For Reference Period 2008: Martin Prosperity Institute, Year 2010 data. The Cultural Location Index (CLI) is an economic indicator that shows the intersection of where people who work in culture occupations live and work, and cultural facilities. This indicator was developed to provide a quantifiable city-wide view of the geographic concentration of Toronto's cultural sector. This indicator is positively influenced in part by the physical presence of cultural facilities, and the concentration of the people who live and work in the cultural sector. The indicator does not capture culture as a set of community values or beliefs. As such a community could have a very active cultural life, and be lower on the Cultural Location Index. The Cultural Location Index (CLI) was produced by the Martin Prosperity Institute for the City of Toronto in 2010. For Reference Period 2011: Data not yet available. For Reference Period 2008: Data not available. For Reference Period 2011: Statistics Canada, 2011 Census, language tables; calculations performed by City of Toronto, Social Policy Analysis & Research (contact spar@toronto.ca). The Linguistic Diversity Index (LDI) is the probability that any two people selected at random would have different mother tongues. Calculated using Greenberg's Linguistic Diversity Index. Lower values mean less diversity, higher values mean more diversity. The Linguistic Diversity Index (LDI) was developed by the City of Toronto, Social Policy Analysis & Research, based on Census 2011 data.

The statistic shows the total population in Canada from 2020 to 2024, with projections up until 2030. In 2024, the total population in Canada amounted to about 41.14 million inhabitants. Population of Canada Canada ranks second among the largest countries in the world in terms of area size, right behind Russia, despite having a relatively low total population. The reason for this is that most of Canada remains uninhabited due to inhospitable conditions. Approximately 90 percent of all Canadians live within about 160 km of the U.S. border because of better living conditions and larger cities. On a year to year basis, Canada’s total population has continued to increase, although not dramatically. Population growth as of 2012 has amounted to its highest values in the past decade, reaching a peak in 2009, but was unstable and constantly fluctuating. Simultaneously, Canada’s fertility rate dropped slightly between 2009 and 2011, after experiencing a decade high birth rate in 2008. Standard of living in Canada has remained stable and has kept the country as one of the top 20 countries with the highest Human Development Index rating. The Human Development Index (HDI) measures quality of life based on several indicators, such as life expectancy at birth, literacy rate, education levels and gross national income per capita. Canada has a relatively high life expectancy compared to many other international countries, earning a spot in the top 20 countries and beating out countries such as the United States and the UK. From an economic standpoint, Canada has been slowly recovering from the 2008 financial crisis. Unemployment has gradually decreased, after reaching a decade high in 2009. Additionally, GDP has dramatically increased since 2009 and is expected to continue to increase for the next several years.

Context

With a population just short of 3 million people, the city of Toronto is the largest in Canada, and one of the largest in North America (behind only Mexico City, New York and Los Angeles). Toronto is also one of the most multicultural cities in the world, making life in Toronto a wonderful multicultural experience for all. More than 140 languages and dialects are spoken in the city, and almost half the population Toronto were born outside Canada.It is a place where people can try the best of each culture, either while they work or just passing through. Toronto is well known for its great food.

Content

This dataset was created by doing webscraping of Toronto wikipedia page . The dataset contains the latitude and longitude of all the neighborhoods and boroughs with postal code of Toronto City,Canada.

The City of Toronto's Hate Activity Policy helps to identify hate activity and appropriate ways to respond. The goal of the policy is to establish and maintain a hate-free City as required under the City of Toronto Human Rights and Anti-Harassment/Discrimination Policy, the Ontario Human Rights Code and the Criminal Code of Canada. Staff in the Equity, Diversity and Human Rights (EDHR) and Legal Divisions have consulted with colleagues in Strategic Communication, City Clerks, Internal Corporate Services, the Toronto Office of Partnerships and with other city staff on public consultations on engagement approaches. City staff undertook a comprehensive online survey which was determined to be the most effective method of engaging such a broad base of stakeholders. Results of the survey will be reported to Council with recommendations in relation to the Hate Activity Policy and Procedures. This survey was not a vote. The City of Toronto's EDHR and Legal Services Divisions sought out stakeholder opinions along with policy considerations to inform any City staff recommendations to City Council. Results of the City of Toronto Hate Activity Policy Survey which was online for public input from June 29, 2018 until September 1, 2018. It was extended once to attempt to facilitate additional stakeholder response, with a final closing date of October 5, 2018 and received 57 completed responses. City Council's Executive Committee requested that the City Manager's office consult with a wide range of stakeholders including: constitutional and Charter rights scholars, the Toronto Police Service, the City Solicitor, the Deputy City Manager, Internal Corporate Services, representatives of the Ministry of the Attorney General of Ontario, various human rights groups and other stakeholders on the City's legal responsibility and options for preventing public gatherings that promote hate and incite discrimination from taking place on the City's property. Limitations: Please note that column including each respondent's IP address has been removed to ensure confidentiality Only data submitted from respondents who have reached the end of the survey has been included - 57/117 respondents The data has been reviewed and information that could reasonably identify an individual (or individuals) has been removed to further ensure safety and confidentiality

