About this dataset

Context

The dataset tabulates the Non-Hispanic population of Toronto by race. It includes the distribution of the Non-Hispanic population of Toronto across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Toronto across relevant racial categories.

Key observations

Of the Non-Hispanic population in Toronto, the largest racial group is White alone with a population of 212 (100% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Toronto
• Population: The population of the racial category (for Non-Hispanic) in the Toronto is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Toronto total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Toronto Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Toronto by race. It includes the population of Toronto across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Toronto across relevant racial categories.

Key observations

The percent distribution of Toronto population by race (across all racial categories recognized by the U.S. Census Bureau): 94% are white, 1.98% are Black or African American, 0.50% are Asian and 3.52% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Toronto
• Population: The population of the racial category (excluding ethnicity) in the Toronto is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Toronto total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Toronto Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Toronto by race. It includes the population of Toronto across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Toronto across relevant racial categories.

Key observations

The percent distribution of Toronto population by race (across all racial categories recognized by the U.S. Census Bureau): 95.65% are white and 4.35% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Toronto
• Population: The population of the racial category (excluding ethnicity) in the Toronto is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Toronto total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Toronto Population by Race & Ethnicity. You can refer the same here

The dataset is based on Statistics Canada census data spanning four census periods (2001, 2006, 2016, and 2021). The dataset captures population statistics disaggregated by ethnicity at the Dissemination Area (DA) level—the smallest standard geographic unit for census data dissemination, covering approximately 400-700 people per unit. For Toronto, this encompasses approximately 3,700 DAs, providing high spatial resolution for analyzing urban dynamics. The dataset includes detailed population counts for the five largest ethnic groups in Toronto: China, India, Philippines, Portugal, and Sri Lanka. The features are also extracted from census datasets and 298 socioeconomic and demographic features from the census data, organized into 12 categories:Demographics: Population age structure, household composition, and family sizeHousing: Dwelling types, ownership status, housing values, and maintenance needsFamily Structure: Marriage patterns, presence of children, household typesIncome: Median household and individual income, income sourcesEmployment: Labor force participation, employment/unemployment ratesMobility & Migration: Internal and external migration patterns, non-permanent residentsVisible Minorities: Population distribution by visible minority statusLanguage: Official language use, mother tongue, and multilingual capabilitiesOccupation: Employment categories across economic sectorsReligion: Religious affiliations and practicesIndustry: Distribution across industry sectorsPlace of Birth: Country of origin information

Ontario was the province with the most immigrants in 2024, with 197,657 immigrants. Nunavut, Canada’s northernmost territory, had 56 immigrants arrive in the same period. Immigration to Canada Over the past 20 years, the number of immigrants to Canada has held steady and is just about evenly split between men and women. Asian countries dominate the list of leading countries of birth for foreign-born residents of Canada, although the United Kingdom, the United States, and Italy all make the list as well. Unemployment among immigrants In 2023, the unemployment rate for immigrants in Canada was highest among those who had been in the country for five years or less. The unemployment rate decreased the longer someone had been in Canada, and unemployment was lowest among those who had been in the country for more than ten years, coming more into line with the average unemployment rate for the whole of Canada.

Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2024.

