Historical dataset of population level and growth rate for the San Pedro Sula, Honduras metro area from 1950 to 2025.

Chile INE Projection: Population: Metropolitan Santiago: San Pedro data was reported at 13.627 Person th in 2035. This records an increase from the previous number of 13.561 Person th for 2034. Chile INE Projection: Population: Metropolitan Santiago: San Pedro data is updated yearly, averaging 11.587 Person th from Jun 2002 (Median) to 2035, with 34 observations. The data reached an all-time high of 13.627 Person th in 2035 and a record low of 8.378 Person th in 2002. Chile INE Projection: Population: Metropolitan Santiago: San Pedro data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G002: Population: Projection.

INE Projection: Population: Biobio: San Pedro de la Paz data was reported at 164.457 Person th in 2035. This records an increase from the previous number of 163.972 Person th for 2034. INE Projection: Population: Biobio: San Pedro de la Paz data is updated yearly, averaging 142.168 Person th from Jun 2002 (Median) to 2035, with 34 observations. The data reached an all-time high of 164.457 Person th in 2035 and a record low of 83.721 Person th in 2002. INE Projection: Population: Biobio: San Pedro de la Paz data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G002: Population: Projection.

Historical dataset of population level and growth rate for the San Pedro, Philippines metro area from 1950 to 2025.

INE Projection: Population: Antofagasta: San Pedro de Atacama data was reported at 11.694 Person th in 2035. This records an increase from the previous number of 11.659 Person th for 2034. INE Projection: Population: Antofagasta: San Pedro de Atacama data is updated yearly, averaging 9.797 Person th from Jun 2002 (Median) to 2035, with 34 observations. The data reached an all-time high of 11.694 Person th in 2035 and a record low of 4.237 Person th in 2002. INE Projection: Population: Antofagasta: San Pedro de Atacama data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G002: Population: Projection.

This is the dataset concerning the distribution of the population grouped by way with the indication of number of males and females, number of citizens and number of families of the municipal territory of the Municipality of San Pietro di Morubio

Saint Pierre and Miquelon is an archipelago located in the Atlantic Ocean, known for its rich cultural history and vibrant community. In this article, we will explore the surnames of the inhabitants of Saint Pierre and Miquelon, highlighting the diversity and roots that characterize this territory. As we delve into the list of most common surnames in Saint Pierre and Miquelon, we will observe how these names reflect the identity of its inhabitants, as well as their connection to local history and traditions. This analysis will not only allow us to better understand the Sanpedrinos and Miqueones, but will also help us understand the evolution of their culture and their community over time. Join us on this journey through the surnames that give life and plurality to Saint Pierre and Miquelon!

Given the characteristics of the COVID-19 pandemic and the limited tools for orienting interventions in surveillance, control, and clinical care, the current article aims to identify areas with greater vulnerability to severe cases of the disease in Rio de Janeiro, Brazil, a city characterized by huge social and spatial heterogeneity. In order to identify these areas, the authors prepared an index of vulnerability to severe cases of COVID-19 based on the construction, weighting, and integration of three levels of information: mean number of residents per household and density of persons 60 years or older (both per census tract) and neighborhood tuberculosis incidence rate in the year 2018. The data on residents per household and density of persons 60 years or older were obtained from the 2010 Population Census, and data on tuberculosis incidence were taken from the Brazilian Information System for Notificable Diseases (SINAN). Weighting of the indicators comprising the index used analytic hierarchy process (AHP), and the levels of information were integrated via weighted linear combination with map algebra. Spatialization of the index of vulnerability to severe COVID-19 in the city of Rio de Janeiro reveals the existence of more vulnerable areas in different parts of the city’s territory, reflecting its urban complexity. The areas with greatest vulnerability are located in the North and West Zones of the city and in poor neighborhoods nested within upper-income parts of the South and West Zones. Understanding these conditions of vulnerability can facilitate the development of strategies to monitor the evolution of COVID-19 and orient measures for prevention and health promotion.

