Author Archives: Reham Youssef

Imagine trying to bake a cake without ever tasting other cakes from other bakers. You’d figure out some things on your own, but you’d probably miss out on tricks, shortcuts, and flavours that others have already mastered. That’s what it’s like running an open data portal without looking at what the best ones around the world are doing. 

We wanted to see what’s out there, how other portals are set up, the tools and features they use, and the ways they make data useful. So we went on a bit of a global tour, scanning some of the most respected portals from national sites to smaller city portals. What we found gave us fresh ideas and a better sense of where we could go next. 

A global tour of portals worth learning from 

From the largest players like the European Union, the UK, and the US federal government, to national leaders like France, Finland, and Singapore, and then down to city portals in New York City, San Francisco, Vancouver, Ottawa, and Seattle, we chose these for their track record of innovation, the myriads of their datasets, and how they’ve turned open data into something more than just a catalogue.  
 

Off the bat, we found that across the list, these portals: 

• Publish high volumes of quality datasets. 

• Offer advanced search, visualization, and developer tools. 

• Have governance models and active engagement strategies. 

• Influence global best practices through open data policy (shout out to SF – you rocked there!) and technology. 

What we’re doing right! 

One thing this review made clear is we’re not starting from scratch. Many of the features we saw elsewhere are already part of our own portal, from solid metadata standards to clear publishing processes. And in some areas, we’re ahead of the pack. Our Data Quality Score is something we didn’t spot on any other portal. It’s a simple but powerful way to help people quickly understand the reliability of a dataset, something most sites leave to guesswork. 

Main features we saw on most portals: 

At a quick glance, a list of features stood out page, after page. Things that make a portal easier to use, more transparent, or more interactive, like:  

• Tagging and categorization improvements 

• Better search, filtering, and browsing  

• User submission tools or request tracking 

• Dashboard metrics 

• Dataset versioning and change history 

• Dedicated sections for APIs and other technical documentation 

• Built-in tools for creating graphs, maps, and charts directly from datasets 

Without further ado, I’ll briefly display some of the key features we found across the board.  

New York City has a a full catalog overview with compliance metrics for agency datasets. This includes: 

• Scheduled dataset releases – tracks upcoming datasets each agency plans to publish  

• Total dataset inventory – shows how many datasets each agency has made available  

• Delayed releases – flags datasets that were planned but not published on time  

A nice feature that helps users ask the right question, get filtered to the proper person and helps track the requests coming in is a standard form to collect requests. NYC helps you:   

• Request a new dataset   

• Ask a question 

• Report an error 

• General inquiry 

Another Government links their divisions/agencies’ datasets right along their page. Some may even use the terms like “you might also like…” if a dataset is related to another one. For example, a tree permit dataset might show you a related dataset called ‘tree canopy’.  

In Finland, they have chosen to highlight their top dataset of the year, including honourable mentions – which I think flexes partnerships and increases participation to release more data. 

In Paris, they outline categories of tools to use:   

• Create a map  

• Create a chart  

• Access all APIs  

• Documentation 

In Singapore, they make it easy to share data by letting you embed charts and tables directly into blogs or articles. It’s a great way to make data more accessible—not just for experts, but for anyone curious about the numbers without having to dig through raw datasets. 

One of the slickest data features comes from Vancouver’s Open Data portal. Shout out to my homeboy Canadian Province. Their dataset page has everything from currency, accuracy, # of downloads, search words, change logs, last modified, websites for more information and more. 
 
 

But THE cake is Seattle’s Homepage. I really like how clean and easy-to-read this page is; more icons, fewer words. It highlights key sections like “About Open Data,” “API Docs,” and “Suggest a Dataset” right in the middle, making them easy to find.  Maybe I am being subjective, but I really like the flow.

I also appreciate how they promote related services like the FOI office, which ties in well with open data. It’s a layout that feels more intuitive and user-friendly. They also highlight different dashboards and agency links, which is a great collaborative tool for users who want a one-stop shop.  

Exploring global open data portals reminded me that innovation thrives on collaboration and curiosity. By studying what others are doing well (from NYC’s compliance dashboards to Singapore’s embeddable charts) we’ve gathered fresh inspiration to improve our own portal. Toronto’s already ahead in some areas, and with ideas like dataset request tracking and our unique Data Quality Score, we’re not just keeping up, I think we’re helping set the pace. 

What features do you think we should introduce or incorporate next on our Toronto Open Data portal? 

Jinesh Dutt and Rishabh Kaushick are recent graduate Master students at Northeastern University Toronto. Under the supervision of Dr. Omar Badreldin, they tinkered around with our portal for a couple months and drew out some cool conclusions. Read more about them and check out their findings.

Our next student guest blog was written by Abbass Sleiman, a third-year undergraduate student at the University of Toronto in the Mathematical Applications in Economics and Finance Specialist. Abbass has experience as a Teaching Assistant in mathematics and wrote this paper for STA302H1 (Methods of Data Analysis) at the University of Toronto with Professor Rohan Alexander.

Background

Lead in drinking water isn’t something most of us think about when we turn on the tap – it certainly hadn’t crossed my mind until I read a CBC News article from 2014. The article, aptly titled “High lead levels found in some Toronto drinking water”, claimed that after analyzing 15,000 water samples provided to the city by homeowners between 2008-2014 through the Residential Lead Testing Program, 13% of Torontonian households exceeded Health Canada’s standards for lead exposure – an alarmingly high portion when you consider the potential health risks associated with lead exposure.

