This dataset tracks annual diversity score from 1991 to 2023 for Toronto City School District vs. Ohio

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Toronto. The dataset can be utilized to gain insights into gender-based income distribution within the Toronto population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Toronto, among individuals aged 15 years and older with income, there were 101 men and 84 women in the workforce. Among them, 40 men were engaged in full-time, year-round employment, while 46 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 15% fell within the income range of under $24,999, while 41.30% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 10% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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

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.

This data is a small collection of plant/tree diversity collected in Grandravine Park, Toronto, Canada and Bratty Park, Toronto, Canada. It shows a sample of species found in both parks and in two areas of both the parks: the forest and field. There is also a collection of invasive species here.

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

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.

Between 2019 and 2023, the proportion of racial minorities in the Canadian workforce of Toronto-Dominion Bank (TD Bank) grew, with increases across the total workforce, senior management, and middle management. As of October 2023, visible minorities represented **** percent of the total workforce, marking an increase of more than six percentage points from the previous year. The share of minorities in senior management also rose, reaching **** percent in 2023.

A research study across the 140 neighborhood‐landscapes (streetscapes) of Toronto was presented through three main intentions. Its foundational goal was to calculate landscape ecology metrics from the 2007 land cover dataset for the City of Toronto; for use in sustainable development planning strategies and to bolster its Wellbeing Toronto data dashboard. In doing so, 130 landscape ecology metrics were computed to serve as a foundational suite for the City of Toronto: 18 class configuration metrics across seven of the City’s eight land cover categories and four landscape diversity metrics. Metrics for agriculture were not included due to very limited neighborhood representation. The 18 class configuration metrics computed for each of the seven land cover types were: class area (CA), percentage of landscape (PLAND), patch density (PD), largest patch index (LPI), landscape shape index (LSI), mean patch area (AREA_MN), area-weighted mean patch area (AREA_AM), area‐weighted mean shape index (SHAPE_AM), area‐weighted mean patch fractal dimension (FRAC_AM), perimeter‐area fractal dimension (PAFRAC), area‐weighted core area distribution (CORE_AM), area‐weighted core area index (CAI_AM), area‐weighted mean Euclidean nearest neighbor distance (ENN_AM), clumpiness index (CLUMPY), percentage‐of‐like‐adjacency (PLADJ), patch cohesion index (COHESION), landscape division index (DIVISION), and effective mesh size (MESH). Additionally, the four landscape diversity metrics were: Patch richness density (PRD), Relative patch richness (RPR), Shannon’s diversity index (SHDI), and Shannon’s evenness index (SHEI). Note that other relationships await discovery using this free database; thus, forthcoming germane research should consider its adoption. The landscape ecology database is provided here via GIS shapefile format and can be used freely with citation.

A university campus provides an opportunity to explore biodiversity, change, and citizen science. Many course offerings such as ecology, experimental design, or environmental science include a laboratory component. York University and The University of Toronto Mississauga collaborated and developed a collaborative platform entitled 'The Campus Ecology Network'. The goal was to connect the data that undergraduate students at each campus collected during hands-on, field exercises. We adopted the same protocols at each campus, and students surveyed each campus in the Autumn, i.e. Fall Term, in 2016. Students used transects to identify sampling locations blocked by major habitat types such as forest, grassland, disturbed sites (i.e. areas with high foot traffic but vegetated), and impermeable sites. Quadrats were then subsequently used to to explore vegetation - 0.5m x 0.5m quadrats to record herbaceous plants and grasses. On these same transects, the total number of vertebrate animals (including humans) were also recorded during the 3-hour sampling instances. Pan traps and sweet nets were used to assess invertebrate diversity. The primary focus was to document structural and species diversity patterns not composition. The data included species richness for key taxa, native versus exotic plants, canopy cover, ground cover, and total number of flowers at that point in time within each quadrat. These data can be used to explore the intermediate disturbance hypothesis, relationships between richness of different taxa, and canopy/ground cover influences on richness. Longitudinal change can also be examined, and the start point of each transect was also georeferenced for mapping or additional research.

