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The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.
In 2023, the population of the Boston-Cambridge-Newton metropolitan area in the United States was about 4.92 million people. This is a slight increase when compared with last year's population, which was about 4.9 million people.
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Demographic Data for Boston’s Neighborhoods, 1950-2019
Boston is a city defined by the unique character of its many neighborhoods. The historical tables created by the BPDA Research Division from U.S. Census Decennial data describe demographic changes in Boston’s neighborhoods from 1950 through 2010 using consistent tract-based geographies. For more analysis of these data, please see Historical Trends in Boston's Neighborhoods. The most recent available neighborhood demographic data come from the 5-year American Community Survey (ACS). The ACS tables also present demographic data for Census-tract approximations of Boston’s neighborhoods. For pdf versions of the data presented here plus earlier versions of the analysis, please see Boston in Context.
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Chart and table of population level and growth rate for the Boston metro area from 1950 to 2025.
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The BPDA Research Division prepared Census data on total population, population by race and ethnicity, voting-age population, group quarters populations, and housing occupancy for use in the 2022 City Council redistricting process. These data reflect 2020 census block-level data from the 2020 Decennial Census P.L. 94-171 Redistricting Data aggregated to the 275 precincts (as amended April 6, 2022) and the 9 current City Council Districts. Also included are 2010 estimates for these geographies based on 2010 census block-level.
Notes on coding of Race and Ethnicity:
The data presented here follow the conventions recommended by the Department of Justice in their September 1, 2021 guidance on the use of race and ethnicity data in redistricting. This differs from other commonly reported race and ethnicity groupings in that it groups those reporting 2 races, one White and one non-White, as being members of the non-White race reported. Thus a person reporting White and Black would be categorized here as Black. All residents of Hispanic or Latino origin, regardless of reported race, are grouped together. This coding appears on page 12 of the guidance that can be found here: https://www.justice.gov/opa/press-release/file/1429486/download
Notes on 2010 data:
For 2010 data the BPDA Research Division crosswalked 2010 census block data to 2020 boundaries using a combination of block assignment and areal interpolation based on Census Tiger shapefiles and the publicly available boundary files for Boston electoral geographies. For blocks split across 2020 boundaries the entire 2010 population was assigned to one side of the boundary if no residential structures within that block existed on the other side of the boundary. In cases where residential structures were present on both sides of the boundary, areal interpolation was used to assign the block's population and housing units based on the share of the land area of the block falling on either side of the boundary. These numbers will differ from those produced using different crosswalking methods.
How racially diverse are residents in Massachusetts? This topic shows the demographic breakdown of residents by race/ethnicity and the increases in the Non-white population since 2010.
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Context
The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Boston. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.
Key observations: Insights from 2023
Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Boston, the median income for all workers aged 15 years and older, regardless of work hours, was $25,556 for males and $17,381 for females.
These income figures highlight a substantial gender-based income gap in Boston. Women, regardless of work hours, earn 68 cents for each dollar earned by men. This significant gender pay gap, approximately 32%, underscores concerning gender-based income inequality in the city of Boston.
- Full-time workers, aged 15 years and older: In Boston, among full-time, year-round workers aged 15 years and older, males earned a median income of $30,982, while females earned $35,563Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.15 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.
Gender classifications include:
Employment type classifications include:
Variables / Data Columns
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.
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 is a part of the main dataset for Boston median household income by race. You can refer the same here
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This map shows a simple summary of the social vulnerability of populations in the United States. Using Census 2010 information, the map answers the question “Where are the areas of relatively greater potential impact from disaster events within the U.S.?” from the perspective of social vulnerability to hazards. In other words, all areas of the U.S. are assessed relative to each other. Local and regional assessments of social vulnerability should apply the same model to their multi-county or multi-state region. For emergency response planning and hazard mitigation, populations can be assessed by their vulnerability to various hazards (fire, flood, etc). Physical vulnerability refers to a population’s exposure to specific potential hazards, such as living in a designated flood plain. There are various methods for calculating the potential or real geographic extents for various types of hazards. Social vulnerability refers to sensitivity to this exposure due to population and housing characteristics: age, low income, disability, home value or other factors. The social vulnerability score presented in this web service is based upon a 2000 article from the Annals of the Association of American Geographers which sums the values of 8 variables as a surrogate for "social vulnerability". For example, low-income seniors may not have access to a car to simply drive away from an ongoing hazard such as a flood. A map of the flood’s extent can be overlaid on the social vulnerability layer to allow planners and responders to better understand the demographics of the people affected by the hazard. This map depicts social vulnerability at the block group level. A high score indicates an area is more vulnerable. This web service provides a simplistic view of social vulnerability. There are more recent methods and metrics for determining and displaying social vulnerability, including the Social Vulnerability Index (SoVI) which capture the multi-dimensional nature of social vulnerability across space. See www.sovius.org for more information on SoVI. The refereed journal article used to guide the creation of the model in ModelBuilder was: Cutter, S. L., J. T. Mitchell, and M. S. Scott, 2000. "Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, South Carolina." Annals of the Association of American Geographers 90(4): 713-737. Additionally, a white paper used to guide creation of the model in ModelBuilder was "Handbook for Conducting a GIS-Based Hazards Assessment at the County Level" by Susan L. Cutter, Jerry T. Mitchell, and Michael S. Scott.Off-the-shelf software and data were used to generate this index. ModelBuilder in ArcGIS 10.1 was used to connect the data sources and run the calculations required by the model.-------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.
