Tag: ArcGIS Pro

Investigating The Distribution of Crime by Type

Studying Toronto’s Peak Crime Month of 2025

Simone Brisbin

sbrisbin

Geo-Vis Project Assignment, TMU Geography, SA8905, Fall 2025

Hello everyone, and welcome to my blog!

Today’s topic addresses the distribution of crime in Toronto. I am seeking to provide the public, and implicated stakeholders with a greater knowledge and understanding of how, where, and why different types of crime are distributed in relation to urban features like commercial buildings, public transit, restaurants, parks, open spaces, and more. We will also be looking at some of the socio-economic indicators of crime, and from there identify ways to implement relevant and context specific crime mitigation and reduction strategies.

This project investigates how crime data analysis can better inform urban planning and the distribution of social services in Toronto, Ontario. Research across diverse global contexts highlights that crime is shaped by a mix of socioeconomic, environmental, and spatial factors, and that evidence-based planning can reduce harm while improving community well-being. The following review synthesizes findings from six key studies, alongside observed crime patterns within Toronto.

Accompanying a literature review, I created a 3D model that displays a range of information including maps made in ArcGIS Pro. The data used was sourced from the Toronto Police Service Public Safety Data Portal, and Toronto’s Neighbourhood Profiles from the 2021 Census. The objective here is to draw insightful conclusions as to what types of crime are clustering where in Toronto, what socio-economic and/or urban infrastructural indicators are contributing to this? and what solutions could be implemented in order to reduce overall crime rates across all of Toronto’s neighbourhoods – keeping equitability in mind ?

The distribution of crime across Toronto’s neighbourhoods reflects a complex interplay of socioeconomic conditions, built environment characteristics, mobility patterns, and levels of community cohesion. Understanding these geographic and social patterns is essential to informing more effective city planning, targeted service delivery, and preventive interventions. Existing research emphasizes the need for long-term, multi-approach strategies that address both immediate safety concerns and the deeper structural inequities that shape crime outcomes. Mansourihanis et al. (2024) highlight that crime is closely linked to urban deprivation, noting that inequitable access to resources and persistent neighbourhood disadvantages influence where and how crime occurs. Their work stresses the importance of integrating crime prevention with broader social and economic development initiatives to create safer, and more resilient urban environments (Mansourihanis et al., 2024).

Mansourihanis, O., Mohammad Javad, M. T., Sheikhfarshi, S., Mohseni, F., & Seyedebrahimi, E. (2024). Addressing Urban Management Challenges for Sustainable Development: Analyzing the Impact of Neighborhood Deprivation on Crime Distribution in Chicago. Societies, 14(8), 139. https://doi.org/10.3390/soc14080139

Click here to view the literature review I conducted on this topic.

How can we use crime data analysis to better inform city planning and the distribution of social services?

Methods – Creating a 3D Interactive Crime Investigation Board

Part 1, Preliminary Research and Mapping

The purpose of this 3D map is to provide an interactive tool that can be regularly updated over time; allowing users to build upon research using various sources of information in varying formats (e.g. literature, images, news reports, raw data, various map types presenting comparable socio-economic data, etc; thread can be used to connect images and other information to associated areas on the map). The model has been designed for easy means of addition, removal and connection of media items by using materials like tacks, clips, and cork board. Crime incidents can be tracked and recorded in real time. This allows for quick identification of where crime is clustering based on geography, socio-economic context, and proximity to different land use types and urban features like transportation networks. We can continue to record and analyze what urban features or amenities could be deterring or attracting/ promoting criminal activity. This will allow for fast, context specific, crime management solutions that will ultimately help reduce overall crime rates in the city.

Heat Map: Assault (pink/purple), Auto Theft (green)
Homicide (red/yellow), Shooting (Orange), Break and Enter (blue)
Toronto Police Open Data Portal, used for crime data
City of Toronto Open Data Portal, Neighbourhood Profiles, for 2021 Census data
1. Conduct a detailed literature review. 
Here is the literature review I conducted to address this topic. 

2. Downloaded the following data from: Open Data | Toronto Police Service Public Safety Data Portal. Each dataset was filtered to show points only from 2025. 

- Dataset: Shooting and Firearm Discharges
- Dataset: Homicides
- Dataset: Assault 
- Dataset: Auto Theft 
- Dataset: Break and Enter

Toronto Neighbourhood Profiles, 2021 Census from: Neighbourhood Profiles - City of Toronto Open Data Portal 
- Average Total Household Income by Neighbourhood
- Unemployment Rates by Neighbourhood

3. After examining the full data sets by year, select a time period to map. In this case, July 2025 which was the month that had the greatest number of crimes to occur this year. 

4. Map Setup
- Coordinate system: NAD 1983 UTM Zone 17N
- Rotation: -17
- Geography:
  - City of Toronto, ON, Canada
  - Neighbourhood boundaries from Toronto Open Data Portal

5. Add the crime incident data reports and Toronto’s Neighbourhood Boundary file. 

Geospatial Analysis Tools Used
     Tool - Select by attribute and delete the data that we are not mapping.               In this case; 
          From the Attribute Table,
          Select by Attribute [OCC_YEAR] [is less than] [2025]

     Tool - Summarize within
          Count the number of crime incidents within each of the neighbourhood's boundary polygons for the 5 selected crime types for preliminary analysis and mapping.

Design Tools and Map Types Used
     - Dot Density
         - 2025 Crime rates, by type, annual and for July of 2025
     - Heat Map
         - 2025 Crime rates, by type, annual and for July of 2025
     - Choropleth
         - Average Total Household Income, City of Toronto by Neighbourhood
         - Unemployment Rates Across Toronto, 2021
     - Design Tools e.g. convert to graphics

Part 2, Creating the 3D Model

Draft 3D Model
Final 3D Model
Break and Enter
Assault
Auto Theft
Based on literature review and analysis of the presented maps,  this model allows for us to further analyze, visually display and record the data and findings. This model will allow for users to see where points are clustering, and examine urban features, land use and the socio-economic context of cluster areas in order to address potential solutions, with equity in mind.

