Graph and download economic data for Unemployed Persons in Northeast Census Region (LAURD910000000000004) from Jan 1976 to May 2025 about Northeast Census Region, household survey, unemployment, persons, and USA.

Graph and download economic data for Employed Persons in Northeast Census Region (LAURD910000000000005) from Jan 1976 to May 2025 about Northeast Census Region, household survey, employment, persons, and USA.

Resident Population in the Northeast Census Region was 57832.93500 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in the Northeast Census Region reached a record high of 57832.93500 in January of 2024 and a record low of 21059.00000 in January of 1900. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in the Northeast Census Region - last updated from the United States Federal Reserve on July of 2025.

About this dataset

Context

The dataset tabulates the North East 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 North East 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 North East was 4,163, a 0.85% increase year-by-year from 2022. Previously, in 2022, North East population was 4,128, an increase of 0.78% compared to a population of 4,096 in 2021. Over the last 20 plus years, between 2000 and 2023, population of North East increased by 1,400. In this period, the peak population was 4,163 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the North East is shown in this column.
• Year on Year Change: This column displays the change in North East population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 North East Population by Year. You can refer the same here

This statistic shows the change in the regional distribution of the U.S. population each decade from 1790 to 2021. In 2021, 17.2 percent of the population in the United States lived in the Northeast.

New York was the most populous state in the union in the year 1900. It had the largest white population, for both native born and foreign born persons, and together these groups made up over 7.1 million of New York's 7.2 million inhabitants at this time. The United States' industrial centers to the north and northeast were one of the most important economic draws during this period, and states in these regions had the largest foreign born white populations. Ethnic minorities Immigration into the agricultural southern states was much lower than the north, and these states had the largest Black populations due to the legacy of slavery - this balance would begin to shift in the following decades as a large share of the Black population migrated to urban centers to the north during the Great Migration. The Japanese and Chinese populations at this time were more concentrated in the West, as these states were the most common point of entry for Asians into the country. The states with the largest Native American populations were to the west and southwest, due to the legacy of forced displacement - this included the Indian Territory, an unorganized and independent territory assigned to the Native American population in the early 1800s, although this was incorporated into Oklahoma when it was admitted into the union in 1907. Additionally, non-taxpaying Native Americans were historically omitted from the U.S. Census, as they usually lived in separate communities and could not vote or hold office - more of an effort was made to count all Native Americans from 1890 onward, although there are likely inaccuracies in the figures given here. Changing distribution Internal migration in the 20th century greatly changed population distribution across the country, with California and Florida now ranking among the three most populous states in the U.S. today, while they were outside the top 20 in 1900. The growth of Western states' populations was largely due to the wave of internal migration during the Great Depression, where unemployment in the east saw many emigrate to "newer" states in search of opportunity, as well as significant immigration from Latin America (especially Mexico) and Asia since the mid-1900s.

About this dataset

Context

The dataset tabulates the North East township 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 North East township 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 North East township was 6,439, a 0.40% decrease year-by-year from 2022. Previously, in 2022, North East township population was 6,465, a decline of 0.55% compared to a population of 6,501 in 2021. Over the last 20 plus years, between 2000 and 2023, population of North East township decreased by 217. In this period, the peak population was 6,714 in the year 2003. 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).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the North East township is shown in this column.
• Year on Year Change: This column displays the change in North East township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 North East township Population by Year. You can refer the same here

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

This graph shows the percentage of movers in the United States in 2018, by geographical region. In 2018, about 7.7 percent of the northeast population in the United States had moved house. The national average is about 10.2 percent.

