Estimation of responses of organisms to their environment using experimental manipulations, and comparison of such responses across sets of species, is one of the primary tools in ecology research. The most common approach is to compare response of a single life stage of species to an environmental factor and use this information to draw conclusions about population dynamics of these species. Such approach ignores the fact that interspecific fitness differences measured at a single life stage are not directly comparable and cannot be extrapolated to lifetime fitness of individuals and thus species’ population dynamics. Comparison of one life stage only while omitting demographic information can strongly bias conclusions, both in experimental studies with a few species, and in large comparative studies. We illustrate the effect of this omission using both an exaggerated fictitious example, and biological data on congeneric species differing in their demography. We are showing, taking sim...

Dasymetric mapping is a technique used to improve the accuracy of population mapping. In the United States, census data is widely used to analyze the spatial distribution of socio-economic factors. For instance, the American Community Survey (ACS, available at https://www.census.gov/programs-surveys/acs) compiles crucial socio-economic data at the census tract level. While census boundaries cover entire states, the population is not evenly distributed but tends to concentrate in residential areas. Dasymetric mapping, in combination with other datasets like land use and land cover, enhances the precision of mapping results.This notebook applies two python packages including:Tobler, a geostatistic pytho package based on PySAL: https://github.com/pysal/tobler.The EnviroAtlas Intelligent Dasymetric Toolbox by the EPA: https://github.com/USEPA/Dasymetric-Toolbox-OpenSource/tree/masterFor more information about dasymetric mapping, see this publication by Baynes, Neale, and Hultgren (2022).Data used:Open Street Map's residential zonesU.S. 2020 Decennial Census at the census block levelNational Land Cover Dataset (NLCD) from 2019 (indexed in the Virginia Data Cube).Data was called and processed in the Virginia Data Cube: https://datacube.vmasc.org/Funding: This work was made possible by the NASA AIST-21-0031 program, grant number 80NSSC22K1407.Data Description for each layer:Open Street Map (OSM) Residential is a free layer provided by the Open Street Map community that are polygons. AIST_regionCensus are census block polygons from the 2020 deciennial US census clipped to the study region. AIST Census - Clipped to OSM are census block polygons that are clipped to the OSM residential area polygons. Tobler_MAI_totPop are hexagons representing total population through the MAI Tobler function. Tobler_MAI_medFrag are hexagons representing total number of medically fragile population through the MAI Tobler function. Tobler_AI_totPop are hexagons representing total population through the AI Tobler function. Tobler_AI_medFrag are hexagons representing total number of medically fragile population through the AI Tobler function. EPA_totPop are hexagons representing total population through the EPA's IDM open source tool without using an uninhabited mask. EPA_medFrag are hexagons representing total medically fragile population through the EPA's IDM open source tool without using an uninhabited mask. Please note the above data with EPA as a prefix does not represent EPA approved products. The EPA's EnviroAtlas has their own dasymetric output. You may find Jupyter Notebooks that show how to gather this data, powered by the Virginia Datacube, here: https://github.com/ODU-GeoSEA/va-datacube

BackgroundCognition covers a range of abilities, such as memory, response time and language, with tests assessing either specific or generic aspects. However differences between measures may be observed within the same individuals.ObjectiveTo investigate the cross-sectional association of cognitive performance and socio-demographic factors using different assessment tools across a range of abilities in a British cohort study.MethodsParticipants of the European Prospective Investigation of Cancer (EPIC) in Norfolk Study, aged 48–92 years, underwent a cognitive assessment between 2006 and 2011 (piloted between 2004 and 2006) and were investigated over a different domains using a range of cognitive tests.ResultsCognitive measures were available on 8584 men and women. Though age, sex, education and social class were all independently associated with cognitive performance in multivariable analysis, different associations were observed for different cognitive tests. Increasing age was associated with increased risk of a poor performance score in all of the tests, except for the National Adult Reading Test (NART), an assessment of crystallized intelligence. Compared to women, men were more likely to have had poor performance for verbal episodic memory, Odds Ratio, OR = 1.99 (95% Confidence Interval, 95% CI 1.72, 2.31), attention OR = 1.62, (95% CI 1.39, 1.88) and prospective memory OR = 1.46, (95% CI 1.29, 1.64); however, no sex difference was observed for global cognition, OR = 1.07 (95%CI 0.93, 1.24). The association with education was strongest for NART, and weakest for processing speed.ConclusionAge, sex, education and social class were all independently associated with performance on cognitive tests assessing a range of different domains. However, the magnitude of associations of these factors with different cognitive tests differed. The varying relationships seen across different tests may help explain discrepancies in results reported in the current literature, and provides insights into influences on cognitive performance in later life.

