In 2022, the real gross domestic product (GDP) of the South Florida metropolitan area amounted to 409.48 billion U.S. dollars. This was an increase from the previous year when the real GDP of the area came to 390.53 billion U.S. dollars.

Graph and download economic data for Total Gross Domestic Product for Miami-Fort Lauderdale-West Palm Beach, FL (MSA) (NGMP33100) from 2001 to 2023 about Miami, FL, industry, GDP, and USA.

About this dataset

Context

The dataset tabulates the data for the South Palm Beach, FL population pyramid, which represents the South Palm Beach 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 South Palm Beach, FL, is 8.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for South Palm Beach, FL, is 164.5.
• Total dependency ratio for South Palm Beach, FL is 172.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for South Palm Beach, FL is 0.6.

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

In 2023, about 22.61 million people lived in Florida. This is an increase from the previous year, when about 22.24 people lived in the state. In 1960, the resident population of Florida stood at about 4.95 million people.

About this dataset

Context

The dataset tabulates the South Daytona 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 South Daytona 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 South Daytona was 13,781, a 0.90% increase year-by-year from 2022. Previously, in 2022, South Daytona population was 13,658, an increase of 2.51% compared to a population of 13,323 in 2021. Over the last 20 plus years, between 2000 and 2023, population of South Daytona increased by 202. In this period, the peak population was 13,797 in the year 2007. 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 South Daytona is shown in this column.
• Year on Year Change: This column displays the change in South Daytona 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 South Daytona Population by Year. You can refer the same here

These data summarize survival and plant size (height, basal diameter) for south Florida slash pine (Pinus elliottii var. densa) in four seasonal ponds at Archbold Biological Station from 1992-2001. Plants were seedlings, saplings, or small trees. The seasonal ponds were favorable for growth (0.1-0.6 m in height per year) and annual survival (usually over 80%) in the absence of fire or major flooding events. Flooding episodes during most of the study ranged from 3-11 months each year. Mortality increased with flooding intensity and decreased with plant size. Growth rates were not affected by flooding. Near the end of the study, two fires killed 72% of these pines (low intensity, prescribed) and 100% of these pines (intense wildfire). The study suggests that most seasonal ponds do not support large south Florida slash pine individuals despite generally favorable conditions, because of periodic flooding and fire.

The federally threatened American crocodile (Crocodylus acutus) is a flagship species and ecological indicator of hydrologic restoration in the Florida Everglades. we conducted a long-term capture-recapture study on the South Florida population of American crocodiles from 1978 to 2015 to evaluate the effects of restoration efforts to restore historic hydrologic conditions. The study produced 10,040 crocodile capture events of 9,865 individuals and more than 90% of captures were of hatchlings. Body condition and growth rates of crocodiles were highly age-structured with younger crocodiles presenting with the poorest body condition and highest growth rates. Average body condition was 2.14±0.35 SD throughout South Florida. Crocodiles exposed to hypersaline conditions (> 40 psu) during the dry season maintained lower body condition scores and reduced growth rate by 13% after one year, by 24% after five years, and by 29% after ten years. Estimated hatchling survival for the South Florida population was 25% increasing with ontogeny and reaching near 90% survival at year six. Hatchling survival was 34% in NE Florida Bay relative to a 69% hatchling survival at Crocodile Lake National Wildlife Refuge and 53% in Flamingo area of Everglades National Park. Hypersaline conditions affected survival, growth and body condition and was most pronounced in NE Florida Bay, where the hydrologic conditions have been most disturbed. The American crocodile, a long-lived animal, with relatively slow growth rate provides an excellent model system to measure the effects of altered hydropatterns in the Everglades landscape. Restoration efforts targeted toward returning freshwater flow and salinity targets of < 20 psu to reflect a more historic state in NE Florida Bay will ensure improved health of the Everglades and illustrates the need for continued long-term monitoring projects to assess system-wide success.

About this dataset

Context

The dataset tabulates the data for the South Daytona, FL population pyramid, which represents the South Daytona 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 South Daytona, FL, is 21.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for South Daytona, FL, is 35.2.
• Total dependency ratio for South Daytona, FL is 56.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for South Daytona, FL is 2.8.

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

Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.

In this time period, the Hispanic population of North Dakota increased by 414 percent, while the Hispanic population of South Dakota increased by 360 percent, the two highest growths in the United States. In 2023, California, Texas, and Florida registered the largest Hispanic or Latino population in the U.S.