To study if human activity impacted species diversity of a given area, I observed two different regions, one with more human activity and one with less. To pick my sites accordingly, I did a pilot experiment to observe which sites had more and less activity. I first began my study at Hillside Gardens at High Park with a tally clicker and a smartphone to use the iNaturalist app for species identification. Each replication lasted for a duration of 20 minutes; keeping a strict timeline helped ensure I spent equal time at both sites and therefore equal time for species identification. Before I began the observation period, I ensured to keep record of the geographical coordinate since point of observation would vary with each visit to the study site. During the observation period, I used the tally clicker to keep track of the human activity surrounding me, this includes walking and socialization within my immediate surrounding that could possibly disrupt organisms in the study. I kept track of the number of species observed, and identified species using the iNaturalist app. After the duration was complete, I continued to the next site of observation which was the Hiking Trail at High Park. I continued this same process, by keeping record of the geographical coordinate, keeping tally of human activity, and observing species diversity and richness. In doing so, I was able to collect data of different species within each study site, species richness, and number of human activity.

This dataset contains Civics and Equity indicators which include information about the diversity index, City grants funding, Salvation Army donors, City Beautification, water main breaks, voter turnout, walk score and neighbourhood equity score. A full description for each column of data provided in worksheets 2 and 3 is available in the 1st worksheet.

This dataset tracks annual diversity score from 2021 to 2023 for Karaffa Elementary School vs. Ohio and Toronto City School District

This dataset tracks annual diversity score from 1990 to 2011 for S C Dennis Elementary School vs. Ohio and Toronto City School District

This data set shows the following indicators: population breakdown by ethnicity, household income, education level, employment, age and sex. Data is broken down by the different Toronto neighbourhoods. CITY OF TORONTO NATIONAL HOUSEHOLD SURVEY METHODOLOGY NOTATION There were changes in the way information was collected for portions of the 2011 Census. This will impact the extent to which comparisons can be made with other Census periods on some Census variables. In general, 2011 Census data on population, dwelling counts, age, sex, families, household living arrangements, marital status, structural types of dwellings types and language can be compared to the data from other Censuses, with due regard for changing definitions of individual variables. Information on Aboriginal peoples, immigration, ethnocultural diversity, education, labour, income and housing was collected differently in 2011 as part of a voluntary National Household Survey (NHS) by Statistics Canada. In general, the 2011 NHS data is less comparable to that of the other Censuses due to non-response bias inherent in voluntary surveys. The risk of a voluntary survey is that the results may only reflect the kinds of individuals who are inclined to participate in a survey in the first place. As the National Household Survey User Guide notes, "because non-respondents tend to have different characteristics from respondents. As a result, there is a risk that the results will not be representative of the actual population." Comparisons between the 2011 NHS and other Censuses should not be considered fully reliable.

The Census of Population is held across Canada every 5 years and collects data about age and sex, families and households, language, immigration and internal migration, ethnocultural diversity, Aboriginal peoples, housing, education, income, and labour. City of Toronto Neighbourhood Profiles use this Census data to provide a portrait of the demographic, social and economic characteristics of the people and households in each City of Toronto neighbourhood. The profiles present selected highlights from the data, but these accompanying data files provide the full data set assembled for each neighbourhood. For an interactive visualization of this data, visit the Neighbourhood Profiles webpage. In these profiles, "neighbourhood" refers to the City of Toronto's 158 social planning neighbourhoods. These social planning neighbourhoods were developed in the late 1990s by the City of Toronto to help government and community organizations with local planning by providing socio-economic data at a meaningful geographic area. The boundaries of these social planning neighbourhoods are consistent over time, allowing for comparison between Census years. Neighbourhood level indicators from sources other than the Census of Population are also available through the City's Wellbeing Toronto mapping application and here on the Open Data portal. Each data point in this file is presented for the City's 158 neighbourhoods or 140 neighbourhoods prior to April 2021. The data is sourced from a number of Census tables released by Statistics Canada. The general Census Profile is the main source table for this data. Data tables are available for the Census years of 2001, 2006, 2011, 2016, and 2021. For definitions of terms and concepts referenced in this data set, as well as limitations imposed by rounding, data suppression standards, and geometry, users should consult the reference materials produced by Statistics Canada for the 2016 Census or the 2021 Census. Please note that social planning neighbourhoods are not an official standard geography produced by Statistics Canada and the data herein is compiled by special request through the Community Data Program.

