This statistics shows a list of the top 20 largest-metropolitan areas in the United States in 2010, by land area. Riverside-San Bernardino-Ontario in California was ranked first enclosing an area of 70,612 square kilometers.

In 2023, New York led the ranking of the largest built-up urban areas worldwide, with a land area of ****** square kilometers. Boston-Providence and Tokyo-Yokohama were the second and third largest megacities globally that year.

This statistic provides projected figures for the Gross Metropolitan Product (GMP) of the United States in 2021, by metropolitan area. Only the 100 leading metropolitan areas are shown here. In 2022, the GMP of the New York-Newark-Jersey City metro area is projected to be around of about **** trillion U.S. dollars. Los Angeles metropolitan areaA metropolitan area in the U.S. is characterized by a relatively high population density and close economic ties through the area, albeit, without the legal incorporation that is found within cities. The Gross Metropolitan Product is measured by the Bureau of Economic Analysis under the U.S. Department of Commerce and includes only metropolitan areas. The GMP of the Los Angeles-Long Beach-Anaheim metropolitan area located in California is projected to be among the highest in the United States in 2021, amounting to *** trillion U.S. dollars. The Houston-The Woodlands-Sugar Land, Texas metro area is estimated to be approximately *** billion U.S. dollars in the same year. The Los Angeles metro area had one of the largest populations in the country, totaling ****** million people in 2021. The Greater Los Angeles region has one of the largest economies in the world and is the U.S. headquarters of many international car manufacturers including Honda, Mazda, and Hyundai. Its entertainment industry has generated plenty of tourism and includes world famous beaches, shopping, motion picture studios, and amusement parks. The Hollywood district is known as the “movie capital of the U.S.” and has its historical roots in the country’s film industry. Its port, the Port of Los Angeles and the Port of Long Beach are aggregately one of the world’s busiest ports. The Port of Los Angelesgenerated some ****** million U.S. dollars in revenue in 2019.

In 2023, the metropolitan area of New York-Newark-Jersey City had the biggest population in the United States. Based on annual estimates from the census, the metropolitan area had around 19.5 million inhabitants, which was a slight decrease from the previous year. The Los Angeles and Chicago metro areas rounded out the top three. What is a metropolitan statistical area? In general, a metropolitan statistical area (MSA) is a core urbanized area with a population of at least 50,000 inhabitants – the smallest MSA is Carson City, with an estimated population of nearly 56,000. The urban area is made bigger by adjacent communities that are socially and economically linked to the center. MSAs are particularly helpful in tracking demographic change over time in large communities and allow officials to see where the largest pockets of inhabitants are in the country. How many MSAs are in the United States? There were 421 metropolitan statistical areas across the U.S. as of July 2021. The largest city in each MSA is designated the principal city and will be the first name in the title. An additional two cities can be added to the title, and these will be listed in population order based on the most recent census. So, in the example of New York-Newark-Jersey City, New York has the highest population, while Jersey City has the lowest. The U.S. Census Bureau conducts an official population count every ten years, and the new count is expected to be announced by the end of 2030.

Metropolitan DivisionsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Metropolitan Divisions within the United States. According to the USCB, "Metropolitan Divisions subdivide a Metropolitan Statistical Area (MSA) containing a single core urban area that has a population of at least 2.5 million to form smaller groupings of counties or equivalent entities. Not all MSAs with urban areas of this size will contain Metropolitan Divisions. Not all MSAs with urban areas of this size will contain Metropolitan Divisions. Metropolitan Division are defined by the Office of Management and Budget (OMB) and consist of one or more main counties or equivalent entities that represent an employment center or centers, plus adjacent counties associated with the main county or counties through commuting ties."Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Metropolitan Divisions) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: TIGER/Line Shapefile, 2019, nation, U.S., Current Metropolitan Division NationalGeoplatform: TIGER/Line Shapefile, 2019, nation, U.S., Current Metropolitan Division NationalFor more information, please visit: Geographic LevelsFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

United States US: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data was reported at 45.896 % in 2017. This records an increase from the previous number of 45.666 % for 2016. United States US: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data is updated yearly, averaging 42.013 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 45.896 % in 2017 and a record low of 38.733 % in 1960. United States US: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Population in urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2018 had a population of more than one million people.; ; United Nations, World Urbanization Prospects.; Weighted average;

Extracted from the 2002 Census of Governments, this dataset provides the number of general-purpose local governments in each United States Metropolitan Statistical Area (MSA). Data from Consolidated Metropolitan Statistical Areas (CMSAs) and their component Primary Metropolitan Statistical Areas (PMSAs) are included. There are nine variables in this study. They contain information on locations (city and state); Metropolitan Statistical Areas; population at each location in the year 2000; number of General-Purpose Governments at each location as well as per 100,000 people; water, land, and total area in square miles; and General-Purpose Governments per 100,000 square miles of land area.