In 2023, about 12.4 percent of the population in Florida was between 25 and 34 years old. In that same year, a further 11.9 percent of Floridians were between the ages of 65 and 74 years old.

About this dataset

Context

The dataset tabulates the South Pasadena population by year. The dataset can be utilized to understand the population trend of South Pasadena.

Content

The dataset constitues the following datasets

• South Pasadena, FL Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

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/.

This data package is comprised of three datasets all pertaining to two dominant palmetto species, Serenoa repens and Sabal etonia, at Archbold Biological Station in south-central Florida. The first dataset, palmetto_data, contains survival and growth data across multiple years, habitats and experimental treatments. The second dataset, seedlings_data, follows the fate of marked putative palmetto seedlings in the field to assess survivorship and growth. The final dataset, harvested_palmetto_data, contains size data and estimated dry mass (biomass in grams) of 33 destructively harvested palmetto plants (17 S. repens and 16 S. etonia) of varying sizes and across habitats. Thirty-two of these were used to calculate estimated biomass, using regression equations, for palmettos sampled in the palmetto_data. Below we summarize experimental setup and data collected for each dataset. Palmetto data Demographic data were collected as three separate components. The first component compared growth among habitats. Starting in 1981, equal numbers of both palmetto species were marked across scrubby flatwoods (oak scrub) and flatwoods habitats (3 sites per habitat) for a total of 240 marked plants. These habitats had not burned within the last decade, but historically had experienced a natural fire return interval of 5 - 20 years prior to this studies initiation. The second component added an additional 400 palmettos (200 of each species), which were marked in sand pine scrub (n = 200) in 1985 and sandhill habitat (n = 200) in 1989 on Archbold's Red Hill. At the time of this project's initiation, all Red Hill management units were last burned in 1927 and were considered long unburned. Part of Archbold's management plan included restoring fire into some management units while leaving others long unburned to serve as reference units. Therefore, for our second component, we were able to create a 2x2 factorial design using habitat types on Red Hill and fire management as factors, with 100 palmettos in each category (50 of each species). The third component involved an experiment to examine the factorial effects of clipping and fertilizing on palmetto flowering. We marked 300 palmettos (150 of each species), all in sand pine scrub habitat on Red Hill, and used the 100 palmettos marked in 1985 as controls. Annual data measures included height, canopy length and width (all in cm), number of new and green leaves and flowering scapes. Data were collected continuously (not for all variables or sites) from 1981 through 1997 then again in 2001 and 2017. Data collection is ongoing at 5-year intervals. Data on the 100 plants in the experimental sandhill on Red Hill were not collected in 2017 due to the removal of marked stakes from roller chopping of the site as part of more recent sandhill restoration efforts. A subset of the plants in the clipping and fertilizing experiment were lost in 2013 when a plow line was established to stop the spread of a wildfire. The locations of all remaining plants were taken in 2017 using a Trimble GPS unit and are included as a separate data file (palmetto_location_data) and shapefile (palmetto_shape). Seedling data In January 1989, we marked 100 putative seedlings in flatwoods habitats and 87 in scrubby flatwoods habitats. Putative seedlings typically cannot be identified using morphology as either S. repens or S. etonia so sample sizes of each are unknown. Annual data recorded included survival, standing height (cm) and maximum crown diameter (cm). In 1991, we started measuring basal stem diameter (cm) with calipers. During annual visits, we noted if the species could be identified as S. repens or S. etonia. Data were collected continuously starting in 1989 through 1997, then again in 2001 and 2008. Data collection is not ongoing for this dataset. Harvested Palmetto data Thirty-three palmettos, 17 S. repens and 16 S. etonia, were destructively harvested at three different sites, from two habitats (scrubby flatwoods and sand pine scrub) in 1985. Basic size measures as taken for palmetto demography data were recorded including height, canopy length and width (all in cm) and the number of green leaves. Additional data measures were recorded on the largest leaf blade including maximum length and width of the palmetto leaf and petiole length and width. Finally, basal diameter at the ground level was recorded. Only 32 palmettos were used to develop biomass regressions (17 S. repens and 15 S. etonia). Biomass is the estimated dry mass (g) of each harvested palmetto. Fresh palmettos were divided into leaf and stem (both above- and below-ground), but roots were not harvested since they grow to depths of several meters, making recovery of all root tissues virtually impossible for fresh-mass determination. Subsamples of fresh mass were oven dried at 80C to constant mass for estimation of dry mass equivalent, which in turn was used to estimate the dry mass of the harvested palmettos.

