In 2023, about 7.43 million people were living in Arizona. This is an increase from the previous year, when 7.36 million people lived in the state. In 1960, the resident population of Arizona was about 1.3 million people.

This map of human habitation was developed, following a modification of Schumacher et al. (2000), by incorporating 2000 U.S Census Data and land ownership. The 2000 U.S. Census Block data and ownership map of the western United States were used to correct the population density for uninhabited public lands. All census blocks in the western United States were merged into one shapefile which was then clipped to contain only those areas found on private or indian reservation lands because human habitation on federal land is negligible. The area (ha) for each corrected polygon was calculated and the 2000 census block data table was joined to the shapefile. In a new field, population density (individuals/ha) corrected for public land in census blocks was calculated . SHAPEGRID in ARC/INFO was used to convert population density values to grid with 90m resolution.

Chart and table of population level and growth rate for the state of Arizona from 1900 to 2024.

File List Plots.csv (MD5: 1af7bca8fed39ea6ed83096a8a5f8a9d) Plot_corners.csv (MD5: 4453f1811df7edeeeca5ba1cabed6b02) Species.csv (MD5: 4748a23f710d2a825641d5bdf1af18d3) Seedling_counts.csv (MD5: ccb55f1a3388cf97c0631af5425f5b2b) Count1906.csv (MD5: e53119e0461f5946f76f76848e1b75c9) SMCover.csv (MD5: d95632355ff80d027c3f86112d9eabbc) SMDensity.csv (MD5: 9cf0072867f8f92ba51ee6210d74e3b7) Stake_info.csv (MD5: 12f70adb84f0800cd464f328b6f885b3) Photo_info.csv (MD5: f01aa5bb1dbbdbd2e608f86cee58ddd7) ZIP files: PlotLayers.zip (MD5: 000f21fd2115dd27a66f1059270fed68) PlantLayers.zip (MD5: feebd3c6951cb1448cfb8e9dfbcaaaeb) CsvFiles.zip (MD5: e4de222b212024ac23cad181dbeacf1a)
We provide four types of data files: (1) *.csv files are comma delimited files with non-spatial information; (2) *.shp are ArcGIS v10 shape files (ESRI) which hold spatially explicit information; (3) *.tif files are scans of original maps; available upon request; and (4) *.zip compressed compilation of *.shp. The following list describes each file: 1. Plots.csv: General information about Spalding-Shreve plots 2. Plot_corners.csv: Coordinates of plot corners 3. px_y_disturbance.shp: Layer depicting disturbance areas for plot “x” in year “y” 4. px_y_boundary.shp: Layer depicting plot boundary for plot “x” in year “y” 5. px_y_control.shp: Layer compiling control points used during census of plot “x” during year “y” 6. px_y_nodata.shp: Areas not surveyed in plot “x” and year “y” due to boundary a mismatch. 7. Plot_Layers.zip: Compressed compilation of files type 3 to 5. 8. px_y_trunks.shp: Layer compiling plant roots for plot “x” in year “y” 9. px_y_crowns.shp: Layer compiling outline of plant canopies in plot “x” during year “y” 10. Plant_Layers.zip: Compressed compilation of file types 7 and 8 11. Species.csv: Summary of plant codes and nomenclature of species detected 12. Seedling_counts.csv: Summary of seedling counts during 1978 census 13. Count1906.csv: Transcription of Spalding’s unpublished notes (1906) 14. SMCover.csv: Summary of plant cover per species, plot, and year 15. SMDensity.csv: Summary of plant densities per species, plot, and year. 16. O_y_Px.tif: Compressed version of scans of original maps for plot “x” in year “y”; available upon request 17. Stake_info.csv: General information on photographic stations 18. Photo_info.csv: Specific information of repeat photographs taken of the Spalding-Shreve plots 19. CsvFiles.zip: Compressed compilation of all *.csv files described above Description This data set constitutes all information associated with the Spalding-Shreve permanent vegetation plots from 1906 through 2012, which is the longest-running plant monitoring program in the world. The program consists of detailed maps of all Sonoran Desert perennial plants in 30 permanent plots located on Tumamoc Hill, near Tucson, Arizona, USA. Most of these plots are 10 m × 10 m quadrats that were established by Volney Spalding and Forrest Shreve between 1906 and 1928. Analyses derived from this data have been pivotal in testing early theories on plant community succession, plant life history traits, plant longevity, and population dynamics. One of the major contributions of this data set is the species-specific demographic traits that derived from estimating individual plant trajectories for more than 106 years. Further use of this data might shed light on spatially explicit population and community dynamics, as well as long-term changes attributable to global change. Data presented here consists of digital versions of original maps created between 1906 and 1984 and digital data from recent censuses between 1993 and 2012. Attributes associated with these maps include location and coverage of all shrubs, and, in some cases, plant height. In addition, we present plot-specific summaries of plant cover and density for each census year and all other information collected, including seedling counts, grass coverage, and annual species enumerations. We reference the repeat photography of these plots, which began with original photography in 1906; these images are stored at the Desert Laboratory Collection of Repeat Photography in Tucson. Initial data collection consisted of grid-mapping the plots manually on graph paper; starting in 1993, Total Stations (which allow a direct digitalization, and more accurate mapping) were used to survey root crowns and canopies.