Supplies
     - Thread, 
     - Painted tooth picks, 
     - Mini clothes pins, 
     - Highlighters, markers etc. 
     - Scissors, 
     - Hot glue
     - Images of indicators
     - Relevant/insightful literature research
     - Socio-Economic Maps: Population Income, unemployment, and density
     - Crime Maps: Dot density crime by type, heat map of crime distribution by type, from the select 5 crime types, all incidents to occur during the month of July, 2025

Process
1. Attach cork board to poster board;

2. Cut out and place down main maps that have been printed (maps created in ArcGIS Pro, some additional design edits made in Canva);

3. Outline the large or central base map with tacks; use string to connect the tacks outlining the City of Toronto's regional boundary line.

4. Using colour painted tooth picks (alternatively, tacks may be used depending on size limitations), crime incidents can be recorded in real time, using different colours to represent different crime types. 

5. Additional data can be added on and joined to other map elements over time. This data could be: images and locations of crime indicators; new literature findings; news reports’ raw data; different map types presenting comparable socio-economic data; community input via email, from consultation meetings, 911 calls, or surveys; graphs; tables; land use type and features and more.

6. Thread is used to connect images and other information to associated areas on the map. In this case, blue string and tacks were used to highlight preventative crime measures and red to represent an indicator of crime.

7. Sticky notes can be used to update the day and month (using a new poster/cork board for each year), under “Time Stamp” 

8. Use of Google Earth was applied to further analyze using satellite imagery, a terrestrial layer, and an urban features layer in order to further analyze land use, type, function, and significant features like Union Station - a major public transit connection point, and located within Toronto’s most dense and overall largest crime hot spot.

9. A satellite imagery base map in ArcGIS was used to compare large green spaces (parks, ravines, golf courses etc.) with the distribution of each incidence point on the dot map created. Select each point field individually for optimal view and map analysis.

10. Video and Photo content used to display the final results were created using an IPhone Camera  and the "iMovie" video editing app.

See photos and videos for reference!

Findings

Socioeconomic and Environmental Indicators of Crime

A consistent theme across the literature and my own findings is the strong connection between neighborhood deprivation and crime. Mansourihanis et al. (2024) emphasize that understanding the “relationship between urban deprivation and crime patterns” supports targeted, long-term strategies for urban safety. Concentrated poverty, population density, and low social cohesion are significant predictors of violence (Mejia & Romero, 2025; M. C. Kondo et al., 2018). Similarly, poverty and weak rule of law correlate more strongly with homicide rates than gun laws alone (Menezes & Kavita, 2025).

Environmental characteristics also influence crime distribution. Multiple studies link greater green space to reduced crime, higher social cohesion, and stronger perceptions of safety (Mejia & Romero, 2025). Exposure to green infrastructure can foster community pride and engagement, further reinforcing crime-preventive effects (Mejia & Romero, 2025). Relatedly, Stalker et al. (2020) show that community violence contributes to poor mental and physical health, with feelings of unsafety directly associated with decreased physical activity and weaker social connectedness.

Other urban form indicators—including land-use mix, connectivity, and residential density—shape mobility patterns that, in turn, affect where crime occurs. Liu, Zhao, and Wang (2025) find that property crimes concentrate in dense commercial districts and transit hubs, while violent crimes occur more often in crowded tourist areas. These patterns reflect the role of population mobility, economic activity, and social network complexity in structuring urban crime.

Crime Prevention and Community-Based Solutions

Several authors highlight the value of integrating built-environment design, green spaces, and community-driven interventions. Baran et al. (2014) show that larger parks, active recreation features, sidewalks, and intersection density all promote park use, while crime, poverty, and disorder decrease utilization. Parks and walkable environments also support psychological health and encourage social interactions that strengthen community safety. In addition, green micro-initiatives—such as community gardens or small landscaped interventions—have been found to enhance residents’ emotional connection to their neighborhoods while reducing local crime (Mejia & Romero, 2025).

At the policy level, optimizing the distribution of public facilities and tailoring safety interventions to local conditions are essential for sustainable crime prevention (Liu, Zhao, & Wang, 2025). For gun violence specifically, trauma-informed mental health care, early childhood interventions, and focused deterrence are recommended as multidimensional responses (Menezes & Kavita, 2025).

Spatial Crime Patterns in Toronto

When mapped across Toronto’s geography, the crime data revealed distinct clustering patterns that mirror many of the relationships described in the literature. Assault, shootings, and homicides form a broad U- or O-shaped distribution that aligns with neighborhoods exhibiting lower average incomes and higher unemployment rates. These patterns echo global findings on deprivation and violence.

Downtown Toronto—particularly the area surrounding Union Station—emerges as the city’s highest-density crime hotspot. This zone features extremely high connectivity, car-centric infrastructure, dense commercial and mixed land use, and limited green space. These conditions resemble those identified by Liu, Zhao, and Wang (2025), where transit hubs and high-traffic commercial districts generate elevated rates of property and violent crime. Google Earth imagery further highlights the concentration of major built-form features that attract large daily populations and mobility flows, reinforcing the clustering of assaults and break-and-enter incidents in the downtown core.

Auto theft is relatively evenly distributed across the city and shows weaker clustering around transit or commercial nodes. However, areas with lower incomes and higher unemployment still show modestly higher auto-theft levels. Break and enter incidents, by contrast, concentrate more strongly in high-income neighborhoods with lower unemployment—suggesting that offenders selectively target areas with greater material assets.

Across all crime categories, one consistent pattern is the notable absence of incidents within large green spaces such as High Park and Rouge National Urban Park. This supports the broader literature connecting green space with lower crime and improved perceptions of safety (Mejia & Romero, 2025; Baran et al., 2014). Furthermore, as described, different kinds of crime occur in low versus high income neighbourhoods emphasizing a need for context specific resolutions that take into consideration crime type and socio-economics.

