Does Downtown Toronto Have Enough Green Space to Support its Residents?

Before anyone gets the wrong idea about what the intention of this article is, I want to be clear that what happened at Trinity Bellwoods Park on Saturday was selfish and inexcusable. Watching the Blog TO video was extraordinarily saddening and frustrating to see, especially for all the people who respected the quarantine measures for months in hopes that everything could return to normal quicker. It is going to be interesting how parks and green spaces will be regulated and enforced, especially if a second spike in cases happens. Whatever happens one thought at the back of my mind while watching that video yesterday was, does downtown Toronto have enough space to accommodate its residents, even if proper social distancing measures are followed?

I made this map a couple of months ago, giving a general view of park space throughout the city of Toronto. We can see that most of the hot spots follow our river and hydro corridors systems. With the Don, Humber, and Rouge River, as well as the Finch and Gatineau hydro corridors ranging in values between 16 and 32. Conversely, downtown Toronto is one of the ‘coldest’ areas in the city, in terms of park space, with only a few parks within the downtown core.

We can also evaluate available park space using population data to calculate Park Space per Person for each Census Tract. Within Toronto’s downtown core 21 of out the 41 census tracts have between 0 – 3 square meters of park space per person.

How can we use this data to evaluate if Toronto has enough green space downtown to accommodate its residential population? First, we have to determine how much space a person needs to be in line with proper social distancing measures. If two people are standing in the middle of their designated square, you need a length of 1 meter from the center of your square to the edge to make up the required 2-meter distance (1 meter plus 1 meter from the other person’s designated square). This means you need a 2 meter by 2-meter square, which is an area of 4 square meters of space per person. With this threshold determined, we can evaluate how many census tracts in Toronto meet this minimum threshold for their local population.

In the city of Toronto, 139 census tracts do not meet this threshold, 22 percent, 31 census tracts, which exist within the downtown and its immediate border. These inefficiencies in our park system create spillover resulting in thousands of people traveling to the few available parks, such as in the case of Trinity Bellwoods. So what can be done? In the short term, we don’t have many options. The best proposition would be to restructure spaces such as streets to make pedestrian-friendly environments and provide spaces for physical activity. While private cars are one of the few transportation options where people feel safe right now, they are inefficient in terms of the volume of people they carry relative to the space they need to operate. The National Association of City Transportation Officials (NACTO) just released a 29-page document regarding re-shaping public spaces and streets in cities to make them safer for pedestrians to maintain a safe distance, which I will link below. Reducing lanes, and even blocking off roads completely to convert them into green open areas would help relieve some spillover effect taking place in Toronto’s downtown core.

This pandemic has made us rethink and evaluate every aspect of how cities are designed and managed. In Toronto, the downtown core simply does not have enough green space required to meet the needs of its local residents. Saturday’s events were extremely upsetting. However, we still need to examine why it happened critically and implement the following changes quickly to prevent it from happening again. Restructuring streets to create more open space can make a profound and immediate impact on Toronto’s need for green space downtown as well as create areas that are more pedestrian-friendly both in the short and long term.

NACTO Article:


City of Toronto Open Data Catalogue. (2019). Parks (WGS84) [Data set].  Retrieved from:

Simply Analytic (2018). Total Population [Dataset]. Retrieved from:

Hot Spot Analysis of Essential Service in Toronto

During this time of COVID-19, social distancing has effected our ability to travel and access essential service as we would typically do. For many without the luxury of a car, active modes of transportation such as walking or biking, are the only alternative, but can these services be reached? On April 17, Statistics Canada, along with the Canada Mortgage and Housing Corporation (CMHC) released a nationwide proximity measure database as part of an initiative to provide the needed information to stakeholders across Canada who are dealing with the COVID-19 pandemic. These proximity measures offer information at the nieghbourhood level of proximity to health facilities, pharmacies, and other essential services/amenities. In this blog, I will be using the optimized hotspot analysis tool to produce a new output feature class identifying areas of Toronto that have high and low values of spatial clustering in terms of these specific essential services. Each result will contain a set of two maps. The first map shows the proximity index of each service normalized on a national scale per dissemination block (closer to 0 being lowest on the national level and 1 being the best). The second map is the optimized hotspot analysis feature class that identifies high and low clusters of the facilities throughout Toronto.