Understanding the biotic consequences of Pleistocene range shifts and fragmentation remains a fundamental goal in historical biogeography and evolutionary biology. Here, we combine species distribution models (SDM) from the present and two late Quaternary time periods with multilocus genetic data (mitochondrial DNA and microsatellites) to evaluate the effect of climate-induced habitat shifts on population genetic structure in the Large-blotched Ensatina (Ensatina eschscholtzii klauberi), a plethodontid salamander endemic to middle and high-elevation conifer forest in the Transverse and Peninsular Ranges of southern California and northern Baja California. A composite SDM representing the range through time predicts two disjunct refugia, one in southern California encompassing the core of the species range and the other in the Sierra San Pedro Mártir of northern Baja California at the southern limit of the species range. Based on our spatial model, we would expect a pattern of high connectivity among populations within the northern refugium and, conversely, a pattern of isolation due to long-term persistence of the Sierra San Pedro Mártir population. Our genetic results are consistent with these predictions based on the hypothetical refugia in that (i) historical measures of population connectivity among stable areas are correlated with gene flow estimates; and (ii) there is strong geographical structure between separate refugia. These results provide evidence for the role of recent climatic change in shaping patterns of population persistence and connectivity within the Transverse and Peninsular Ranges, an evolutionary hotspot.

This is the dataset concerning the population grouped by year of birth of the municipal territory of the Municipality of San Pietro di Morubio

Dormant life stages are often critical for population viability in stochastic environments, but accurate field data characterizing them are difficult to collect. Such limitations may translate into uncertainties in demographic parameters describing these stages, which then may propagate errors in the examination of population-level responses to environmental variation. Expanding on current methods, we 1) apply data-driven approaches to estimate parameter uncertainty in vital rates of dormant life stages and 2) test whether such estimates provide more robust inferences about population dynamics. We built integral projection models (IPMs) for a fire-adapted, carnivorous plant species using a Bayesian framework to estimate uncertainty in parameters of three vital rates of dormant seeds – seed-bank ingression, stasis and egression. We used stochastic population projections and elasticity analyses to quantify the relative sensitivity of the stochastic population growth rate (log λs) to changes in these vital rates at different fire return intervals. We then ran stochastic projections of log λs for 1000 posterior samples of the three seed-bank vital rates and assessed how strongly their parameter uncertainty propagated into uncertainty in estimates of log λs and the probability of quasi-extinction, Pq(t). Elasticity analyses indicated that changes in seed-bank stasis and egression had large effects on log λs across fire return intervals. In turn, uncertainty in the estimates of these two vital rates explained > 50% of the variation in log λs estimates at several fire-return intervals. Inferences about population viability became less certain as the time between fires widened, with estimates of Pq(t) potentially > 20% higher when considering parameter uncertainty. Our results suggest that, for species with dormant stages, where data is often limited, failing to account for parameter uncertainty in population models may result in incorrect interpretations of population viability.

Abstract

The objectives of the survey included the following: 1. Estimate current levels of fertility. 2. Estimate levels of neonatal, infant and child mortality. 3. Determine the prevalence, pattern and mean duration of breastfeeding. 4. Determine the prevalence and severity of diarrhea and acute respiratory infections among children under age five, the percentage that received treatment, and the type of treatment received. 5. Determine the percentage of children under five that have been immunized against various childhood diseases. 6. Determine the sources of prenatal, delivery and postpartum care and for women delivering at home, the care provided by traditional birth attendants. 7. Determine contraceptive prevalence by method and by source. 8. Identify reasons for terminating the use of family planning and for not using contraception: determine the extent of unmet need for contraception. 9. Determine the percentage of women who have experienced unplanned pregnancies, the pregnancy intentions of all women, ideal family size and the percentage of women who want more children. 10. Determine what advice is given and the impact of this advice on the decision to discontinue pill use among women with problems related to oral contraception. 11. Explore characteristics of adolescents and young women 20-24 as they relate to first intercourse and patterns of family building. 12. Determine levels of knowledge concerning STDs and AIDS.

Geographic coverage

National, except two departments which were excluded from the sample, Gracias a Dios and the Bay Islands, due to difficult access and their small and scattered population. Together they make up only 1.5 percent of the total population of Honduras.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 1987 National Epidemiology and Family Health Survey (EFHS) employed a multistage probability sample of over 11,000 households. Two departments were excluded from the sample, Gracias a Dios and the Bay Islands, due to difficult access and their small and scattered population. Together they make up only 1.5 percent of the total population of Honduras.