Lead exposure can have serious health consequences, especially for children. It can cause damage to the brain and nervous system, slowed growth and development, learning and behavior problems, and decreased IQ. For adults, long-term exposure can lead to cardiovascular issues, kidney damage, and reproductive problems. This, coupled with the fact that lead can’t be seen, smelled, or tasted, means that testing our water becomes all the more crucial. So, how did the city of Toronto address this issue? Back in 2011, Toronto’s City Council took action against this silent threat by approving a strategy to reduce lead in our drinking water. Fast forward to 2014, and the city began adding phosphate to the water treatment process which forms a protective coating in the pipes, helping to keep lead from leaching into the water we drink every day.

But did it work? To find out, I delved into a dataset of 12,810 water samples from Toronto homes, collected between 2014 and 2024. My goal was to determine if the phosphate treatment made a difference in reducing dangerous lead levels in our water, how efficient it may have been at doing so, and whether certain areas in Toronto are more at risk than others by analyzing the data using the programming language R.

Understanding Lead Levels

To understand the impact of lead concentrations on our drinking water, we need to first understand how these levels are measured. Lead concentrations are generally measured in parts per billion (ppb), effectively how many tiny amounts of lead are present in every billion drops of water, or equivalently in micrograms per liter.

Back in 2014, Health Canada set the safety threshold of lead exposure in water at 10 ppb, meaning that the CBC news article’s claim that 13% of Toronto households were facing high lead levels essentially meant that 13% of households provided water samples with a lead concentration of at least 10 ppb. However, in 2019, Health Canada tightened the standard, lowering it to just 5 ppb. So, when we talk about ‘high lead concentrations’, we’ll distinguish between whether we’re referencing the older threshold or the updated one.

The Data

The data used in this analysis was derived from Open Data Toronto, under “Non Regulated Lead Sample”. Published by Toronto Water, this data features data from Toronto’s Residential Lead Testing Program – the same source data used in the CBC news article. It includes details on various houses’ lead concentrations based on water samples provided by the households themselves. The data is refreshed daily and the particular dataset used was up-to-date as of January 22, 2024.

The raw data set features the lead concentration in parts per million (ppm) of 12,810 water samples, where 1 ppm is equivalent to 1000 ppb, the date that each sample was collected, and the household’s partial postal code (only the first three digits of the resident’s postal code for privacy reasons).

Cleaning the Data

Given that I was interested in the after-effects of the phosphate addition to the drinking water treatment process in 2014, all entries from 2014 were eliminated to ensure the analysis focused on water samples taken after the policy was put into effect. Additionally, entries with missing values were removed, and the column for lead concentrations was converted to ppb for consistency.

Extreme outliers were also excluded to avoid skewing the analysis. In the context of this study, I defined a lead concentration to be an outlier if it was any value exceeding (and including) 100 ppb, 20 times Health Canada’s standard of 5 ppb, making it reasonable to assume that the value is so extreme as a result of an error in the particular household’s sample collection process. This left us with a cleaned dataset of 9,302 samples ready for analysis. A sample of this cleaned data can be seen in Table 1 below, and all observations are visualized in the scatter plot in Figure 1.

Summary Statistics of the Data

Before diving further into the analysis, it was important to examine the structure of the dataset, particularly the number of observations per year, to ensure that any conclusions drawn later down the line would be based on a sufficient amount of data. Table 2 shows us that we have access to much fewer data points in the year 2020, likely as a result of the COVID-19 pandemic, and only a single observation available for 2024, meaning that any information pertaining to that particular year is likely meaningless and should be taken with a grain of salt.

Then, to get a better sense of the data as a whole, both the mean and standard deviation of lead concentrations were calculated and are presented in Table 3. The mean lead concentration of all samples was approximately 1.04 ppb, well below both the old and new safety limits. However, the standard deviation of the lead concentrations, essentially a measure of how spread out the lead concentrations are from the mean, was a rather large 4.05 ppb, indicating considerable variability.

Examining the Portion of Households Exceeding the Lead Concentration Limit

I was mainly interested in whether the portion of households exceeding the lead concentration limit of 10 ppb had changed from the past portion of 13%. Additionally, it was important to examine the portion of households exceeding the newer limit of 5 ppb. To do so, I decided to look at the distribution of households across four lead concentration categories (<5 ppb, 5-10 ppb, 10-20 ppb, >20 ppb), all of which are shown in Table 4.

Using this information, we can see that the vast majority of water samples (98.73%) contained a lead concentration below the previous limit of 10 ppb, a striking improvement from the original 87%. Even more so, approximately 97.14% of water samples are below the new limit of 5 ppb. Overall, this provides some fairly compelling evidence in favor of the benefits of adding phosphate to the water treatment plan.

Investigating the Relationship Between Time and Lead Concentration

But how quickly did the lead concentrations improve? Were they instantaneous or were they gradual? These are key questions to consider when evaluating the effectiveness of the phosphate treatment. To answer this, I created a scatter plot (Figure 2) to see how the mean lead concentration changed across each year.

We can see a clear and consistent decline over time, with 2015 featuring the highest mean lead concentration of 1.53 ppb, and 2024 with the lowest at a mere 0.15 ppb (though the 2024 data point is based on only one observation and should be viewed cautiously). To gain a deeper insight, I examined how the proportion of households with water samples exceeding lead concentrations of 10 ppb and 5 ppb changed over time in Figures 3 and 4, respectively (note that 2024 was omitted given that there is only one observation).