Survey participants plotted activity points using an interactive mapping tool.The 2012 Northeast Recreational Boater Survey was conducted by SeaPlan, the Northeast Regional Ocean Council (NROC), states’ coastal agencies, marine trade associations composed of many private industry representatives, and the First Coast Guard District. The methodology for the 2012 Northeast Recreational Boater Survey follows a protocol similar to the 2010 Massachusetts Survey with modifications based on the lessons learned and recommendations suggested in the Massachusetts Survey Final Report.The methodology consists of surveying a random sample of selected boat owners throughout the Northeast through a series of monthly online surveys. The surveying period lasted throughout the 2012 boating season (May 1 through October 31, 2012), which was identified by the advisory committee (consisting of NROC and representatives from the recreational boating industry).The project team decided to use a random sample survey approach because it successfully gathered statistically robust economic and spatial data on recreational boating activity by Massachusetts registered boaters during the 2010 boating season. This was also the only approach that would allow for the calculation of statistically robust economic impact estimates for both the states and the region, which was identified as a priority (along withspatial data) by both NROC and the boating industry.

This dataset can be used by coastal planners in ocean planning activities to develop a better understanding of how and where humans use the ocean in the Northeast to inform regional ocean planning and minimize ocean use conflicts. This effort also fulfilled a recommendation from the 2010 Massachusetts Survey to expand the survey’s geographic range to the Northeast Region, allowing for the capture of interstate traffic between states in the Northeast. Furthermore, this dataset can also be used by the boating industry to show the importance of recreational boating to the region and to inform business planning.