Quality information on the mid-year population estimates at local authority and region level for England and Wales, by age and sex.

As of February 2025, 5.56 billion individuals worldwide were internet users, which amounted to 67.9 percent of the global population. Of this total, 5.24 billion, or 63.9 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 20254. In The Netherlands, Norway and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide – over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a ten percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most significant usage penetration, 98 percent. In comparison, the worldwide average for the age group 15–24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

Population genetic data from multiple taxa can address comparative phylogeographic questions about community-scale response to environmental shifts, and a useful strategy to this end is to employ hierarchical co-demographic models that directly test multi-taxa hypotheses within a single, unified analysis while benefiting in statistical power from aggregating datasets. This approach has been applied to classical phylogeographic datasets such as mitochondrial barcodes as well as reduced-genome polymorphism datasets that can yield 10,000s of SNPs, produced by emergent technologies such as RAD-seq and GBS. A strategy for the latter had been accomplished by adapting the site frequency spectrum to a novel summarization of population genomic data across multiple taxa called the aggregate site frequency spectrum (aSFS), which potentially can be deployed under various inferential frameworks including approximate Bayesian computation, random forest, and composite likelihood optimization. Here, we introduce the R package Multi-DICE, a wrapper program that exploits existing simulation software for straight-forward and flexible execution of hierarchical model-based inference using the aSFS, which is derived from genomic-scale data, as well as mitochondrial data. We validate several novel software features such as applying alternative inferential frameworks, enforcing a minimal threshold of time surrounding event pulses, and specifying flexible hyperprior distributions. In sum, Multi-DICE provides comparative analysis within the familiar R environment while allowing a high degree of user customization, and will thus serve as a valuable tool for comparative phylogeography and population genomics.

We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for greater sage-grouse across the western United States. Polygons (clusters) within each cluster level group a population of sage-grouse leks (sage-grouse breeding grounds) and each level increasingly groups lek clusters from previous levels. We developed the hierarchical clustering approach by identifying biologically relevant population units aimed to use a statistical and repeatable approach and include biologically relevant landscape and habitat characteristics. We desired a framework that was spatially hierarchical, discretized the landscape while capturing connectivity (habitat and movements), and supported management questions at different spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates at local scales affected by changes in habitat quality compared to larger scaled processes affecting growth rates, such as regional climate/vegetation communities. Therefore, the use of multiple scales (hierarchical cluster levels) that group demographic data can provide information driving population changes at different spatial scales, thereby providing a tool for population monitoring and adaptive management.

Use of absolute and derived values in assessing Population health and the activities of healthcare

Submitted by Riya Patil & Rutuja Sonar, to Moldoev Murzali Ilyazovich Osh state University

ABSTRACT

In contrast, derived values involve the use of statistical techniques to calculate indirect indicators from absolute values. These include metrics like disability-adjusted life years (DALYs), quality-adjusted life years (QALYs), and health-adjusted life expectancy (HALE). Derived values are instrumental in understanding the broader context of population health, as they often combine both mortality and morbidity data to reflect the overall burden of disease.

In healthcare institutions, these values are integral in guiding resource allocation, evaluating the effectiveness of interventions, and shaping policies aimed at improving health outcomes. While absolute values provide essential raw data, derived values offer nuanced insights into the quality and long-term impact of healthcare services. Together, they form a comprehensive approach to measuring and improving population health, helping healthcare institutions prioritize actions and allocate resources more effectively.

This paper explores the role of absolute and derived values in assessing population health and their relevance to healthcare institutions, examining how both types of values support decision-making and influence health policy.

Keywords: Population health, absolute values, derived values, healthcare institutions, mortality rates, morbidity, Disability-Adjusted Life Years (DALYs), Quality-Adjusted Life Years (QALYs), Health-Adjusted Life Expectancy (HALE), health policy, healthcare interventions.