Though these figures show a slight rise in mean lead concentrations from 2019-2022, we can still see an overall fairly consistent decline over time in the portion of households exceeding both the 10 ppb and 5 ppb limits. Moreover, the portion of households across all years with high lead concentrations still remains well below the 13% found in 2014, indicating that the addition of phosphate was indeed effective in improving water quality.

Exploring the Relationship Between Location and Lead Concentration

Lastly, I wanted to uncover whether certain locations were more prone to having contaminated water than others. Given that the dataset had dozens of unique partial postal codes, I grouped them by their first 2 characters (for e.g. M2L and M2K would both fall under the “M2-” group) to create a more readable figure. Doing so allowed me to calculate the mean lead concentration for households in each group, showcased in Figure 5.

While we do see some variation in lead concentrations between locations, with the highest mean concentration being 1.27 ppb in the “M6-” group and the lowest being 0.29 ppb in the “M8-” group, the range of concentrations is within 1 ppb. This means that we can safely conclude that location doesn’t significantly impact the likelihood of a household having high lead concentrations.

Summary

Consuming water contaminated with lead can cause serious health issues, including brain damage and slowed growth, especially in children. To tackle this problem, Toronto started adding phosphate to its drinking water treatment process in 2014 to reduce lead contamination. After analyzing water samples from post-2014, it’s clear that mean lead concentrations have decreased, and fewer households are exceeding the safe lead exposure limit. There’s also little evidence to suggest that where the water samples were taken from has a significant impact on lead levels.

Next Steps

Given that the data used was based on samples collected by the individuals residing in each household, future analysis could be better improved by incorporating sources of data from more controlled sources, collected by qualified individuals.

Another improvement could be the use of time-series data showcasing changes in lead concentrations over time from the same source. This could provide deeper, and possibly more accurate, insights into the effects of various water treatments of water quality and safety.

Thomas Fox is a 4th year undergraduate student at the University of Toronto’s Faculty of Information. His research focuses on information policy and the human impact of socio-technical systems. This paper was written for Professor Rohan Alexander’s course Worlds Become Data (INF312) at the University of Toronto. 

This post explores issues of equity, access, and demand surrounding licensed child care spaces throughout Toronto’s 25 wards. By examining information made available through Open Data Toronto related to licensed child care centres as well as the demographic information related to each ward from the 2021 Canada census, a detailed analysis is made possible. This data is easily accessible on Open Data Toronto’s website and is a useful resource for researchers and community members to explore and locate child care information. An interactive map can be found in the “Data Preview” tab at this link: https://open.toronto.ca/dataset/licensed-childcare-centres/.

Child care is essential to the social and economic health of a community such as the City of Toronto. Access to child care has been shown to have a positive impact on occupational and educational opportunities for parents, especially those in lower income brackets (Gunaseelan 2021). These economic advantages for parents and families bring benefits to their physical and social well-being. Child care access also has a positive impact on the health and development of children attending these facilities, especially those who are most vulnerable (Rhijn et al. 2021; Underwood and Frankel 2012). Equitable access to child care is therefore a vital facet of community health and development.

The findings of this analysis demonstrate inequitable access to child care across the city of Toronto based on each ward’s average household income, English language prevalence, and proportion of the population identifying as racialized. As child care plays a central role in the social and economic well-being of communities and has an especially positive impact on vulnerable populations, these findings support measures and initiatives aimed at ensuring more equitable access to child care in the city of Toronto.

The “Licensed Child Care Centres” data set is provided to Open Data Toronto by the City of Toronto’s Children’s Services division. The data is updated on an ongoing basis with the data used in this post being current as of April 19, 2024. The data set contains one entry for each licensed child care facility in Toronto. Variables in the data set include facility names and addresses, the ward number in which each facility is located, and the total number of individual child care spaces at the facility, amongst others. The data set also contains each facility’s operation type, whether it be non-profit, commercial, or public (City operated). Information about whether each facility has a fee subsidy contract or participates in the Canada-Wide Early Learning & Child Care (CWELCC) system is also included. Figure 1 utilizes the data set’s location information to map each of Toronto’s licensed child care facilities.

The “Ward Profiles (25-Ward Model)” data set is provided to Open Data Toronto by Toronto City Planning. The data set of interest found through this resource is the “2023-WardProfiles-2011-2021-CensusData” set which is used to determine demographic information of interest for each ward. Variables of interest for this analysis include ward numbers and names, total population, population under 15, average yearly household income, number of households where English is spoken most often, and population identifying as racialized. This data set is useful when exploring relationships between social or demographic factors and the various data sets made available through Open Data Toronto.

To explore the effect that household income, language, and racialized population may have on child care access, both data sets are used to determine the number of children under the age of 15 for every existing child care space in each ward, as shown in Figure 2. These values help indicate the demand for child care spaces across Toronto’s 25 wards.

Figure 3 aims to assess the relationship between average household income and children per child care space in Toronto’s 25 wards. The plot shows a negative correlation between average household income and children per child care space. The seven wards with the lowest average household income have an average of 6.8 children per child care space, while the seven wards with the highest average household income have an average of 3.9. These findings suggest that there is increased competition for child care spaces in wards with lower incomes and decreased competition for spaces in wards with higher incomes.

