Canada, with 3.33 people per square kilometre, has one of the lowest population densities in the world. In 2001, most of Canada's population of 30,007,094 lived within 200 kilometres of the United States (along Canada's south). In fact, the inhabitants of our three biggest cities -- Toronto, Montréal and Vancouver -- can drive to the border in less than two hours. Thousands of kilometres to the north, our polar region -- the Yukon, the Northwest Territories and Nunavut -- is relatively empty, embracing 41% of our land mass but only 0.3% of our population. An inset map shows in greater detail the Windsor-Québec Corridor where a high concentration of Canadians live.

This dataset contains unit density profiles of Social Housing in the City of Toronto for the 140 neighbourhoods that make up the City of Toronto. For Reference Period 2014: Social housing units including Toronto Community Housing Corporation locations, Housing Connections locations, non-profits and co-op developments participating in the Social Housing Wait List.

Clutter Classification : 0 Unclassified1 Inland Water 2 Open 3 Low Tree Density 4 High Tree Density 5 Rural areas/low residential 6 Suburban 7 Suburban With Tree 8 Urban ow Density 9 Urban Medium Dense 10 Urban High Dense 11 Skyscrapers/ High-rise 12 Industrial Area/ Commercial areas 13 Airstrip 14 River 15 Coast Note that original file was in ASCII Grid format. All other formats are derivatives of the original grid file.

Available on CD Rom through the Map and Data Library. CD # 384

Extreme heat is the most common climate-related hazard globally, with rising temperatures and more frequent heat waves affecting cities, ecosystems, and food production. Urban heat islands (UHIs), where city temperatures are higher than surrounding rural areas, are becoming more prevalent due to climate change. This occurs because urban structures like buildings and roads trap more heat than natural landscapes. To address this, creating a heat risk index (HRI) is essential for developing localized adaptation plans and prioritizing areas most at risk. This web map showing health risk index (HRI), temperature variations, population density, tree canopy cover across Toronto city. The inputs for this HRI was derived from multiple data sources from the ArcGIS Living Atlas of the World.

With 3.5 persons per square kilometre, Canada is one of the countries with the lowest population densities in the world. Census metropolitan areas (CMAs) with the highest population densities—Toronto (866), Montréal (854), Vancouver (735), Kitchener (546), Hamilton (505), and Victoria (475)—were located close to United States border.

The accuracy, precision values, recall values, and F1-score values comparison according to different state-of-the-art methods of the Toronto-3D dataset.

Number of points (thousand) per category in training and test sets of the Toronto-3D dataset.

This dataset contains spatial and temporal data on urban parks and their usage in Toronto, Canada. It was used to examine how anonymized mobility data from Mapbox can help identify and reduce inequality in the availability and use of green spaces. The dataset consists of four files:Toronto 2021 Census.shp: A shapefile that contains census data for the park catchments in Toronto, including variables such as housing density and car ownership.greenSpaceActivityWithWeather.csv: A spreadsheet that contains the daily Mapbox activity value for each park, as well as the average temperature and total precipitation from local weather stations.Simplified Large Parks.shp: A shapefile that contains the polygons of the target parks used in the study, which are larger than 10 hectares and have more than 1000 visits per year.Park amenities.csv: A spreadsheet that contains the amenities available in each park, such as sports fields, transportation options, gardens, and playgrounds.The dataset supports a manuscript published in People and Nature titled: “Using anonymized mobility data to reduce inequality in the availability and use of urban parks”. The manuscript presents the methods and results of the analysis, as well as the implications and recommendations for urban planning and policy.

Canada, with 3.33 people per square kilometre, has one of the lowest population densities in the world. In 2001, most of Canada's population of 30,007,094 lived within 200 kilometres of the United States (along Canada's south). In fact, the inhabitants of our three biggest cities -- Toronto, Montréal and Vancouver -- can drive to the border in less than two hours. Thousands of kilometres to the north, our polar region -- the Yukon, the Northwest Territories and Nunavut -- is relatively empty, embracing 41% of our land mass but only 0.3% of our population. An inset map shows in greater detail the Windsor-Québec Corridor where a high concentration of Canadians live.