Proximity Measure Database

The proximity measure database contains ten unique services that can be measured at the dissemination block level to provide the highest level of geographic resolution possible. The proximity measures of each service for a dissemination block are created using a gravity model. A gravity model accounts for the network distance between the centroid of an origin dissemination block and the DB’s where the specific service is present within a given range, as well as accounting for the presence of services within its dissemination block. DB’s that have a high value of services within them or nearby will have a high gravity score, while DB’s that only have facilities located very far away or not at all will have low gravity scores. The final measures are computed as a normalized index value between 0 and 1 on a national scale. Values of 0 indicate the lowest level of proximity to a specific service in Canada, and 1 shows the highest level.

In this article, we will be evaluating four essential services:

Grocery Stores – measures the closeness of a dissemination block to any dissemination block with a grocery store within walking distance of 1 km.

Pharmacies – measures the closeness of a dissemination block to any dissemination block with a pharmacy or a drug store within walking distance of 1 km.

Health Care – measures the proximity of a dissemination block to any dissemination block with a health care facility within a driving distance of 3 km.

Employment – measures the proximity of a dissemination block to any dissemination block within a driving distance of 10 km.

Overview of the Optimized Hotspot Analysis Tool

The optimized hotspot analysis tool identifies significant spatial clusters of high and low values. Similar to the way that your camera has an automatic setting to adjust lighting and other factors to get the best picture, the Optimized Hot Spot Analysis tool interrogates your data to obtain the parameters that will yield optimal hot spot results. The results of this tool create a new Output Feature Class with a resulting z-score, p-value, and confidence level bin (Gi_Bin) for each feature. The Gi_Bin identifies and groups statistically significant hot and cold spots. Elements in the +/-3 bins are statistically significant at the 99 percent confidence level; features in the +/-2 bins reflect a 95 percent confidence level; features in the +/-1 bins reflect a 90 percent confidence level, and the clustering for features with 0 for the Gi_Bin field is not statistically significant. If you would like a more in-depth analysis of the methods and algorithms to optimize your hotspot analysis when using this tool, I encourage you to look at the How Optimized Hot Spot Analysis works tool reference linked below.


Grocery Stores



Employment Areas


When using optimized hot spot analysis to identify spatial clustering of essential services within the City of Toronto, some consistent patterns start to emerge. Throughout all the maps, we can see significant clustering within Toronto’s downtown core, especially in maps evaluating employment and healthcare. This pattern was expected given that the downtown core is the economic engine of the Greater Golden Horseshoe and has a world-class medical and pharmaceutical cluster downtown as well. For pharmacies and grocery stores specifically, other than the downtown core, significant hot spots were identified traveling along Yonge Street, Toronto’s main north-south arterial road, and Bloor Street, Toronto’s main east-west arterial road. While most of Toronto was identified as not significant, many areas along Toronto’s edges were identified as cold spots, specifically the inner suburbs of Scarborough and Etobicoke. One area of Scarborough that low proximity of services throughout, but most notably for employment and health care facilities, was Kingston Road, a major arterial road stretching from the east side of downtown to Toronto’s eastern border. For Etobicoke, there were not as many significant clusters when compared to Scarborough but still many smaller pockets that were identified as cold spots throughout the maps. Some areas were Rexdale, in northern Etobicoke, as well as areas along Highway 427 in southern Etobicoke home to areas such as the East and West mall. 

Tool Reference

How Optimized Hotspot Analysis Works:


Statistics Canada. (2020). Proximity Measures Database – Early Release [Data set]. Retrieved from:

Toronto Island Opportunities Study

Back in September of this academic year, I had the opportunity to work as part of a team to create an opportunities study for the Toronto Island that would be submitted and reviewed by the City of Toronto planning staff. In our report, we outline how Toronto Island serves as the primary park, not only downtown residents but the wider city as well. Also, we provide potential solutions for how Toronto Island can be resilient to increasing environmental threats and establish better connections within the park itself and to the broader city. The report can be found to download in the “Toronto Island Opportunities Study” tab.