Figure 4 explores the relationship between English-speaking household populations and children per child care space in Toronto’s 25 wards. The plot shows a negative correlation between the proportion of English-speaking populations and children per child care space. The seven wards with the lowest proportion of English-speaking households have an average of 6.42 children per child care space, while the seven wards with the highest proportion of English-speaking households have an average of 3.9. These findings suggest that wards with large proportions English-speaking population proportions have decreased competition for child care spaces.

Figure 5 examines the relationship between racialized populations and children per child care space in Toronto’s 25 wards. The plot shows a negative correlation between the proportion of populations identifying as non-racialized and children per child care space. The seven wards with the lowest proportion of non-racialized residents have an average of 6.62 children per child care space, while the seven wards with the highest proportion of non-racialized residents have an average of 3.8. These findings suggest that wards with large proportions of their populations identifying as non-racialized have decreased competition for, and increased supply of child care spaces.

As outlined in Figure 3, a negative correlation exists between average household income and children per child care space across Toronto. In Figure 4, a similar trend is displayed with fewer children per existing child care space in wards with higher proportions of English-speaking households. Figure 5 shows a negative relationship between the proportion of residents identifying as non-racialized and the number of children per space. These findings suggest wards with higher incomes, higher proportions of English-speaking households, and lower proportions of racialized residents contain fewer children per existing child care space, and therefore, increased accessibility to licensed child care.

These findings are troubling for a variety of reasons. Children with at least one parent who speaks a language other than English in the home benefit disproportionately from child care access when compared to children from English-speaking households (Maki Park and Giang 2022). Income disparity related to child care access is especially concerning as both dual language and racialized children are more likely to experience poverty (Tome 2021). With Toronto having the highest rate of income inequality between racialized and non-racialized individuals in Canada, these findings are particularly relevant (Tome 2021). As quality child care is an instrumental facet of community, family, and individual health, these findings support any steps taken to ensure equitable access to quality child care across the city of Toronto.

This post was derived from the paper: “Inequitable Access: An Analysis of Licensed Child Care in Toronto’s 25 Wards in 2024”, where these topics are explored in more detail. The paper can be found here: https://github.com/ThomasWilliamFox/child_care_access.git.

Gunaseelan, Vinusha. 2021. “A New Normal for Child Care in Canada: Accessible, Affordable, Universal. Wellesley Institute.”https://www.wellesleyinstitute.com/children-youth/a-newnormal-for-child-care-in canada-affordable-accessible-universal/; broken link.

Maki Park, Jacob Hofstetter, and Ivana Tú Nhi Giang. 2022. “Overlooked but Essential: Language Access in Early Childhood Programs.” https://www.migrationpolicy.org/sites/default/files/publications/mpi_ecec-language-access-2022_final.pdf.

Rhijn, Tricia van, Kathryn Underwood, Elaine Frankel, Donna S. Lero, Karen Spalding, Magdalena Janus, Martha Friendly, and Arlene Haché. 2021. “Role of Child Care in Creating Inclusive Communities and Access for All.” Canadian Public Policy 47 (3): 399–409. https://doi.org/10.3138/cpp.2021-010.

Tome, Samantha. 2021. “Racialization of Poverty.” https://horizonsforyouth.org/blog/racializationofpoverty#:~:text=Racial%20disparities%20therefore%20ccur%20in,the%20%27racialization%20of%20poverty%27.

Underwood, Kathryn, and Elaine B Frankel. 2012. “The Developmental Systems Approach to Early Intervention in Canada.” Infants & Young Children 25 (4): 286–96. https://doi.org/10.1097/IYC.0b013e3182673dfc.

Our first student guest blog was written by Benny Rochwerg, a fourth-year undergraduate student at the University of Toronto, St. George Campus in the Chemical Physics Specialist, Statistics Minor, and Mathematics Minor programs. He has several years of professional and volunteer tutoring experience in mathematics, physics, and chemistry. This paper was written for STA302H1 (Methods of Data Analysis) at the University of Toronto with Professor Rohan Alexander.

Background

Following the World Health Organization’s COVID-19 pandemic proclamation in 2020, 81% of deaths from COVID-19 in Canada occurred among long-term care residents. Since Toronto recently experienced high influenza and COVID-19 activity, it is critical to evaluate disease outbreaks in Toronto healthcare facilities.

Toronto Public Health defines an outbreak as “a localized increase (e.g. in an institution, or a specific ward or floor within an institution) in the rate of infection or illness, above that which is expected.” To gain insight into this issue, the R programming language and several R packages were used to examine Toronto Public Health “Outbreaks in Toronto Healthcare Institutions” open data from 2023.

Results

Figure 1. Number of outbreaks at each Toronto healthcare location type in 2023

As demonstrated in Figure 1, the majority of Toronto healthcare outbreaks in 2023 occurred in long-term care homes, followed by retirement homes, chronic care hospital settings, acute care hospital settings, psychiatric hospital settings, and transitional care facilities.

Also, Figure 2 highlights that approximately 95% of Toronto healthcare outbreaks in 2023 were respiratory with relatively few enteric outbreaks or other outbreak types.

Figure 3. Number of outbreaks for each first known cause in Toronto healthcare facilities in 2023.

Moreover, Figure 3 shows that COVID-19 was the first known cause of approximately two-thirds of Toronto healthcare outbreaks in 2023. In contrast, other agents were represented to a much lesser extent.