Supplemental Information; SURVEY SAMPLING METHODOLOGY - The sample for this survey came from seven databases, including the U.S. Coast Guard Documented Vessel Database and databases of state registered boaters from New York, Connecticut, Rhode Island, Massachusetts, New Hampshire, and Maine. Recreational boaters who owned vessels that met the following criteria were eligible for the survey: * Registration: Currently registered with a state in the Northeast and/or registered as a documented vessel with the U.S. Coast Guard, with a hailing port in the Northeast * Primary Use: Recreational use designation * Length: At least 10 feet in length * Saltwater (if specified; only Maine and New Hampshire required this information) * Location: Located in a “coastal county”. The survey team defined “coastal counties” as those that border saltwater, or those that were highlighted by state coastal planners as likely containing large amount of saltwater boating activity. Based on the 2010 Massachusetts Survey and budgetary considerations, the project team determined an overall sample size that would provide sufficient spatial and economic data for both each state, as well as the whole Northeast. Because of the, at times, large discrepancies between the number of eligible boats in some states, the team decided that certain states with fewer eligible boats should also have a supplemental sample of boats in addition to the pure random sample. To ensure the sample represented the total population of registered boats in the Northeast, the sampling method included considerations of state, geography and size class. Of the 373,766 boats eligible for the survey, the base of randomly sampled boats included 50,000 boats from across all six states. In addition to this base, the survey team sampled 17,772 boats as a supplemental sample, including: 1,772 boats of 26 feet in length or more from across all six states to increase the number of large boats in the sample, and 16,000 additional boats to ensure each state had enough responses for the statistical analysis. These included 10,000 boats from Maine, 2,500 boats from Rhode Island, 2,000 boats from New Hampshire and 1,500 boats from Connecticut. This resulted in a total of 67,772 boaters invited to participate in the study. Boater Recruitment and Response: In the survey invitation package, the survey team also sent invited boaters a questionnaire to verify eligibility to participate in the survey. Eligibility requirements consist of: boat is used in saltwater; boat is used for recreational purposes; and boaters have access to the internet with a working email address. 12,218 boaters responded to the invitation; however only 7,800 of these respondents were found to meet all of the above criteria. From this sample, 4,297 individual boaters completed at least one monthly survey. Surveying Process: The study consisted of six monthly surveys and one end of season survey. The online monthly surveys gathered spatial and economic data on recreational boating activity that occurred during the previous month. The online survey had two parts: 1) a survey with questions about general boating activity during the previous month, and the boater’s last trip of the month (specifically focusing on spending), and 2) a mapping application developed by Ecotrust where boaters plotted their boating route and identified any areas where they participated in activities, such as fishing, diving, wildlife viewing, swimming and relaxing at anchor. The end of season survey gathered a variety of information that could not be gathered in the monthly surveys. The end of season survey contained questions about yearly boating-related expenditures (e.g., dockage, storage, taxes, yearly maintenance), feedback on the survey itself, and general boating-related questions (e.g. whether boaters have taken a boating safety course). Density Analysis: The density analysis described in the following paragraphs was vetted by a technical advisory team consisting of representatives from the Massachusetts Office of Coastal Zone Management (MA CZM), NROC, Maine Coastal Program and Applied Science Associates (ASA) and was based on mapping and analysis protocols from the 2010 Massachusetts Survey. To develop the density layer, vessel routes were drawn in WGS 1984 in the Ecotrust mapping application and were imported into Excel, then ArcMap using a data frame in that coordinate system. Routes from the random sample were selected from that data layer, and the data layer was re-projected into two separate shapefiles, one in UTM 18 and one in UTM 19. A line density analysis using a 250 m square grid cell with a 675 m neighborhood was applied to each shapefile. The 675 m neighborhood was applied to account for inherent user error in the mapping tool. The line density analysis resulted in a raster grid for each UTM zone. Each raster was clipped by the boundaries of its UTM zone, re-projected into the North American Albers Equal Area Conic Projection, and the separate rasters were mosaicked together. At the boundary of the two raster grids there was a line of cells with no data value. This was a result of mosaicking rasters that originated in different coordinate systems. To approximate values in the blank cells, each blank cell was populated by a value from a focal statistics calculation. The focal statistics expression took the mean of all cells in a 4x4 neighborhood around each blank cell. The values were then converted to Z-scores using the raster calculator by taking the log of the density values, subtracting the mean value, and dividing the resulting value by the standard deviation of the value. This layer was clipped again using the NOAA medium resolution shoreline dataset. DATA PROCESSING Processing environment: ArcGIS 10.05, Windows 7 Ultimate SP5, Intel Xeon CPU Process Steps Description 1 Raw routes from mapping application imported into ArcMap 2 Routes from random sample selected using select by attributes query 3 Routes projected into two separate shapefiles (UTM Zones 18 & 19) 4 LINE DENSITY tool in spatial analyst applied to each shapefile using a 250 m square grid with a 675 m neighborhood 5 Resulting rasters clipped to their respective UTM Zones using the EXTRACT BY MASK tool 6 Rasters reprojected to North America Albers Equal Area Conic Projection, using PROJECT tool 7 MOSAIC tool used to merge rasters 8 Focal mean expression (4x4 neighborhood) used to approximate and fill cells with no data at the boundary between mosaicked rasters 9 Raster calculator used to calculated Z-scores ([(Ln(Value))-Mean]/Std. Deviation) 10 Raster clipped by NOAA Medium Resolution Shoreline data using EXTRACT BY POLYGON tool QUALITY PROCESS Attribute Accuracy: The lines used to generate the density grid were derived from a mapping tool used by boaters to reconstruct their boating routes. To ensure that boaters included their round-trip route the mapping applications would send the user an error message asking them to re-plot the route or the program would automatically return the route to the starting point. This application also restricted the scale at which users could draw their routes, reducing the amount of error that could occur from plotting routes at too small a scale. Clipping this layer with a regional ocean shapefile derived from the NOAA medium resolution shoreline dataset excluded route density resulting from routes drawn over land, in freshwater, or outside of northeastern waters. Logical Consistency: None Completeness: Only reported routes from the random sample were included. Routes from the supplemental sample were excluded from this analysis. Route density occurring over land, freshwater areas, or outside northeastern waters was excluded by the final geoprocessing step. Positional Accuracy: The positional accuracy of the routes is dependent on the individual reporting routes through the

Prepared by the Inter-university Consortium for Political and Social Research, the block group subset was extracted from the Census of Population and Housing, 2000, Summary File 3 (SF3). The SF3 data contain information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, household relationship, marital status, caregiving by grandparents, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry to the United States, migration, place of work, journey to work, school enrollment, educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include housing unit vacancy status, housing unit tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, rent, and shelter costs. The information in SF3 is presented in 813 tables, one variable per table cell, plus additional variables with geographic information. However, only 409 of these tables are shown for the block group and higher levels of geography. The remaining 404 tables, which are shown for the census tract and higher levels of geography, were excluded from the block group subset. Cases in the summary file data are classified by levels of observation, known as "summary levels" in the Census Bureau's nomenclature. The block group subset comprises all of the cases in the SF3 data for summary level 150. Five data files are provided with this collection. There is a block group subset for each of the four census regions (Northeast, Midwest, South, and West), plus a national subset that covers all of the regions.