In Florida, the mining, quarrying, and oil and gas extraction industry added around 990 million chained 2017 U.S. dollars of value to the state's gross domestic product (GDP) in 2023. The total value added to Florida's GDP by all industries was about 1.29 trillion U.S. dollars that year.

In 2023, the real gross domestic product (GDP) of Florida was about 1.28 trillion U.S. dollars. This is an increase from the previous year, when the state's GDP stood at around 1.22 trillion U.S. dollars.

The state of Florida experienced the most significant GDP growth in 2023, growing by 9.8 percent from 2022. Washington, South Carolina, and Nebraska also experienced high amounts of growth in the same period. Wyoming saw the smallest increase, at only two percent.

Significant population declines in Acropora cervicornis and A. palmata began in the 1970s and now exceed over 90%. The losses were caused by a combination of coral disease and bleaching, with possible contributions from other stressors, including pollution and predation. Reproduction in the wild by fragment regeneration and sexual recruitment is inadequate to offset population declines. Starting in 2007, the Coral Restoration Foundation™ evaluated the feasibility of outplanting A. cervicornis colonies to reefs in the Florida Keys to restore populations at sites where the species was previously abundant. Reported here are the results of 20 coral outplanting projects with each project defined as a cohort of colonies outplanted at the same time and location. Photogrammetric analysis and in situ monitoring (2007 to 2015) measured survivorship, growth, and condition of 2419 colonies. Survivorship was initially high but generally decreased after two years. Survivorship among projects based on colony counts ranged from 4% to 89% for seven cohorts monitored at least five years. Weibull survival models were used to estimate survivorship beyond the duration of the projects and ranged from approximately 0% to over 35% after five years and 0% to 10% after seven years. Growth rate averaged 10 cm/year during the first two years then plateaued in subsequent years. After four years, approximately one-third of surviving colonies were ≥ 50 cm in maximum diameter. Projects used three to sixteen different genotypes and significant differences did not occur in survivorship, condition, or growth. Restoration times for three reefs were calculated based on NOAA Recovery Plan (NRP) metrics (colony abundance and size) and the findings from projects reported here. Results support NRP conclusions that reducing stressors is required before significant population growth and recovery will occur. Until then, outplanting protects against local extinction and helps to maintain genetic diversity in the wild.

Methods Survivorship (the percentage of colonies with any living tissue in a cohort) and condition (percentage of living tissue to the nearest 5%) [76] were obtained from the photographs using CANVAS software [77] and in situ using SCUBA. Weibull survival analysis models (using the statistical software package JMP, version 12 SAS) were used to project survivorship beyond the length of the studies [78]. Percent live tissue was analyzed in addition to survivorship because the survivorship metric is binary (dead =0; alive=1), while partial mortality is a continuous variable (0% to 100%) that impacts survivorship.

The maximum skeletal diameter of colonies was measured using scale references in the photographs that included identifier tags (indicating genotype) of known sizes or PVC bars, or by direct measurements underwater using SCUBA. Growth was estimated using Gompertz growth functions. Due to gaps in the photographic record and reduced sample sizes, colonies that survived four years or longer were combined into a single group. Size measurements were not normally distributed, based on Wilks tests; therefore, log transformation was performed to better approximate a normal distribution. Statistical analyses using generalized linear models were performed using log-transformed data in R [79], including analyses to determine whether or not there were genotype effects on survivorship, condition and growth.

The time (years) and effort (the number of outplanted colonies) required to restore Carysfort Reef, Molasses Reef, and Conch Reef (Fig 1) were estimated using results from this study and metrics in the NRP. Specifically, reef areas delineated by GIS were divided by survivorship estimates after four years for cohorts that started with 1050 colonies. Based on the NRP abundance and size metrics, each surviving colony that reaches ≥ 50 cm maximum diameter restores 1 m2 of the reef. Two depth ranges were used to calculate restoration areas for the three reefs: 5 to 10 m approximates the historical distribution of the species in the Caribbean and Florida [2, 80] and 5 to 20 m water depth as identified in the NRP. The three reefs are management zones in the Florida Keys National Marine Sanctuary, with boundaries that constrained the area estimates. Areas were delineated in GIS using a two-step geoprocessing intersect procedure. First, the Florida Keys 100 m grid cell habitat layer [17, 81] was clipped using the FKNMS management-zone layer. Then, the resulting habitats-within-zones layer was overlaid with the South Florida water depth layer. The final clipped and intersected layer contained polygons annotated with zone, habitat, and depth information.