INTRODUCTION

Use of Absolute and Derived Values in Assessing Population Health and the Activities of Healthcare Institutions**

Population health is a key focus of public health systems and healthcare institutions worldwide. Assessing the health of a population requires robust metrics to understand the current state of health, identify risks, and track trends over time. One of the essential tools in evaluating population health is the use of **absolute values** and **derived values**. These metrics offer complementary insights into both the health status of individuals within a population and the effectiveness of healthcare interventions.

**Absolute values** are straightforward measures that provide direct data points, such as the total number of people suffering from a specific disease, the number of hospital admissions, or the total expenditure on healthcare services. These values are critical for understanding the scale of health issues and resource needs within a community.

**Derived values**, on the other hand, are ratios or indices calculated from absolute values. They allow for more meaningful comparisons across populations, time periods, or geographical areas. Examples include rates such as morbidity or mortality rates, life expectancy, and disease prevalence, which are essential for assessing public health outcomes and guiding healthcare policy and decision-making.

By integrating both absolute and derived values, healthcare institutions can gain a comprehensive picture of population health, identify areas for improvement, allocate resources more efficiently, and track the effectiveness of healthcare initiatives. This approach helps ensure that healthcare systems are responsive to the needs of the population and can adapt to emerging health challenges.

METHODOLOGY

Method and analysis which is performed by the google worksheet and google forms

Absolute Values in Assessing Population Health:

Absolute values refer to raw, unadjusted data points that provide a direct measure of a population's health status. These values are fundamental for initial assessments, as they provide baseline data for various health indicators.

Definition and Examples

Absolute values refer to concrete figures that represent the total counts or occurrences of specific health events or conditions. For example:

Total Mortality Rate: The number of deaths in a population over a specific time period (e.g., deaths per 100,000 people).

Prevalence Rates: The proportion of individuals in a population diagnosed with a specific condition at a particular time (e.g., diabetes prevalence).

Incidence Rates: The number of new or newly diagnosed cases of a disease over a given period (e.g., cancer incidence).

Life Expectancy: The average number of years a person is expected to live based on current mortality rates.

Use in Population Health

Health Monitoring: Absolute values allow public health authorities to monitor trends in population health, such as increases in mortality or the spread of disease.

Resource Allocation: These values help in determining the burden of disease in different populations, aiding in the efficient distribution of healthcare resources.

Derived Values in Assessing Population Health

Derived values involve the use of mathematical formulas or statistical techniques to adjust or combine absolute values to create composite indices or ratios that provide deeper insights into health outcomes and healthcare activities.

Definition

Derived values are statistical measures that offer context to absolute

by relating them to population characteristics. Common examples include:

Age-Standardized Mortality Rate: Adjusts the mortality rate for differences in the age structure of different populations, allowing comparisons between populations with different age distributions.

Disability-Adjusted Life Years (DALY): A composite measure that combines years of life lost due to premature death and years lived with disability. DALY provides a more comprehensive understanding of the burden of disease.

Quality-Adjusted Life Years (QALY): A measure used to evaluate the effectiveness of healthcare interventions by combining quantity and quality of life.

Health Inequality Index: Derived by comparing health disparities between different subgroups within a population.

Use in Population Health

Risk Assessment: Derived values like DALYs or QALYs enable healthcare providers and policymakers to assess the relative impact of different diseases or health conditions on the population’s overall health.

Health Outcomes Comparison: Derived values facilitate comparisons across different populations or regions, adjusting for factors like age, gender, or socioeconomic status.

Policy and Program Evaluation: Derived values are used to evaluate the effectiveness of public health interventions or healthcare programs, such as whether a vaccination program reduces disease burden over time.

Significance

Contextualizing Health Trends: Absolute values alone may not offer a clear picture. For instance, while an increase in the number of cancer cases might be alarming, derived values like the cancer incidence rate allow us to understand if the increase is due to an actual rise in cases or simply a result of population growth.

Comparative Analysis: Derived values are essential when comparing different populations or regions. For example, comparing the infant mortality rate in different countries provides insights into healthcare system performance, whereas absolute numbers may mislead without considering population size differences.