United States Urban Areas Dataset

Spatial and attribute data for US urban areas

By Homeland Infrastructure Foundation [source]

About this dataset

The Urban Areas dataset provides comprehensive spatial and attribute data on urban areas in the United States. These urban areas represent densely developed territories consisting of residential, commercial, and other nonresidential land uses. The dataset includes geospatial information such as longitude and latitude coordinates, area measurements (land and water), shape length, and shape area for each urban area.

Each urban area is identified by a unique 5-character numeric census code, which distinguishes between two types of urban areas: urbanized areas (UAs) with populations of 50,000 or more people, and urban clusters (UCs) with populations ranging between 2,500 to 50,000 people (except in the U.S. Virgin Islands and Guam where some UCs have populations greater than 50,000).

Other important attributes provided include the functional status code indicating the functional classification of the urban area along with its name description. The land area measurement gives insight into the extent of developed territory in square meters for each urban area.

Furthermore, this dataset contains MAF/TIGER feature class codes that provide additional information about specific features within an urban area. These codes help in identifying various characteristics or components within an urban footprint.

By utilizing this dataset researchers can analyze different aspects related to population density patterns across various urbanscapes within the United States. This includes studying demographic trends as well as exploring correlations between land usage patterns - whether residential or commercial - in relation to geographical location.

Overall, this dataset serves as a valuable resource for conducting detailed spatial analysis on a wide range of topics related to population distribution and development across diverse metropolitan areas throughout the United States

How to use the dataset

1. Understanding the Urban Area Types

The dataset categorizes urban areas into two types: Urbanized Areas (UAs) and Urban Clusters (UCs). UAs are densely developed territories with populations of 50,000 or more people. UCs are also densely developed territories but have populations ranging from at least 2,500 people to fewer than 50,000 people.

2. Familiarize Yourself with Key Attributes

The dataset includes various attributes that provide valuable information about each urban area:

• NAME10: The name of the urban area.
• NAMELSAD10: The legal/statistical area description of the urban area.
• UACE10: A unique 5-character numeric census code that identifies each urban area.
• FUNCSTAT10: The functional status code for the urban area.
• ALAND10: The land area of the urban area in square meters.
• AWATER10: The water area of the urbareaan in square meters.
• INTPTLAT10: The latitude coordinate of an interior point within the geographic extent of an individual block group building footprint.(Numeric) -**INTPTLON150 longitude coordinate of an interior point within markset tabulation block group или Tract_LP tificatlpublic Land Survey System™ (PLSS)-based apices location

Understanding these attributes will allow you to gain insights into each specific type.

3. Analyzing Land and Water Area

By focusing on ALAND10 and AWATER10, you can explore the land and water areas of each urban area. This information could be valuable for understanding urban sprawl, planning infrastructure projects, or conducting environmental studies.

4. Exploring Functional Status

FUNCSTAT10 provides information about the functional status of an urban area. This attribute can help you identify whether an area serves as a single principal city or represents part of a larger metropolitan region.

5. Utilizing the Geographic Coordinates

INTPTLAT10 and INTPTLON10 provide latitude and longitude coordinates for each urban area's interior point. You can leverage this data to plot locations on maps

Research Ideas

• Urban Planning Analysis: This dataset can be used for urban planning analysis, such as identifying the land area and water area of different urban areas. It can help city officials and planners understand the spatial distribution of urban areas, assess population density, and make informed decisions regarding infrastructure development and resource allocation.
• Market Research: The dataset can be utilized for market research purposes by identifying different types of urban areas (UAs or UCs) based on their po...

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

Graph and download economic data for Total Real Gross Domestic Product for United States Metropolitan Portion (RGMPUSMP) from 2001 to 2023 about metropolitan portion, real, industry, GDP, and USA.

A Difficult Development Area (DDA) for the Low Income Housing Tax Credit program is an area designated by the U.S. Department of Housing and Urban Development (HUD) with high construction, land, and utility costs relative to its Area Median Gross Income (AMGI). All designated DDAs in Metropolitan Statistical Areas (MSA) or Primary Metropolitan Statistical Areas (PMSA) may not contain more than 20% of the aggregate population of all MSAs/PMSAs, and all designated areas not in metropolitan areas may not contain more than 20% of the aggregate population of the non-metropolitan counties.