Synthesis and Relevance for Toronto

Collectively, these findings indicate that crime in Toronto is shaped by intersecting socioeconomic factors, environmental features, and mobility patterns. Downtown crime clustering reflects high density, transit connectivity, and land-use complexity; outer-neighborhood violence aligns with deprivation; and green spaces consistently correspond with lower crime. These patterns mirror global research emphasizing the role of social cohesion, urban form, and economic inequality in shaping crime distribution.

Understanding these relationships is essential for planning decisions around green infrastructure investments, targeted social services, transit-area safety strategies, and neighborhood-specific interventions. Ultimately, integrating environmental design, socioeconomic supports, and community-based programs that support safer, healthier, and more equitable outcomes for Toronto residents.

The Intersection of Geography and Athletic Effort

SA8905 – Master of Spatial Analysis, Toronto Metropolitan University

A Geovizualization Project by Yulia Olexiuk.

Introduction

A common known fact is that all marathons are the same length, but are they created equal? Long distance running performance depends on more than just fitness and training. The physical environment plays a significant role in how runners exert effort. Whether it be terrain, slope, humidity, or temperature, marathons around the world present distinct geographic challenges. In this case, three races in three continents are compared. Boston’s rolling topography often masks the difficulty of its course such as its infamous Heartbreak Hill, and Singapore’s hot and humid climate has athletes start running before dawn to beat the sun.

Data

  • GPS data for the Boston, Berlin, and Singapore Marathons were sourced from publicly available Strava activities, limited to routes that runners had marked as public. The marathon data was ensured that it consisted of  dense point resolution, clean timestamps, and minimal GPS noise and then downloaded as .GPX files. 

Figure 1. Getting .GPX data from Strava.

  • Using QGIS, the .GPX files were first inspected and cleaned and then converted to GeoPackage format and imported into ArcGIS Pro, where they were transformed into both point feature classes and polyline feature classes. The polyline class was then projected using appropriate city-specific coordinate systems (ETRS89 / UTM Zone 33N and NAD83 / Massachusetts Mainland, etc). The DEMs were sourced from the LivingAtlas database and are labeled as Terrain3D.
  •  I used the Open-Meteo API to make queries for each marathon’s specific race day, narrowing the geographic coordinates, local timezone, and hourly variables including temperature (degC), humidity (%), wind speed (km/h), and precipitation(mm). It was integrated into ArcGIS Pro’s Add Surface Information and Extract Multi-Values to Points tools to derive slope, elevation range, and elevation gain per kilometre. The climate data was collected through an API which returned the data in JSON format. It was converted to .CSVs with Excel Power Query.

Software/Tools

  • ArcGISPro: Used to transform the data and make web layers, map routes, and calculate the field to get valuable runner information.
  • QGIS: Used to clean and overlook the .gpx files imported from Strava.
  • Experience Builder: Used to create an interactive dashboard for the geospatial data.

Methodology

  • The workflow for this project began with extensive preprocessing of GPS track data sourced from public Strava activities. Each GPX file was inspected, cleaned, and converted into usable spatial geometry and re-projecting all layers into city-appropriate projected coordinate reference systems. The fields were then calculated for pace per kilometre, elevation gain per kilometre, maximum slope, and mean slope, using a combination of the Generate Points Along Lines, Split Line at Measure, and Add Surface Information tools.

Figure 2. GPX point layer undergoing a spatial join. 

  • The visualization design was the main cornerstone of the project’s approach. Thus, race maps employed accessible, easy-to-comprehend gradients to represent sequential variables such as pace, slope, and elevation gain, while the dashboard created through Experience Builder enabled dynamic comparison across the three cities.

Figure 3. Slider showing the patterns and relationships between average pace and elevation of the Berlin marathon.

Results and Discussion

Relationship between Pace and Terrain

  • Berlin displays the most consistent and fastest pacing profile, with minimal variation in both slope and elevation gain of only 27 metres of elevation difference.
  • On the other hand Boston showed more variability by each consecutive marker due to its hilly terrain. The geovisualizations clearly highlight slowdowns associated with climb leading to Heartbreak Hill, followed by pace recoveries on downhill segments.
  • Surprisingly, the Singapore marathon route had a different performance dynamic but not in the way that was initially assumed. In addition to its exact elevation difference as Boston of 135 metres. Participants would also face more environmentally-centred constraints, not only terrain-based difficulty.
  • Pacing inconsistency can coincide with high humidity and hot overnight temperatures really showing viewers how tropical climate conditions can inflict a different form of endurance.

Figure 4. Chart demonstrating the recorded temperature in degrees Celsius at the time of each race day. Note that the date was omitted due to the differing years, days, and months of each marathon so the duration of the race is the primary focus.

Figure 5. Chart comparing the relative humidity (%) between the marathon cities during race day.

Environmental Conditions and Weather During Race Day

  • It’s interesting to note that each city hosts their marathon at very different times throughout the year. For example, the Boston marathon used in the case study was held on April 17th, 2023. Berlin hosted their race on September 24th, 2023, and Singapore hosted their annual marathon on December 2nd, 2012. Boston usually started their race around 8:00 AM, Berlin usually starts an hour later at 9:00am local time. Lastly, Singapore begins the marathon at 4:30AM, assumingly to avoid the midday heat, which reaches high 30 degrees Celsius by noon.
  • This integration of hourly weather data highlights how climate interacts with geography to shape athletic effort. Berlin demonstrates ideal running conditions having cool and stable temperature along with stable wind speeds, which makes sense of the fast, consistent pacing. Boston shows slightly more variable weather, perhaps being on the New England Coast, Singapore saw the most influential weather impact with the humidity exceeding 80% for majority of the race (Figure 5) and persistent hot temperatures even throughout the night before.