I hope to get some more blog posts out soon!

Toronto Island Neighborhood Profile

While often overlooked, the Toronto Island has the potential to be a vital part of Toronto’s park system especially in the downtown core where additional public space is required to meet the increasing population demand. This quantitative report first outlines a neighbourhood profile looking at demographic, social and economic characteristics of both the Toronto Island, its surrounding area and the City of Toronto. Part two looks at the issue of park space in the downtown core as well as outlines important infrastructure on the Toronto Island through a series of maps.

Part 1: Quantative Neighbourhood Profile

            The study area chosen for this neighbourhood profile is comprised of both the Toronto Island and its surrounding waterfront communities. Specifically, areas that are located along the waterfront from Bathurst Street to the Don Valley Parkway. In terms of census tracts five were used in the analysis of the neighbourhood profile which are outlined in figure 1.1 (535002, 5350012.01, 5350012.04, 5350013.02 and 5350017).

            We begin by assessing the demographic statistics of the study area such as: population and age of the Toronto Island and its surrounding area. In terms of population there are roughly 155,000 living within the study area accounting for 5.7 percent of Toronto’s total population. Figure 1.2 can give us sense of the age of people living within this area. The highest age demographic is between the ages of 25 to 34 years old comprising of 56,460 people, roughly 36 percent of area population. In contrast, the lowest age demographic is from 65 years old and over. This demographic only accounts 9.7 percent of the total population for the study area with just over 15,000 people. However, when looking at just the Toronto Island specifically these demographics flip. Age 65 and over accounts for roughly 30 percent of the islands total population while age 25 to 44 only accounts for 15 percent.

           When assessing low income residents within the study area (figure 1.3) there are approximately 11,000 households within low-income standing resulting in 16.5 percent of residents compared to the city average of 20.2 percent. The Toronto Island has the lowest amount with only 140 households accounting for only 1.27 percent of total households within the study area. The highest household type within low-income standing is the “one-person household” making up 41 percent of the total in the study area. The lowest percentage for housing type is “other households of two or more” accounting for only 8.6 percent of the study area.

            This analysis of demographic and economic data tells us quite a bit about the Toronto Island and its surrounding area. We can see its is a younger neighbourhood with most of its inhabitants being of working age (24 to 44). However, the Toronto Island specifically is an older neighbourhood with a larger percentage of age 65 and older. Moreover, most of the households with in the study area above low-income in terms of median household income and most of the households are either defined as census families, or one couple families without other persons, with this demographic group earning the most with respect to the other family types.

Part 2: Mapping Neighbourhood Infrastructure

            In part 2 we outline the social and physical infrastructure of the Toronto Island and its surrounding area. First, looking at the provision of park space when compared to the rest of Toronto, followed by a detailed look at the services and areas of the Toronto Island.

            Figure 2.1 and 2.2 gives us a detailed look at the provision of park space within Toronto and in the downtown core. In this map we use a kernel density estimate or “cluster analysis” to identify high and low areas of the city in terms of available park space. From this map we can identify four areas that performed very well in terms of available park space. The Humber River, Don River Valley, Rouge National Park, and the Toronto Island all had high KDE scores ranging from 8 to 32 in some areas. This is mainly due to the high number of parks clustered within close proximity of each other in addition to the presence of large-scale parks as well. From these two maps we also realize that downtown Toronto has very low KDE scores, ranging from 0 to 4 and is one of the “lowest” areas in Toronto in terms of available park space due to the lack of parks coupled with only small-scale parks located in downtown core.

