About this dataset

Context

The dataset tabulates the Seattle population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Seattle. The dataset can be utilized to understand the population distribution of Seattle by age. For example, using this dataset, we can identify the largest age group in Seattle.

Key observations

The largest age group in Seattle, WA was for the group of age 25 to 29 years years with a population of 94,551 (12.75%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Seattle, WA was the 80 to 84 years years with a population of 10,073 (1.36%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

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 in consideration
• Population: The population for the specific age group in the Seattle is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Seattle 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 Seattle Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Seattle population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Seattle. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 542,899 (73.22% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Seattle population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Seattle is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Seattle 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 Seattle Population by Age. You can refer the same here

Table from the American Community Survey (ACS) 5-year series on age and gender related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B01001 Sex by Age, B01002 Median Age by Sex. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Vintages: 2024 ACS Table(s): B01001, B01002Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Table from the American Community Survey (ACS) B01001A-I sex by age by race - data is grouped into three age group categories for each race, under 18, 18-64 and 65 and older. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually. Data on total number of people by each race alone and in combination by each census tract has been transposed to support dashboard visualizations. King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field. The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves. Vintages: 2010, 2015, 2020, 2021, 2022, 2023, 2024ACS Table(s): B01001Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the Survey Geography & ACS Technical Documentation News & Updates This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate. The data for this geographic area cannot be displayed because the number of sample cases is too small.

About this dataset

Context

The dataset tabulates the data for the Seattle, WA population pyramid, which represents the Seattle 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 Seattle, WA, is 15.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Seattle, WA, is 17.0.
• Total dependency ratio for Seattle, WA is 32.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Seattle, WA is 5.9.

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 Seattle population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Seattle for the selected age group is shown in the following column.
• Population (Female): The female population in the Seattle for the selected age group is shown in the following column.
• Total Population: The total population of the Seattle 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 Seattle Population by Age. You can refer the same here

Comprehensive demographic dataset for Pioneer Square, Seattle, WA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

The dataset tabulates the Seattle population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Seattle. The dataset can be utilized to understand the population distribution of Seattle by age. For example, using this dataset, we can identify the largest age group in Seattle.

Key observations

The largest age group in Seattle, WA was for the group of age 25 to 29 years years with a population of 94,815 (12.91%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Seattle, WA was the 80 to 84 years years with a population of 9,812 (1.34%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 in consideration
• Population: The population for the specific age group in the Seattle is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Seattle 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 Seattle Population by Age. You can refer the same here

Graph and download economic data for Estimate, Median Age by Sex, Total Population (5-year estimate) in King County, WA (B01002001E053033) from 2009 to 2024 about King County, WA; Seattle; age; WA; 5-year; median; and USA.

Comprehensive demographic dataset for Roosevelt, Seattle, WA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Comprehensive demographic dataset for West Seattle, Seattle, WA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

In 2023, the population of the Seattle-Tacoma-Bellevue metropolitan area in the United States was about 4.04 million people. This was a slight decrease from the previous year, when the population was about 4.03 million.

Comprehensive demographic dataset for Fremont, Seattle, WA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Graph and download economic data for Estimate, Median Age by Sex, Total Population (5-year estimate) in Snohomish County, WA (B01002001E053061) from 2009 to 2024 about Snohomish County, WA; Seattle; age; WA; 5-year; median; and USA.

Comprehensive demographic dataset for Capitol Hill, Seattle, WA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

The dataset tabulates the population of Seattle by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Seattle. The dataset can be utilized to understand the population distribution of Seattle by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Seattle. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Seattle.

Key observations

Largest age group (population): Male # 25-29 years (49,749) | Female # 25-29 years (44,802). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Seattle population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Seattle is shown in the following column.
• Population (Female): The female population in the Seattle is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Seattle for each age group.

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 Seattle Population by Gender. You can refer the same here

Graph and download economic data for Estimate, Median Age by Sex, Total Population (5-year estimate) in Pierce County, WA (B01002001E053053) from 2009 to 2024 about Pierce County, WA; Seattle; age; WA; 5-year; median; and USA.

Table from the American Community Survey (ACS) B01001 of total population count by sex and age group. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2010 shown by the corresponding census tract vintage. Also includes the most recent release annually. King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field. The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves. Vintages: 2010, 2015, 2020, 2021, 2022, 2023, 2024ACS Table(s): B01001Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the Survey Geography & ACS Technical Documentation News & Updates This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate. The data for this geographic area cannot be displayed because the number of sample cases is too small.

Comprehensive demographic dataset for Central Area, Seattle, WA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Provides data on people staying still in the space, including total number observed, demographic data, group size, postures, and activities.

The City of Seattle Department of Transportation (SDOT) is providing data from the public life studies it has conducted since 2017. These studies consist of measuring the number of people using public space and the types of activities present on select sidewalks across the city, as well as several parks and plazas. The data set is continually updated as SDOT and other parties conduct public life studies using Gehl Institute’s Public Life Data Protocol.

This dataset consists of four component spreadsheets and a GeoJSON file, which provide public life data as well as information about the study design and study locations:

1 Public Life Study: provides details on the different studies that have been conducted, including project information. https://data.seattle.gov/Transportation/Public-Life-Data-Study/7qru-sdcp

2 Public Life Location: provides details on the sites selected for each study, including various attributes to allow for comparison across sites. https://data.seattle.gov/Transportation/Public-Life-Data-Locations/fg6z-cn3y

3 Public Life People Moving: provides data on people moving through space, including total number observed, gender breakdown, group size, and age groups. https://data.seattle.gov/Transportation/Public-Life-Data-People-Moving/7rx6-5pgd

4 Public Life People Staying: provides data on people staying still in the space, including total number observed, demographic data, group size, postures, and activities.

5 Public Life Geography: A GeoJSON file with polygons of every location studied. https://data.seattle.gov/Transportation/Public-Life-Data-Geography/v4q3-5hvp

Please download and refer to the Public Life metadata document - in the attachment section below - for comprehensive information about all of the Public Life datasets.