In 2025, Moscow was the largest city in Europe with an estimated urban agglomeration of 12.74 million people. The French capital, Paris, was the second largest city in 2025 at 11.35 million, followed by the capitals of the United Kingdom and Spain, with London at 9.84 million and Madrid at 6.81 million people. Istanbul, which would otherwise be the largest city in Europe in 2025, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population is almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in these seventy years. Before the turn of the millennium, Europe was the second-most populated continent, before it was overtaken by Africa, which saw its population increase from 228 million in 1950 to 817 million by 2000. Asia has consistently had the largest population of the world’s continents and was estimated to have a population of 4.6 billion. Europe’s largest countries Including its territory in Asia, Russia is by far the largest country in the world, with a territory of around 17 million square kilometers, almost double that of the next largest country, Canada. Within Europe, Russia also has the continent's largest population at 145 million, followed by Germany at 83 million and the United Kingdom at almost 68 million. By contrast, Europe is also home to various micro-states such as San Marino, which has a population of just 30 thousand.

End-users in Canada face varying electricity costs, with a national monthly average of **** Canadian cents per kilowatt-hour, as of September 2023. Due to their remote location, electric energy is most expensive in the Northwest Territories, where the price per kilowatt-hour stands at ** Canadian cents. Electricity generation in the Northwest Territories is largely reliant on hydropower and petroleum. How is electricity produced in Canada? Canada’s electricity generation employs a diverse array of energy sources; however, the country is primarily reliant on hydroelectric power. Hydraulic turbines, propelled by flowing water, drive generators that produce electricity. The widespread use of hydroelectricity, particularly in the province of Quebec, has contributed to the province having the cheapest electricity prices nationwide, at *** Canadian cents per kilowatt-hour. In 2022, Canada generated nearly *** terawatt-hours of hydroelectric power. Average industrial and residential electricity prices in Canada Industry electricity prices within Canadian cities differ, and the average industrial electricity price in Canada fluctuated between **** and ***** Canadian cents per kilowatt-hour in 2022. By comparison, the average residential electricity prices in Canada oscillated between **** and ***** Canadian cents per kilowatt-hour, depending on the city. Notably, residential energy costs across the country tend to be higher when compared to the industrial sector.

The Canadian flexible workspace market, valued at $1.22 billion in 2025, is poised for robust growth, projected to expand at a compound annual growth rate (CAGR) of 7.58% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of hybrid work models, driven by technological advancements and evolving employee preferences for work-life balance, is a significant factor. Furthermore, the burgeoning startup ecosystem in major Canadian cities like Toronto, Vancouver, and Montreal, coupled with the cost-effectiveness and flexibility offered by shared workspaces, are contributing significantly to market growth. The demand for adaptable and scalable office solutions is particularly strong within the IT and telecommunications, media and entertainment, and retail sectors. While competition among providers like Regus, WeWork, and iQ Offices is intense, the market offers opportunities for specialized providers catering to niche segments, such as those focusing on specific industries or offering unique amenities. The market segmentation, encompassing private offices, co-working spaces, and virtual offices, caters to diverse business needs and preferences, further fueling market expansion. The growth is expected to be most pronounced in major metropolitan areas, with Toronto, Vancouver, and Montreal likely leading the way due to their robust economies and established business landscapes. However, the market's growth trajectory is tempered by potential economic fluctuations and the possibility of reduced demand during economic downturns. The forecast period of 2025-2033 suggests a substantial increase in market size. Assuming a consistent CAGR of 7.58%, the market is expected to exceed $2 billion by 2033. This growth will likely be influenced by factors such as further technological advancements in workspace solutions, increased government support for entrepreneurship, and a continued shift toward flexible work arrangements. The competitive landscape is likely to remain dynamic, with existing players expanding their offerings and new entrants vying for market share. Success will hinge on providers' ability to adapt to changing market demands, offer innovative services, and cultivate strong customer relationships in a rapidly evolving landscape. Specific growth areas are likely to include the integration of technology and sustainable practices within these spaces. Recent developments include: June 2023: As the need for flexible workspace grows across the country, IWG, the world's leading provider of hybrid working solutions, including the Spaces and Regus brands, is building a new facility in Red Deer, Alberta. This opening is being done in collaboration with East Lincoln Properties, which has a management agreement with the Regus brand. This is the city of Red Deer's first flexible workspace. The new Regus facility, located at 4909 - 50th Street, will open in early 2024 in a recognized historical structure originally held by the Government of Canada. The 10,000-square-foot Regus will be on the building's top level and will include coworking space, private offices, conference rooms, and creative areas., January 2023: Workhaus teamed up with Techstars to build out the newest tech startup hub in Toronto. Techstars Toronto is now running its winter class out of one of Workhaus’ downtown coworking locations. This collaboration is providing a fantastic opportunity to give a great hybrid experience to new Techstars founders and companies.. Key drivers for this market are: The rise of remote work and flexible work arrangements has led to increased demand for flexible office spaces, Canada has a thriving entrepreneurial culture, with a growing number of startups, freelancers, and small businesses. Potential restraints include: The rise of remote work and flexible work arrangements has led to increased demand for flexible office spaces, Canada has a thriving entrepreneurial culture, with a growing number of startups, freelancers, and small businesses. Notable trends are: Toronto is set to revolutionize the market in the years to come..

Police-reported hate crime, number of incidents and rate per 100,000 population, Census Metropolitan Areas and Canadian Forces Military Police, 2014 to 2024.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...