United States CPI U: Northeast: Size Class A data was reported at 268.504 1982-1984=100 in Jun 2018. This records an increase from the previous number of 268.295 1982-1984=100 for May 2018. United States CPI U: Northeast: Size Class A data is updated monthly, averaging 179.700 1982-1984=100 from Dec 1977 (Median) to Jun 2018, with 433 observations. The data reached an all-time high of 268.504 1982-1984=100 in Jun 2018 and a record low of 64.700 1982-1984=100 in Dec 1977. United States CPI U: Northeast: Size Class A data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I014: Consumer Price Index: Urban: By Region. All metropolitan areas with population over 1.5 million

Prepared by the Inter-university Consortium for Political and Social Research, this data collection consists of selected subsets extracted from the Census of Population and Housing, 2000, Summary File 3 (SF3). The SF3 data contain information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, household relationship, marital status, caregiving by grandparents, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry to the United States, migration, place of work, journey to work, school enrollment, educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include housing unit vacancy status, housing unit tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, rent, and shelter costs. The information in SF3 is presented in 813 tables, one variable per table cell, plus additional variables with geographic information. Cases in the summary file data are classified by levels of observation, known as "summary levels" in the Census Bureau's nomenclature, which served as the selection criteria for the subsets generated by ICPSR. Each subset comprises all of the cases in one of 10 summary levels: the nation (summary level 010), states (summary level 040), Metropolitan Statistical Areas (MSA)/Consolidated Metropolitan Statistical Areas (CMSA) (summary level 380), Primary Metropolitan Statistical Areas (PMSA) (summary level 385), places (summary level 160), counties (summary level 050), county subdivisions (summary level 060), whole census tracts (summary level 140), census tracts in places (summary level 158), and 5-Digit ZIP Code Tabulation Areas (ZCTA) (summary level 860). Four files are supplied for the summary level 860 subset: a single file that contains all of the SF3 tables, plus three smaller files, each of which contains about one third of the tables. Five files are supplied for each of the summary level 010, 040, 380, 385, 160, and 050 subsets: a single file that contains all of the SF3 tables, plus four smaller files, each of which contains approximately one quarter of the tables. Fifteen files are provided for each of the summary level 140 and 158 subsets. There is a national file with all of the SF3 tables, plus two smaller national files, each of which contains approximately one half of the tables. Additionally, there are three files for each of the four census regions (Northeast, Midwest, South, and West): a file with all tables and two smaller files each containing about one half of the tables. Twenty files are supplied for summary level 060. There is a national file with all tables, plus three smaller national files, each of which contains approximately one third of the tables. In addition, there are four files for each of the four census regions: a file with all tables and three smaller files each containing about one third of the tables.

Graph and download economic data for Unemployment Rate in Northeast Census Region (LAURD910000000000003A) from 1976 to 2024 about Northeast Census Region, household survey, unemployment, rate, and USA.

United States CPI U: Northeast: Size Class B/C data was reported at 156.752 Dec1996=100 in Oct 2018. This records a decrease from the previous number of 156.961 Dec1996=100 for Sep 2018. United States CPI U: Northeast: Size Class B/C data is updated monthly, averaging 132.049 Dec1996=100 from Dec 1996 (Median) to Oct 2018, with 263 observations. The data reached an all-time high of 157.350 Dec1996=100 in Aug 2018 and a record low of 100.000 Dec1996=100 in Jan 1997. United States CPI U: Northeast: Size Class B/C data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I014: Consumer Price Index: Urban: By Region. All metropolitan areas with population smaller than 1.5 million

These files contain the spatial boundaries of the NOAA Fisheries Bottom-trawl surveys. This data set covers 8 regions of the United States: Northeast, Southeast, Gulf of Mexico, West Coast, Eastern Bering Sea, Aleutian Islands, Gulf of Alaska, and Hawai'i Islands.