Evaluating Healthcare Efficiency: Derived values such as cost-effectiveness or patient outcomes per healthcare dollar provide insights into the efficiency of healthcare institutions. This helps identify areas of improvement in resource allocation and delivery of services.

Policy and Planning: Derived values play a crucial role in informing public health policies and healthcare strategies. For example, the quality-adjusted life year (QALY), derived from health outcome measures, is commonly used in health economics to assess the effectiveness of medical treatments and interventions.

Conclusion

Both absolute and derived values are integral to assessing population health and healthcare institution activities. Absolute values provide raw data, while derived values allow for deeper analysis, trends, and comparisons, giving a more comprehensive picture of health outcomes and healthcare performance.

REFERENCE

1.Kindig D, Stoddart G (March 2003). "What is population health?". American Journal of Public Health. 93 (3): 380–3. doi:10.2105/ajph.93.3.380. PMC 1447747. PMID 12604476.

2. McGinnis JM, Williams-Russo P, Knickman JR (2002). "The case for more active policy attention to health promotion". Health Aff (Millwood). 21 (2): 78–93. doi:10.1377/hlthaff.21.2.78. PMID 11900188.. See also National Academies Press free publication: The Future of Public Health in the 21st Century.

3. World Health Organization. 2006. Constitution of the World Health Organization – Basic Documents, Forty-fifth edition, Supplement, October 2006.

4. Jeffery RW. 2001. Public health strategies for obesity treatment and prevention. American Journal of Health Behavior 25:252–259.

5. Buunk BP, Verhoeven K. 1991. Companionship and support at work: a microanalysis of the stress-reducing features of social interactions. Basic and Applied Social Psychology 12:243–258.

6. CDC. 2001. a. CDC FactBook 2000/2001: Profile of the Nation's Health. Atlanta, GA: CDC.

7. What is the WHO definition of health? from the Preamble to the Constitution of WHO as adopted by the

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Global biodiversity is facing a crisis, which must be solved through effective policies and on-the-ground conservation. But governments, NGOs, and scientists need reliable indicators to guide research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. But without a basis for real-world comparison, there is no way to directly assess an indicator’s accuracy or reliability. Instead, a modelling approach can be used. We developed a model of trend reliability, using simulated datasets as stand-ins for the "real world", degraded samples as stand-ins for indicator datasets (e.g. the Living Planet Database), and a distance measure to quantify reliability by comparing sampled to unsampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, and that revisiting previously-studied populations is a quick and efficient way to improve trend reliability until new long-term studies can be completed and made available. Methods These data are entirely simulated. We used R code to generate simulated population time series. We added observation error to the simulated time series, degraded them by randomly removing observations, then sampled repeatedly and calculated both the partially and fully sampled trends using the method of the Living Planet Index. The partially sampled trends were then compared with the fully sampled trends using a distance metric. We generated thousands of time series datasets with different underlying properties and tested to see which parameters affected the distance values. We then used the responsible parameters to build a model of trend accuracy and applied that model to regional taxonomic groups in the Living Planet Database. The simulated time series in both raw and degraded form as well as the trends and distance values are included here, divided into archives which are further described in the README file.

If you would like to view a straightforward comparison between the Population density (by State) of Nigeria as at 2006 and 2016, this is just for you.

This web app showcases a simple and at-a-glance comparison between the Population density of Nigeria in 2006 and 2016. It features side-by-side, two individual web apps that display the population density, by state, for each corresponding year (2006, 2016). The population density was calculated by dividing the states total population by the area of its landmass in m². Within the app, there are easy-to-use navigation tools that have been configured to help users better access its features. Examples of these include the zoom tool, Expand tool, synced pop-ups, legend and many more. Clicking on any state on either map enables its pop-up from which you can access that particular states population details. One wonderful feature of this app is that popups for the 2 maps are synced! This means that clicking on a state in one map to get its pop-up details, will effect the same in the second map. (How cool is that!) Don't hesitate to leave comment about your experience with this web app, as well as suggestions on what can be done to make it even better.Thank you!