This dataset tracks annual diversity score from 1988 to 2007 for Lincoln Elementary School vs. Ohio and Toronto City School District

Community change is one of the few constants in nature, and the balance of mechanisms influencing this change is central to understanding the structure and functioning of communities and ecosystems. Newly established communities undergo succession and can change in diversity and composition as local environmental change, interspecific interactions, and immigration play out over time. Understanding the influence of initial conditions and priority effects (long-term consequences of the initial community composition and species identity) on community change is critically important for both evaluating ecological theory and predicting restoration outcomes. Here I evaluate how initial experimental conditions in 2012, such as initial sown species richness, phylogenetic diversity, and early biomass production, along with priority effects caused by the identity of sown species, influence subsequent plant community composition and the number of colonizing species after nine years of uninter...

Data includes: board and school information, grade 3 and 6 EQAO student achievements for reading, writing and mathematics, and grade 9 mathematics EQAO and OSSLT. Data excludes private schools, Education and Community Partnership Programs (ECPP), summer, night and continuing education schools.

How Are We Protecting Privacy?

Results for OnSIS and Statistics Canada variables are suppressed based on school population size to better protect student privacy. In order to achieve this additional level of protection, the Ministry has used a methodology that randomly rounds a percentage either up or down depending on school enrolment. In order to protect privacy, the ministry does not publicly report on data when there are fewer than 10 individuals represented.

* Percentages depicted as 0 may not always be 0 values as in certain situations the values have been randomly rounded down or there are no reported results at a school for the respective indicator. * Percentages depicted as 100 are not always 100, in certain situations the values have been randomly rounded up.

The information in the School Information Finder is the most current available to the Ministry of Education at this time, as reported by schools, school boards, EQAO and Statistics Canada. The information is updated as frequently as possible.

This information is also available on the Ministry of Education's School Information Finder website by individual school.

Descriptions for some of the data types can be found in our glossary.

School/school board and school authority contact information are updated and maintained by school boards and may not be the most current version. For the most recent information please visit: https://data.ontario.ca/dataset/ontario-public-school-contact-information.

Climate warming is a major disruptor of fish community structure globally. We use large-scale geospatial analyses of 447,077 Canadian Arctic lakes to predict how climate change would impact lake thermal habitat diversity across the Arctic landscape. Increases in maximum surface temperature (+2.4–6.7 °C), ice-free period (+14–38 days), and thermal stratification presence (+4.2–18.9%) occur under all climate scenarios. Lakes, currently fishless due to deep winter ice, open up; many thermally uniform lakes become thermally diverse. Resilient coldwater habitat supply is predicted; however, thermally diverse lakes shift from providing almost exclusively coldwater habitat to providing substantial coolwater habitat and previously absent warmwater habitat. Across terrestrial ecozones, most lakes exhibit major shifts in thermal habitat. The prevalence of thermally diverse lakes more than doubles, providing refuge for coldwater taxa. Ecozone-specific differences in the distribution of thermally d..., Overview of the Methods Used in This Paper  The following is an overview of the methods that we used in this paper. Each paragraph has an accompanying sub-section within the Methods section that provides more details. To develop the approach used in this paper, we applied both empirical and semi-mechanistic methods to build the set of predictive models needed to fulfill our primary objective: (i) predicting the impacts of climate change on the seasonal progression of thermal structure in Canadian Arctic lakes: and (ii) assessing how those impacts would change the character and diversity of the fish communities resident in those lakes24,35. A summary of issues addressed, and methods used follows:  (i) Ground-Truthing Lake Morphometry: Lake shape is a primary determinant of lake thermal structure. We used the GIS-based estimates of Canadian Arctic lake morphometry as the basis for our study, hereafter the Arctic GIS lake database13. Our Arctic GIS lake database provides the basic inform..., Spreadsheet editor to view files (e.g., Microsoft Excel). R to execute code., ## This README file was generated on 2024-02-09 by Daniel P. Gillis.