In terms of park space the downtown core only has 3721.76m2 compared to the 52,018m2 of park space on the Toronto Island. Figure 2.3 builds on the idea of lack of park space in the downtown core when looking at park space per person for each census tract area. Within the TO core planning area 36 of out the 41 census tracts are in the bottom 40 percent of park space per person averaging between 0 – 9 m2 of park space per person. In contrast, the Toronto Island is in the top 20 percent of park space per person with 3767.23m2 of park space per person.

            Moving to the Island itself, figures 2.4 and 2.5 give us a more detailed view of the topography on the Toronto Island looking at area uses and points of interest. Figures 2.4 looks at the areas that make up the Toronto Island which is divided into five main parts: Parks, Water Treatment, Residential, Centers, and Yacht Clubs. As discussed above the Toronto Island is predominantly covered by parks with 52,018m2 of continuous park space across the island also consisting smaller inner islands. There are three small residential neighbourhoods on the Toronto Island all located on the far east side of island with two of them connected to the inner harbour. The Toronto Island has two main centers which are the main tourist areas of the island. The first is Centerville, a small theme park. The second center, located to the south, is the main boardwalk heading towards Lake Ontario. The Toronto Island is home to two private yacht clubs, the Royal Canadian Yacht Club located in the center of the island facing the inner harbour, and the Toronto Island Sailing Club located on the west side of the island. The last area on the Toronto Island is the section of the island dedicated to the water treatment facility on the south side of the island.

            The last map figure 2.5 looks at areas of interest for the public visiting the Toronto Island. There are three ferry terminals connecting the Toronto Island to downtown Toronto on the east, west and center of the island. Along with the tourist centers the Toronto Island also has four beaches all along the south side of the island facing Lake Ontario. Lastly, the island has 16 family picnic areas spread out across the island although many are near the tourist centers.

In conclusion, we see there is a need to provide more park space in the downtown core especially due to increasing population and employment demand. The Toronto Island with its abundance of parks and social infrastructure such as: tourist centers, beaches, picnic areas, as well as the accessibility to downtown through the islands three ferry terminals make it a potential solution to this issue and a vital part of Toronto’s park system.


City of Toronto Open Data Catalogue. (2019). Parks (WGS84) [Data set].  Retrieved from:

Statistics Canada. (2016). Census – Age (in Single Years) and Average Age (127) and Sex (3) [Data set]. Retrieved from: eng.cfm?Temporal=2016&Theme=115&VNAMEE=&GA=8&S=0

Statistics Canada. (2016). Census – Low-income Measures (2), Household Low-income Status (5) and Household Type (5) for Private Households [Data set]. Retrieved from:

Statistics Canada. (2016). Census – Household Income Statistics (3) and Household Type Including Census Family Structure (11) for Private Households [Dataset]. Retrieved from:

Simply Analytic (2018). Total Population [Dataset]. Retrieved from:

Toronto’s Parks and Green Space Lit up

This afternoon I was just messing around in QGIS while doing a mapping assignment for a planning class (which will get posted shortly) and I stumbled across this. I don’t think I have ever seen a map like this before but if Toronto’s parks and green spaces were lit up at night this would be the view from space. From here we can begin to identify some key parks and natural spaces across the city. Starting at the top right of the city you can see Rouge National Urban Park one of the brightest spots on the map and if you look closely you can see two hydro corridors emerging from the park and running across the city. The first one running across the top is the hydro corridor just north of Finch Avenue and the one running diagonal into downtown is the Gatineau hydro corridor. Working left from there, the brightest spot in the middle of the city is the Don River Valley as it converges and works down towards the Lake stopping at the Lower Donlands and Portlands area. The lone bright spot at the bottom of the map is the Toronto Island just below the huge dark spot that is downtown Toronto (more about that in my next post). Staying downtown and continuing left to the west end of Toronto we can see High Park and the mouth of the Humber river. If we work up from there we can see it snake its way up to the biggest area of the Humber River valley between Weston Road and Royal York. Travelling north from there the last notable bright spot is the area of Black Creek and the two hydro corridors working there way down to Pearson Airport. I hope you enjoyed looking at Toronto through this lens it definitely opened my eyes when I first saw it.