The natural forest ecosystems of South Florida, USA, support a high biodiversity of plant and animal species and provide valuable ecosystem services. However, these ecosystems remain poorly represented in global studies, primarily due to a paucity of standardized data. Here, we present previously unpublished data from 332 censuses of 54 permanent 1-hectare tree inventory plots in the Racoon Point area of Big Cypress National Preserve, Florida, USA, including a total of nearly 100,000 measurements (diameter or height) of >17,000 individual living trees and palms (with additional measurements of nearly 6,000 dead pine snags) collected sporadically over a 19-year period (1993 – 2012). These data, which were originally collected as part of a project to investigate tree responses to different experimental burning regimes, provide unique insight into the diversity, composition, structure, and dynamics of South Florida’s unique and endangered pine forest ecosystems. Data files include the species identity, size (dbh = diameter at breast height), and location of all trees ≥5 cm dbh in 54 individual tree plots. Additional data are provided about heights of palm trees, and the location and burn history of each plot. Users are encouraged to cite this dataset. Methods The Raccoon Point area in eastern Big Cypress National Preserve is the largest remaining area of mature south Florida slash pine (P. elliottii var. densa) forest, having escaped the logging activities of 1900-1960 (Patterson & Robertson, 1981). The pinelands consist of a mosaic of small, slightly elevated and dryer "islands" isolated among wetland swamp areas of dwarf cypress prairies and cypress domes. The isolation and inaccessibility of the Raccoon Point pine islands provided de facto protection against logging. However, the federal government did not acquire the mineral rights in Big Cypress when the preserve was created in 1974 and in 1977 Exxon Company, USA, constructed an all-weather private access road running north from U.S. Highway 41 to multiple active oil well pads in the Raccoon Point oil field in BCNP. The study area surrounds the Raccoon Point oil field. It is divided into 18 experimental burn units. Each burn unit includes at least 50 ha of pine forest. Within each unit, three permanent 1-ha tree plots were established and repeatedly censused (Snyder & Belles, 2000) starting in the early- to mid-1990s. The experimental burn treatments consisted of burning at three seasons (spring, or early wet season [May-June], when the largest human-caused or lightning-caused wildfires occur; summer, or mid wet season [July-August], when there are frequent, but generally smaller, lightning-ignited fires; and winter, or mid dry season [December 15- February 15], when conditions are frequently favorable for prescribed burning) and two burn frequencies (every 3 years and every 6 years) for a total of six treatment combinations. Each treatment was replicated three times, with one replicate originally scheduled to burn per year for three years. Actual burn treatments did not always follow the prescribed schedule due to abnormally wet conditions, state-wide burning bans brought on by drought conditions, or the occurrence of natural unplanned fires. All pinelands in the Raccoon Point study area were burned twice by the National Park Service as initializing burns before the start of this study (January 3 - February 9, 1990, and February 27 - April 4, 1994). A table of burn dates is included in the data files. The permanent 1-hectare tree plots were all established between 1993 and 1995 (Snyder & Belles, 2000). Within each burn unit, the tree plot locations were chosen by overlaying a grid on enlarged aerial photographs and randomly choosing points that fell within areas that appeared to be pineland. If it was subsequently determined during visits to the field that the area was not large enough to contain the 1-ha plot, or if more than 10% of the area was dominated by cypress trees, another point was chosen. The final geographic coordinates of all 54 tree plots are included in the data files (coordinates indicate approximate plot centers). Plots were oriented in a north-south direction so that the permanent markers would be easier to locate in the future. To set up each 1.0 ha tree plot, an east-west 100 m line was first established by driving 60 or 90 cm pieces of 1.3 cm diameter steel reinforcement bar (i.e., rebar) into the ground at 0, 25, 75, and 100 m. Two north-south 100 m lines were then established from the 25 and 75 m rebars using a double right-angle prism to place 2 additional rebar stakes 50 m apart. The two remaining plot corners were put into place last. A total of 10 rebar stakes were placed in each plot. In most cases a rotary hammer demolition drill was needed to secure the rebar in the very hard Tamiami limestone that is close to the surface throughout BCNP. Round aluminum tags with unit and plot numbers were wired to the four corner stakes of each tree plot. Each tree plot was then divided into quarters to make tree mapping more efficient. The quarters were identified by compass orientation: NW, NE, SW, and SE. Each quarter was further subdivided into halves. A 50 m tape was laid out