Limitations

  • I experienced many limitations making this geovisualization including the fact that the project relies on public Strava .GPX data, which could vary in precision due to the accuracy of runner’s device whether it be phone or watch, or even satellite reception.
  • Also, though it was a good idea to use the data of some top performers of the marathon to get a good idea of where a well conditioned athlete naturally takes more time and slows their pace, I wished more average participant data was available to have a more averaged experience mapped.
  • Furthermore, I was unable to match the weather data directly to specific kilometres and instead had it serve as contextual aids rather than precise environmental measurements. 

Conclusion

I think this geovisualization project does an effective job demonstrating how terrain, and climate distinctly shape marathon performance across Boston, Berlin, and Singapore and I believe that visuals like these can be super fascinating just to satisfy curiosity or plan strategically for a race in the future. Happy Mapping!

Mapping and Printing Toronto’s Socioeconomic Status Index in 3D

Menusan Anantharajah, Geovis Project Assignment, TMU Geography, SA8905, Fall 2025

Hello, this is my blog post!

My Geovis project will explore the realms of 3D mapping and printing through a multi-stage process that utilizes various tools. I have always had a small interest in 3D modelling and printing, so I selected this medium for the project. Although this is my first attempt, I was quite pleased with the process and the results.

I decided to map out a simplified Socioeconomic Status (SES) Index of Toronto’s neighbourhoods in 2021 using the following three variables:

  • Median household income
  • Percentage of population with a university degree
  • Employment rate

It should be noted that since these variables exist on different scales, they were standardized using z-scores and then scaled to a 0-100 range. The neighbourhoods will be extruded by the SES index value, meaning that neighbourhoods scoring high will be taller in height. I chose SES as my variable of choice since it would be interesting to physically visualize the disparities and differences between the neighbourhoods by height.

Data Sources

Software

A variety of tools were used for this project, including:

  • Excel (calculating the SES index and formatting the table for spatial analysis)
  • ArcGIS Pro (spatially joining the neighbourhood shapefile with the SES table)
  • shp2stl* (takes the spatially joined shapefile and converts it to a 3D model)
  • Blender (used to add other elements such as title, north arrow, legend, etc.)
  • Microsoft 3D Builder** (cleaning and fixing the 3D model)
  • Ultimaker Cura (preparing the model for printing)

* shp2stl would require an older node.js installation
** Microsoft 3D Builder is discontinued, though you can sideload it

Process

Step 1: Calculate the SES index values from the Neighbourhood Profiles

The three SES variables (median household income, percentage of population with a university degree, employment rate) were extracted from the Neighbourhood Profiles table. Using Microsoft Excel, these variables were standardized using z-scores, then combined into a single average score, and finally rescaled to a 0-100 range. I then prepared the final table for use in ArcGIS Pro, which included the identifiers (neighbourhood names) with their corresponding SES values. After this was done, the table was exported as a .csv file and brought over to ArcGIS Pro.

Step 2: Create the Spatially Joined Shapefile using ArcGIS Pro

The neighbourhood boundary file and the newly created SES table were imported into ArcGIS Pro. Using the Add Join feature, the two data sets were combined into one unified shapefile, which was then exported as a .shp file.

The figure above shows what the SES map looks like in a two-dimensional view. The areas with lighter hues represent neighbourhoods with low SES values, while the ones in dark green represent neighbourhoods with high SES values.

Step 3: Convert the shapefile into a 3D model file using shp2stl

Before using shp2stl, make sure that you have an older version of node.js (v11.15.0) and npm (6.7.0) installed. I would also recommend placing your shapefile in a new directory, as it can later be utilized as a Node project folder. Once the shapefile is placed in a new folder, you can open the folder in Windows Terminal (or Command Prompt) and run the following:

npm install shp2stl

This will bring in all the necessary modules into the project folder. After that, the script can be written. I created the following script:

const fs = require('fs');
const shp2stl = require('shp2stl');

shp2stl.shp2stl('TO_SES.shp', {
  width: 150,
  height: 25,
  extraBaseHeight: 3,
  extrudeBy: "SES_z",
  binary: true,
  verbose: true
}, function(err, stl) {
  if (err) throw err;
  fs.writeFileSync('TO_NH_SES.stl', stl);
});

This script was ‘compiled’ using Visual Studio Code; however, you can use any compiler or processor (even Notepad works). This script was then saved to a .js file in the project folder. The script was then executed in Terminal using this:

node shapefile_convert.js

The result is a 3D model that looks like this:

Since we only have Toronto’s neighbourhoods, we have to import this into Blender and create the other elements.

Step 4: Add the Title, Legend, North Arrow and Scale Bar in Blender

The 3D model was brought into Blender, where the other map elements were created and added alongside the core model. To create the scale bar for the map, the 3D model was overlaid onto a 2D map that already contained a scale bar, as shown in the following image.

After creating the necessary elements, the model needs to be cleaned for printing.

Step 5: Cleaning the model using Microsoft 3D Builder

When importing the model into 3D Builder, you may encounter this:

Once you click to repair, the program should be able to fix various mesh errors like non-manifold edges, inverted faces or holes.

After running the repair tool, the model can be brought into Ultimaker Cura.

Step 6: Preparing the model for printing

The model was imported into Ultimaker Cura to determine the optimal printing settings. As I had to send this model to my local library to print, this step was crucial to see how the changes in the print settings (layer height, infill density, support structures) could impact the print time and quality. As the library had an 8-hour print limit, I had to ensure that the model was able to be printed out within that time limit.

With this tool, I was able to determine the best print settings (0.1 mm fine resolution, 10% infill density).

With everything finalized from my side, I sent the model over to be printed at the library; this was the result:

Overall, the print of the model was mostly successful. Most of the elements were printed out cleanly and as intended. However, the 3D text could not be printed with the same clarity, so I decided to print out the textual elements on paper and layer them on top of the 3D forms.