Research Study Update: 3

Hi everyone! In my last research study update we were still half way through digitizing the FASTT time use diaries and I am now happy to say that process was finished about two weeks ago and we are moving on to the next steps in the data entry process. Since then we were able to digitize the rest of the short surveys as well as provide a quality check on the time use diaries by swapping the one’s we filled out with each other to prevent input error. The final step now is to geocode the locations that FASTT study participants identified as home, work and grocery store locations in their short surveys into ArcGIS which can then be matched with the GPS data taken from the FASTT study app downloaded on their phone during the study period. Although I have only completed a few of the participants entries I have started to notice a little problem with data quality for work and grocery store locations in this step for a few of the participants.

The ArcGIS Online geocoder uses the postal code of the address entered in a csv file format to find and input that location on the web map as a point data file. In the short survey FASTT participants were required to fill out their full home address but for work and grocery stores the name of the store and/or general intersection was okay and is now a potential problem. The store name is okay as I am able to use Google to find the rest of the address information but its general intersections for example, Yonge and Finch, where we now either have to find out more about the participant or just literally find the closest address to Yonge and Finch and input that even though that might not be accurate. Fortunately this has only happened a handful of times but we are so early into this process that it is something to think about.

Update: I was messing around with the settings in the ArcGIS Online geocoder yesterday and I got it to use the street address’ from the csv file instead of the postal code which is beneficial for two reasons. Firstly, the address gives a more accurate data point instead of using a postal code centroid and secondly, street intersections can be put into the address field of the csv and are accepted by the geocoder which means all locations are working!

Evaluating the Accessibility of the new Finch-West LRT Stops

Hi everyone. Finally another post on the blog, sorry for the delay this month I have just been a little busy with the research work, an update for that coming this week, but its nice to have some time to make a couple of maps for an infrastructure project that very literally hits close to home.

Background: Finch LRT

The Finch-West LRT proposal can be traced back all the way to 2007 with the transit city plan proposed by Mayor David Miller and Chair of the TTC Adam Giambrone. In this plan Finch Avenue, among many other suburban corridors,were highlighted as priority transit expansion segments where redevelopment and growth is to be encouraged to create new housing, jobs as well as improvements to public realm along side the development of rapid high-quality public transit.

The Finch-West LRT is comprised of 18 stops along 11 kilometers of track with rapid transit connections to the subway, Finch West Subway Station, as well as connections to other regional transit systems such as York and Peel Region Transit.

Accessibility to the new Finch-West LRT Stops

The point of the Finch-West LRT in my eyes is to encourage modal-shift along Finch Avenue West from cars to public and active modes of transportation while also encouraging and supporting redevelopment in the area. Based on this hypothesis the area for which my analysis was based on was the 10-minute walk time area used by the Ontario Growth Plan to measure densities for transit-oriented development.

The first map looks at the area of each walk-time polygon from each station on the Finch-West LRT line. A station with a bigger walk-time area is of course more accessible but also shows a more connected street grid in this area making active modes of transit like walking and biking more accessible to more people. The top three stations with the largest walk-time areas were: Martin Grove (1.52 km), Sentinel (1.49) and Albion (1.46) with the bottom three being: Humber College (1.09), Norfinch Oakdale (1.03) and Signet Arrow (0.89).

The second map looks at the number of low-density households within each stations specific walk-time area divided by the walk-time area for each station. The top three stations with the largest number of low-density households per walk-time area was: Sentinel (677.38), Duncanwoods (625.44), Pearldale (583.09). Conversely, the station with the smallest number of low-density households per walk area were: Emery (191.96), Norfinch Oakdale (190.36), and Signet Arrow (0).

The last map looks at the number of high-density households within each stations walk area. The top three stations with the largest number of high-density households were: Duncanwoods (18), Pearldale (16) and Rowntree Mills (16). On the other hand the three stations with the lowest number of high-density households were: Westmore, Norfinch Oakdale and Signet Arrow all with 0.