Not many studies have documented climate and air quality changes of settlements at early stages of development. This is because high quality climate and air quality records are deficient for the periods of the early 18th century to mid 20th century when many U.S. cities were formed and grew. Dramatic landscape change induces substantial local climate change during the incipient stage of development. Rapid growth along the urban fringe in Phoenix, coupled with a fine-grained climate monitoring system, provide a unique opportunity to study the climate impacts of urban development as it unfolds. Generally, heat islands form, particularly at night, in proportion to city population size and morphological characteristics. Drier air is produced by replacement of the countryside's moist landscapes with dry, hot urbanized surfaces. Wind is increased due to turbulence induced by the built-up urban fabric and its morphology; although, depending on spatial densities of buildings on the land, wind may also decrease. Air quality conditions are worsened due to increased city emissions and surface disturbances. Depending on the diversity of microclimates in pre-existing rural landscapes and the land-use mosaic in cities, the introduction of settlements over time and space can increase or decrease the variety of microclimates within and near urban regions. These differences in microclimatic conditions can influence variations in health, ecological, architectural, economic, energy and water resources, and quality-of-life conditions in the city. Therefore, studying microclimatic conditions which change in the urban fringe over time and space is at the core of urban ecological goals as part of LTER aims. In analyzing Phoenix and Baltimore long-term rural/urban weather and climate stations, Brazel et al. (In progress) have discovered that long-term (i.e., 100 years) temperature changes do not correlate with populations changes in a linear manner, but rather in a third-order nonlinear response fashion. This nonlinear temporal change is consistent with the theories in boundary layer climatology that describe and explain the leading edge transition and energy balance theory. This pattern of urban vs. rural temperature response has been demonstrated in relation to spatial range of city sizes (using population data) for 305 rural vs. urban climate stations in the U.S. Our recent work on the two urban LTER sites has shown that a similar climate response pattern also occurs over time for climate stations that were initially located in rural locations have been overrun bu the urban fringe and subsequent urbanization (e.g., stations in Baltimore, Mesa, Phoenix, and Tempe). Lack of substantial numbers of weather and climate stations in cities has previously precluded small-scale analyses of geographic variations of urban climate, and the links to land-use change processes. With the advent of automated weather and climate station networks, remote-sensing technology, land-use history, and the focus on urban ecology, researchers can now analyze local climate responses as a function of the details of land-use change. Therefore, the basic research question of this study is: How does urban climate change over time and space at the place of maximum disturbance on the urban fringe? Hypotheses 1. Based on the leading edge theory of boundary layer climate change, largest changes should occur during the period of peak development of the land when land is being rapidly transformed from open desert and agriculture to residential, commercial, and industrial uses. 2. One would expect to observe, on average and on a temporal basis (several years), nonlinear temperature and humidity alterations across the station network at varying levels of urban development. 3. Based on past research on urban climate, one would expect to see in areas of the urban fringe, rapid changes in temperature (increases at night particularly), humidity (decreases in areas from agriculture to urban; increases from desert to urban), and wind speed (increases due to urban heating). 4. Changes of the surface climate on the urban fringe are expected to be altered as a function of various energy, moisture, and momentum control parameters, such as albedo, surface moisture, aerodynamic surface roughness, and thermal admittance. These parameters relate directly to population and land-use change (Lougeay et al. 1996).

DATA DESCRIPTION:-

1- land_area : size in square miles

2-percent_city : percent of population in central city/cities

3-percent_senior : percent of population ≤ 65 years

4-physicians : number of professionally active physicians

5-hospital_beds : total number of hospital beds

6-graduates : percent of adults that finished high school

7-work_force : number of persons in work force in thousands

8-income : total income in 1976 in millions of dollars

9-crime_rate: Ratio of number of serious crimes by total population

10-region: geographic region according to US Census

We can see that the regions have 4 values, where:

1 = North-East

2 = North-Central

3 = South

4 = West

An ESRI Shapfile containing spatially generalized built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical settlement layers from the Historical settlement data compilation for the U.S. (HISDAC-US, Leyk & Uhl 2018). See Burghardt et al. (2022) for details on the data processing.

Additionally, there is a CSV file (HISDAC-US_patch_statistics.csv) containing the counts of built-up property records (BUPR), and -locations (BUPL), as well as total building indoor area (BUI) and built-up area (BUA) per CBSA, year, and patch, extraced from the HISDAC-US data (Uhl & Leyk 2018, Uhl et al. 2021). This CSV can be joined to the shapefile (column uid2) by concatenating the columns msaid_year_Id.

Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. Scientific data, 5(1), 1-14. DOI: https://doi.org/10.1038/sdata.2018.175 Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D. S., & Balk, D. (2021). Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States. Earth system science data, 13(1), 119-153. DOI: https://doi.org/10.5194/essd-13-119-2021

This statistics shows the top 20 fastest growing large-metropolitan areas in the United States between July 1st, 2022 and July 1st, 2023. The total population in the Wilmington, North Carolina, metropolitan area increased by 0.05 percent from 2022 to 2023.