The following is the final resulting product:

Limitations

While I am still satisfied with the end result, there were some limitations to the model. The model still required further modifications and cleaning before printing; this was handled by the library staff at Burnhamthorpe and Central Library in Mississauga (huge shoutout to them). The text elements were also messy, which was expected given the size and width of the typeface used. One improvement to the model would be to print the elements separately and at a larger scale; this would ensure that each part is printed more clearly.

Closing Thoughts

This project was a great learning experience, especially for someone who had never tried 3D modelling and printing before. It was also interesting to see the 3D map highlighting the disparities between neighbourhoods; some neighbourhoods with high SES index values were literally towering over the disadvantaged bordering neighbourhoods. Although this project began as an experimental and exploratory endeavour, the process of 3D mapping revealed another dimension of data visualization.

References

City of Toronto. (2025). Neighbourhoods [Data set]. City of Toronto Open Data Portal. https://open.toronto.ca/dataset/neighbourhoods/ 

City of Toronto. (2023). Neighbourhood profiles [Data set]. City of Toronto Open Data Portal. https://open.toronto.ca/dataset/neighbourhood-profiles/

Mapping Toronto’s Post-War Urban Sprawl & Infill Growth (1945-2021)

A Geovizualization Project by Mandeep Rainal.

SA8905 – Master of Spatial Analysis, Toronto Metropolitan University.

For this project, I explore how Toronto has grown and intensified over time, by creating a 3D animated geovisualization using Kepler.gl. I will be using 3D building massing data from the City of Toronto and construction period information from the 2021 Census data (CHASS).

Instead of showing a static before and after map, I decided to build a 3D animated geovizualization that reveals how the city “fills in” over time showing the early suburban expansion, mid-era infill, and rapid post-2000 intensification.

To do this, I combined the following:

  • Toronto’s 3D Massing Building Footprints
  • Age-Class construction era categories
  • A Custom “Built-Year” proxy
  • A timeline animation created in Kepler. gl and Microsoft Windows.

The result is a dynamic sequence showing how Toronto physically grew upward and outward.

BACKGROUND

Toronto is Canada’s largest and fastest growing city. Understanding where and when the built environment expanded helps explain patterns of suburbanization, identify older and newer development areas and see infill and intensification. This also helps contextualize shifts in density and planning priorities for future development.

Although building-level construction years are not publicly available, the City of Toronto provides detailed 3D massing geometry, and Statistics Canada provides construction periods at the census tract level for private dwellings.

By combining these sources into a single animated geovizualization, we can vizualize Toronto’s physical growth pattern over 75 years.

DATA

  • City of Toronto – 3D Building Massing (Polygon Data)
    1. Height attributes (average height)
    2. Building Footprints
    3. Used for 3D extrusions
  • City of Toronto – Muncipal Boundary (Polygon Data)
    1. Used to isolate from the Census metropolitan area to the Toronto city core.
  • 2021 Census Tract Boundary
  • CHASS (2021 Census) – Construction Periods for Dwellings
    1. Total dwellings
    2. 1960 and before
    3. 1961-1980
    4. 1981-1990
    5. 1991-2010
    6. 2011-2015
    7. 2016-2021
    8. Used to assign Age classes and a generalized “BuiltYear” for each building.

METHODOLOGY

Step 1: Cleaning and Preparing the Data in ArcGIS Pro

  • I first imported the collected data into ArcGIS. I clipped the census tract layers to the City of Toronto boundary to get census tracts for Toronto only.
  • Next, I joined the census tract polygon layer we created to the construction period data that was imported. This gives us census tracts with construction period counts.
  • Because Toronto does not have building-year data, I assigned construction era categories from the census as proxies for building age, and created an age classification system using proportions. Adding periods and dividing / total dwellings to get proportions, and assigned them into three classes:
    • Mostly Pre-1981 dwellings
    • Mixed-era dwellings
    • Mostly 2000+dwellings
  • Next, I needed a numeric date field for Kepler to activate the time field. I assigned a representative year to each tract using the Age classes.
    • if age = Mostly Pre-1981 dwellings = 1945
    • if age = Mixed-era dwellings = 1990
    • if age = Mostly 2000+dwellings = 2010
  • And to make the built year Kepler-compatible a new date field was created to format as 1945-01-01.
  • The data was then exported as GeoJSON files to import into Kepler.gl. The built year data was also exported as a CSV because Kepler doesn’t pick up on the time field in geoJSON easily.

Stage 2: Visualizing the Growth in Kepler

  • Once the layers are loaded into Kepler the tool allows you manipulate and vizualize different attributes quickly.
  • First the 3D Massing GeoJSON was set to show height extrusion based on the average height field. The colour of the layer was muted and set to be based on the age classes and dwelling eras of the buildings.
  • Second layer, was a point layer also based on the age-classes. This would fill in the 3D massings as the time slider progressed, and was based on brighter colours.
  • The Built Date CSV was added as a time-based filter for the build date field.

The final visualization was screen recorded and shows an animation of Toronto being built from 1945 to 2021.

  • Teal = Mixed-era dwellings
  • Amber = Mostly 2000+ dwellings
  • Dark purple = Mostly Pre-1981 dwellings

RESULTS

The animation reveals key patterns on development in the city.

  • Pre-1981 areas dominate older neighbourhoods, the purple shaded areas show Old Toronto, Riverdale, Highpark, North York.
  • Mixed-era dwellings appear in more transitional suburbs, filling in teal, and showing subdividisions with infill.
  • Mostly 2000+ dwellings are filling in amber and highlight the rapid intensification in areas like downtown with high-rise booms, North York centre, Scarborough Town Centre.

The animation shows suburban sprawl expanding outward, before the vertical intensification era begins around the year 2000.

Because Kepler.gl cannot export 3D time-slider animations as standalone HTML files, I captured the final visualization using Microsoft Windows screen recording instead.