Discussion

As illustrated in the Results section, most of the Toronto healthcare facility outbreaks occurred in long-term care homes, were respiratory, and had COVID-19 as their first known cause. These outcomes may have been exacerbated by the Government of Ontario’s 2022 decision to eliminate mandatory masking in long-term care homes for visitors and caregivers. This is despite the fact that long-term care residents tend to be at least 65 years old as of 2018, an age group that is more susceptible to worse health impacts from COVID-19.

The outbreak data examined here was likely an underestimate of the true total given that asymptomatic disease may not have been detected and recorded and that secondary drivers of each outbreak were not assessed. Consequently, long-term care home disease outbreaks in Toronto and in Canada overall should be investigated to gain a better understanding of this significant issue.

Attribution Statement

“Contains information licensed under the Open Government Licence – Toronto.”

Original Paper

This blog post is a summary of my 2024 paper titled “Long-term care homes were hit hardest by 2023 disease outbreaks in Toronto healthcare facilities” (available here). The GitHub Repository associated with this paper is available here.

Welcome to our newest blog post, where we’re exploring a game-changer in Toronto’s public transit scene! Meet time2reach, an interactive transit map created by university student Henry Nguyen. This clever tool uses data from Open Data, including TTC Routes and Schedules, along with information from other regional transit agencies, to give you the best travel routes and times across the city. From Scarborough to Clarkson, time2reach makes navigating Toronto’s public transit system a breeze. Whether you’re a daily commuter or just planning a day out, this map is your new go-to guide. Join us as we delve into how this innovative map is simplifying travel in Toronto! Written by Henry Nguyen.

Time2reach is a map that shows you how fast you can travel from a place using public transit. The more “yellow” an area is, the quicker you can reach it from your starting point. Hover over any point to see which trains or buses you need to take to get there. The default starting location is at Union Station. To change the starting point, double click anywhere on the map. The map updates to show commute times from your starting point.

Just for fun, I included a few filters that let you choose your mode of transit. For example, if you want to exclude Pearson Express and GO trips, then you can deselect the agencies from the right menu. The resulting map without GO Transit looks like this:

As another example, to see where you can go from Sheppard-Yonge without using any subway line (e.g. Line 1 is down), then you can also unselect the “Subway” mode.

The resulting map looks like this:

There are a lot of other filters for you to play around with as well! For example, you can change the time (midnight transit options are different!) and adjust the maximum duration of the trip as well. 

Just for fun, I also tried to make an animation of the transit access map over the course of a day. I thought it’d be interesting to see how transit changes around our commuting patterns. Here is the 24 hour animation for Toronto.

If you noticed the ~15 minute blips at Weston / Pearson Airport, that’s because of the Union Pearson express! The trains have a 15 minute frequency, so it’s extremely quick to reach Pearson Airport if you time the train just right. You might also notice lower frequency blips near the GO Stations in Mississauga or on the Lakeshore line. If you time your departures just right to catch the train, then it’s extremely fast to reach any station along the line.

Just for comparison, here is a similar 24 hour timeline for New York City.

The animations show the “heartbeat” of each city. Frequent rapid transit makes large distances feel small. It’s also cool how this perceived distance changes over the course of a day. The Finch area is normally very accessible, but not past midnight when the trains stop running. But above all, the animations show how transit infrastructure connects neighbourhoods. This is the best evidence for running buses and trains more frequently, or for 24-hour transit. The city literally becomes farther without transit.

Dive into the world of ‘Open Water Data,’ a must-have digital companion for every open water swimmer and enthusiast. Developed by Mitch Bechtel, this platform offers real-time data on water quality, temperature, and weather conditions for beaches in Toronto and around the globe. With an interactive map to explore popular swim spots and an array of data sources, you’re assured a safer and more informed swim. Let’s explore this essential tool that’s making waves in the swimming community.

Open Water Data informs open water swimmers and other water recreation users with a wealth of information about popular beaches in Toronto, and around the world.

Overview

Water quality, temperature, waves and weather are all critical to open water swimmers. Open Water Data informs open water swimmers and other water recreation users about:
• Water quality pass/fail
• E. coli & enterococcus levels
• Water temperature
• Wave height, period and direction
• Wind speed, gusts and direction
• Air temperature
• Weather

The website can be accessed at https://www.openwaterdata.com/

The Map

The map displays nearby beaches and swim spots. Map pins indicate nice places to swim, boat or just relax by the water, and if recent water quality tests passed (green), failed (red) or are not available (grey).

Beach Details

Click a map pin to get more details, including the latest known conditions, historic graphs, open data downloads, and even see where people have been swimming in the water. A beach photo, description, features and links to relevant information are also available for most beaches.

Swim Data

If you have a Garmin smartwatch, you can also track and share your swims with others. Review past swims, including locations, routes, distance, duration, pace, heart rate, strokes per minute and water temperature. Your swim paths can also be shared anonymously to let others see where people are swim at the same location.

Data Sources

In addition to swim paths and water temperature data collected by participating swimmers, data is collected from a number of third-party data sources, including:

• City of Toronto
• City of Hamilton
• Niagara Region
• Swim Drink Fish
• Surfrider Foundation
• WindFinder
• Weather API
• and many more municipalities and agencies around the world

Smart Buoys

Open Water Data has also designed, built and deployed several IoT water temperature buoys at beaches throughout the Greater Toronto Area, which update the website with near-realtime water temperature measurements at their corresponding locations. This is particularly useful in Lake Ontario, where the water can “flip” causing the temperature to drop more than 10 degrees in a matter of hours. For example, check out early October at Kew-Balmy Beach.