Studying mediums of vegetation with different permeabilities will help understand anthropocentric impacts in the City of Toronto, as artificial and impervious materials are used in urban infrastructures. Abundance and diversity of herbaceous plants, woody plants, vertebrates, and invertebrates were sampled.Observations were collected by three ecology students between 2:45 PM and 4:15 PM EST on Wednesday, October 26, 2016. On York University’s Keele campus, permeable spaces near Stong Pond and impermeable surfaces of the baseball diamond were used. It was a windy day with temperatures around 5°C. census: week number of campus ecology experimentscalendar.date: Gregorian calendar date of the field work, stating month and daycampus: university location of experimentsgroup_ID: unique name assigned to a group in York University’s campus ecologyhabitat: general environment of survey arealat: latitude of area, determined by GPSlong: longitude of area, determined by GPSelevation: elevation of area, provided by laboratory instructorrep: repetition number of a particular experiment/methodHerbaceous PlantsHypothesis: The number of native plants, exotic plants, and flowers near Stong Pond and the impermeable area will be different.Predictions: There will be a greater number of native plants, exotic plants, and total number of flowers near Stong Pond compared to the impermeable area due to the fact that changed environmental conditions (human disturbance and gravel) would not allow for plant life to thrive.Outcome: The hypothesis was supported. Greater numbers of native plants, exotic plants, and total number of flowers were near Stong Pond. It is possible that human disturbance and the lack of soil structure and nutrients in the impermeable area contributed to lack of plant growth.Method: Every 2m, a quadrat (1mx1m) was placed along a 50m transect (alternating between right and left sides). Generally, exotic grass were counted through systematic estimation. Plant origin was determined using a search engine. This process was used in permeable and impermeable areas. abundance.native.plants: total number of native herbaceous plants counted within each quadrat. Native plants occur naturally in a particular ecosystem. Discrete data: positive integers. Unit: individuals.abundance.exotic.plants: total number of exotic herbaceous plants counted within each quadrat. Exotic plants are not native to the area of study. Discrete data: positive integers. Unit: individuals.total.number.flowers (quadrat): total number of flowers counted within each quadrat. Discrete data: positive integers. Unit: individuals.Woody PlantsHypothesis: There will be a difference between abundance of woody plants, total amount of flowers, canopy coverage, and ground coverage near Stong Pond and in baseball diamond.Predictions: There will be higher amounts of woody plants and flowers, higher canopy coverage, and ground coverage near Stong Pond compared to the baseball diamond. The gravel and human activities will interfere with abiotic and biotic interactions in the impermeable area.Outcome: The hypothesis was supported. There were no flowers, woody plants, grass, and vegetation found in impermeable areas; hence, there were 0% canopy cover and ground cover. Human disturbances, such as people travelling through the baseball diamond, affected soil compaction and nutrient uptake. Moreover, trampled grass and flowers needed more rainfall and nutrients to recover, but gravel absorbs water and blocks sunlight, which caused the soil to be dry and the plants, flowers, trees or any form of vegetation to wilt and die.Method: Two 25m transects were placed near the Stong Pond. Every 2m, total number of trees, canopy coverage, ground coverage, and total number of flowers within 0.5m of the transect were recorded. Trees taller than 1.5m were counted. To measure canopy coverage, a square was made with pointer and thumb, and held up to estimate percentage of canopy coverage. This was also done with ground coverage, but holding the square down. These steps were also done in the impermeable area. abundance.woody.plants: total number of trees taller than 1.5 meters, counted within 0.5 meters at each 2m interval along the 50m transect. Discrete data. Unit:individuals.canopy.cover: estimated by creating a square with researcher’s fingers, and seeing percentage of square covered by leaves.Continuous data. Units:percentage.ground.cover: estimated by creating a square with researcher’s fingers, and seeing percentage of square covered by plants, grass, and shrubs. Continuous data. Units:percentage.total.flower.numbers (transect): total number of flowers found within 0.5 meters per 2m intervals along 50m of transect tape. Discrete data. Unit:number of individuals.Vertebrates & InvertebratesHypothesis: The number of vertebrates and invertebrates will differ between permeable and impermeable areas (i.e. between pond banks and gravel). Predictions: Permeable land was predicted to support more vertebrate and invertebrate life because of factors such as aeration, water movement, and nutrient absorption. Artificial, impervious space was predicted to negatively affect vertebrate and invertebrate populations because of restrictions of abiotic and biotic interactions would impact autotrophic populations, which would have implications on heterotroph populations. Outcome: The hypothesis was supported: more animals appeared by the lake than the baseball diamond. Resources must be more plentiful near the pond. Lakeside animals included loons (the only animals in the water), humans, and seagulls. Animals by the gravel were humans and Canada geese. All non-human animals were flying animals, so it was possible that terrestrial animals were keeping warm or preparing for hibernation. Invertebrates abundances were little; possibly, chilly weather affected their activity. Also, plant experiments reported little vegetation in impermeable surfaces, which correlate with little abundances of vertebrates in the same location. Thus, permeability of the ground seems to affect all trophic levels.Method: As a guideline, 50m of transect tape was placed on the ground (two 25m transect tapes were used). Using 0m as the centre, the number of animals and humans were counted within a 50 metre radius formed by the transect. The survey lasted 15 minutes. Subsequently, invertebrate abundances were measured. Using 5m of transect tape, 0m was used as the centre. For 15 minutes, the number of invertebrates were counted within the 5m radius. The two surveys were replicated in the permeable and impermeable landscapes.abundance.vertebrates: total number of animals observed within a 50m radius for 15 minutes. Discrete data. Unit: individuals.vertebrate.species: total amount of species observed within a 50m radius for 15 minutes. Discrete data. Unit: numbers of species.abundance.human: total number of humans observed within a 50m radius for 15 minutes. Discrete data. Unit: individuals.abundance.invertebrates.observed: total number of invertebrates observed within a 5m radius for 15 minutes. Discrete data. Unit:individuals. InvertebratesHypothesis: There would be a difference between pond and impermeable surface abundances of invertebrates (data collected using pan trap and sweep net sampling methods).Predictions: There would be more invertebrates collected by pan traps and sweep netting near Stong Pond than impermeable surface area (baseball diamond). This is probably due to the lack of soil and vegetation that exists in the baseball diamond area to house invertebrates. Thus, impermeable surface conditions were not suitable for existence of invertebrates and do not promote plant growth.Outcome: The hypothesis was supported. More insects were collected near Stong Pond for pan trap and sweep net sampling techniques. This was probably due to the soil and vegetation that surrounds the area. This shows that the area near Stong Pond provided more suitable habitat for invertebrates.Method: Using the pan trap method, 6 pan traps were filled with soapy water. These traps were each distributed 3m apart along the length of a 50m transect. After 45 minutes, the number of invertebrates in pan traps were counted. A sweep net was used and replicated 10 times. Next to the transects, sweeps were performed and the number of invertebrates in the net were counted. This procedure was repeated for the impermeable surface area (baseball diamond). abundance.invertebrates.pantraps: total number of invertebrates collected and counted using pan trap sampling method near Stong pond and at the baseball diamond. Discrete data. Unit: individuals.abundance.invertebrates.sweepnet: total number of invertebrates collected and counted using the sweep net sampling method near Stong Pond and at the baseball diamond. Discrete data. Unit: individuals.

About this dataset

Context

The dataset presents the median household income across different racial categories in Toronto. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Toronto population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 77.94% of the total residents in Toronto. Notably, the median household income for White households is $56,250. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $56,250.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Toronto.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Toronto median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Toronto. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Toronto, the median household income for the households where the householder is White decreased by $21,260(32.91%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $64,593 in 2013 and $43,333 in 2023.
• Black or African American: As per the U.S. Census Bureau population data, in Toronto, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Toronto.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Toronto median household income by race. You can refer the same here