LIMITATIONS

This visualization used census tract–level construction-period data as a proxy for building age, which means the timing of development is generalized rather than precise. I had to collapse the CHASS construction ranges into age classes because the census periods span multiple decades and cannot be animated in Kepler.gl’s time slider, which only accepts a single built-year value per feature. Because all buildings within a tract inherit the same age class, fine-grained variation is lost and the results are affected by aggregation. Census construction categories are broad, and assigning a single representative “built year” further simplifies patterns. The Kepler animation therefore illustrates symbolic patterns of sprawl, infill, and intensification, not exact chronological construction patterns.

CONCLUSION

This project demonstrates how multiple datasets can be combined to produce a compelling 3D time-based visualization of a city’s growth. By integrating ArcGIS Pro preprocessing with Kepler’s dynamic visualization tools, I was able to:

  • Simplify census construction-era data
  • Generate meaningful urban age classes
  • Create temporal building representations
  • Visualize 75+ years of urban development in a single interactive tool

Kepler’s time slider adds an intuitive, animated story of how Toronto grew, revealing patterns of change that static maps cannot communicate.

Geospatial Visualization of Runner Health Data: Toronto Waterfront Marathon

Geovis Project Assignment, TMU Geography, SA8905, Fall 2025

Hello everyone! I am excited to share my running geovisualization blog with you all. This blog will allow you to transform the way you use GPS data from your phone or smart watch!

This idea came to me as I recorded my half marathon run on my apple watch in 2023 in the app “Strava”. Since then, I developed an interest in health tracking data and when assigned this project, I thought, hmm maybe I can make this data my own.

As a result, I explored the options and was able to create a 3D representation of my run and how I was doing physically throughout.

Here is a Youtube link to the final product!

The steps are as followed if you want to give this type of geospatial analysis a try yourself!

Step 1.

You will need to have installed the app Strava. This health and fitness app will track your GPS data from either your phone or watch and track your speed, elevation and heartrate (watch only). Apart from this, you will also need the app RunGap. This app will allow you to transfer your activity data and export it to a “.fit” file. A .fit file is a special data source that can track heartrate, speed and elevation that is geolocated by x and y coordinates every second (each row).

Step 2.

Once you have the apps downloaded, start a health activity on the Strava app. From there you can transfer your Strava data to RunGap.

After you sign in and import the Strava data, go to the activity you want to export as a .fit file. Save the .fit file and transfer it to your computer.

Step 3.

Now that you have the .fit file, you will need to download a tool to convert it to a CSV. This can be found at https://developer.garmin.com/fit/overview/. In Step 1 of this page you will need to download the https://developer.garmin.com/fit/download/ Fit SDK. The file will be in your downloaded folder under FitSDKRelease_21.171.00.zip. You will need to unzip this file and navigate to >java>FitToCSV.bat. This is the tool that you will use on the .fit file. To do this, go to your .fit file properties and change the “Open with:” application to your >java>FitToCSV.bat path.

Now simply run the .fit file and the tool will open and covert it to a CSV in the same folder after pressing any key to continue…

Step 4.

Now, open your CSV. The data is initially messy, and the fields are mixed. To clean it I added a new sheet, and then deleted from the original, continuing to narrow it down using the filter function. In the end, you only want the “data” rows in the Type column and rows with lat and long coordinates to create a point feature class. I also renamed the fields. For example, value 1 became Timestamp(s), which is used as the primary key to differentiate the rows. To get the coordinates in degrees, I used this calculation:

  • Lat_Degrees: Lat_semicircles / 11930464.71
  • Long_Degrees: Long_semicircles / 11930464.71

Furthermore, to display the points as lines in the final map, 4 more fields are needed to be added to the excel sheet. This is the start lat, start long, end lat and end long fields. These can simply be calculated by taking the coordinates of the next row for the end lat and end long. You will also need to do this with altitude to make a 3D representation of the elevation.

Step 5.

Now that your CSV is cleaned, it is ready to be exported as spatial data. Open ArcGIS Pro and create a new project. From here, load your CSV into a new map. This table will be used in the XY to line geoprocessing tool using the start and end coordinates for the WGS_1984_UTM_Zone_17N projection in Toronto.

Once you run the tool, your data should look something like this, displaying lines connecting each initial point/row.

Step 6.

Now it is time to bring your route to life! Start by running the Feature To 3D By Attribute geoprocessing tool on your feature class using the height field as your elevation/altitude.

Your line should now be 3D when opening a 3D Map Scene and importing the 3D layer

Step 7.

To add more dimensions to the symbology colours, I used “Bivariate Colours”. This provides a unique combination of speed and heart rate at each leg of the race.

To make the elevation more visually appealing, I used the extrusion function on the line feature class. Then, I used the “Min Height” category with the expression “$feature.Altitude__m_ / 3”. To further add realism, I added the ground elevation surfaces layer called WorldElevation3D/Terrain3D, so that the surrounding topography pops out more.

Step 8.

Now that the layer and symbology are refined, the final part of the visualization is creating a Birdseye view of the race trail from start to finish. To do this, I once again used ArcGIS Pro and added an animation in the view tab. From here I continuously added key frames throughout the path until the end. Finally, I directly exported the video to my computer.

Step 9. Canva

To conclude, I used Canva to add the legend to the map, add music, and a nice-looking title.

And now, you have a 3D running animation…! I hope you have learned something from this blog and give it a try yourself. It was very satisfying taking a real-life achievement and converting it to a in-depth geospatial representation. :)

Demographics of Chicago Neighbourhoods and Gang Boundaries in 2024

By: Ganesha Loree

Geovis Project Assignment, TMU Geography, SA8905, Fall 2025

INTRODUCTION

`Chicago is considered the most gang-occupied city in the United States, with 150,000 gang-affiliated residents, representing more than 100 gangs. In 2024, 46 gangs and their boundaries across Chicago were mapped by the City of Chicago. Factors about the formation of gangs have been of interest and a topic of research for many years all over the world (Assari et al., 2020), but for the purpose of this project, these factors are going to stem from demographics of Chicago. For instance, Chicago has deep roots within gang history and culture. Not only gangs but violent crimes are also dense. Demographics such as income, education, housing, race, etc., play factors within the neighbourhoods of Chicago and could be part of the cause of gang history.

