This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2011-2014. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information.

Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2006-2010. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information.

Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

This dataset contains data from the P.L. 94-171 2020 Census Redistricting Program. The 2020 Census Redistricting Data Program provides states the opportunity to delineate voting districts and to suggest census block boundaries for use in the 2020 Census redistricting data tabulations (Public Law 94-171 Redistricting Data File). In addition, the Redistricting Data Program will periodically collect state legislative and congressional district boundaries if they are changed by the states. The program is also responsible for the effective delivery of the 2020 Census P.L. 94-171 Redistricting Data statutorily required by one year from Census Day. The program ensures continued dialogue with the states in regard to 2020 Census planning, thereby allowing states ample time for their planning, response, and participation. The U.S. Census Bureau will deliver the Public Law 94-171 redistricting data to all states by Sept. 30, 2021. COVID-19-related delays and prioritizing the delivery of the apportionment results delayed the Census Bureau’s original plan to deliver the redistricting data to the states by April 1, 2021.

Data in this dataset contains information on population, diversity, race, ethnicity, housing, household, vacancy rate for 2020 for various geographies (county, MCD, Philadelphia Planning Districts (referred to as county planning areas [CPAs] internally, Census designated places, tracts, block groups, and blocks)

For more information on the 2020 Census, visit https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.html

PLEASE NOTE: 2020 Decennial Census data has had noise injected into it because of the Census's new Disclosure Avoidance System (DAS). This can mean that population counts and characteristics, especially when they are particularly small, may not exactly correspond to the data as collected. As such, caution should be exercised when examining areas with small counts. Ron Jarmin, acting director of the Census Bureau posted a discussion of the redistricting data, which outlines what to expect with the new DAS. For more details on accuracy you can read it here: https://www.census.gov/newsroom/blogs/director/2021/07/redistricting-data.html

This dataset provides modeled predictions of ozone levels from the EPA's Downscaler model. Data are at the census tract level for 2001-2005. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2016-2020. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information. Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action. By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

About this dataset

Context

The dataset tabulates the Day town median household income by race. The dataset can be utilized to understand the racial distribution of Day town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Day, New York median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Day, New York (2021, in 2022 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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Day town median household income by race. You can refer the same here

A. SUMMARY This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2016-2020 American Community Survey (ACS) population estimates are included to calculate the cumulative rate per 10,000 residents. Dataset covers cases going back to 3/2/2020 when testing began. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date). COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 05:00 Pacific Time. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). This dataset can be used to track the spread of COVID-19 throughout the city, in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date. Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. Cases are dropped altogether for areas where acs_population < 1000 4. Deaths data are not included in this dataset for privacy reasons. The low COVID-19 death rate in San Francisco, along with other publicly available information on deaths, means that deaths data by geography and day is too granular and potentially risky. Read more in our privacy guidelines Rate suppression in effect where counts lower than 20 Rates are not calculated unless the cumulative case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are spec

About this dataset

Context

The dataset tabulates the Day town population by age. The dataset can be utilized to understand the age distribution and demographics of Day town.

Content

The dataset constitues the following three datasets

• Day, New York Age Group Population Dataset: A complete breakdown of Day town age demographics from 0 to 85 years, distributed across 18 age groups
• Day, New York Age Cohorts Dataset: Children, Working Adults, and Seniors in Day town - Population and Percentage Analysis
• Day, New York Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2001-2005. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information.

Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Day town: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 26(6.25%) households where the householder is under 25 years old, 101(24.28%) households with a householder aged between 25 and 44 years, 151(36.30%) households with a householder aged between 45 and 64 years, and 138(33.17%) households where the householder is over 65 years old.
• In Day town, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

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:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Day town median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the John Day, OR population pyramid, which represents the John Day population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for John Day, OR, is 27.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for John Day, OR, is 28.9.
• Total dependency ratio for John Day, OR is 56.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for John Day, OR is 3.5.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the John Day population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the John Day for the selected age group is shown in the following column.
• Population (Female): The female population in the John Day for the selected age group is shown in the following column.
• Total Population: The total population of the John Day for the selected age group is shown in the following column.

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 John Day Population by Age. You can refer the same here

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2011-2015. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information.

Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

This dataset provides modeled predictions of ozone levels from the EPA's Downscaler model. Data are at the census tract level for 2006-2010. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information.

Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Day Township, Michigan, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Day township did not include 5, 6, or 7-person households. Across the different household sizes in Day township the mean income is $60,014, and the standard deviation is $17,810. The coefficient of variation (CV) is 29.68%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $36,604. It then further increased to $72,624 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/day-township-mi-median-household-income-by-household-size.jpeg" alt="Day Township, Michigan median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 Day township median household income. You can refer the same here

A. SUMMARY This dataset includes aggregate data on the type, status, population served, and individuals placed at each alternative housing site under contract with HSA. B. HOW THE DATASET IS CREATED Site Type, Status, and Population The HSA DOC leadership inform the data tracker owner when the legal status, site type, or intended population to serve changes. Daily Census and Units Available The site monitors at each site inform the data tracker owner at the HSA DOC at least once daily with the updates to the daily census. C. UPDATE PROCESS Updated several times daily, whenever new information is shared with the data tracker owner. The data tracker owner inputs the data directly into the underlying SharePoint spreadsheet. D. HOW TO USE THIS DATASET Use the data for aggregate data on the site type, status, and daily census of individuals placed in the sites. Do not use this spreadsheet for individual-level information. There is no personally identifying or medical information in this dataset.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes (institutional)

UNIT DESCRIPTIONS: - Dwellings: Not available - Households: An individual or group of people who inhabit part or all of the physical or census building, usually live together, who eat from one kitchen or organize daily needs together as one unit. - Group quarters: A special household includes people living in dormitories, barracks, or institutions in which daily needs are under the responsibility of a foundation or other organization. Also includes groups of people in lodging houses or buildings, where the total number of lodgers is ten or more.

Universe

All population residing in the geographic area of Indonesia regardless of residence status. Diplomats and their families residing in Indonesia were excluded.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Statistics Indonesia

SAMPLE DESIGN: Geographically stratified systematic sample (drawn by MPC).

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 20,112,539

Mode of data collection

Face-to-face [f2f]

Research instrument

L1 questionnaire for buildings and households; L2 questionnaire for permanent residents; and L3 questionnaire for non-permanent residents (boat people, homeless persons, etc).

This dataset provides modeled predictions of ozone levels from the EPA's Downscaler model. Data are at the census tract level for 2001-2005. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information.

Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

Analysis of ‘COVID-19 Alternative Housing Sites’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0cb625c4-7d2f-41d4-8d8f-5bfc357e7efb on 26 January 2022.

--- Dataset description provided by original source is as follows ---

A. SUMMARY This dataset includes aggregate data on the type, status, population served, and individuals placed at each alternative housing site under contract with HSA.

B. HOW THE DATASET IS CREATED Site Type, Status, and Population The HSA DOC leadership inform the data tracker owner when the legal status, site type, or intended population to serve changes.

Daily Census and Units Available The site monitors at each site inform the data tracker owner at the HSA DOC at least once daily with the updates to the daily census.

C. UPDATE PROCESS Updated several times daily, whenever new information is shared with the data tracker owner. The data tracker owner inputs the data directly into the underlying SharePoint spreadsheet.

D. HOW TO USE THIS DATASET Use the data for aggregate data on the site type, status, and daily census of individuals placed in the sites. Do not use this spreadsheet for individual-level information. There is no personally identifying or medical information in this dataset.

--- Original source retains full ownership of the source dataset ---

This dataset provides modeled predictions of PM2.5 levels from the EPA's Downscaler model. Data are at the census tract level for 2001-2005. These data are used by the CDC's National Environmental Public Health Tracking Network to generate air quality measures. Census tract-level datasets contain estimates of the mean predicted concentration and associated standard error. Please refer to the metadata attachment for more information.

Learn more about outdoor air quality on the Tracking Network's website: https://ephtracking.cdc.gov/showAirLanding.action.

By using these data, you signify your agreement to comply with the following requirements: 1. Use the data for statistical reporting and analysis only. 2. Do not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. 3. Do not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. 4. Do not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. 5. Acknowledge, in all reports or presentations based on these data, the original source of the data and CDC. 6. Suggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking.

Analysis of ‘COVID-19 Cases and Deaths Summarized by Geography’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d2e381bb-f395-4b40-979e-920a79a3db88 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change. Due to the changes, starting on January 22, 2022, the number of new cases reported daily will be higher than under the old system as cases that would have taken longer to process will be reported earlier.

Note: As of April 16, 2021, this dataset will update daily with a five-day data lag.

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2019 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

Dataset is cumulative and covers cases going back to March 2nd, 2020 when testing began.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2019 ACS estimates for population provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time.

D. HOW TO USE THIS DATASET Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling bases.

--- Original source retains full ownership of the source dataset ---