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us at http://goto.arcgisonline.com/landscape7/World_Population_Density_Estimate_2016.This layer is a global estimate of human population density for 2016. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

This project addresses major gaps in knowledge on vital rates such as age to maturity, survival, sex ratios, and population size (including the males)whcih have made it difficult to conduct meaningful population and risk assessments. Although vital rates are difficult to observe directly, genetic analysis provides a practical approach to understand these processes. Understanding the proportion of males to females in any population has important consequences for population demographic studies. Using hatchling and maternal DNA fingerprints, one can deduce the paternal genotypes ? from one to many fathers per clutch. The resulting genotypes represent individual males that are actively breeding in the population. This means that males can effectively be sampled without ever having seen them or having to catch them in the field. The nesting population on St. Croix is an important US Index Population for leatherbacks that has been intensively monitored using a variety of Capture-Mark-Recapture (CMR) methods since 1981 (Dutton et al. 2005). Due to the richness and consistency of the demographic data, this population offers unique opportunities for research and development of tools & approaches for getting at vital rate parameters that are needed to improve stock assessments in sea turtles, as identified in the recent NRC Report (2010). These approaches can then be applied to other populations, e.g. the critically endangered Pacific leatherback. We have developed non-injurious in-situ techniques to mass sample large numbers of live hatchlings for genetic fingerprinting as part of a long term CMR experiment, and also demonstrated the feasibility of using hatchling genotyping and kinship analysis to determine the genotypes and number of breeding males in the population (Stewart & Dutton 2011). We have sampled a total of 17,087 hatchlings between 2009-2011 as part of this project, will continue field effort in 2012 toward the goal of a minimum sampling of 50,000 hatchlings over the next 2-4 years. At an appropriate time in the future, we will use high throughput genotyping methods currently being developed in the next 2-4 years to create a database of individual hatchling identifications (?genetic tags?) that will be compared to those first time nesters sampled annually into the future. This project will also genotype a subset of the samples collected in 2011 to assess males in two consecutive seasons for a more accurate census of the number of males in the breeding population and to determine the extent of male fidelity and breeding periodicity. Objectives include 1) mass-tagging of leatherback hatchlings for Capture-Mark-Recapture (CMR) studies to determine age at first reproduction and age-specific survival rates and 2) application of kinship approaches to reconstruct parental genotypes from mother-offspring comparison to census males, determine operational sex ratios (OSR) of the breeding population, reproductive success of males and mating system.

The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

Dispersal, the movement of individuals between populations, is crucial in many ecological and genetic processes. However, direct identification of dispersing individuals is difficult or impossible in natural populations. By using genetic assignment methods, individuals with unknown genetic origin can be assigned to source populations. This knowledge is necessary in studying many key questions in ecology, evolution and conservation.

We introduce a network-based tool BONE (Baseline Oriented Network Estimation) for genetic population assignment, which borrows concepts from undirected graph inference. In particular, we use sparse multinomial Least Absolute Shrinkage and Selection Operator (LASSO) regression to estimate probability of the origin of all mixture individuals and their mixture proportions without tedious selection of the LASSO tuning parameter. We compare BONE with three genetic assignment methods implemented in R packages radmixture, assignPOP and RUBIAS.

Probability of the origin and mixture proportion estimates of both simulated and real data (an insular house sparrow metapopulation and Chinook salmon populations) given by BONE are competitive or superior compared to other assignment methods. Our examples illustrate how the network estimation method adapts to population assignment, combining the efficiency and attractive properties of sparse network representation and model selection properties of the L1 regularization. As far as we know, this is the first approach showing how one can use network tools for genetic identification of individuals' source populations.

BONE is aimed at any researcher performing genetic assignment and trying to infer the genetic population structure. Compared to other methods, our approach also identifies outlying mixture individuals that could originate outside of the baseline populations. BONE is a freely available R package under the GPL license and can be downloaded at GitHub. In addition to the R package, a tutorial for BONE is available at https://github.com/markkukuismin/BONE/.

Methods The house sparrow study metapopulation consists of 18 islands located in an archipelago off the coast of Helgeland in northern Norway. Monitoring of the metapopulation, that covers more than 1600 km2 started in 1993 and is still ongoing. Population-level data on population sizes, and individual data on year and island of birth, information on individual survival, and blood samples for DNA were collected annually in the breeding season (May-August) and post-breeding season (September-November). Most nestlings, fledged juveniles and adults in the study metapopulation have been ringed with a numbered metal ring and a unique combination of plastic colour rings. This, in combination with intensive recapture and re-sighting efforts, has provided high recapture rates (mean: 0.75) and good ecological dispersal data, that can be used to, e.g., test the accuracy of genetic assignment methods.