      Key words: Arizona; community dynamics; longevity; long-term monitoring; permanent plots; population dynamics; Sonoran Desert; vegetation change.

The 2023 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 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 and beyond, 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.

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Santa Cruz County, AZ, is 35.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Santa Cruz County, AZ, is 31.7.
• Total dependency ratio for Santa Cruz County, AZ is 67.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Santa Cruz County, AZ is 3.2.

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Santa Cruz County Population by Age. You can refer the same here

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The cartographic boundary files include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The generalized boundaries of most incorporated places in this file are based on those as of January 1, 2020, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CDPs based on those delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Azerbaijan Population Density: People per Square Km data was reported at 122.707 Person/sq km in 2022. This records an increase from the previous number of 122.659 Person/sq km for 2021. Azerbaijan Population Density: People per Square Km data is updated yearly, averaging 103.853 Person/sq km from Dec 1992 (Median) to 2022, with 31 observations. The data reached an all-time high of 122.707 Person/sq km in 2022 and a record low of 88.708 Person/sq km in 1992. Azerbaijan Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Azerbaijan – Table AZ.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Phoenix, AZ, is 28.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Phoenix, AZ, is 17.4.
• Total dependency ratio for Phoenix, AZ is 46.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Phoenix, AZ is 5.8.

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Phoenix Population by Age. You can refer the same here

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Central Arizona - Phoenix Urban LTER (CAP) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

These data provide a spatial representation of the population change 1980 - 2000. Map Shows the census tracts that have experienced a doubling of population between 1980 and 1990 and between 1990 and 2000 in the central Arizona-Phoenix area.

This map viewer presents select topics from Census 2010 and the most recent American Community Survey (ACS) for Arizona. ACS data in this viewer is updated on an annual basis as new 5-year estimates are released.

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Star Valley, AZ, is 2.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Star Valley, AZ, is 121.3.
• Total dependency ratio for Star Valley, AZ is 123.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Star Valley, AZ is 0.8.

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Star Valley Population by Age. You can refer the same here

This layer is being made accessible on this platform as part of a larger collaborative project under development by Arizona Water Company, University of Arizona Water Resources Research Center, Babbitt Center for Land and Water Policy, and Center for Geospatial Solutions. This visualization for Pinal County expresses 2022 Assured and Adequate Water data and was altered to display this information with the boundaries of Pinal County, Arizona.The main sources of data present in this map were taken from the following locations:Arizona Department of Water Resources (2022)https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/aaws-issued-determination/explore?location=34.152689%2C-112.003340%2C7.24University of Arizona (2008)https://repository.arizona.edu/handle/10150/188734

This geodatabase contains statistical geographies from the U.S. Census Bureau and have had selected attributes from the 2018-2022 American Community Survey (ACS) 5-Year Estimates attached. A list of fields contained in each feature class' attribute table is also included in this geodatabase. ACS data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate is represented through the use of a margin of error (MOE). In addition to sampling variability, the ACS estimates are subject to nonsampling error. The MOE and effect of nonsampling error is not represented in these tables. Supporting documentation on subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section. Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. The MOE for individual data elements can be found on the Census website.Note: Although the ACS produces population, demographic and housing unit estimates, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities and towns.

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

Population Estimate, Total, Not Hispanic or Latino, Black or African American Alone (5-year estimate) in Greenlee County, AZ was 139.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Population Estimate, Total, Not Hispanic or Latino, Black or African American Alone (5-year estimate) in Greenlee County, AZ reached a record high of 203.00000 in January of 2018 and a record low of 9.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Not Hispanic or Latino, Black or African American Alone (5-year estimate) in Greenlee County, AZ - last updated from the United States Federal Reserve on March of 2025.

Plan submitted by: WBeard on 10/21/2021 USER DESCRIPTION: The areas of Arizona City/Eloy and South Casa Grande have a greater community of interest being in the same district as Marana (CD 6 on map) than with Yuma. The population balance is created by adjusting the amount of the City of Tucson within CD 7 on this map. USER PLAN OBJECTIVE: The area east of Tohono reservation in Pinal County (ELoy/S Casa Grande/AZ City) have more in common with Marana than with Yuma. Population balanced in City of Tucson. Maintains more communities of interest and doesn't affect VRA requirements.

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Yuma County, AZ, is 34.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Yuma County, AZ, is 34.4.
• Total dependency ratio for Yuma County, AZ is 69.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Yuma County, AZ is 2.9.

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Yuma County Population by Age. You can refer the same here

Plan submitted by: redistrictadmin on 10/15/2021 USER DESCRIPTION: In this version based off the newly adopted LD Test Map Version 2.0, Flagstaff is moved from District 7 to District 6 in its entirety only as a demonstration. District 7 also has moved east to Highway 191, including Springerville, Eager and St. John’s. Including Yavapai County in District 7 as well as all eastern parts currently included in the district not meeting the allowable population deviation; without Yavapai it is 113,000 short of target. The only option to rectify population is to include in District 7: Maricopa, or Gila and Pinal. In this version, Flagstaff cannot be kept whole in a single district and allow us to achieve the population balancing that is needed. For more information on the methodology used to create these boundaries, please visit: https://redistricting-irc-az.hub.arcgis.com/pages/draft-maps USER PLAN OBJECTIVE: N/A