Semantic segmentation of urban areas using Light Detection and Ranging (LiDAR) point cloud data is challenging due to the complexity, outliers, and heterogeneous nature of the input point cloud data. The machine learning-based methods for segmenting point clouds suffer from the imprecise computation of the training feature values. The most important factor that influences how precisely the feature values are computed is the neighborhood chosen by each point. This research addresses this issue and proposes a suitable adaptive neighborhood selection approach for individual points by completely considering the complex and heterogeneous nature of the input LiDAR point cloud data. The proposed approach is evaluated on high-density mobile and low-density aerial LiDAR point cloud datasets using the Random Forest machine learning classifier. In the context of performance evaluation, the proposed approach confirms the competitive performance over the state-of-the-art approaches. The computed accuracy and F1-score for the high-density Toronto and low-density Vaihingen datasets are greater than 91% and 82%, respectively.

The legacy Section 37 density bonusing framework in the Planning Act, 1990 enabled the City to negotiate contributions towards local community benefits for development applications that exceeded a site’s zoned height and density. Toronto’s Official Plan provided that Section 37 may be used for development, excepting non-profit developments, with more than 10,000 square metres of gross floor area where the zoning by-law amendment increases the permitted density by at least 1,500 square metres and/or significantly increases the permitted height. When a developer submitted an application requesting more height and density than allowed, City Planning staff initiated a review process. Section 37 benefits were negotiated on a case-by-case basis with developers, incorporating consultation with the Ward Councillor, the community, and various City Divisions and Agencies. In-kind contributions occur where a development applicant agrees to directly provide the negotiated benefit, such as dedicating physical space within a building for use by non-profit groups. Alternatively, a development applicant can provide cash-in-lieu of the negotiated benefit, transferring responsibility for implementation to the City. After approval by City Council, the proposed Section 37 benefits are considered to be “secured”, and the developer has a legal obligation to provide the benefits at a future date, usually when applying for a building permit. Similar to Section 37, community benefits can also be obtained pursuant to Section 45(9) of the Planning Act, 1990, where the Committee of Adjustment adds a condition on the approval of a minor variance to the zoning by-law. Note: There is often a lag between when the City secures and receives a benefit as, in some cases, it can take years before an approved development submits an application for a building permit. Delays in receiving the benefit can also occur when large investments require the pooling of funds from multiple projects. To access the full dataset, use the “Download Data” section to download a CSV, JSON, or XML file.

2005 Digital Elevation Model of GTA & Hamilton derived from aerial photography acquired in the Spring of 2005. The basis of the DEM will be points and breaklines, density and accuracy to support 20cm orthoimagery.

Coverage Area includes all lands lying within the Regions of Durham, York, Peel and the City of Hamilton (Note: the Region of Halton and the City of Toronto are not included)

This dataset contains data that are part of the Zoning By-law 569-2013, was approved by Council but it is still subject to an Ontario Municipal Board (OMB) hearing for final approval.The Zoning By-law team is responsible for the revising the city-wide zoning bylaw. Zoning bylaws regulate the use, size, height, density and location of buildings on properties and affect every property in the City.Data Source: Open Data Toronto: https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/#8fef077c-9a14-e922-0c57-f390cd68b8a0Data Owner: City PlanningCurrency (as of upload): September 2014

Zoning By-law 569-2013 has been updated to include amendments up to June 18th, 2023. This dataset also contains static shapefiles that are part of the Zoning By-law 569-2013 (as well as amendments from 2019), which were approved by Council but with sections that still remain under appeal. The Zoning By-law team is responsible for the revising the city-wide zoning bylaw. Zoning bylaws regulate the use, size, height, density and location of buildings on properties and affect every property in the City. View Amendments to Zoning By-law 569-2013 website

