A population is a subgroup of individuals within the same species that are living and breeding within a geographic area. The number of individuals living within that specific location determines the population density, or the number of individuals divided by the size of the area.Population density can be used to describe the location, growth, and migration of many organisms. In the case of humans, population density is often discussed in relation to urbanization, immigration, and population demographics.Globally, statistics related to population density are tracked by the United Nations Statistics Division, and the United States Constitution requires population data to be collected every 10 years, an operation carried out by the U.S. Census Bureau. However, data on human population density at the country level, and even at regional levels, may not be very informative; society tends to form clusters that can be surrounded by sparsely inhabited areas. Therefore, the most useful data describes smaller, more discrete population centers.Dense population clusters generally coincide with geographical locations often referred to as city, or as an urban or metropolitan area; sparsely populated areas are often referred to as rural. These terms do not have globally agreed upon definitions, but they are useful in general discussions about population density and geographic location.Population density data can be important for many related studies, including studies of ecosystems and improvements to human health and infrastructure. For example, the World Health Organization, the U.S. Energy Information Administration, the U.S. Global Change Research Program, and the U.S. Departments of Energy and Agriculture all use population data from the U.S. Census or UN statistics to understand and better predict resource use and health trends.Key areas of study include the following:Ecology: how increasing population density in certain areas impacts biodiversity and use of natural resources.Epidemiology: how densely populated areas differ with respect to incidence, prevalence, and transmission of infectious disease.Infrastructure: how population density drives specific requirements for energy use and the transport of goods.This list is not inclusive—the way society structures its living spaces affects many other fields of study as well. Scientists have even studied how happiness correlates with population density. A substantial area of study, however, focuses on demographics of populations as they relate to density. Areas of demographic breakdown and study include, but are not limited to:age (including tracking of elderly population centers);sex (biological classification as male or female); andrace and ethnic group, or cultural characteristics (ethnic origin and language use).

This dataset presents a fine-grained population map of Zambiawith a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Zambia. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

The COVID-19 pandemic, which began in China in late 2019, and subsequently spread across the world during the first several months of 2020, has had a dramatic impact on all facets of life. At the same time, it has not manifested in the same way in every nation. Some countries experienced a large initial spike in cases and deaths, followed by a rapid decline, whereas others had relatively low rates of both outcomes throughout the first half of 2020. The United States experienced a unique pattern of the virus, with a large initial spike, followed by a moderate decline in cases, followed by second and then third spikes. In addition, research has shown that in the United States the severity of the pandemic has been associated with poverty and access to health care services. This study was designed to examine whether the course of the pandemic has been uniform across America, and if not how it differed, particularly with respect to poverty. Results of a random intercept multilevel mixture model revealed that the pandemic followed four distinct paths in the country. The least ethnically diverse (85.1% white population) and most rural (82.8% rural residents) counties had the lowest death rates (0.06/1000) and the weakest link between deaths due to COVID-19 and poverty (b = 0.03). In contrast, counties with the highest proportion of urban residents (100%), greatest ethnic diversity (48.2% nonwhite), and highest population density (751.4 people per square mile) had the highest COVID-19 death rates (0.33/1000), and strongest relationship between the COVID-19 death rate and poverty (b = 46.21). Given these findings, American policy makers need to consider developing responses to future pandemics that account for local characteristics. These responses must take special account of pandemic responses among people of color, who suffered the highest death rates in the nation.

This dataset presents a fine-grained population map of Rwanda with a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Rwanda. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