To carry out such a survey, adequate maps must be available to construct small area units to serve as sampling units in the later stages of selection. However, for the 1987 survey, current maps were not readily available for all parts of the country. At the time of sample preparation, the Honduran Bureau of Census and Statistics was updating maps for the 1988 Census and had updated maps of municipios (counties) representing a little less than half of the country's population. Included in the updated maps were those of 16 major cities. These cities had been selected for their importance in terms of employment by the Bureau of Census and Statistics which in September of 1986 carried out the Encuesta de Hogares (Labor Force Survey) to determine employment patterns of the labor force. Thus for the 1987 survey, two groups of recently updated maps were available: urban maps of 16 cities divided into colonias (neighborhoods) which were divided into sectors for the Encuesta de Hogares, and census maps of rural municipios divided into aldeas (villages) which Ministry of Health and Management Sciences for Health staff subsequently divided into sectors.

In the remainder of the country where Census personnel had not updated maps, the 1974 Census sector maps were used for the first stage of selection. Once these area units were selected, teams composed of survey staff and cartographers from the Vector Control Division of the Ministry of Public Health visited the sites and updated the maps.

The partial availability of current maps led to the designation of the following four cells (see Table II B1 of the Final Report) which served as the first level of stratification in the primary sample.

This four cell partitioning of the primary sample is not equivalent to stratification using the official definition of urbanization since Cells 2, 3 and 4 in Table II B1 contained both urban and rural areas. However, it is important to note that this feature of the design did not compromise our ability to produce estimates by official urban or rural designations.

Once the interview was completed and questionnaires were coded for data processing, all sectors were classified according to strata: 1) urban Tegucigalpa and urban San Pedro Sula, 2) other urban areas, and 3) rural areas. This classification of strata maintains the final strata designation used in the 1984 MCH/FP Survey. Urban was defined according to criteria used by the Bureau of Census and Statistics: population greater than 2000 inhabitants with public utilities, water and sewage. Since population estimates are based on 1974 census information, rural areas with less than but close to 2000 inhabitants were assumed to have grown and were reclassified as urban.

The 1986 population of Honduras based on estimates from the Latin American Center for Demography (CELADE) and the Secretary for Planning and Finances (SECPLAN) was about 4.2 million inhabitants. With about 5.5 persons per household, this corresponds to 763,636 households. Thus the overall sampling rate in selecting 11,660 households was 0.0153, or about one out of every 65 households. Our aim was to select a sample in which the sample and Census distributions by cell were approximately equal.

In January of 1987 the Ministry of Public Health implemented the National Nutrition Survey which used the sampling frame originally designed for the 1987 Epidemiology and Family Health Survey. Cell 1 of the Nutrition Survey was a subsample of the Encuesta de Hogares.

We selected 550 primary sampling units (PSUs) for the EFHS, 275 of which had been chosen earlier for the Nutrition Survey. However, new segments or secondary sampling units (SSUs) were selected for the EFHS in the previously used PSUs. An additional 275 PSUs were chosen independently from the four original stratified listings which correspond to the 4 design cells. To facilitate selection of the additional PSUs, we doubled the number of PSUs in each cell. To achieve the desired sample size for the EFHS, we increased the segment size used in the Nutrition Survey by 50 percent. The targeted number of households per segment varied.

Selection Protocol for each Cell

Cell 1

In preparation for the Encuesta de Hogares, cartography staff made a rapid enumeration of blocks and households in the 16 selected cities. Blocks were grouped in units that averaged 50 households. However, the units ranged from 25 to 125 households. These units were called PSUs and were listed and sorted hierarchically by health region, city, socioeconomic status, and population size or geographic proximity. Since probability proportional to size (PPS) systematic sampling was used ultimately to choose the PSU sample for each design cell, ordering according to the above variables implicitly stratified the sample. In the Encuesta de Hogares, the measure of size for PPS selection was the number of HHs. PSUs were selected with PPS. For the EFHS and Nutrition Surveys, a subsample of the Encuesta de Hogares sample was chosen. PSUs for these surveys were selected at random using equal probability.

Once the appropriate number of PSUs was selected, the SSUs or segments were delineated to contain approximately 15 households in Tegucigalpa and San Pedro Sula and about 8 households in the other 14 cities. Survey staff chose one segment at random and identified it on the map. In the field, the supervisor counted the number of households in the segment and when there were more than the predetermined cluster size, she chose at random a house with which to begin. Following a clockwise direction, interviewers visited households until the appropriate number was attained. An interview was attempted with all eligible respondents (women aged 15 to 44) in each household selected.

Cell 2

The updated (as of October 24, 1986) rural and urban maps not included in Cell 1, were used to define Cell 2. Municipios were divided into villages which were designated as the PSUs. PSU selection was done by PPS systematic sampling where the selection probability for each PSU was proportional to its size (total number of households) and an interval of fixed length was applied to the frame after a random start. The list of PSUs was ordered according to health region, department, municipio and geographical proximity which means a serpentine route was followed so that any two consecutive PSUs on the list were neighbors geographically. Once chosen, the PSU was divided into segments of about 33 households and one segment was chosen at random. All households in that chosen segment were contacted.

Cells 3 and 4

These cells used 1974 Census sector maps at the first stage of selection. The PSUs for urban areas were ordered according to health region, municipio, city and SES status when possible or geographic proximity. In rural areas, lists were sorted by health region, department, municipio and geographic proximity. PSUs were chosen separately in each cell by PPS systematic sampling and these "census sector maps" were updated for the EFHS and Nutrition Survey. The updated sectors averaged 70 households but ranged from 25 to 130 households. As in Cell 2, one segment of about 33 households was chosen at random for each PSU.

Summary of the sample for the 1987 Epidemiology and Family Health Survey

General description: Two stage area sample of households with stratification in the first stage and area segments of 8-33 households as the ultimate sampling unit. Expected number of selected households: 11,660 Overall household sampling rate: About 1 in 65 households Expected number of responding households: 9,736* Expected number of responding eligible women in selected households:

Abstract copyright UK Data Service and data collection copyright owner. This project explored the implications of social change for collaborative approaches to conservation, through an analysis and participatory review of one case study, the Tamshiyacu Tahuayo Communal Reserve in Amazonian Peru, and an overview of the Peruvian communal reserve system as a whole. Over the past twenty years, many conservationists have moved from a policy of excluding local communities from protected areas to one of collaborating with them. However, there is a growing backlash against this approach, partly because of concern that collaborative approaches will not survive processes of social change particularly population growth and increasing demand for consumer goods. This project informed the debate by examining implications of these processes for an established collaborative initiative. Specifically, it explored changes in settlement patterns and social organisation, attitudes to natural resource use, and attitudes to the Reserve itself over the past 25 years, and tracked their influence on the evolution of co-management. This mixed methods data collection includes qualitative life histories of the residents of three communities, and quantitative demographic data. Further information about the project and links to publications may be found on the Collaborative Wildlife Management and Changing Social Contexts web page. Main Topics: The local and life history data are held in three files, one for each community in Tamshiyacu: San Pedro, Diamante, and 7 de Julio. Topics covered include life history, family networks and migration in and out of the communities concerned. The demographic data cover gender, age, birthplace and year of migration to the community. Users should note that the textual data are in Spanish, with no English translation available.

Characteristics of study population and Leptospira seroprevalence by microscopic agglutination test, Espaillat and San Pedro de Macoris Provinces, Dominican Republic, 2021.

INE预测：人口：安托法加斯塔：San Pedro de Atacama在06-01-2035达11.694千人，相较于06-01-2034的11.659千人有所增长。INE预测：人口：安托法加斯塔：San Pedro de Atacama数据按年更新，06-01-2002至06-01-2035期间平均值为9.797千人，共34份观测结果。该数据的历史最高值出现于06-01-2035，达11.694千人，而历史最低值则出现于06-01-2002，为4.237千人。CEIC提供的INE预测：人口：安托法加斯塔：San Pedro de Atacama数据处于定期更新的状态，数据来源于Instituto Nacional de Estadísticas，数据归类于全球数据库的智利 – Table CL.G002: Population: Projection。

INE预测：人口：圣地亚哥首都大区：圣佩德罗在06-01-2035达13.627千人，相较于06-01-2034的13.561千人有所增长。INE预测：人口：圣地亚哥首都大区：圣佩德罗数据按年更新，06-01-2002至06-01-2035期间平均值为11.587千人，共34份观测结果。该数据的历史最高值出现于06-01-2035，达13.627千人，而历史最低值则出现于06-01-2002，为8.378千人。CEIC提供的INE预测：人口：圣地亚哥首都大区：圣佩德罗数据处于定期更新的状态，数据来源于Instituto Nacional de Estadísticas，数据归类于全球数据库的智利 – Table CL.G002: Population: Projection。