Open Data

All open data collected by the site from various data sources, including the smart buoys, is aggregated and shared as open data. Each open data beach measurement can be downloaded in CSV and JSON format from the corresponding beach location and data tile. In addition, open data is also available from a powerful API, available upon request. For more information about open data available from the Open Water Data website and API, see https://www.openwaterdata.com/open-data/.

Photo Attribution

Promo Photo by Todd Quackenbush on Unsplash.

Cycling is a pivotal aspect of urban commuting, but one recurring challenge for riders is finding suitable parking. Even in a city as expansive as Toronto, safe and accessible bicycle parking spots are often difficult. Imagine a tool that highlights those gaps, allowing cyclists to voice their parking concerns and provide actionable data to the city. Enter BikeSpace: a community-driven solution that pinpoints where Toronto can improve its bike parking infrastructure. Through this web app, riders can report issues and suggest potential parking spots, actively shaping Toronto’s cycling future. Dive in to discover how BikeSpace works, its roots in the Civic Tech community, and what the future holds for Toronto’s cycle enthusiasts. Come out and meet (support) the volunteers behind this app – http://civictech.ca

In this blog post: Matt Elliott, Publisher of City Hall Watcher newsletter and Toronto Star contributor, shares his experience of how the city’s Open Data Portal transformed his reporting during the COVID-19 pandemic. Stranded without traditional municipal news to cover, Elliott dove into a wealth of available datasets, discovering a myriad of untold stories about Toronto. Grab a cup of coffee, sit back, and join Matt as he takes you on an intriguing journey of discovery, right here in Toronto.

In this captivating blog post, Ingrid, a local resident, shares her personal journey of uncovering the impact of noise pollution on her life and her quest to better understand and quantify its effects. Amid the COVID-19 pandemic, Ingrid noticed changes in her soundscape that negatively affected her quality of life, leading her to take action. Ingrid’s story aligns with the goals of the Open Data Master Plan, advocating for data-driven solutions and emphasizing the importance of including community-sourced and third-party datasets. This approach not only encourages innovation but also bolsters civic engagement, empowering citizens to actively contribute to improving their urban environment. Ingrid’s efforts to collect citizen-submitted data showcase the power of grassroots initiatives and the significance of community involvement in creating better urban spaces.

So, if you too have embarked on your own journey of data collection and would like to share your story, please reach out to us at opendata@toronto.ca. We’d love to hear from you! Now, dive into her story.

My story (Ingrid’s story)

Before COVID I started to notice changes in my soundscape. When I was in my bathroom I could hear a car’s muffler on the highway, bikes and cars started to be so loud that the noise interrupted my conversation with my partner on the 25^th floor of my condo. I couldn’t enjoy my balcony anymore.  Then they started to wake me up at night, more frequently. My residence hadn’t changed, so something in my environment had. 

One morning around 3am, during the lockdown when our streets and highways were mostly empty, I was woken up by one single motorbike on the highway. I heard it for 10 minutes (for about 7 km) and knew where it was by the sound. For example, it got softer when it was in a depression and louder when it used an elevated ramp to connect to another highway. 

This one driver, this one engine, this single source of sound, disturbed my sleep and surely the sleep of many others. I wondered how many people’s sleep had been disturbed? How loud was it? This was one driver passing by my place, one time. What about when there are more? How many people does one motorbike, or one modified vehicle affect in its daily use? What are the health effects of this noise, other than feeling terrible the next day? I decided to learn about sound – and unwanted sound – called noise. 

Health Effects of Noise

Toronto Public Health commissioned a Noise Study in 2016, the results are in How Loud is Too Loud.  I learned noise pollution is the #2 Urban Environmental Health Hazard, right after air pollution. Negative health effects start at 55 decibels (db) and the World Health Organization (WHO) recommends 45 db for a restorative sleep. Not only does noise impact our quality of life, but also has other health impacts, such as cardiovascular effects, cognitive impacts, sleep disturbance and mental health effects. The Ontario Ministry of Environment and Climate Change has recommendations for road-related noise thresholds: for sensitive land uses, such as residential uses, mitigation measures are required if outdoor levels at the centre of a window or door opening exceed 55 dBA daytime or 50 dBA nighttime. The study found that 88.7% of the population is estimated to be exposed to levels above 55 dBA during the day, and 43.4% is estimated to be exposed to above this level at night.

In the report, heat maps showed that our highways and arterial roads produce between 70 to 90 db and were loud, yet they didn’t reflect what I was living with. I wondered if I was alone and started tweeting about excessive vehicle noise. I learned that I was not the only one annoyed at this small percentage of people who created this unnecessary noise that was impacting our health and quality of life. My anxiety climbed along with my level of annoyance at the increasing frequency of the noise events most days and nights.

Data Collection

My background in HR and IT taught me that data tells a story, so I knew that I needed to start collecting data about what I was experiencing. I bought an environmental sound meter in July 2021 and immediately felt vindicated when it logged multiple noise spikes through the night, going from 60 or 70 db to 80 and even over 90db.  

Then a friend forwarded a link to Toronto Metropolitan University’s YouTube on the Health Effects of Urban Traffic Noise featuring Tor Oiamo. Not only was it informative, when he said that “we don’t know enough about noise at the bedroom window” I shouted, “Yes!” The models use averages, because without access to a “bedroom window” this was the best data they had. Here was my niche. First, I needed to measure my soundscape and eventually also from other people’s bedroom windows to understand what we live with. Modelling can only go so far, it was time to collect citizen data. 