GENERAL INFORMATION

1. Title of Dataset: Major changes in fish thermal habitat diversity in Canada’s Arctic lakes due to climate change

2. Author Information A. Corresponding Author: Name: Daniel P. Gillis Institution: 1) University of Toronto, Toronto, ON, Canada 2) Fisheries and Oceans Canada, Vancouver, BC Email: B. Co-Author 1: Name: Charles K. Minns Institution: University of Toronto, Toronto, ON, Canada C. Co-Author 2: Name: Steven E. Campana Institution: University of Iceland, Reykjavik, Iceland D. Co-Author 3: Name: Brian J. Shuter Institution: University of Toronto, Toronto, ON, Canada

3. Geographical location: Canadian Arctic. Focus of analysis is on areas at least 60 degrees N latitude.

4. Funding sources: A. This work was supported by grants from the Canadian Network for Aquatic Ecosystems Services and the Discovery Grant program of the Natur...

This study was done in collaboration with 4 other team members consisting of Muhammad Akram, Daniel Germani, and Markian Plawiuk, and Nicole Gallagher. The purpose of this study was to collect a dataset of organisms gathered from using pan traps in order to familiarize group members with this method of sampling. Pan traps are particularly applicable towards the estimation of the abundance and diversity of flying insects, and are great at capturing pollinators. The experiment took place at the Danby Woodlot and Danby Grassland at York University Keele campus in Toronto, Ontario, on Tuesday September 29th, 2015. The Danby Grassland consisted of a large abundance of plant species no taller than knee height, with a clumped dispersion pattern, as patches of individuals crowding together were common. The Danby Woodlot consisted of an abundance of different species of trees of various heights and canopy coverage. Overall, there were high amounts of canopy coverage within the entire Woodlot, as low amounts of sunlight were able to pass through. This resulted in less precipitation present within the Woodlot compared to the amount of precipitation present in the Grassland. There were minor amounts of debris present within both areas at the time of study. The temperature was approximately 17 degrees Celsius, and it was rainy and gloomy with cloudy conditions, with continuous and constant wind travelling at approximately 23 km/h. The precipitation continued throughout the entire duration of experimentation, which started at approximately 3:19PM. Pan traps were completely set up by 3:37PM, data was collected at 4:20PM, and the experiment concluded by 4:34PM. Within the Grassland, nine pan traps were set up in a linear formation 2 metres apart each (18 metres on length total), alternating in colour: white, blue, yellow. The differences in colour of the traps were to account for the ability to attract different species of pollinators. Soapy water was filled into each trap approximately 1cm deep to account for the precipitation that was occurring to minimize the possibility of overflow, and to minimize the escape of insects as much as possible. The pan traps were set up at the edge of the Grassland closest to the Woodlot, where there was the least chance of disturbance to the traps by activity ocurring in the Grassland. This setup also allowed maximal sunlight exposure (although it was a rather gloomy day), and the pan traps were ensured to not be placed under heavy vegetative cover. Within the Woodlot, the same procedures of setup were repeated – however, the nine pan traps alternated in colour in a white, yellow, blue arrangement instead. The pan traps were placed close to the centre of the Woodlot in a linear formation. The pan traps were left undisturbed in both areas for about 1 hour. Upon data collection, the contents of the bowls were poured through a sieve for easier examination of the specimen collected, and for counting the frequency of individuals collected in each bowl. Further inspection was conducted by using metal prongs to pick up each organism to take a closer look at the species in order to assign an RTU to it. Overall, 18 pan traps were set up – 9 were located in the Grassland, while the other 9 were located in the Woodlot. Efficiency and collaborative efforts within the group was ensured by having a discussion between all group members in regards to experimental setup and design prior to the actual setup within the areas of study. While Bonnie Duong and Nicole Gallagher were the main coordinators for this particular data set, other group members worked efficiently by helping gathering materials needed, ensuring setup was done correctly and provided input, and aided in cleanup efforts after data collection was completed. Due to the constant precipitation during experimentation, it is likely to have affected the overall data collected. The rain likely played a role in lowering the number of organisms collected, as well as lowering the number of different RTU’s collected since more organisms were likely to be caught in the absence of rainy weather.