These files contain the spatial boundaries of the NOAA Fisheries Bottom-trawl surveys. This data set covers 8 regions of the United States: Northeast, Southeast, Gulf of Mexico, West Coast, Eastern Bering Sea, Aleutian Islands, Gulf of Alaska, and Hawai'i Islands.

Census Designated Places are the statistical counterparts of incorporated places. CDPs are settled concentrations of population that are identifiable by name but not legally incorporated under the laws of the state in which the CDPs are located. The Census Bureau defines CDP boundaries in cooperation with local partners as part of the PSAP. CDP boundaries usually coincide with visible features or the boundary of an adjacent Incorporated Place or another legal entity boundary. CDPs have no legal status and do not have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary. There are no population size requirements for CDPs. In the nine states of the Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont) as well as Michigan, Minnesota, and Wisconsin, a CDP may represent a densely settled concentration of population within a town or township; in other instances, a CDP represents an entire town or township.Additional resources to obtain Place geography is listed below.Consolidated City Shapefile – https://www2.census.gov/geo/tiger/TIGER2020/CONCITY/Place Shapefile (Includes Incorporated Place and Census Designated Place) – https://www2.census.gov/geo/tiger/TIGER2020/PLACE/

ABSTRACT This study aims to analyze educational inequality in the Northeast Region of Brazil based on data from the 2010 Census. For this purpose, the Educational Gini Index (IGE) has been estimated for the portion of the economically active population aged 15 years and over residing in the 1793 municipalities of the Northeast. Analytical techniques included Spatial Data (ESDA) and Spatial Regression Analysis to detect the importance of a number of variables related to household, education and the economy of counties accounted for in the IGE. Results suggest that the state of Bahia shows the lowest educational inequality rates among all Northeast states, while Alagoas is the one with the highest inequality rate (0.467). As for the spatial analysis of educational inequality, spatial dependence has been detected regarding the municipalities and their neighbors. It has also been found that per capita income, net school attendance, IES presence and municipal per capita PIB contribute to the reduction of inequality. And the low impact of educational variables can be attributed to their long-term effect; as a result, investment and public policies directed to educational become really important, since they will only have an impact on the reduction of educational inequality among the municipalities in the long run.

Original provider: University of Maine

Dataset credits: NOAA Northeast Fisheries Science Center James Gilbert, University of Maine Wendy Dow, Duke University

Abstract: Seal populations in the northwest Atlantic are thriving, yet few grant dollars go to seal projects in the northeast region. According to many researchers and managers in the region, the healthy state of stocks is exactly why we should be studying seals. Seal and human activities along the coast often result in conflicts, which will undoubtedly increase as the population and range of both seals and humans increase. The east coast of the United States lacks a management plan for seals. A problem that seriously impedes management is that managers do not know where the seals are and more specifically, where haul-out sites are. This atlas is designed to aid managers and researchers in the management and conservation of seals in the northwest Atlantic. The atlas and the data used to create the atlas can be accessed through OBIS-SEAMAP.

Purpose: The purpose of this dataset and atlas is to provide data from a series of aerial surveys conducted between 1981 and 2001 of harbor and gray seal haul-out sites in Maine.

Supplemental information: The original data were transposed so that each record represents seals abundance at a single ledge on a single day. As this dataset is a time-series, an appropriate way to represent it is to show it with time-series charts by region. Currently, OBIS-SEAMAP lets you see it on Google Earth with the charts. Not all seasons are represented in this dataset, and distributions of seals may be different in other seasons. Lack of a count for a ledge on a particular date does not necessarily mean the ledge was not observed.