The Canadian residential real estate market, valued at approximately $XX million in 2025, is projected to experience steady growth with a Compound Annual Growth Rate (CAGR) of 3.20% from 2025 to 2033. This growth is driven by several factors, including a growing population, particularly in major urban centers like Toronto, Vancouver, and Montreal, increasing urbanization, and a persistent demand for housing across various segments, from apartments and condominiums to villas and landed houses. Strong immigration numbers and a relatively robust economy contribute to sustained demand, although affordability concerns, particularly in high-density areas, represent a significant challenge. Government policies aimed at addressing housing affordability and supply shortages will play a crucial role in shaping the market's trajectory in the coming years. Competition among major developers like Aquilini Development, Bosa Properties, and Brookfield Asset Management, along with numerous smaller players, will continue to influence pricing and innovation within the sector. The market segmentation reveals significant regional disparities. Toronto, Vancouver, and Montreal consistently dominate the market share due to their economic dynamism and population density. However, cities like Calgary and Ottawa also contribute substantially, reflecting regional economic variations and the distribution of population growth across the country. While the apartment and condominium segment holds a considerable share, the demand for villas and landed houses remains significant, particularly in suburban and rural areas. The forecast period anticipates continued growth, but at a moderated pace compared to previous periods of rapid expansion, reflecting a more balanced market characterized by increasing affordability concerns and adjustments in government regulations. The consistent presence of established players and emerging developers indicates a dynamic and competitive landscape. Recent developments include: October 2022: Dye & Durham Limited ("Dye & Durham") and Lone Wolf Technologies ("Lone Wolf") have announced a brand-new integration that was created specifically for CREA WEBForms powered by Transactions (TransactionDesk Edition) to enable access to and communication with legal services., September 2022: ApartmentLove Inc., based in Calgary, has recently acquired OwnerDirect.com and finalized a rental listing license agreement with a significant U.S. aggregator as part of its ongoing acquisition and partnership plans. In 30 countries, ApartmentLove (APLV-CN) offers online house, apartment, and vacation rental marketing services.. Key drivers for this market are: Population Growth is the main driving factor, Government Initiatives and Regulatory Aspects for the Residential Real Estate Sector. Potential restraints include: Housing Supply Shortage, Interest rates and Financing. Notable trends are: Immigration Policies are Driving the Market.

Point feature layer representing major transit stations pertaining to density intensification in the City of Barrie. Relevant fields within the layer include (but not limited to): Name, Type, Address, DensityThe City of Barrie is situated in the heart of Central Ontario, a premier waterfront community on Lake Simcoe, conveniently located an hour north of Toronto. With a growing population of 143,000 the City of Barrie is the 34th largest city in Canada. Visit barrie.ca for more information or contact Service Barrie at 705-726-4242 or ServiceBarrie@barrie.ca

The Canadian luxury housing market, encompassing high-end apartments, condominiums, villas, and landed houses, is experiencing robust growth, driven by several factors. Strong economic performance in major cities like Toronto, Vancouver, and Calgary, coupled with increasing high-net-worth individuals and foreign investment, fuels demand for premium properties. The limited supply of luxury housing, particularly in desirable urban locations, further contributes to price escalation. While rising interest rates present a potential headwind, the overall market remains resilient due to persistent demand from domestic and international buyers seeking exclusive residences. The market segmentation reveals variations in performance across property types and cities. Toronto and Vancouver consistently rank among the most expensive markets globally, attracting significant investment. While the "Other Cities" segment experiences growth, its pace lags behind the top-tier urban centres due to factors such as lower population density and reduced economic activity compared to the major hubs. This dynamic creates opportunities for developers catering to the specific preferences within each segment. Looking ahead, the Canadian luxury housing market is projected to maintain a compound annual growth rate (CAGR) exceeding 10% throughout the forecast period (2025-2033). Several trends are expected to shape market evolution, including the growing popularity of sustainable and smart-home features, an increasing preference for larger living spaces, and a rise in demand for properties with proximity to amenities and green spaces. However, regulatory changes aiming to cool down the market, such as stricter mortgage rules or increased property taxes, could act as restraints on future growth. Key players such as Westbank Corp, Mattamy Homes, and Oxford Properties Group, amongst others, continue to dominate the market through strategic acquisitions and new development projects. International market dynamics and global economic conditions may also impact investment flows into the Canadian luxury housing sector, shaping overall market performance in the coming years. Recent developments include: October 2021: The CHEO Foundation gave the first look inside Minto Dream Home, the 'Caraway.' The Minto Dream Home on Skysail Place is a customized bungalow, situated on an oversized corner lot. It's a collaboration by the Minto Group (a Canadian real estate company) with Tanya Collins Design (a residential and commercial interior designer). The Caraway features beautiful views of the Mahogany Pond with an incredible wrap-around porch to enjoy the views and the outdoors, while inside the 4,603 square-foot floor plan offers plenty of space. The Minto Dream Home has a net-zero approach to minimize its carbon footprint and improve the wellness of the planet., March 2021: Skydev (a real estate development and construction oversight company), held a private ceremony to celebrate the start of the development's construction. The new development, called Southfield Green, is owned by Skyline Apartment REIT (a private Canadian real estate investment trust). Once the development is complete, the complex will be managed by Skyline Living (a Canadian residential property management company). The Southfield Green development will comprise a four-storey complex with luxury suites and on-site amenities, including an indoor/outdoor lounge and terrace, a dog run, and an on-site gym and yoga studio. The site is well located within walking distance of grocery stores, restaurants, and transit. The suites will boast fantastic views of the adjacent Southfield Park.. Notable trends are: Pandemic Accelerated Luxury Home Sales in Major Canadian Markets.