Although it is very likely to assume that the development of high-quality rapid public transportation will bring about growth and new development to the Finch Avenue West Corridor as outlined in Toronto’s transit city plan as of right now stations such as: Sentinel, Rowntree Mills, Jane and Finch, and Pearldale have the combination of large walk-time service areas as well as a relatively high amount of low and high density households making these stations very accessible to the population along the Finch West LRT corridor.


City of Toronto Open Data Catalogue. (2019). Address Points – Municipal (WGS84) [Data set].

Research Study Update: 2

Hi everyone. Its been about a month since my last update on the research study I am helping with at SAUSy Labs and just wanted to keep you updated on the progress we have made with the study. We just passed the half way point in digitizing the paper time use journals and paper surveys unfortunately the memory stick holding some of the surveys (not the journals) got corrupted last week and we have to input that again, to be honest if the forty or so time use journals I filled out were lost I would have snapped cause they are taking so long. Anyways, while this is going on we have also started to build the structure for the database in PostGres which will match up the data from the GPS app with the time use journals by matching to the corresponding user ID. I was also able to talk to my professor, Dr. Widener, on a bit of the analysis and what we are looking for with the data but a more general explanation of that will be explained in the next post as more of a process going forward update once all the data is digitized.

Another exciting thing is that my position at the lab has been extended from June 14th to the end of the summer and hopefully into the school year which was amazing news to hear. A task for me going forward after digitizing is to start geo-coding some locations from the surveys such as: work and grocery store locations, into spatial data for further analysis. This will be done on ArcPro a GIS program that I personally have not used. I just got a copy to download from UofT’s map and data library so I am excited to start messing around and in the next couple of blog post’s I will be using ArcPro in order to gain some personal experience with the program.

So that’s pretty much it for a little research update as far as my normal blog posts go I have a couple of ideas ready to go for July so I’ll be back soon. Bye!

Spatial Analysis of Bicycle Parking in Toronto

Hi all! Today is a shorter blog post than usual using two different methods to analyze bicycle parking in downtown Toronto. The first map uses graduated symbols and color to indicate the number of available spots at each bike rack. The weighted mean takes into account the location of each bike rack (lat, long. coordinates) but also the capacity of available spots to find the average location in Toronto which is just west of Queens Park in King’s College Circle. The pink ring is a standard deviational ellipse centered around the weighted mean which was weighted by bike rack capacity as well. Within the circle contains 68% of all bicycle parking spots in Toronto.

The second map is a basic heat map of bicycle parking spots in Toronto using the Kernel Density Estimate tool in QGIS. The bandwidth for each kernel was 500m using the tri-weight function. This map unlike the first map is better at identifying clusters and is not affected by outliers in data unlike the weighted mean and standard deviational ellipse which is better for giving an overall summary of spatial data. With this heat map we can clearly identify three significant bicycle parking hot spots in downtown Toronto. First, is an area between Yonge St. to the east and University Ave. to the west, and between Dundas St. to the north and Queen St. to the south. Second, is Union Station and third is a stretch along Bloor St. between Christie St. to the west and Bathurst to the east.


City of Toronto Open Data Catelogue. (2018). Bicycle Parking Racks (WGS84) [Data set]. Retrieved from:

Toronto’s Zoning By-Law Diversity

An integral part of the success of our urban systems in cities is predicated on the placement and arrangement of our zoning and land use. In particular land-use diversity can help to promote sustainable development by allowing citizens close proximity to where they can work, shop and live as well as giving them the infrastructure to connect to the wider city. But how diverse is Toronto’s Land use and how does this effect our daily lives. In this blog I will use Shannon’s Index and Moran’s I to evaluate Toronto’s land use diversity using Toronto’s Zoning By-Law data taken from Toronto’s Open Data Catalogue.

