About this dataset

Context

The dataset tabulates the Speed 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 Speed 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 Speed was 65, a 0% decrease year-by-year from 2022. Previously, in 2022, Speed population was 65, an increase of 3.17% compared to a population of 63 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Speed decreased by 4. In this period, the peak population was 80 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

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 Speed is shown in this column.
• Year on Year Change: This column displays the change in Speed 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 Speed Population by Year. You can refer the same here

This statistic illustrates the share of population covered by broadband lines in the Italian province of Siena in 2018, broken down by speed. According to data, the share of population covered by broadband lines with speed included in the range from * Mbit/s to ** Mbit/s reached ***** percent.

About this dataset

Context

The dataset tabulates the Speed population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Speed. The dataset can be utilized to understand the population distribution of Speed by age. For example, using this dataset, we can identify the largest age group in Speed.

Key observations

The largest age group in Speed, KS was for the group of age 60 to 64 years years with a population of 20 (86.96%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Speed, KS was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

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 in consideration
• Population: The population for the specific age group in the Speed is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Speed total population. 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 Speed Population by Age. You can refer the same here

This statistic illustrates the share of population covered by broadband lines in the Italian region of Abruzzo in 2018, broken down by speed. According to data, the share of population covered by broadband lines with speed included in the range from * Mbit/s to ** Mbit/s reached ***** percent.

Dataset

This dataset was created by Abhigyan Basak

Contents

Population Exposed to Wind Speed Zones

This statistic illustrates the share of population covered by broadband lines in the Italian region of Lombardy in 2018, broken down by speed. According to data, the share of population covered by broadband lines with speed included in the range from * Mbit/s to ** Mbit/s reached 38.68 percent.

This statistic illustrates the share of population covered by broadband lines in the Italian region of Calabria in 2018, broken down by speed. According to data, the share of population covered by broadband lines with speed included in the range from * Mbit/s to ** Mbit/s reached ***** percent.

The speed of evolution on structured populations is crucial for biological and social systems. The likelihood of invasion is key for evolutionary stability, but it makes little sense if it takes long. It is far from known what population structure slows down evolution. We investigate the absorption time of a single neutral mutant for all the 112 non-isomorphic undirected graphs of size 6. We find that about three-quarters of the graphs have an absorption time close to that of the complete graph, less than one-third are accelerators, and more than two-thirds are decelerators. Surprisingly, determining whether a graph has a long absorption time is too complicated to be captured by the joint degree distribution. Via the largest sojourn time, we find that echo-chamber-like graphs, which consist of two homogeneous graphs connected by few sparse links, are likely to slow down absorption. These results are robust for large graphs, mutation patterns as well as evolutionary processes. This work serves as a benchmark for timing evolution with complex interactions and fosters the understanding of polarization in opinion formation.

These data were compiled here to fit various versions of Bayesian population models and compare their performance, primarily the time required to make inferences using different softwares and versions of code. The humpback chub data were collected by US Geological Survey and US Fish and Wildlife service in the Colorado and Little Colorado Rivers from April 2009 to October 2017. Adult fish were captured using hoop nets and electro-fishing, measured for total length and given individual marks using passive integrated transponders that were scanned when fish were recaptured. The other three datasets were collected by US Forest Service. Owl data for the N-occupancy model was collected between 1990 and 2015. Owl data for the two-species example was collected between 1990 and 2011. Both owl data sets were collected in a ~1000 km2 area in the Roseburg District of the Bureau of Land Management in western Oregon, USA. Owl vocalizations (vocal lures) were used to detect barred owl or spotte ...

As populations spread into new territory, environmental heterogeneities can shape the population front and genetic composition. We focus here on the effects of an important building block of heterogeneous environments, isolated obstacles. With a combination of experiments, theory, and simulation, we show how isolated obstacles both create long-lived distortions of the front shape and amplify the effect of genetic drift. A system of bacteriophage T7 spreading on a spatially heterogeneous Escherichia coli lawn serves as an experimental model system to study population expansions. Using an inkjet printer, we create well-defined replicates of the lawn and quantitatively study the population expansion of phage T7. The transient perturbations of the population front found in the experiments are well described by a model in which the front moves with constant speed. Independent of the precise details of the expansion, we show that obstacles create a kink in the front that persists over large distances and is insensitive to the details of the obstacle’s shape. The small deviations between experimental findings and the predictions of the constant speed model can be understood with a more general reaction-diffusion model, which reduces to the constant speed model when the obstacle size is large compared to the front width. Using this framework, we demonstrate that frontier genotypes just grazing the side of an isolated obstacle increase in abundance, a phenomenon we call ‘geometry-enhanced genetic drift’, complementary to the founder effect associated with spatial bottlenecks. Bacterial range expansions around nutrient-poor barriers and stochastic simulations confirm this prediction. The effect of the obstacle on the genealogy of individuals at the front is characterized by simulations and rationalized using the constant speed model. Lastly, we consider the effect of two obstacles on front shape and genetic composition of the population illuminating the effects expected from complex environments with many obstacles.