Definition of Walkable Destinations. (DOC)

Polygon feature layer representing areas of focus and planning in urban growth intensification initiatives in the City of Barrie. Relevant fields within the layer include (but not limited to): Type, Description, Previous Density, Target Density and Area.The City of Barrie is situated in the heart of Central Ontario, a premier waterfront community on Lake Simcoe, conveniently located an hour north of Toronto. With a growing population of 143,000 the City of Barrie is the 34th largest city in Canada. Visit barrie.ca for more information or contact Service Barrie at 705-726-4242 or ServiceBarrie@barrie.ca

Data used in manuscript, Dynamic connectivity assessment for a terrestrial predator in a metropolitan region, containing coyote steps within the Greater Toronto Area, Canada with vegetation density (NDVI), impervious surface (building density), human population density, and distance to linear features extracted for each start and end step. Linear features labeled following traffic: low (LT), medium (MT), high (HT), and function hiking trails (NT) and public service (PS; (ie. railways and transmission lines).

Code Repository: https://github.com/tgelmi-candusso/Dynamic-connectivity-assessment-for-a-terrestrial-predator-in-a-metropolitan-region

Coyotes (n = 27; Figure 1) were monitored between 2012 and 2021 for 245 ± 136 days (mean ± standard deviation). Coyotes were live-trapped with padded foothold traps, approved by the Ontario Ministry of Natural Resources Wildlife Animal Care Committee (protocols 75-12, 75-13, 75-14 ) or captured with nets by the Toronto Wildlife Centre, and fitted with self-releasing GPS-collars (Lotek Wildcell SG, Newmarket, Canada), recording location data, resampled following the median sampling frequency in order to maintain a constant sampling frequency for each individual (1-3 hours; Appendix S1: Table S1, http://doi.org/10.1002/fee.2633 ). The data were well balanced in terms of demographic traits (12 females/15 males, 19 adults/eight juveniles, 22 residents/15 transients). Residents and transients were distinguished based on movement characteristics.

From consecutive GPS-collar locations, we calculated the turning angle and step length with the steps_by_burst() function from the R package amt (Signer et al. 2019). After fitting the distributions to observed step lengths and turning angles, we generated nine random available steps for each observed step using the random_steps() function from the R package amt (Signer et al. 2019). We standardized the fixed variables included in the model and extracted their values at the endpoint of each step.

The fixed variables included four urban landscape covariates: vegetation density (normalized difference vegetation index or NDVI), human population density, impervious surface, and linear features. To measure the spatiotemporal dynamic responses of coyotes, we included the interaction of the fixed variables with three temporal scales (diel cycles, biological seasons, and climate seasons) and three demographic traits (coyote age, sex, and social status).

More information on the data and how it was used available at http://doi.org/10.1002/fee.2633