Between 1935 and 1940 the federal government’s Home Owners’ Loan Corporation (HOLC) classified the neighborhoods of 239 cities according to their perceived investment risk. This practice has since been referred to as “redlining,” as the neighborhoods classified as being the highest risk for investment were often colored red on the resultant maps. The Mapping Inequality project, a collaboration of faculty at the University of Richmond’s Digital Scholarship Lab, the University of Maryland’s Digital Curation Innovation Center, Virginia Tech, and Johns Hopkins University has digitized and georectified all 239 HOLC maps and made them publicly available, including the HOLC map of Boston from 1938. The Boston Area Research Initiative has coordinated (i.e., spatial joined) the districts from the 1938 HOLC map of Boston with census tracts from the 2010 U.S. Census. This dataset contains the original shapefile and the spatially joined tract-level data.
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Context
The dataset tabulates the New Boston population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of New Boston across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.
Key observations
In 2022, the population of New Boston was 4,550, a 1.04% decrease year-by-year from 2021. Previously, in 2021, New Boston population was 4,598, a decline of 0.50% compared to a population of 4,621 in 2020. Over the last 20 plus years, between 2000 and 2022, population of New Boston decreased by 115. In this period, the peak population was 4,795 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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 is a part of the main dataset for New Boston Population by Year. You can refer the same here
2010 Androscoggin County Census Data, includes population and housing data. From Maine Office of GIS.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the New Boston population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of New Boston across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.
Key observations
In 2023, the population of New Boston was 4,548, a 0.07% increase year-by-year from 2022. Previously, in 2022, New Boston population was 4,545, a decline of 1.15% compared to a population of 4,598 in 2021. Over the last 20 plus years, between 2000 and 2023, population of New Boston decreased by 117. In this period, the peak population was 4,795 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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 is a part of the main dataset for New Boston Population by Year. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in New Boston. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.
Key observations: Insights from 2023
Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In New Boston, the median income for all workers aged 15 years and older, regardless of work hours, was $22,250 for males and $15,860 for females.
These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 29% between the median incomes of males and females in New Boston. With women, regardless of work hours, earning 71 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thevillage of New Boston.
- Full-time workers, aged 15 years and older: In New Boston, among full-time, year-round workers aged 15 years and older, males earned a median income of $36,545, while females earned $27,115, leading to a 26% gender pay gap among full-time workers. This illustrates that women earn 74 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in New Boston, showcasing a consistent income pattern irrespective of employment status.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.
Gender classifications include:
Employment type classifications include:
Variables / Data Columns
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.
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 is a part of the main dataset for New Boston median household income by race. You can refer the same here
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Graph and download economic data for Burdened Households (5-year estimate) in Middlesex County, MA (DP04ACS025017) from 2010 to 2023 about Middlesex County, MA; burdened; Boston; MA; households; 5-year; and USA.
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Boston Neighborhood Boundaries represent a combination of zoning neighborhood boundaries, zip code boundaries and 2010 census tract boundaries. These boundaries are used in the broad sense for visualization purposes, research analysis and planning studies. However these boundaries are not official neighborhood boundaries for the City of Boston. The BPDA is not responsible for any districts or boundaries within the City of Boston except for the districts we use for planning purposes.
The gate receipts of the Boston Bruins franchise of the National Hockey League fluctuated between 2010 and 2024. In the 2023/24 season, the gate receipts of the franchise amounted to 109 million U.S. dollars. The significant drop in gate receipts in the 2020/21 season can be attributed to the fact that the Bruins played their games in front of a restricted audience as a result of the coronavirus pandemic.
In the 2023/24 season, the total home attendance of the Boston Celtics during the regular NBA season totaled around 785 thousand. This worked out at approximately 19 thousand attendees per game for the franchise.
These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. Researchers compiled datasets on prison admissions and releases that would be comparable across places and geocoded and mapped those data onto crime rates across those same places. The data used were panel data. The data were quarterly or annual data, depending on the location, from a mix of urban (Boston, Newark and Trenton) and rural communities in New Jersey covering various years between 2000 and 2010. The crime, release, and admission data were individual level data that were then aggregated from the individual incident level to the census tract level by quarter (in Boston and Newark) or year (in Trenton). The analyses centered on the effects of rates of prison removals and returns on rates of crime in communities (defined as census tracts) in the cities of Boston, Massachusetts, Newark, New Jersey, and Trenton, New Jersey, and across rural municipalities in New Jersey. There are 4 Stata data files. The Boston data file has 6,862 cases, and 44 variables. The Newark data file has 1,440 cases, and 45 variables. The Trenton data file has 66 cases, and 32 variables. The New Jersey Rural data file has 1,170 cases, and 32 variables.
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Graph and download economic data for Burdened Households (5-year estimate) in Plymouth County, MA (DP04ACS025023) from 2010 to 2023 about Plymouth County, MA; burdened; Boston; MA; households; 5-year; and USA.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.