I learned that when taking measurements, the average sound level (LEQ) is like a high water mark on a lake, obscuring what lies below. And it was those events that maybe didn’t break the surface, but were audibly different frequencies and events that annoy us and impact our health. 

I was lucky enough to have my meter on a balcony when the Gardiner was closed for the Ride for Brain Health.  In the graph below, you can see the drop of the average when the Gardiner was closed at 2:30am, which exposed the resulting noise events that a model will not show. The orange line is the World Health Organization’s recommended level for a restorative sleep – 45 db. The measurement below was taken with an Environmental Sound Meter from Cirrus Research.

In the graphs above, these spikes are caused by vehicles and motorbikes.  Combined there were over 70 spikes between when the highway was closed at 2:30am and reopened at 2pm. Looking at the spike at 12:30 am from 78 to 98 db, our perception of that loudness is not an increase of 20 db. We perceive that to be an increase 12x louder than it was at 78 due to sound being logarithmic, for example to go from from normal conversation to someone pointing a hairdryer directly at your ear. Now it was easier to “see” what we feel and live with.

The graph below is from the location with the loudest spikes, which was the 15^th floor at Sherbourne and Adelaide. Over the course of 7 days there were at least 44 spikes over 90 db (double the WHO recommendation) both day and night, due to sound bouncing off of the buildings. This measurement was taken with a noise sentry meter built by Convergence Instruments.

I have only one location that met the WHO guideline of 45 db, a leafy residential neighbourhood, The Kingsway.

I have now collected 1,000s of hours of recordings from my balcony and other locations, that produce csv’s of sound levels by the second.  I am working with people from Civic Tech Toronto to build a database to query this data and to possibly make it available to the public.

City of Toronto Noise Bylaws

The city’s noise bylaws were being discussed at City Hall in Spring of 2022, specifically vehicle noise. The city received over 900 emails regarding noise from combustion engines. I heard from both the city and people that catching moving violations is difficult for everyone and that residents had lost hope in reporting noise complaints. 

To help people know that they were not alone, and maybe to help TPS target problem areas, I created the Not 311 Noise Report. Through this web app, people can identify the location of the noise source, what produced it and when. Different from 311, people can see the location of all noise reports in the Noise Report Dashboard. In two months, we had almost 1,800 reports. In the graph below you can see a chart of the different noise sources and day of the week; cars, pickup trucks and motorcycles being the most common complaints.

I am pleased to announce that I have just released a more comprehensive  “Not 311” Noise Report in advance of the full launch on May 4th, in conjunction with  Hot Docs Citizen Minutes Program.

You can view the Not 311 Noise Report Dashboard and learn more here: No More Noise Toronto.

Conclusion

We learned during COVID that cities aren’t loud, vehicles are loud. And I also learned that what changed in my environment was the removal of the Drive Clean program by the Provincial Government, eliminating vehicle inspections. 

With the transition to EVs there is an opportunity to advocate for stronger noise regulations on all levels of government to ensure that those who need to hear vehicles do, and those of us that don’t need to hear them for our safety – don’t.  Dodge should not be able to produce a vehicle that emits 126db of unnecessary vanity noise. 

We also know that Toronto is building more condos than any other city in North America and these people will be living above any type of noise mitigation (trees, green spaces & noise barriers). While noise dissipates over distances, clearly one engine can be heard from 7 km away and be heard over 50 stories high. This will place a greater burden on our healthcare system and reduce the quality of life and negatively impact the health of those living within earshot. 

We can make a healthier, calmer city and citizen submitted data is the story that will help us do that.

This supports the city’s vision of an inclusive and collaborative open data ecosystem, where diverse contributors can actively participate and add value to the wealth of information available to the public.

As members of the Open Data Team, we were thrilled to dive into our own dataset and analyze the numbers to gain insights about our own portal’s performance and impact. The process was enlightening, and we were eager to share our findings with others.

To shed light on our journey of self-discovery, we interviewed Reza Ghasemzadeh, a technical expert on our team who led the analysis. Our conversation delved into the details of his analysis, revealing key findings and insights that we believe will be valuable when working with this dataset. Let’s first define some terms before jumping in:

• A session is a set of interactions from a single identified user in a given time frame to each page.  
• A visit refers to a single instance of a user accessing a webpage
• Download refers to clicking on the “Download” button on our dataset page 
• A dataset (dataset package) is a collection of structured or unstructured data 
• A file is a collection of information or data that is typically characterized by a specific format (ex. csv, xlsx, etc)
• Dataset visits are analyzed for the entire year while the dataset downloads are analyzed from March to December of 2022 (due to the system not tracking any data from January and February of 2022). 
• At the time of analysis, we assumed the City had 41 divisions; however, we confirmed the City has 44 divisions, some which were added in 2022. We used the number 41 because at that time it represented the number of different Publishers/Divisions we had in the open data portal.
• Civic issues are based on datasets that have been #tagged with a civic issue. Datasets that are not tagged will not be part of this analysis. 
• The data is available for you to analyze here: Open Data Web Analytics – City of Toronto Open Data Portal 

Top 10 Divisional Datasets visits

The City has 41 divisions (not including Agencies, Boards & Corporations). Some have plenty of datasets; other divisions don’t have as much. Our first exercise involved examining the traffic and engagement patterns of different datasets across the various divisions that publish data throughout the portal.  