Shannon’s Diversity Index

A Shannon’s Index is a quantitative measure that reflects the variation of types in a community while also looking at how evenly these types are distributed. This is commonly used in ecology to evaluate the diversity of species such as trees within a certain area, but can also be used for text strings and in this case land-use type. The equation of Shannon Index is as follows: pi the proportion of the individual in the community is multiplied by the log of pi which is them summed with the other proportions of species in that community and then multiplied by negative 1 to produce a Shannon Index value. The higher the number the more diverse the community where equal diversity of all species produces a value of ln(R). Conversely, the lower the value the less diverse with only one type in the community equating to a value of zero.

Instead of looking at the proportion of species in a community Shannon’s Index will be used to look at the proportion of each land use type within a community by using the total area of each land use type divided by the total community area to calculate pi, which can then be used to give a Shannon Index Value for different areas of the city.


Above is a map of Toronto’s Zoning By-law categorized by land-use type. In order to look at the diversity of different areas across Toronto I overlaid a two square kilometer hexagon grid across the city which was clipped by Toronto’s municipal boarder so that no empty space factored into the proportion along communities bordering the city. After the grid was overlaid I used the Tabulate Intersection tool to find the proportion of each land-use type per hexagon in that area. Finally the table was converted to an excel spreadsheet to complete the Shannon Index calculations for each area before being spatially joined back into the hexagon grid to display areas of low and high zoning diversity.

Results of Shannon’s Index and Moran’s I

When Shannon’s Index was used to calculate the diversity of Zoning land-use throughout Toronto it showed many hot spots across the city of both low (dark blue) and high zoning diversity (red). The first and most notable area of high diversity can be seen on the lower east side of Toronto around the Beaches and East York area. Shannon Index values in this area ranged from 1.31 to 1.79 indicating high diversity mainly due to the areas mix of low and high density residential, industrial employment, green space as well as small pockets of commercial spaces in the Beaches area. Other areas of high zoning diversity in Toronto were centered around Toronto’s regional shopping centers of: Sherway Gardens, Yorkdale, and Scarborough Town Center due to these areas mix of Industrial, Commercial and Residential zoning along with Utility and Transportation land use due to the regional highway network.

Along with areas of high zoning diversity Shannon’s Index also revealed areas of low zoning diversity throughout Toronto. Disregarding Rouge National Park and The Waterfront which was classified as undefined the most notable area of low diversity land use was in an area just west of Yonge Street continuing down south of Allen Road. In this area Shannon Index values ranged from 0.00 to 0.67 indicating very low zoning diversity due to large areas mainly comprised of low density residential housing. Two other areas in Toronto that also had low Shannon Index values were Pearson Airport zoned almost exclusively and Industrial Employment and low density residential communities surrounding the downtown core mainly on the east side of downtown such as: St. James Town, Regent Park, Corktown, and Cabbage Town South created during the small urban renewal movement of the late 1960s in Toronto which was centered around large housing complexes and separation of land uses.

Along with using Shannon’s Index to calculate zoning diversity Moran’s I was also used to calculate the spatial auto correlation of the Shannon Index values to determine if these areas are dispersed or clustered throughout Toronto. When compared to an expected index of -0.002 a value of 0.381 indicated significant clustering of low and high zoning diversity areas throughout the city. Moreover, when testing for statistical significance a z-score of 12.38 coupled with a p-value of 0.00 indicates a less the 1% chance that this the result of a random spatial process. As a result the significant clustering of these high and low diversity areas has made Toronto more auto-dependent in suburban areas as citizen in lower diversity areas have to travel further distances in order to complete daily tasks such as travel to and from work especially with a lack of high-quality public transportation connecting these areas. In addition travel to large single-use employment areas like Pearson Airport can compound this problem of auto-dependency as most of these areas are only accessible by car.


In conclusion, while the use of Shannon’s Index to identify zoning diversity throughout the city showed that Toronto is more diverse and mixed use in its allocation of land use zoning than a typical sprawling city such as Chicago and Atlanta. However, the significant clustering of these high and low diversity areas coupled with a lack of public transportation has made Toronto just as auto-dependent.


City of Toronto Open Data Catalogue. (2014). Zoning By-Law (WGS84) [Data set]. Retrieved from: