This dataset represents the Region of Waterloo, broken down into 45 Neighbourhood boundaries. Neighbourhood's were created by ROW staff by digitizing boundaries identified in coordination with area municipalities, the EDI advisory group, local service providers and local community members. lt Neighbourhood geography is consistent with block face representative points to allow for data extraction by Statistics Canada.
https://borealisdata.ca/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.5683/SP3/TMVOHPhttps://borealisdata.ca/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.5683/SP3/TMVOHP
Background: At the onset of the COVID-19 pandemic, Canadian universities, including the University of Waterloo, shifted primarily to online learning to minimize in-person gatherings. Despite these measures, some students remained at risk due to in-person classes, living arrangements near campus, and travel related to co-op programs. Aims of CITF funded study: The study aims to identify groups on the university campus who are more susceptible to SARS-CoV-2 infection and factors that contribute to increased exposure. Data collected from this study was shared with regional health authorities and other universities to help understand COVID-19 spread in educational institutions. Methods: This cohort study enrolled students, faculty and staff from the University of Waterloo and other local institutions. Blood samples were collected at three and six-month intervals to track participants’ exposure risk and immunity to SARS-CoV-2 infection over time. Contributed dataset contents: The datasets include 153 participants who completed their baseline survey between June 2021 and October 2022. All participants gave one or more blood samples to track exposure risk and immunity to SARS-CoV-2 infection between June 2021 and October 2022. A total of 344 samples were collected. Variables include data in the following areas of information: demographics (age, sex, ethnicity and indigeneity, province, occupation), general health (weight and height, smoke, other diseases, flu vaccine), exposure risk factors (household, travel history, gathering), longitudinal follow-up for COVID infections (COVID test, symptoms, hospitalization), SARS-CoV-2 vaccination, and serology (IgA, IgG, and IgM against SARS-CoV-2 RBD and spike proteins).
Comprehensive demographic dataset for Waterloo, ON, CA including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.
https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for Regional Price Parities: Goods for Waterloo-Cedar Falls, IA (MSA) (RPPGOOD47940) from 2008 to 2023 about Waterloo, IA, PPP, goods, price, and USA.
The Waterloo Region Community Profile, providing information on Homelessness, Housing and Social Assistance data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Regional Price Parities: Services: Rents for Waterloo-Cedar Falls, IA (MSA) was 62.20800 Index in January of 2023, according to the United States Federal Reserve. Historically, Regional Price Parities: Services: Rents for Waterloo-Cedar Falls, IA (MSA) reached a record high of 76.30600 in January of 2010 and a record low of 62.20800 in January of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Regional Price Parities: Services: Rents for Waterloo-Cedar Falls, IA (MSA) - last updated from the United States Federal Reserve on September of 2025.
Identifying information such as names and addresses haven been removed from the data to protect personal identification. A notification such as {name removed} has been used to indicate where information was removed.
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This dataset represents the Region of Waterloo, broken down into 45 Neighbourhood boundaries. Neighbourhood's were created by ROW staff by digitizing boundaries identified in coordination with area municipalities, the EDI advisory group, local service providers and local community members. lt Neighbourhood geography is consistent with block face representative points to allow for data extraction by Statistics Canada.