The total number of dataset visits on our portal was 174,593 sessions in 2022. To put this into perspective, we hosted around 500 dataset visitors, on average, every day in 2022.  

This analysis helped us answer questions such as, “which divisions attract the most traffic?” The following table displays the most popular divisions based on the sum of dataset visits (sorted by total sessions) in 2022: 

The sessions share percentage (pct) is simply derived by dividing the divisional value by total value. This is done to make sense of the numbers.  

Reza thought it would be interesting to create a cumulative sum plot of these shared percentages, after sorting them from highest to lowest. To do so, he took the highest share percentage (11.39%) as the first point (X=1) in our plot. So, the first point has x=1 and y₁=11.39%. For the second data point (X=2), he added the second highest share percentage (11.24%) to all the values bigger to it. So, y₂=11.39+11.24=22.63%. He continued this until he exhausted all 41 division rows. 

The resulting graph presented below, shows us that the top 4 divisions (out of 41) are responsible for 40% of visits; the top 10 divisions attract nearly 80% of our portal dataset visit traffic in terms of total number of sessions.  

Analyzing this data provided us with some insights into which datasets are most popular or useful for different groups of users, as well as identifying potential areas for improvement or further development, which led us to our next analysis.

Top 5 most visited dataset packages 

We then asked, which datasets/packages are the most frequently accessed, what types of data are most popular, and which areas of interest are most represented among users? Analyzing the top 5 most visited dataset packages on the open data portal provides valuable insights for optimizing the user experience, as well as informing strategic decision-making around resource allocation. 

Currently we are hosting more than 430 dataset packages on our portal.

 It was interesting to find that the top dataset package visited – Neighbourhood Profiles – did not correlate to the top division visited – which was Transportation Services, as seen in the above analysis of Top divisional dataset visits. 

The top visited datasets were the following: 

Neighbourhood profiles – The Neighbourhood Profiles provide a portrait of the demographic, social and economic characteristics of the people and households in each City of Toronto neighbourhood. The data is based on tabulations of 2016 Census of Population data from Statistics Canada.  

3d Massing – This is a geospatial 3D ESRI shape / 3D CAD format file of building shapes for City of Toronto 

Outbreaks in Toronto Healthcare Institutions – This dataset includes list of outbreaks in Toronto healthcare institutions – including hospitals, long-term care homes, and retirement homes – which are currently active, those that have been declared over for the current calendar year. Year-to-date data for the current year are updated weekly, each Thursday. 

Identifying Trends Across Datasets 

We also analyzed the trendline of the top 5 most visited datasets of 2022 based on session count. We observed that Outbreaks in Toronto’s Healthcare Institutions was particularly popular in January, which coincided with the peak of cold and flu season. Additionally, Elections saw a surge in views in October, aligning with the timing of the fall 2022 elections. Interestingly, 3D massing maintained consistently high levels of views throughout the year, suggesting a high level of interest among urban planners, architects, real estate developers, and geospatial analysts.  

From the above plot, we could also identify potential trends or patterns in user behavior, such as whether certain datasets are more likely to be accessed during certain times of the year.  

Top 5 most downloaded datasets  

As we delved into the realm of data analytics, it was intriguing to explore the trends in dataset downloads. The accompanying bar plot represents the total number of downloads for each available file within a dataset, highlighting the five most frequently downloaded datasets in 2022. A noteworthy observation is that while some datasets (dataset pages) may have high “visits,” the most downloaded datasets may differ significantly, as evidenced by the inclusion of only two datasets, “outbreaks in Toronto healthcare institutions” and “neighbourhood profiles,” in the top five downloaded datasets. 

Top 5 most downloaded files 

This analysis helped us understand which files are the most popular, what types of data are in high demand, and which areas of interest are most represented among users. It could also help identify areas for potential improvements, such as improving the accessibility or usability of certain files. 

Outbreaks in Toronto Healthcare Institutions was by far the most accessed dataset file in 2022, more likely because of the surge of the COVID virus.   

Civic issues people are most concerned about 

The Civic Issue Campaign aimed to prioritize the release of datasets based on five key civic issues: affordable housing, poverty reduction, fiscal responsibility, climate change, and mobility. These issues were identified through a combination of strategic priorities for the city. We’ve written previously about them and our civic issues campaign. The campaign considered the impact of these issues to determine which datasets to release next. 

According to the available data and related tags, it appears that certain civic issues are of greater interest to people than others. These issues have been ranked based on the number of times they were visited, with Mobility being the most popular, followed closely by Affordable Housing, Poverty Reduction, Climate Change, and finally Fiscal Responsibility.

There are a few possible reasons why Mobility is the most searched civic issue. One reason could be that Toronto is a large and densely populated city, which often leads to traffic and transportation being major concerns for residents. Another explanation may be that the Transportation Services division was the most visited divisional data on the portal, which suggests that people are particularly interested in issues related to transportation in Toronto. 

(Keep in mind not every dataset is associated with a civic issue tag and therefore this is only a count from 189 out of 443 datasets, that’s 42% of the entire catalogue as of when this blog was written). 

Based on the insights and findings shared in this blog post, what are some potential actions or use cases that you can think of for these analytics? How might this information be helpful to you or your organization? We’re always happy to collaborate.