SNP-genotyping was carried out on house sparrows present on 8 islands that differ in microenvironments and habitat types depending on the occurrence of livestock and dairy farms (five farm islands, and three non-farm islands) and distance from the mainland. On the five farm islands, all ringed adult individuals present from 1998 to 2013 were genotyped, whereas all ringed adult individuals present from 2003 or 2004 to 2013 were genotyped on the three non-farm islands. A total of 3269 adults were genotyped on a custom 200K Affymetrix Axiom SNP array. These SNPs are evenly distributed throughout the genome. After rigorous quality control, 183,145 SNPs with minor allele frequency above 0.01, and 3116 individuals with genotyping rate above 0.90 were found suitable. This is data of adults present in the year 2012. The data is anonymized.

Abstract

The 2010 Population and Housing Census was Conducted between 11-17 November 2010. Over 750,000 household forms were completed by over 12,000 enumerators. More than 30,000 persons were directly involved in census conducting. The Population and Housing Census is the biggest event organized by the National Statistical Office. The unique feature of the Census is that it covers a wide range of entities starting from the primary unit of the local government up to the highest levels of the government as well as all citizens and conducted with the highest levels of organization. For the 2010 Population and Housing Census, the management team to coordinate the preparatory work was established, a detailed work plan was prepared and the plan was successfully implemented. The preliminary condition for the successful conduct of the Census was the development of a detailed plan. The well thought-out, step by step plan and carefully evidenced estimation of the expenditure and expected results were crucial for the successful Census. Every stage of the Census including preparation, training, enumeration, data processing, analysis, evaluation and dissemination of the results to users should be reflected in the Census Plan.

Geographic coverage

National

Analysis unit

• Household;
• Indivudual.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Processing System

The introduction of internet technology and GIS in the 2010 Population and Housing Census has made the census more technically advanced than the previous ones. Compared to the data processing of the 2000 Population and Housing Census the techniques and technological abilities of the NSO have advanced. The central office - National Statistical Office has used an internal network with 1000 Mbps speed, an independent internet line with 2048 Kbps speed and server computers with special equipments to ensure the reliable function of internal and external networks and confidentiality. The Law on Statistics, the Law on Population and Housing Census, the guidelines of the safety of statistical information systems and policies, the provisional guidelines on the use of census and survey raw data by the users, the guidelines on receiving, entering and validating census data have created a legal basis for census data processing.

The data-entry network was set up separately from the network of the organization in order to ensure the safety and confidentiality of the data. The network was organized by using the windows platform and managed by a separate domain controller. Computers where the census data will be entered were linked to this server computer and a safety devise was set up to protect data loss and fixing. Data backup was done twice daily at 15:10 hour and 22:10 hour by auto archive and the full day archive was stored in tape at 23:00 hour everyday.

The essential resources of important equipments and tools were prepared in order to provide continuous function of all equipment, to be able to carry out urgent repairs when needed, and to return the equipment to normal function. The computer where the census data would be entered and other necessary equipment were purchased by the state budget. For the data processing, the latest packages of software programs (CSPro, SPSS) were used. Also, software programs for the computer assisted coding and checking were developed on NET within the network framework.

INTERNET CENSUS DATA PROCESSING

One of the specific features of the 2010 Population and Housing Census was e-enumeration of Mongolian citizens living abroad for longer period. The development of a web based software and a website, and other specific measures were taken in line with the coordination of the General Authority for State Registration, the National Data Centre, and the Central Intelligence Agency in relation to ensuring the confidentiality of data. Some difficulties were encountered in sharing information between government agencies and ensuring the safety and confidentiality of census data due to limited professional and organizational experience, also because it was the first attempt to enumerate its citizens online.

The main software to be used for online registration, getting permission to get login and filling in the census questionnaire online as well as receiving a reply was developed by the NSO using a symphony framework and the web service was provided by the National Data Centre. Due to the different technological conditions for citizens living and working abroad and the lack of certain levels of technological knowledge for some people the diplomatic representative offices from Mongolia in different countries printed out the online-census questionnaire and asked citizens to fill in and deliver them to the NSO in Mongolia. During the data processing stage these filled in questionnaires were key-entered into the system and checked against the main census database to avoid duplication.

CODING OF DATA, DATA-ENTRY AND VALIDATION

Additional 136 workers were contracted temporarily to complete the census data processing and disseminate the results to the users within a short period of time. Due to limited work spaces all of them were divided into six groups and worked in two shifts with equipments set up in three rooms and connected to the network. A total of six team leaders and 130 operators worked on data processing. The census questionnaires were checked by the ad hoc bureau staff at the respective levels and submitted to the NSO according to the intended schedule.

These organizational measures were taken to ensure continuity of the census data processing that included stages of receiving the census documents, coding the questionnaire, key-entering into the system and validating the data. Coding was started on December 13, 2010 and the data-entry on January 7, 2011. Data entering of the post-enumeration survey and verification were completed by April 16, 2011. Data checking and validation started on April 18, 2011 and was completed on May 5, 2011. The automatic editing and imputation based on scripts written by the PHCB staff was completed on May 10, 2011 and the results tabulation was started.

Conservation biologists often deal with species that have small, fragmented populations throughout their range, making it necessary to prioritize populations for management. Genetics provides tools to assist with prioritization according to the levels and distribution of genetic diversity and evolutionary distinctiveness. Many studies have used nuclear microsatellite loci to measure genetic diversity in disparate populations and mitochondrial DNA to assess genetic distinctiveness. However, comparing metrics based on microsatellite genotypes ascertained in different laboratories is complicated by the selection of different loci with distinct nucleotide repeat motifs. This issue may be resolved by comparing metrics to a well-characterized reference population with shared microsatellite markers. The Asian elephant, Elephas maximus, is an endangered species with 50–60% of populations in India and Sri Lanka, and small, fragmented populations throughout southeast and insular Asia. We assessed range-wide genetic diversity of the Asian elephant by directly comparing allelic diversity and heterozygosity estimates from 35 populations, overcoming marker selection bias by calibrating metrics to a large population on the Nakai Plateau, Lao PDR, genotyped at 25 loci. We coupled these results with mtDNA analysis to evaluate genetic distinctiveness and identify potential conservation management units. We found the greatest diversity in the populations of southeast Asia and the greatest genetic distinctiveness among the subspecies designations, particularly Borneo and Sumatra, and other southeast Asian populations. The populations of southeast Asia, albeit small, fragmented, and at high risk of extirpation, contain valuable diversity and distinctiveness and are thus of high priority for the preservation of the Asian elephant.

Daytime population - The estimated number of people in a borough in the daytime during an average day, broken down by component sub-groups.

The figures given are an average day during school term-time. No account has been made for seasonal variations, or for people who are usually in London (resident, at school or working), but are away visiting another place.

Sources include the Business Register and Employment Survey (BRES) (available under license), Annual Population Survey (APS), 2011 Census, Department for Education (DfE), International Passenger Survey (IPS), GB Tourism Survey (GBTS), Great Britain Day Visit Survey (GBDVS), GLA Population Projections, and GLA Economics estimates (GLAE).

The figures published in these sources have been used exactly as they appear - no further adjustments have been made to account for possible sampling errors or questionnaire design flaws.

Day trip visitors are defined as those on day trips away from home for three hours or more and not undertaking activities that would regularly constitute part of their work or would be a regular leisure activity.

International visitors – people from a country other than the UK visiting the location;
Domestic overnight tourists – people from other parts of the UK staying in the location for at least one night.
All visitor data is modelled and unrounded.

This edition was released on 14 January 2015 and replaces the previous estimates for 2012.

GLA resident population, 2011 Census resident population, and 2011 Census workday populations (by sex) included for comparison.

For more workday population data by age use the Custom Age-Range Tool for Census 2011 Workday population , or download data for a range of geographical levels from NOMIS.

Odds of high estimated 10-year cardiovascular disease risk using 5 different risk estimator tools, by selected social determinants of health.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564