The overall objective of this study was to determine population growth, sustainable yield, and factors influencing population dynamics of the species. We studied black bears (Ursus americanus) on 2 study areas in the Okefenokee-Osceola ecosystem in north Florida and southeast Georgia from 1995-1999 to determine population characteristics (size, density, relative abundance, distribution, sex and age structure, mortality rates, natality, and recruitment) and habitat needs. We captured 205 different black bears (124M: 81F) 345 times from June 1995 to September 1998. Overall, adult bears on Osceola were 19% heavier than those on Okefenokee (t = 2.96, df = 148, P = 0.0036).We obtained 13,573 radiolocations from 87 (16M:71F) individual bears during the period of study. Seventeen mortalities of radio collared bears were documented on Okefenokee, with hunting mortality accounting for 70.6% of these deaths. We documented only 2 (8%) mortalities of radiocollared females from Osceola; both were illegally killed. Annual survival rates for radio collared females were lower on Okefenokee ( x = 0.87, 95% CI = 0.80-0.93) than on Osceola ( x = 0.97, 95% CI =0.92-1.00; 2 0.05 = 3.98, 1 df, P = 0.0460). Overall, 67 bears (51M:16F) were taken by hunters on the Okefenokee study area from 1995-1999. Including the bears that were in protected areas and unavailable to harvest, the annual harvest rate was 10.1%. Our annual survival estimate for Osceola females (0.97) was among the highest reported from any southeastern bear population, no doubt influenced by the closing of the bear hunting season in and around Osceola NF in 1992. When survival estimates for Okefenokee females were recalculated without hunting mortality, overall survival rates increased from 0.87 to 0.95, similar to that of the Osceola females. To estimate population size, we maintained 88 and 94 barbed wire hair traps during 1999 on the Okefenokee and Osceola study areas, respectively. Complete multilocus genotypes were obtained for 78 (99%) of the Okefenokee samples, of which 39individual bears were identified. On the Osceola study area, complete genotypes were obtained for 84 (96%) samples representing 37 individuals. After considering a number of mark-recapture estimators, we concluded that the within-year estimate of 71 bears (95% CI = 59-91) produced by the jackknife heterogeneity model Mh was the most appropriate for the Okefenokee hair-trapping data. Likewise, we selected the estimate of 44 bears (95% CI = 40-57) on the Osceola study area provided by the null model Mo as most appropriate during 1999. The estimated densities of black bears on the Okefenokee and Osceola study areas were 0.14 and 0.12 bears/km2, respectively. Based on a weighted average density of 0.135 bears/km2 and assuming a homogeneous distribution, we estimate that approximately 830 bears (95% CI = 707-1,045) inhabit the 6,147-km2Okefenokee-Osceola ecosystem. We monitored 66 radiocollared bears (8M:58F) from 1995-1998 for 132 possible denning occasions. Denning durations for females ( x = 96.7 days, n = 109, SE = 2.7)were longer than for male bears ( x = 71.6 days, n = 9, SE = 8.8; Z = -2.38, P = 0.0174).Two male bears from Okefenokee denned in ground nests whereas tree cavities (n = 18)and ground nests (n = 16) accounted for 65% of all dens used by Okefenokee females. In contrast, ground nests accounted for 100% (n = 37) of all documented den types used by female bears on the Osceola study area. Bears on Okefenokee used shrub, blackgum, mixed shrub, and cypress habitat types on 24, 23, 21, and 13 occasions, respectively. Interestingly, 90% (n = 74) of all radiocollared bears on the Okefenokee study area denned within the boundaries of ONWR during 1995-1998. Only 1 radiocollared female from each area denned in pine habitat during this study.

Medical Service Study Areas - Census Detail, 2010

Medical Service Study Areas (MSSAs) are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSAs were developed in 1976 by the California Healthcare Workforce Policy Commission (formerly California Health Manpower Policy Commission) to respond to legislative mandates requiring it to determine "areas of unmet priority need for primary care family physicians" (Song-Brown Act of 1973) and "geographical rural areas where unmet priority need for medical services exist" (Garamendi Rural Health Services Act of 1976).

MSSAs are recognized by the U.S. Health Resources and Services Administration, Bureau of Health Professions' Office of Shortage Designation as rational service areas for purposes of designating Health Professional Shortage Areas (HPSAs), and Medically Underserved Areas and Medically Underserved Populations (MUAs/MUPs).

The MSSAs incorporate the U.S. Census total population, socioeconomic and demographic data and are updated with each decadal census. Office of Statewide Health Planning and Development provides updated data for each County's MSSAs to the County and Communities, and will schedule meetings for areas of significant population change. Community meetings will be scheduled throughout the State as needed.

Adopted by the California Healthcare Workforce Policy Commission on May 15, 2002.

Each MSSA is composed of one or more complete census tracts. MSSAs will not cross county lines. All population centers within the MSSA are within 30 minutes travel time to the largest population center.

Urban MSSA - Population range 75,000 to 125,000. Reflect recognized community and neighborhood boundaries. Similar demographic and socio-economic characteristics.

Rural MSSA - Population density of less than 250 persons per square mile. No population center exceeds 50,000.

Frontier MSSA - Population density of less than 11 persons per square mile.

Medical Service Study Areas - Census Detail, 2010

Medical Service Study Areas (MSSAs) are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSAs were developed in 1976 by the California Healthcare Workforce Policy Commission (formerly California Health Manpower Policy Commission) to respond to legislative mandates requiring it to determine "areas of unmet priority need for primary care family physicians" (Song-Brown Act of 1973) and "geographical rural areas where unmet priority need for medical services exist" (Garamendi Rural Health Services Act of 1976).

MSSAs are recognized by the U.S. Health Resources and Services Administration, Bureau of Health Professions' Office of Shortage Designation as rational service areas for purposes of designating Health Professional Shortage Areas (HPSAs), and Medically Underserved Areas and Medically Underserved Populations (MUAs/MUPs).

The MSSAs incorporate the U.S. Census total population, socioeconomic and demographic data and are updated with each decadal census. Office of Statewide Health Planning and Development provides updated data for each County's MSSAs to the County and Communities, and will schedule meetings for areas of significant population change. Community meetings will be scheduled throughout the State as needed.

Adopted by the California Healthcare Workforce Policy Commission on May 15, 2002.

Each MSSA is composed of one or more complete census tracts. MSSAs will not cross county lines. All population centers within the MSSA are within 30 minutes travel time to the largest population center.

Urban MSSA - Population range 75,000 to 125,000. Reflect recognized community and neighborhood boundaries. Similar demographic and socio-economic characteristics.

Rural MSSA - Population density of less than 250 persons per square mile. No population center exceeds 50,000.

Frontier MSSA - Population density of less than 11 persons per square mile.

Amphibians currently represent the most at-risk as well as the least-conserved vertebrate class on earth and factors contributing to these declines are a subject of active research. However, amphibians present a unique challenge to scientists and conservation managers mainly because of their bi-phasic life cycles. Furthermore, it is now generally accepted that each amphibian life history trait is affected differently by anthropogenic threats. In exploring this aspect further, focusing on the amphibians of the southern African sub-continent, frog species were grouped according to their breeding habitats (stream-, permanent aquatic-, temporary aquatic- and terrestrial-breeding groups). First, using a random draw technique focusing specifically on South Africa, at both the national and the biogeographical scales (the latter being defined as sub-regions within the study area identified based on similarities in frog species distributions; see Chapter 2); I evaluate whether areas where different frog breeding-groups occur are characterised by higher levels of anthropogenic threats (human population density, percentage land transformation, percentage protected area, and invasive alien plant richness) than expected by chance. Terrestrial-breeders were often spatially congruent with areas of high threat than expected by chance at both national and biogeographical scales with land transformation and invasive alien plant richness being most significant. Areas where stream-breeders occur were spatially congruent with anthropogenic threats (with alien plants being most consistent) in five of the seven regions examined while protected areas were well-represented in four of the seven regions. Non-significant results were found for permanent and temporary aquatic-breeders at both national and biogeographic scales. Second, I then evaluated the relationship between different frog breeding-group richness and the proportion of protected areas per grid cell (i.e., the proportion of the grid cell that constitutes protected land) and how this relationship is affected by controlling for the wider unprotected matrix. I found that the current southern African conservation portfolio (that is, all proclaimed statutory conservation areas of the sub-continent) only moderately captures anuran distributions. I found a negative relationship between terrestrial-breeding species and the proportion of protected areas. Furthermore, I found that the strength of the model (irrespective of the anuran breeding group) decreased after controlling for the wider unprotected matrix suggesting, at least for now, that amphibian species, in general, are maintaining viable populations within the wider unprotected matrix of the sub-continent. This study indicates that in addition to species with an aquatic tadpole stage, terrestrial-breeding anurans of the southern African sub-continent are in dire need of conservation action. Submitted to fulfil the requirements for an MSc degree.

Analysis of ‘MSSA Detail 2010c1 public’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b0ad8a2d-468b-47bf-8c4e-270779b1d841 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

Medical Service Study Areas - Census Detail, 2010

Medical Service Study Areas (MSSAs) are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSAs were developed in 1976 by the California Healthcare Workforce Policy Commission (formerly California Health Manpower Policy Commission) to respond to legislative mandates requiring it to determine "areas of unmet priority need for primary care family physicians" (Song-Brown Act of 1973) and "geographical rural areas where unmet priority need for medical services exist" (Garamendi Rural Health Services Act of 1976).

MSSAs are recognized by the U.S. Health Resources and Services Administration, Bureau of Health Professions' Office of Shortage Designation as rational service areas for purposes of designating Health Professional Shortage Areas (HPSAs), and Medically Underserved Areas and Medically Underserved Populations (MUAs/MUPs).

The MSSAs incorporate the U.S. Census total population, socioeconomic and demographic data and are updated with each decadal census. Office of Statewide Health Planning and Development provides updated data for each County's MSSAs to the County and Communities, and will schedule meetings for areas of significant population change. Community meetings will be scheduled throughout the State as needed.

Adopted by the California Healthcare Workforce Policy Commission on May 15, 2002.

Each MSSA is composed of one or more complete census tracts. MSSAs will not cross county lines. All population centers within the MSSA are within 30 minutes travel time to the largest population center.

Urban MSSA - Population range 75,000 to 125,000. Reflect recognized community and neighborhood boundaries. Similar demographic and socio-economic characteristics.

Rural MSSA - Population density of less than 250 persons per square mile. No population center exceeds 50,000.

Frontier MSSA - Population density of less than 11 persons per square mile.

--- Original source retains full ownership of the source dataset ---

Species at the periphery of their range are typically limited in density by lower habitat quality. As a result, the Central-Marginal Hypothesis (CMH) predicts a decline in genetic diversity of populations towards the periphery of a species’ range. Grizzly bears (Ursus arctos) once ranged throughout most of North America but have been extirpated from nearly half of their former range, mainly in the south. They are considered a species at risk even in Canada’s remote North, where they occupy the northernmost edge of the species’ continental distribution in a low-productivity tundra environment. With climate change, one of their main food items in the tundra (caribou), which has always shown yearly fluctuations, is declining, but simultaneously, grizzlies appear to be expanding their range northward, in tundra environment. Yet, a lack of population density estimates across the North is hindering effective conservation action. The CMH has implications for the viability of peripheral populations, and the links between population fluctuations, potential bottlenecks and genetic diversity need to be determined to contribute to species’ conservation. Using non-invasive genetic sampling from 2012 to 2014, and autosomal DNA genotyping (via-microsatellites), we estimated bear density using a spatial capture-recapture framework and analysed genetic diversity using observed heterozygosity (Ho), Allelic Richness (AR), and expected heterozygosity (He). We compared our findings to other studies that used comparable methodologies on this and a related species (Black bears; Ursus americanus). We found densities of grizzly bears that were low for the species but characteristic for the region (5.9 ± 0.4 bears/1000 km2), but with high Ho (0.81 ± 0.05), AR (7 ± 0.78) and He (0.71 ± 0.03), despite a signal of recent bottlenecks. In both species, peripherality was not correlated with Ho but was negatively correlated with density. We suggest that the apparent growth of this expanding population of grizzlies offsets the negative impacts of recent bottlenecks on Ho. Indigenous Knowledge provides historical context (on the order of centuries – e.g., arctic large mammal fluctuations, grizzly bear bottlenecks) for the current bear population dynamics (on the order of decades – e.g., climate change, northern grizzly bear expansion). Methods Study area The 30,000km2 study area, centred at N 64.2°, W 110.0°, was located in the Southern Arctic (Coppermine River Upland Ecoregion, CRU) and Taiga Shield (Takijuq Lake Upland Ecoregion, TLU) Ecozones (Figure 1) (Ecological Stratification Working Group Canada 1995). Its northern limit extended to the border of NT and Nunavut, and its southern limit to the tree line. The mean annual temperature of the CRU ecoregion was -7.5 ˚C, and -10.5 ˚C for the TLU ecoregion (Ecological Stratification Working Group Canada 1995). Average temperatures at the time of the study were likely higher than those cited above, due to a changing climate (Post et al. 2009). The tundra area is considered semi-arid, with mean annual precipitation ranging from 200-300 mm, with mostly continuous permafrost characterized by a rolling landscape of uplands, lowlands, and plateaus (Ecological Stratification Working Group Canada 1995). Eskers, created through glaciation and composed of stratified sand and gravel, are found throughout the landscape and form most of the relief in this area. Lowlands are generally a mosaic of sedge dominated wetlands composed of fens and bogs as well as lakes and rivers. Vegetative cover is typically dominated by heath and shrub species such as Labrador tea (Rhododendron spp.), dwarf birch (Betula nana), and willow (Salix herbacea). Mammals inhabiting the area include barren-ground caribou, moose (Alces alces) but at very low densities, grizzly bears, wolves (Canis spp), red and arctic foxes (Vulpes spp.), and small rodents. Grizzly bear sampling approach Non-invasive genetic sampling of bears can be conducted using natural rub sites (e.g., trees), or human-made objects usually placed within predetermined grid cells delineated prior to detector deployment (Woods et al. 1999, Karamanlidis et al. 2010, Dumond et al. 2015, Boulanger et al. 2018). The spatial organization of a detector array for use in capture-recapture studies depends on the movement ecology of species (Sollmann et al. 2012, Royle et al. 2014). We divided the study area into 221 square grid cells of 144 km2 each. This area was based on a rough approximation of 14-day home ranges of barren ground female grizzly bears, which was based on previous studies (McLoughlin et al. 1999) (Fig. 1). Owing to the lack of natural rub sites in the tundra, we constructed hair snare posts from wooden boards wrapped in barbed wire and fastened together into a tripod shape so that posts could be transported by helicopter and deployed in the field (Dumond et al. 2015) (Figure 2). Hair snares were deployed near the centre of each grid cell, while avoiding locations on lakes or waterbodies, which also influenced logistics of access. They were baited with a non-reward scent lure corresponding to the seasonal availability of food sources to attract grizzly bears, as identified during a Traditional Ecological Knowledge (TEK) workshop held in the community of Lutsel K’e, NT in 2015 (Jessen 2017), and based on previous studies (Gau et al. 2002b). In early and late summer, rotten cow’s blood and fish oil were used while bergamot, raspberry, and cranberry oil were used in the mid-summer (Supplementary Table S1). We conducted six sampling occasions (rounds of site visits) per session (year), each lasting 10-14 days to minimize hair sample degradation from weather exposure, which increases with time (Dumond et al. 2015)(Supplementary Methods; Supplementary Table S1). Hair samples, i.e., clumps of hair captured by a single barb, were collected during each visit, and placed in labelled paper envelopes which were stored in a cool, dry place. Sampling of the northern half of the study area was carried out from mid-June to mid-September of 2012 and 2013, and sampling of the southern half of the study area was identically conducted from mid-June to mid-September of 2013 and 2014 (Fig. 1). Laboratory and statistical analyses of genetic data A high-quality set of hair (either ≥ 30 underfur or ≥ 2 guard hair roots) was chosen from each hair sample and analyzed for species confirmation, sex, and genotype using established techniques, including established genotyping error-checking protocols (Paetkau 2003). We used a ZFX/ZFY gender marker, plus 8 microsatellite markers (G10B, CXX110, G1D, G10H, G10J, G10M, G10P, MU59) (Paetkau et al. 1999). Using 5 loci is considered sufficient for accurately detecting individuals in brown bears (Waits et al. 2001), and typically 7-8 loci are used in studies of brown bear populations (Boulanger et al. 2001, Dumond et al. 2015). All genetic analyses were conducted by Wildlife Genetics International (WGI) in Nelson, British Columbia. Scoring errors and the presence of null alleles were detected using MICROCHECKER v2.2.3 (Van Oosterhout et al. 2004). We also tested markers for deviations from Hardy-Weinberg Equilibrium (HWE) and for Linkage Disequilibrium (LD), using the exact probability test in Genepop v4.2 (Rousset 2008) (Supplemental Methods S2). Allelic richness (AR), Ho, and Nei’s unbiased expected heterozygosity (He; (Nei and Roychoudhury 1974)) were used as measures of genetic diversity. Both were used because allelic richness measures the number of alleles in a population standardized by sample size and is a measure of the raw amount of variation at loci, while expected heterozygosity accounts for both the number of alleles and the evenness of allele frequencies. AR was calculated using the rarefaction method implemented in FSTAT v2.9.4 (Goudet 2003), and He was obtained using Genetix v4.05.2 (Belkhir et al. 2004). We used the program NeEstimator to estimate effective population size (Ne) with its linkage disequilibrium single‐sample estimator without lowest allele frequency restriction (Do et al. 2014). We also tested for heterozygosity excesses and signs of a genetic bottleneck using the program Bottleneck (Cornuet and Luikart 1996, Cristescu et al. 2010). Its infinite allele model (IAM), two-phase model (TPM), and stepwise mutation model (SMM) were all applied. It should be noted that all are legitimate, although imperfect models of mutations, and simulations results are inconclusive on which is the most appropriate for autosomal microsatellite data (i.e., our data) in particular (Shriver et al. 1993). Ultimately these models, for which no consensus currently exists on which is best, allow assessing genetic signatures of population bottlenecks or of population expansions; however, these cannot be distinguished from genetic signatures resulting from natural fluctuations in density. Estimating density using spatial capture-recapture We developed spatial capture-recapture encounter histories of individual bears based on the hair samples (Woods et al. 1999, Mowat and Strobeck 2000, Boulanger et al. 2018). We used spatial capture-recapture (SCR) models to estimate density. SCR models join an observation model that describes the decreasing probability of detecting an individual as a function of the distance between a detector location and an animal’s home range centre, to a spatial point process model that describes the distribution of animal home range centres on a landscape (Efford 2004, Royle and Young 2008, Efford and Fewster 2013). We estimated bear density in the study area for the years 2012, 2013, and 2014 separately, using the secr package version 4.5.5 (Efford 2022) in the program R, Version 4.1.3 (R Core Team 2022). To account for peripheral individuals with home range centres outside the detector array, we applied a 48 km buffer zone, approximately double the greatest Root Pooled Spatial Variance for

The red sea urchin fishery has a long harvest and management history along the Northeastern Pacific coast. In Mexico, it has been commercially harvested since 1972, and although it is one of the most important fisheries in Baja California, efforts to assess the condition and dynamics of harvestable stocks have been focused on certain harvested areas with scarce fisheries independent data. Additionally, the analysis of yearly information for small geographic areas has obscured the actual status of harvested populations. This study aims to re-assess population trends, fishing effort, and catches, incorporating all available information from the last 19 years. Information was grouped based on 14 landing sites along Baja California’s Pacific coast. Length based virtual population analysis (LVPA) was implemented to estimate site-specific catch rates and densities. Red sea urchin catches/landings varied widely within and between areas. Population density was below 1 urchin m–2 in most of the sites, and was composed of higher recruits and juvenile densities that may partially mitigate for fishery removals. LVPA produced biomass estimations that double previous estimates. We suggest that the model parameters used in previous estimations might not reflect key biological traits of the red sea urchin, failing to reproduce population trends accurately. Results from this study allowed identifying the specific sites where population attributes (biomass, densities), fishery data (catch, effort), and the combination of both (Kobe plots), suggest that urchin populations may need attention. New management measures must be adopted: maximum legal size of 110 mm, improvement on fishery logs and analysis, continuous fishery independent surveys to track changes in the population that might not be so apparent when observing only catch/biomass data. Reinforce the under legal size management strategy, since results suggest that sites with high abundances of small urchins can support higher catches.

GEM's Global Socio-Economic Vulnerability Maps

The Global Social Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-human-vulnerability) is a composite index that was developed to measure characteristics or qualities of social systems that create the potential for loss or harm. Here, social vulnerability helps to explain why some countries will experience adverse impacts from earthquakes differentially where the linking of social capacities with demographic attributes suggests that communities with higher percentages of age-dependent populations, homeless, disabled, under-educated, and foreign migrants are likely to exhibit higher social vulnerability than communities lacking these characteristics. Other relevant factors that affect the social vulnerability of populations include in-migration from foreign countries, population density, an accounting of slum populations, and international tourist arrivals.

The Global Economic Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-economic-vulnerability) is a composite index that was designed primarily to measure the potential for economic losses from earthquakes due to a country’s macroeconomic exposure. This index is also an appraisal of the ability of countries to respond to shocks to their economic systems. Relevant indicators include the density of exposed economic assets such as commercial and industrial infrastructure. Metrics used to measure the ability of a country to withstand shocks to its economic system include reliance on imports/exports, government debt, and purchasing power. The economic vulnerability category also considers the economic vitality of countries since the economic vitality of a country can be directly related to the vulnerability and resilience of its populations. The latter includes measurements of single-sector economic dependence, income inequality, and employment status.

The Recovery/Reconstruction Potential Map (viewable here: https://maps.openquake.org/map/sv-global-recovery-and-reconstruction) is closely aligned with the concept of disaster resilience. Enhancing a country’s resilience to earthquakes is to improve its capacity to anticipate threats, to reduce its overall vulnerability, and to allow its communities to recover from adverse impacts from earthquakes when they occur. The measurement of recovery and reconstruction potential includes capturing inherent conditions that allow communities within a country to absorb impacts and cope with a damaging earthquake event, such as the density of the built environment, education levels, and political participation. It also encompasses post-event processes that facilitate a population’s ability to reorganize, change, and learn in response to a damaging earthquake.

Criteria for indicator selection
To choose indicators contextually exclusive for use in each map, the starting point was an exhaustive review of the literature on earthquake social vulnerability and resilience. For a variable to be considered appropriate and selected, three equally important criteria were met:

- variables were justified based on the literature regarding its relevance to one or more of the indices.
- variables needed to be of consistent quality and freely available from sources such as the United Nations and the World Bank; and
- variables must be scalable or available at various levels of geography to promote sub-country level analyses.

This procedure resulted in a ‘wish list’ of approximately 300 variables of which 78 were available and fit for use based on the three criteria.

Process for indicator selection
For variables to be allocated to an index, a two-tiered validation procedure was utilized. For the first tier, variables were assigned to each of the respective indices based on how each variable was cited within the literature, i.e., as being part of an index of social vulnerability, economic vulnerability, or recovery/resilience. For the second tier, machine learning and a multivariate ordinal logistic regression modelling procedure was used for external validation. Here, focus was placed on the statistical association between the socio-economic vulnerability indicators and the adverse impacts from historical earthquakes on a country-by country-basis.

The Global Significant Earthquake Database provided the external validation metrics that were used as dependent variables in the statistical analysis. To include both severe and moderate earthquakes within the dependent variables, adverse impact data was collected from damaging earthquake events that conformed to at least one of five criteria: 1) caused deaths, 2) caused moderate damage (approximately 1 million USD or more), 3) had a magnitude 7.5 or greater 4) had a Modified Mercalli Intensity (MMI) X or greater, or 5) generated a tsunami. This database was chosen because it considers low magnitude earthquakes that were damaging (e.g., MW >=2.5 & MW<=5.5) and contains socio-economic data such as the total number of fatalities, injuries, houses damaged or destroyed, and dollar loss estimates in USD.

Countries not demonstrating at least a minimal earthquake risk, i.e., seismicity <0.05 PGA (Pagani et al. 2018) and <$10,000 USD in predicted average annual losses (Silva et al. 2018) were eliminated from the analyses so as not to include countries with minimal to no earthquake risk. A total study area consists of 136 countries.

Global Proline Market to grow from US$ 653.4 Million in 2023 to US$ 1077.11 Million by 2032, at a CAGR of 5.3%.

In nature species react to a variety of endogenous and exogenous ecological factors. Understanding the mechanisms by which these factors interact and drive population dynamics is a need for understanding and managing ecosystems. In this study we assess, using laboratory experiments, the effects that the combinations of two exogenous factors exert on the endogenous structure of the population dynamics of a size-structured population of Daphnia. One exogenous factor was size-selective predation, which was applied on experimental populations through simulating: (a) selective predation on small prey, (b) selective predation on large prey and (c) non-selective predation. The second exogenous factor was pesticide exposure, applied experimentally in a quasi-continuous regime. Our analysis combined theoretical models and statistical testing of experimental data for analyzing how the density dependence structure of the population dynamics was shifted by the different exogenous factors. Our results showed that pesticide exposure interacted with the mode of predation in determining the endogenous dynamics. Populations exposed to the pesticide and to either selective predation on newborns or selective predation on adults exhibited marked nonlinear effects of pesticide exposure. However, the specific mechanisms behind such nonlinear effects were dependent on the mode of size-selectivity. In populations under non-selective predation the pesticide exposure exerted a weak lateral effect. The ways in which endogenous process and exogenous factors may interact determine population dynamics. Increases in equilibrium density results in higher variance of population fluctuations but do not modify the stability properties of the system, while changes in the maximum growth rate induce changes in the dynamic regimes and stability properties of the population. Future consideration for research includes the consequences of the seasonal variation in the composition and activity of the predator assembly in interaction with the seasonal variation in exposure to agrochemicals on freshwater population dynamics.

Abstract (en): The Local Arts Index was developed in response to an interest in "scaling-down" the National Arts Index (NAI) to the community level and to the growing demand for comparative information on arts at the community level. The LAI was developed in partnership with arts leadership organizations in over 100 communities and is comprised of a variety of indicators to understand who we are as a community and how that manifests itself through cultural activities and participation. Indicators are a systematic data collection initiative that is conducted regularly over time. The LAI compresses many arts indicators into one number that is calculated the same way and at regular time intervals, making it easy to compare performance between time periods. The LAI collected county level data such as nonprofit arts revenue and expenditures, creative businesses and nonprofit arts organizations per 100,000 residents, arts share of businesses, employees, establishments, and payroll, estimated expenditures on arts equipment, number of visual and performing arts degrees, and adult population attending arts and culture activities. Demographic information includes median measures of age, household income, and year housing was built, as well as population density, and population share that was over 65, non-English speakers, and non-white. The purpose of the Local Arts Index (LAI) is to provide a set of measures to understand the breadth, depth and character of the cultural life of a community, as well as provide a framework for relating arts and culture to community priorities and aspirations. The P.I.s used the county as the unit of analysis; the 2010 Census lists 3,143 counties or equivalents in the 50 states plus the District of Columbia. To measure a wide range of local arts and culture activity, the P.I.s gathered several hundred micro-level, specific measures of arts activity, resources, participation, and character, from which a smaller number of useful county-level indicators of arts and culture were produced. The P.I.s set each of the indicators in a conceptual framework, the Community Arts Vitality Model. The secondary data sources provide information for varying numbers of counties. Typically, there is ample data to describe urban counties, less for rural counties. The indicators span multiple years, and almost all are from 2011 or later. This study has 3146 cases and 147 variables. Variables include county-level information on adult cultural participation, nonprofit arts expenditures, per capita arts expenditures, nonprofit art program revenues, government art grants per capita, arts-related establishments per 100,000 residents, weight of arts sector in community's business population, grant success rate, institutional or entrepreneurial factor of cultural character, number of historic places per 100,000 residents, and professional arts training. Demographic information includes bureau of economic analysis region, population density, median age, population share over 65, population share that are non-English speakers, population share that is non-white, median year housing built, population share with a bachelor's degree, household income, population share commuting to work, per capita income, and total population. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Datasets:DS1: Local Arts Index (LAI), 2009-2015 [United States] Counties within the United States. Smallest Geographic Unit: County Secondary data was obtained from over 25 different sources including: the federal government; private membership organizations, professional societies, and trade groups; research institutions; and commercial data providers. Criteria for including a particular data point in the Local Arts Index are:

The indicator has at its core a meaningful measurement of arts and culture activity; The data are measured at the county level; The data are produced annually by a reputable organization; The data are statistically valid, even if based on sample; Future years of data are ex...

This study contains selected electoral and demographic national data for nine nations in the 1950s and 1960s. The data were prepared for the Data Confrontation Seminar on the Use of Ecological Data in Comparative Cross-National Research held under the auspices of the Inter-university Consortium for Political and Social Research on April 1-18, 1969. One of the primary concerns of this international seminar was the need for cooperation in the development of data resources in order to facilitate exchange of data among individual scholars and research groups. Election returns for two or more national and/or local elections are provided for each of the nine nations, as well as ecological materials for at least two time points in the general period of the 1950s and 1960s. While each dataset was received at a single level of aggregation, the data have been further aggregated to at least a second level of aggregation. In most cases, the data can be supplied at the commune or municipality level and at the province or district level as well. Part 1 (Germany, Regierungsbezirke), Part 2 (Germany, Kreise), Part 3 (Germany, Lander), and Part 4 (Germany, Wahlkreise) contain data for all kreise, laender (states), administrative districts, and electoral districts for national elections in the period 1957-1969, and for state elections in the period 1946-1969, and ecological data from 1951 and 1961. Part 5 (France, Canton), and Part 6 (France, Departemente) contain data for the cantons and departements of two regions of France (West and Central) for the national elections of 1956, 1962, and 1967, and ecological data for the years 1954 and 1962. Data are provided for election returns for selected parties: Communist, Socialist, Radical, Federation de Gauche, and the Fifth Republic. Included are raw votes and percentage of total votes for each party. Ecological data provide information on total population, proportion of total population in rural areas, agriculture, industry, labor force, and middle class in 1954, as well as urbanization, crime rates, vital statistics, migration, housing, and the index of "comforts." Part 7 (Japan, Kanagawa Prefecture), Part 8 (Japan, House of Representatives Time Series), Part 9 (Japan, House of (Councilors (Time Series)), and Part 10 (Japan, Prefecture) contain data for the 46 prefectures for 15 national elections between 1949 and 1968, including data for all communities in the prefecture of Kanagawa for 13 national elections, returns for 8 House of Representatives' elections, 7 House of Councilors' elections, descriptive data from 4 national censuses, and ecological data for 1950, 1955, 1960, and 1965. Data are provided for total number of electorate, voters, valid votes, and votes cast by such groups as the Jiyu, Minshu, Kokkyo, Minji, Shakai, Kyosan, and Mushozoku for the Communist, Socialist, Conservative, Komei, and Independent parties for all the 46 prefectures. Population characteristics include age, sex, employment, marriage and divorce rates, total number of live births, deaths, households, suicides, Shintoists, Buddhists, and Christians, and labor union members, news media subscriptions, savings rate, and population density. Part 11 (India, Administrative Districts) and Part 12 (India, State) contain data for all administrative districts and all states and union territories for the national and state elections in 1952, 1957, 1962, 1965, and 1967, the 1958 legislative election, and ecological data from the national censuses of 1951 and 1961. Data are provided for total number of votes cast for the Congress, Communist, Jan Sangh, Kisan Mazdoor Praja, Socialist, Republican, Regional, and other parties, contesting candidates, electorate, valid votes, and the percentage of valid votes cast. Also included are votes cast for the Rightist, Christian Democratic, Center, Socialist, and Communist parties in the 1958 legislative election. Ecological data include total population, urban population, sex distribution, occupation, economically active population, education, literate population, and number of Buddhists, Christians, Hindus, Jainis, Moslems, Sikhs, and other religious groups. Part 13 (Norway, Province), and Part 14 (Norway, Commune) consist of the returns for four national elections in 1949, 1953, 1957, and 1961, and descriptive data from two national censuses. Data are provided for the total number

Medical Service Study Areas - Census Detail, 2010California Health & Human Services Agency Open Data Portal DescriptionMedical Service Study Areas (MSSAs) are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSAs were developed in 1976 by the California Healthcare Workforce Policy Commission (formerly California Health Manpower Policy Commission) to respond to legislative mandates requiring it to determine "areas of unmet priority need for primary care family physicians" (Song-Brown Act of 1973) and "geographical rural areas where unmet priority need for medical services exist" (Garamendi Rural Health Services Act of 1976).MSSAs are recognized by the U.S. Health Resources and Services Administration, Bureau of Health Professions' Office of Shortage Designation as rational service areas for purposes of designating Health Professional Shortage Areas (HPSAs), and Medically Underserved Areas and Medically Underserved Populations (MUAs/MUPs).The MSSAs incorporate the U.S. Census total population, socioeconomic and demographic data and are updated with each decadal census. Office of Statewide Health Planning and Development provides updated data for each County's MSSAs to the County and Communities, and will schedule meetings for areas of significant population change. Community meetings will be scheduled throughout the State as needed.Adopted by the California Healthcare Workforce Policy Commission on May 15, 2002.Each MSSA is composed of one or more complete census tracts. MSSAs will not cross county lines. All population centers within the MSSA are within 30 minutes travel time to the largest population center.Urban MSSA - Population range 75,000 to 125,000. Reflect recognized community and neighborhood boundaries. Similar demographic and socio-economic characteristics.Rural MSSA - Population density of less than 250 persons per square mile. No population center exceeds 50,000.Frontier MSSA - Population density of less than 11 persons per square mile.

The dataset includes the CDR of Riyadh city, the Traffic Analysis Zones (TAZs) locations, proposed metro stations, demographic data about the city, and the road network. Those will be explored in the following.CDRCDR data represents a digital record containing information about a telephone call or communication session. A CDR typically includes information such as the caller's and recipient's phone numbers, the date and time of the call, the duration of the call, and any additional services or features used during the call (e.g., call forwarding, call waiting). In addition, CDRs may contain information about the type of call (voice, video, data), the caller's or recipient's location, and details about any supplementary services utilized during the call.The CDR file used is provided by STC for Riyadh and collected from 1,712 towers over a month, separated by hours. Each hour contains call details during that hour. These details include information about where calls originated and ended and at what hour of the day. TAZTraffic Analysis Zones (TAZs) are geographical areas defined and used for transportation planning and traffic analysis purposes. TAZs are created by dividing a large region or area into smaller sub-areas based on population density, land use patterns, transportation infrastructure, and socio-economic characteristics.TAZs are defined to facilitate transportation planning and analysis by providing a more granular and manageable unit for studying travel patterns and forecasting transportation demand. TAZs are often used in transportation models and simulations to estimate and analyze traffic flows, travel behavior, and travel demand within specific areas. As part of this study, Riyadh city is defined as 1,492 TAZs.Metro StationsIn this study, we consider the 84 metro stations. Based on the spatial information of each TAZ, we associated each TAZ with the closest metro station. This made it easier to predict metro usage.Demographic DataThe demographic data is available in TAZ. Each TAZ includes data on the total population of males, females, and non-Saudis. Females and non-Saudis are expected to utilize the metro more based on the sociocultural implications of the region. Hence, areas with higher concentrations of those populations expect more metro usage.Road NetworkThe road network comprises several thousand lines, each represented by numerous points defined by latitude and longitude. These points constitute nodes. These road lines are the pathways that users travel on while making a call.

Background A key aim in wildlife disease ecology is to understand how host and parasite characteristics influence parasite transmission and persistence. Variation in host population density can have strong impacts on transmission and outbreaks, and theory predicts particular transmission-density patterns depending on how parasites are transmitted between individuals. Here, we present the results of a study on the dynamics of Morogoro arenavirus in a population of multimammate mice (Mastomys natalensis). This widespread African rodent, which is also the reservoir host of Lassa arenavirus in West Africa, is known for its strong seasonal density fluctuations driven by food availability. Goal We investigated to what degree virus transmission changes with host population density and how the virus might be able to persist during periods of low host density. Methods A seven-year capture-mark-recapture study was conducted in Tanzania where rodents were trapped monthly and screened for the presence of antibodies against Morogoro virus. Observed seasonal seroprevalence patterns were compared with those generated by mathematical transmission models to test different hypotheses regarding the degree of density-dependence and the role of chronically infected individuals. Results We observed that Morogoro virus seroprevalence correlates positively with host density with a lag of one to four months. Model results suggest that the observed seasonal seroprevalence dynamics can be best explained by a combination of vertical and horizontal transmission, and that a small number of animals needs to be infected chronically to ensure viral persistence. Broad context Transmission dynamics and viral persistence were best explained by the existence of both acutely and chronically infected individuals, and by seasonally changing transmission rates. Due to the presence of chronically infected rodents, rodent control is unlikely to be a feasible approach for eliminating arenaviruses such as Lassa virus from Mastomys populations.

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219