METHODOLOGY

Step 1: Data Preparation

Chicago Neighbourhood Census Data (2025): Over 200 socioeconomic and demographic data for each neighbourhood was obtained from the Chicago Metropolitan Agency for Planning (CMAP) (Figure 1). In July 2025 their Community Data Snapshot portal released granular insights into population characteristics, income levels, housing, education, and employment metrics across Chicago’s neighbourhoods.

Figure 1: Census data for Chicago, 2024

Chicago Neighbourhood Boundary Files: official geographic boundaries for Chicago neighbourhoods were downloaded from the City of Chicago’s open data portal (Figure 2). These shapefiles were used to spatially join census data and support geospatial visualization.

Figure 2: Chicago Data Portal – Neighborhood Boundaries

Chicago Gang Territory Boundaries (2024): Gang territory data from 2024 was sourced from the Chicago Police Department’s GIS portal (Figure 3). These boundaries depict areas of known gang influence and were integrated into the spatial database to support comparative analysis with neighbourhood-level census indicators.

Figure 3a: 2024 Gang Boundaries Data Portal
Figure3b: 2024 Gang Boundaries Data Portal

Step 2: Technology

Once the data was downloaded, they were applied to software to visualize the data. A combination of technologies was used, ArcGIS Pro and Sketchup (Web). ArcGIS Pro was used to import all boundary files, where neighbourhood census data was joined to Chicago boundary shapefile using unique identifier such as Neighbourhood Name (Figure 4).

Figure 4: ArcGIS Pro Data Join Table

Gang territory boundary polygons overlaid with neighborhood boundaries to enable spatial intersection and proximity analysis (Figure 5).

Figure 5: Shapefiles of Chicago’s Neighbourhoods and Gangs

Within ArcGIS Pro, the combined map of both boundaries allowed for analysis of the neighbourhoods with the most gang boundaries. Rough sketch of these neighbourhoods was made by circling the neighbourhoods of a clean map of Chicago, where the bigger circles show the areas with more gang areas and the stars indicate the neighborhoods with no gang boundaries (Figure 6). The CMAP was used to look at the demographics of the neighborhoods with the most area of gangs and compared to the areas with no gang areas (e.g. O’Hare).

Figure 6: Chicago neighborhood outlines with markers

SketchUp

SketchUp is 3D modeling tool that is used to generate and manipulate 3D models and is often used in architecture and interior design. Using this software for this project was a different purpose of the software; by importing Chicago neighborhoods outline as an image I was able to trace the neighborhoods.

Step 3: Visualization with 3D Extrusions (Sketch Up)

Determined the highest height of the 3D maps models, was based on the total number of neighborhoods (98) and total number of gangs records/areas (46). Determining which neighbourhoods had the most gang boundaries was based on the gang area number which was provided in the Gang Boundary file. The gang with the most area totaled to shape area of 587,893,900m2, where the smallest shape area is 217,949m2. Similar process was done with neighbourhood area measurements. Neighbourhoods were raised based on the number of gang areas that were present within that neighbourhood (as previously shown in Figure 5). 5’ (feet) is the highest neighbourhood, and 4” (inches) is the lowest neighbourhood where gangs are present, neighbourhoods that do not have gangs are not elevated.

A different approach was applied to the top 3 gangs map model, where the highest remains same in each gang, but are placed in the neighbourhoods that have that gang present. For instance, Gangster Disciples were set at approximately 5 feet (5′ 3/16″ or 1528.8 mm), Black P Stones at almost 4 feet (3′ 7/8″ or 936.6 mm), and Latin Kings at a little over 1 foot (1′ 8 1/4″ or 514.4 mm).

Map Design

Determined what demographic factors were going to be used to compare with gang areas, for example, income, race, and top 3 gangs (Gangster Disciples, Black P Stones, and Latin Kings). Two elements present with the two demographic maps (height and colour), where colour indicates the demographic factor and the height represents the gang presence (Figure 7).

Figure 7: 3D map models of Chicago gangs based on Race and Income

LIMITATIONS

There was limited information available about the gang areas, which only consisted of gang name, shape area, and length measurements. In terms of SketchUp’s limitations, the free web version as some restrictions, had to manually draw the outline of Chicago neighbourhoods which was time consuming. In addition, SketchUp scale system was complex and was not consistent between maps. To address tis, each corner of the map was measured with the Tape Measure Tool to ensure uniformity. Lastly, when the final product was viewed in augmented reality (AR), the map quality was limited such as the neighbourhood outlines were gone, and the only parts that were visible were the colour parts of the models.

CONCLUSION (Key Insights)

The most visual pattern shown from the race map is the areas with more gang activity have a large population of African Americans (Figure 7). For the income map, indicated in green, more gang areas have lower income whereas the areas with higher income do not have gangs in those neighborhoods. Based on the top three gangs, Gangster Disciples have the most gang boundaries across Chicago neighborhoods (Figure 8). Gangster Disciples takes up 33.6% of the area in km2, founded in 1964 in Englewood.

Figure 8: 3D map of the top 3 gangs in Chicago, 2024

FINAL PRODUCT

The final product, is user interactive through a QR code that allows viewers to look at the map models using augmented reality (AR) just by pointing your mobile device camera at the QR code below.

Being aware that the quality of the AR has its limits, the SketchUp map models can be viewed using the Geovis Map Models button below.

Geovis map models

Reference

Assari, S., Boyce, S., Caldwell, C. H., Bazargan, M., & Mincy, R. (2020). Family income and gang presence in the neighborhood: Diminished returns of black families. Urban Science, 4(2), 29.

Paint by Raster: Watercolour Cartography Illustrating Landform Expansion at Leslie Street Spit, Toronto (1972 – 2025)

Emma Hauser

Geovis Project Assignment, TMU Geography, SA8905, Fall 2025

Hi everyone, welcome to my final Geovisualization Project tutorial. With this project, I wanted to combine my love of watercolour painting with cartography. I used Catalyst Professional, ArcGIS Pro, and watercolours to transform Landsat imagery spanning the years 1972 to 2025 into blocks of colour representing periods of landform expansion at Leslie Street Spit. I also made an animated GIF to help illustrate the process.

Study Area

Just to give you a bit of background to the site, Leslie Spit is a manmade peninsula on the Toronto waterfront, made up of brick and concrete rubble from construction sites in Toronto starting in 1959. It was intended to be a port-related facility, but by the early 1970s, this use case was no longer relevant, and natural succession of vegetation had begun. The landform continued to expand through lakefilling, as did the vegetation and wildlife, and by 1995 the Toronto and Region Conservation Authority started enhancing natural habitats, founding Tommy Thompson Park.

Post Classification Change Detection

The Landsat program has been providing remotely sensed imagery since 1972, at which time the Baselands and “Spine Road” had been constructed. Pairs of Landsat images can be compared by classifying the pixels as land or water in Catalyst Professional using an unsupervised classification algorithm, and performing “delta” or post classification change detection in ArcGIS Pro using the Raster Calculator to determine areas that have undergone landform expansion in that time period. The tool literally subtracts the pixel values denoting land or water of a raster at an earlier date from a raster at a later date in order to compare them and detect change. If we perform this process seven times, up until 2025, we can get a near complete picture of the land base formation of the Spit and can visualize these changes.

Let’s begin!

Step 1: Data Collection from USGS EarthExplorer

The first step is to collect 9 images forming 7 image pairs from USGS EarthExplorer. I searched for images that had minimal cloud cover covering the extent of Toronto.

For the year 1985, we need to double up on images in order to transition from the Multispectral Scanner sensor with 60m resolution to the Thematic Mapper sensor with 30m resolution. 1980 MSS and 1985 MSS will form a pair, and 1985 TM and 1990 TM will form a pair.

Step 2: Data Processing in Catalyst Professional

Now we can begin processing our images. All images must be data merged either manually (using the Translate and Transfer Layers Utilities) or using the metadata MTL.txt files (using the Data Merge tool) to join each image band together and subset (using the Clipping/Subsetting tool) to the same extent. The geocoded extent is:

Upper left: 630217.500 P / 4836247.500 L
Lower right: 637717.500 P / 4828747.500 L

Using the 2025 image as an example, my window looked like this:

I started a new session of Unsupervised Classification and added two 8 bit channels.

I specified the K-Means algorithm with 20 maximum classes and 20 maximum iterations.

I used Post-Classification Analysis (Aggregation) to assign each of the 20 classes to an information class. These classes are Water and Land. I made sure all classes were assigned and I applied the result to the Output Channel.

I got this result:

I repeated this process for all images. For example, 1972 looked like this:

I saved all of the aggregation results as .pix files using the Clipping/Subsetting tool.

Step 3: Data Processing, Visualization, and GIF-making in ArcGIS Pro

We are ready to move onto our processing and visualization in ArcGIS Pro. Here, we will be performing the post classification or “delta” change detection.

I added the aggregation result .pix files to ArcGIS Pro. I exported the rasters to GRID format. The rasters now had values of 0 (No Data), 21 (Water), and 22 (Land). I used the Raster Calculator (Spatial Analyst) to subtract each earlier dated image from the next image in the sequence. So, 1974 minus 1972, 1976 minus 1974, and so on.

I got this result (with masking polygon included, explanation to follow):

The green (0) represents no change, the red (1) represents change from Water to Land (22 – 21), and the grey (-1) represents change from Land to Water (21 – 22).

I drew a polygon (shown in white) around the Spit so we can perform Extract by Mask (Spatial Analyst). This will clip the raster to a more specific extent.

I symbolized the extracted raster’s values of 0 and -1 with no colour and value 1 as red. We now have the first land area change raster for 1972 to 1974.

I repeated this for all time periods, symbolizing the portions of the raster with value 1 as orange, yellow, green, blue, indigo, and purple.

We can now begin our animation. I assigned each change raster its appropriate time period in the Layer Properties. A time slider appeared at the top of my map.

I added a keyframe for each time period to my animation by sliding to the correct time and pressing the green “+” button on the timeline. I used Fixed transitions of 1.5 seconds for each Key Length and extra time (3.0 seconds) at beginning and end to showcase the base raster and the finished product.

I added overlays (a legend and title) to my map. I ensured the Start Key was 1 (first) and the End Key was 9 (last) so that the overlays were visible throughout the entire 13.5 second animation.

I exported the animation as a GIF – voila!

Step 4: Watercolour Map Painting

To begin my watercolour painting, I used these materials:

  • Pencil and eraser
  • Drafting scale (or ruler)
  • Watercolour paper (Fabriano, cold press, 25% cotton, 12” x 15.75”)
  • Watercolour brushes (Cotman and Deserres)
  • Watercolour palettes (plastic and ceramic)
  • Watercolour drawing pad for test colour swatches
  • Water container
  • Lightbox (Artograph LightTracer)
  • Leslie Spit colour-printed reference image
  • Black India ink artist pen (Faber-Castell, not pictured)
  • Masking tape (not pictured)
  • Lots of natural light
  • JAZZ FM 91.1 playing on radio (optional)

I first sketched out in pencil some necessary map elements on the watercolour paper: title, subtitle, neatline, legend, etc. I then taped the reference image down onto the lightbox, and then taped the watercolour paper overtop.

I mixed colour and water until I achieved the desired hues and saturations.

From red to purple, I painted colours one by one, using the reference illuminated through the lightbox. When the last colour (purple) was complete, I added the Baselands and Spine Road in grey as well as all colours for the legend.

To achieve the final product, I added light grey paint for the surrounding land and used a black artist pen to go over my pencil lines and add a scale bar and north arrow.

The painting is complete – I hope you enjoyed this tutorial!

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

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.

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 360o.

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