along the center line that separated these two halves. A right-angle prism was used to locate the position of each tree along the center line and a second tape was used to measure the distance from the center line to the tree. The field distances were subsequently adjusted to a single plot-level XY cartesian coordinate system (Snyder & Belles, 2000). Within each plot, all tree stems with a diameter at breast height (dbh) ≥ 5.0 cm and palms with a height to the apical bud ≥ 1.4 m were tagged with a round, 3.2 cm diameter, pre-numbered aluminum tag using a 5.4 cm aluminum nail. The tags were placed on live trees at breast height (approximately 1.4 m) and the diameter was measured just above the nail. Tags were placed in the most secure location on palm trees (i.e., a smooth area of the stem without remnant leaf bases). In addition to live trees, dead pine snags were also tagged and measured (Snyder & Belles, 2000). Data collected on individuals included tag number, XY cartesian coordinates within the plot, dbh, and species identity. For palms, the height to the apical bud was recorded rather than dbh since palm diameters do not increase with age. The heights of palms and some trees were measured using a telescoping pole up to a maximum of ~6 meters; taller trees were not measured (with the exception of some uprooted, but still living, trees that could be measured with measuring tapes). Tree plots were recensused between 3 and 10 times each, with the last censuses completed in 2012 (the last censuses for most plots were prior to 2003). During the plot recensuses, it was determined whether trees were living or had died since the previous census, and the dbhs of all living individuals were remeasured (along with dead pine snags). Additional notes were taken on features such as basal fire scars on pine trees. Tags were replaced as necessary. In addition, all new recruits with dbh ≥ 5.0 cm were measured and tagged. Attempts were made to census plots immediately before and after prescribed burns. In 2024, the original tree census datasets were cleaned and reformatted to facilitate data sharing and statistical analyses. Data cleaning consisted primarily of correcting typographic and data entry errors (e.g., remove leading and trialing blank spaces, etc.). In addition, we screened the dbh data for outlier values; if any value appeared to be erroneous based on the other measurements of the same individual tree, we adjusted it accordingly (in the vast majority of these cases the incorrect values had been recorded in the wrong unit - i.e., 10× too small or too large - so the required corrections were obvious). Notable to the reformatting, since the dbhs of palms were not measured or recorded in the censuses, they were set to 35 cm and 10 cm for Sabal palmetto and Serenoa repens, which are the approximate mean dbh’s for these species, respectively (Jones 1995). Also, all cypress trees were reassigned to Taxodium ascendens in accord with updated nomenclature. While the censuses did measure dead pine snags, the dbhs for all dead trees were set to “NA” in the reformatted data files to minimize the risk of including dead trees in aboveground biomass or growth estimates. The original dbh values for all stems are included. REFERENCES Black, D. W., & Black, S. (1980). Plants of Big Cypress National Preserve: a preliminary checklist of vascular plants: US National Park Service, South Florida Research Center, Everglades National Park. Duever, M. J. (2005). Big Cypress regional ecosystem conceptual ecological model. Wetlands, 25(4), 843-853. Gunderson, L. H., & Loope, L. L. (1982). An Inventory of the Plant Communities in the Levee-28 Tieback Area, Big Cypress National Preserve: National Park Service, South Florida Research Center, Everglades National Park. Jones, D.L. (1995). Palms throughout the World. Smithsonian Institution Press, Washington DC. Muss, J. D., Austin, D. F., & Snyder, J. R. (2003). Plants of the Big Cypress National Preserve, Florida. Journal of the Torrey Botanical Society, 119-142. Patterson, G. A., & Robertson, W. B. (1981). Distribution and habitat of the red-cockaded woodpecker in Big Cypress National Preserve: National Park Service, South Florida Research Center, Everglades National Park. Snyder, J. R., & Belles, H. (2000). Long-term study of fire season and frequency in pine forest and associated cypress wetlands, Big Cypress National Preserve: project description and preliminary data. US Geological Survey, Big Cypress National Preserve Field Station, Ochopee, Florida. Snyder, J. R. (1991). Fire regimes in subtropical south Florida. Proceedings of the Tall Timbers Fire Ecology Conference 17, 303-319.

A dataset listing Florida counties by population for 2024.

About this dataset

Context

The dataset tabulates the data for the Sewall''S Point, FL population pyramid, which represents the Sewall'S Point 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 Sewall''S Point, FL, is 16.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Sewall''S Point, FL, is 95.0.
• Total dependency ratio for Sewall''S Point, FL is 111.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Sewall''S Point, FL is 1.1.

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