Context

The emergence in the United States of large-scale “megaregions” centered on major metropolitan areas is a phenomenon often taken for granted in both scholarly studies and popular accounts of contemporary economic geography.

This dataset comes from a paper (Nelson & Rae, 2016. An Economic Geography of the United States: From Commutes to Megaregions) that uses a data set of more than 4,000,000 commuter flows as the basis for an empirical approach to the identification of such megaregions.

Content

This dataset consists of two files: one contains the commuting data, and one is a gazetteer describing the population and locations of the census tracts referred to by the commuting data. The fields Ofips and Dfips (FIPS codes for the originating and destination census tracts, respectively) in commute_data.csv refer to the GEOID field in census_tracts_2010.csv.

commute_data

This file contains information on over 4 million commute flows. It has the following fields:

• Ofips: the full FIPS code for the origin census tract of an individual flow line
• **Dfips **: the full FIPS code for the destination census tract of an individual flow line
• Ostfips: the FIPS code for the origin state of an individual flow line
• Octfips: the FIPS code for the origin county of an individual flow line
• Otrfips: the FIPS code for the destination census tract of an individual flow line
• Dstfips: the FIPS code for the destination state of an individual flow line
• Dctfips: the FIPS code for the destination county of an individual flow line
• Dtrfips: the FIPS code for the destination census tract of an individual flow line
• Flow: the total number of commuters associated with this individual point to point flow line (i.e. the total number of journeys to work)
• Moe: margin of error of the Flow value above
• LenKM: length of each flow line, in Kilometers
• ESTDIVMOE: the Flow value divided by the Margin of Error of the estimate

census_tracts_2010

This file contains the following fields, which represent information about different U.S. Census Tracts:

• USPS: United States Postal Service State Abbreviation
• GEOID: Geographic Identifier - fully concatenated geographic code (State FIPS and County FIPS)
• ANSICODE: American National Standards Institute code
• NAME: Name
• POP10: 2010 Census population count.
• HU10: 2010 Census housing unit count.
• ALAND: Land Area (square meters) - Created for statistical purposes only.
• AWATER: Water Area (square meters) - Created for statistical purposes only.
• ALAND_SQMI: Land Area (square miles) - Created for statistical purposes only.
• AWATER_SQMI: Water Area (square miles) - Created for statistical purposes only.
• INTPTLAT: Latitude (decimal degrees) First character is blank or "-" denoting North or South latitude respectively.
• INTPTLONG: Longitude (decimal degrees) First character is blank or "-" denoting East or West longitude respectively.

Acknowledgements

This dataset comes from the following article:

Nelson & Rae, 2016. An Economic Geography of the United States: From Commutes to Megaregions

The full dataset (in GIS shapefile format) can be found on figshare here

Population growth will result in a significant anthropogenic environmental change worldwide through increases in developed land (DL) consumption. DL consumption is an important environmental and socioeconomic process affecting humans and ecosystems. Attention has been given to DL modeling inside highly populated cities. However, modeling DL consumption should expand to non-metropolitan areas where arguably the environmental consequences are more significant. Here, we study all counties within the conterminous U.S. and based on satellite-derived product (National Land Cover Dataset 2001) we calculate the associated DL for each county. By using county population data from the 2000 census we present a comparative study on DL consumption and we propose a model linking population with expected DL consumption. Results indicate distinct geographic patterns of comparatively low and high consuming counties moving from east to west. We also demonstrate that the relationship of DL consumption with population is mostly linear, altering the notion that expected population growth will have lower DL consumption if added in counties with larger population. Added DL consumption is independent of a county’s starting population and only dependent on whether the county belongs to a Metropolitan Statistical Area (MSA). In the overlapping MSA and non-MSA population range there is also a constant DL efficiency gain of approximately 20km2 for a given population for MSA counties which suggests that transitioning from rural to urban counties has significantly higher benefits in lower populations. In addition, we analyze the socioeconomic composition of counties with extremely high or low DL consumption. High DL consumption counties have statistically lower Black/African American population, higher poverty rate and lower income per capita than average in both NMSA and MSA counties. Our analysis offers a baseline to investigate further land consumption strategies in anticipation of growing population pressures.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

The State Legislative District Summary File Supplement contains geographic identification codes that relate each 2000 Census block to pre-2010 Census state legislative districts. Both upper and lower chamber districts are identified. In addition, these block-level data contain variables on land area, water area, latitude, longitude, total population size, and number of housing units, as well as geographic identification variables for other levels of observation such as states, metropolitan statistical areas, urban areas, congressional districts, counties, county subdivisions, places, census tracts, block groups, and ZIP code tabulation areas. There is one data file for each state, the District of Columbia, and Puerto Rico which are bundled together in a single ZIP archive. A second ZIP archive contains the codebook and other documentation.