot. Ensure that “Use Time Span” is checked and the Start and End date is set between 2014 and 2018. See the image below for settings.

Step 7: Create a time Slider Steps for Animation Purposes

Under Animation tab, select the appropriate “Append Time” (the transition time between each frame). Usually 1 second is good enough, anything higher will be too slow. Make sure to check off maintain speed and append front before Importing the time Slider Steps. See below image.

Step 8: Editing additional cosmetics onto the animation.

Once the animation is created, you may add any additional layers to the frames such as Titles, Time Bar and Paragraphs.

There is a drop down section in the Animation tab that will allow you to add these cosmetic layers onto the frame.

Animation Timeline by frames will look like this below.

Step 9: Exporting to Video

There are many types of exports the user can choose to create. Such as Youtube, Vimeo, Twitter, Instagram, HD1080 and Gif. See below image for the settings to export the create animation video. You can also choose the number of frames per second, as this is a time-series snapshot no more than 30 frames per second is needed. Choose a place where you would like to export the video and lastly, click on Export.

Conclusion/Recommendation/Limitation

As this was one of my first-time using ArcGIS Pro software, I find it very intuitive to learn as all the functions were easy to find and ready to use. I got lucky in finding a dataset that I didn’t have to format too much as the main fields I required were already there and the only thing required was editing the date format. The number of data in the dataset was sufficient for me to create a time series map that shows enough data across the city of Toronto spanning 3 months at a time. If there was less data, I would have to increase my time span. The 3D scene on ArcGIS Pro is very slow and created a lot of problems for me when trying to load my video onto set time frames. As a result of the high-quality 3D setting, I decided to use, it took couple of hours to render my video through the export tool. As the ArcGIS Pro software wasn’t made to create videos, I felt that there was lack of user video modification tools.

Heat Map Videos Export

Theft in Downtown Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.
Robbery in Downtown Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.
Break and Enter in Downtown Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.
Auto Theft across the City of Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.
Assault across the City of Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.