Population Exposure Wind Speed Zones Matthew-16

Dataset

This dataset was created by Amit kumar

Released under Apache 2.0

Contents

Speed setting table for calculating cost distance.

This statistic illustrates the share of population covered by broadband lines in the Italian region of Umbria in 2018, broken down by speed. According to data, the share of population covered by broadband lines with speed included in the range from * Mbit/s to ** Mbit/s reached ***** percent.

See "01_ASDN_readme.txt" (under "Download Data" tab) for data author and contact information. Field data on shorebird ecology and environmental conditions were collected from 1993-2014 at 16 field sites in Alaska, Canada, and Russia. Data were not collected in every year at all sites. Studies of the population ecology of these birds included nest-monitoring to determine timing of reproduction and reproductive success; live capture of birds to collect blood samples, feathers, and fecal samples for investigations of population structure and pathogens; banding of birds to determine annual survival rates; resighting of color-banded birds to determine space use and site fidelity; and use of light-sensitive geolocators to investigate migratory movements. Data on climatic conditions, prey abundance, and predators were also collected. Environmental data included weather stations that recorded daily climatic conditions, surveys of seasonal snowmelt, weekly sampling of terrestrial and aquatic invertebrates that are prey of shorebirds, live trapping of small mammals (alternate prey for shorebird predators), and daily counts of potential predators (jaegers, falcons, foxes). Detailed field methods for each year are available in the ASDN_protocol_201X.pdf files. All research was conducted under permits from relevant federal, state and university authorities. Potential users of these data should first contact the relevant data author(s), listed below. This will enable coordination in terms of updates/corrections to the data and ongoing analyses. Key analyses of the data are in progress and will be included in the theses and dissertations of graduate students who collected these field data. Please acknowledge this dataset and the authors in any analysis, publication, presentation, or other output that uses these data. If you use the full dataset, we suggest you cite it as: Lanctot, RB, SC Brown, and BK Sandercock. 2016. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. If you use data from only one or a few sites, we suggest you cite data for each site as per this example, using the corresponding site PIs as the authors: Lanctot, RB and ST Saalfeld. 2016. Barrow, 2014. Arctic Shorebird Demographics Network. NSF Arctic Data Center. doi: INSERT HERE. Note that each updated version of the full dataset has its own unique DOI. Disclaimers: The dataset is distributed “as is” and with absolutely no warranty. The data providers have invested considerable effort to ensure that the data are of highest quality, but it is possible that undetected errors remain. Data have been processed with several steps for quality assurance, but the data providers accept no liability or guarantee that the data are up-to-date, correct, or complete. Access to data is provided on the understanding that the data providers are not responsible for any damages from inaccuracies in the data. Note: An up-to-date version of data from Barrow/Utqiagvik, including corrected and more recent data, is now housed here: https://arcticdata.io/catalog/view/doi:10.18739/A2VT1GP7Q . Please contact the relevant site PIs to seek recent data (after 2014) from any other site.

Population Exposure Data Haiti

Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator-prey interaction between sea otters and butter clams (Saxidomus gigantea), one of the dominant large bivalves in Glacier Bay and a favored prey of sea otters. To do so, we developed a modeling framework to account for both bottom-up and top-down drivers of butter clam abundance and dynamics. For the bottom-up driver, we used the root-mean-square current speed (m/s) predicted by a tidal circulation model of Glacier Bay developed by Drew et al. (2013). For top-down sea otter dynamics, we used the posterior mean sea otter abundance estimates from Lu et al. (2019). This repository contains the current speed raster (100m x 100m resolution) produced by Drew et al. (2013) and the files and model output from Lu et al. (2019) necessary to generate a time series of rasters (400m x 400m resolution raster brick with 26 layers for the years 1993-2018) of estimated posterior mean sea otter abundance. These data layers are used in Leach et al. (2023) to model butter clam dynamics at sampling sites across Glacier Bay.

Dataset

This dataset was created by sundarML

Contents

Thailand High Speed Internet: Access Rate: Per Population data was reported at 14.510 % in Sep 2019. This records an increase from the previous number of 14.070 % for Jun 2019. Thailand High Speed Internet: Access Rate: Per Population data is updated quarterly, averaging 12.130 % from Mar 2016 (Median) to Sep 2019, with 15 observations. The data reached an all-time high of 14.510 % in Sep 2019 and a record low of 9.620 % in Mar 2016. Thailand High Speed Internet: Access Rate: Per Population data remains active status in CEIC and is reported by Office of The National Broadcasting and Telecommunications Commission. The data is categorized under Global Database’s Thailand – Table TH.TB003: Internet Statistics: Office of The National Broadcasting and Telecommunications Commission .