The population density in Costa Rica was 99.53 people in 2022. In a steady upward trend, the population density rose by 72.54 people from 1961.

Costa Rica: Population density, people per square km: The latest value from 2021 is 101 people per square km, an increase from 100 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for Costa Rica from 1961 to 2021 is 64 people per square km. The minimum value, 27 people per square km, was reached in 1961 while the maximum of 101 people per square km was recorded in 2021.

Costa Rica CR: Population Density: People per Square Km data was reported at 100.939 Person/sq km in 2021. This records an increase from the previous number of 100.335 Person/sq km for 2020. Costa Rica CR: Population Density: People per Square Km data is updated yearly, averaging 63.443 Person/sq km from Dec 1961 (Median) to 2021, with 61 observations. The data reached an all-time high of 100.939 Person/sq km in 2021 and a record low of 27.343 Person/sq km in 1961. Costa Rica CR: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization and World Bank population estimates.;Weighted average;

Historical dataset showing Costa Rica population density by year from 1961 to 2022.

Population density (people per sq. km of land area) in Costa Rica was reported at 99.99 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Costa Rica - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

The density of dentists in Costa Rica increased from *** to **** professionals per 10,000 people from 2012 to 2022. During the last year depicted, the density of dentists estimated for the country was the highest in the analyzed period.

Comprehensive socio-economic dataset for Costa Rica including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

The density of dentists in Panama fluctuated between *** and *** professionals per 10,000 people between 2010 and 2022. During the last year depicted, there were an estimated *** dentists per 10,000 people in the country.

We inferred the population densities of blue whales (Balaenoptera musculus) and short-beaked common dolphins (Delphinus delphis) in the Northeast Pacific Ocean as functions of the water-column’s physical structure by implementing hierarchical models in a Bayesian framework. This approach allowed us to propagate the uncertainty of the field observations into the inference of species-habitat relationships and to generate spatially explicit population density predictions with reduced effects of sampling heterogeneity. Our hypothesis was that the large-scale spatial distributions of these two cetacean species respond primarily to ecological processes resulting from shoaling and outcropping of the pycnocline in regions of wind-forced upwelling and eddy-like circulation. Physically, these processes affect the thermodynamic balance of the water column, decreasing its volume and thus the height of the absolute dynamic topography (ADT). Biologically, they lead to elevated primary productivity and persistent aggregation of low-trophic-level prey. Unlike other remotely sensed variables, ADT provides information about the structure of the entire water column and it is also routinely measured at high spatial-temporal resolution by satellite altimeters with uniform global coverage. Our models provide spatially explicit population density predictions for both species, even in areas where the pycnocline shoals but does not outcrop (e.g. the Costa Rica Dome and the North Equatorial Countercurrent thermocline ridge). Interannual variations in distribution during El Niño anomalies suggest that the population density of both species decreases dramatically in the Equatorial Cold Tongue and the Costa Rica Dome, and that their distributions retract to particular areas that remain productive, such as the more oceanic waters in the central California Current System, the northern Gulf of California, the North Equatorial Countercurrent thermocline ridge, and the more southern portion of the Humboldt Current System. We posit that such reductions in available foraging habitats during climatic disturbances could incur high energetic costs on these populations, ultimately affecting individual fitness and survival.

Resource specialists persist on a narrow range of resources. Consequently, the abundance of key resources should drive vital rates, individual fitness and population viability. While Neotropical forests feature both high levels of biodiversity and numbers of specialist species, no studies have directly evaluated how the variation of key resources affects the fitness of a tropical specialist. Here, we quantified the effect of key tree species density and forest cover on the fitness of three-toed sloths (Bradypus variegatus), an arboreal folivore strongly associated with Cecropia trees, in Costa Rica using a multi-year demographic, genetic and space use dataset. We found that the density of Cecropia trees was strongly and positively related to both adult survival and reproductive output. A matrix model parameterized with Cecropia-demography relationships suggested positive growth of sloth populations, even at low densities of Cecropia (0.7 trees/ha). Our study shows the first direct link ...

Simulation of the effect of density on population growth rates in Brachyrhaphis rhabdophora populations.

Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps:

* Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years.
* Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years.
* Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added.
* The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years.
* Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.

As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.

Dogs (Canis lupus familiaris) are one of the most common pets around the world but ownership patterns and human-dog interactions have been changing, particularly in developing nations. We conducted household surveys in Costa Rica to characterize dog ownership, the owned dog population, where dogs were confined at night and in the morning, and behaviors regarding selected dog care issues. We also compared these results to similar questionnaires used in Costa Rica over the past 20 years. We found 76% of households in Costa Rica owned at least one dog and on average there were about 1.4 dogs owned per household. These dog ownership rates are higher than previous estimates. The probability of owning a dog was highest on farms and lowest in single family dwellings without a yard, higher among respondents that owned their homes and decreasing with increasing human population density The total number of owned dogs in Costa Rica was estimated to be 2,222,032 (95% confidence intervals: 1,981,497–2,503,751). The sterilization rate for homed dogs in 2020 was approximately 62% (females: 67%, males: 61%) which is higher than the 18% of owned dogs that were sterilized in a 2003 survey. Overall, only 1.2% (95% CI: 0.3–2.5%) of owned dogs slept on the street with a slightly higher proportion on the street at 8 am. The number of owned dogs roaming the streets at night nation-wide was estimated to be 27,208 (95% CI: 7,557–56,619) compared to 43,142 (95% CI: 20,118–73,618) on the street at 8 am. The number of unowned free-roaming dogs in Costa Rica has never been estimated but we can generate some idea of the size of the unowned dog population by determining the proportion of free-roaming dogs on the street wearing collars. There was a negative relationship between human population density and owned dogs being on the street meaning fewer dogs roam the streets in highly populated areas compared to less populated areas. Overall, we identify trends against which future progress can be measured and provide information that are critical in designing effective humane dog management programs in Costa Rica in the future.

The data sets in this directory were provided by Mr. Gregory Yetman and Drs. Stuart Gaffin and Deborah Balk from the Center for International Earth Science Information Network (CIESIN) at Columbia University. There are three data files at three spatial resolutions of 0.25, 0.5 and 1.0 degree in both latitude and longitude and for the reference year of 1990.

Estimates of Gross Domestic Product (GDP) are commonly given for nations as a single aggregated number. This data set generates estimates of GDP density distributed subnationally to facilitate the integration of GDP with other data at a sub-national level and to promote interdisciplinary studies that include socioeconomic aspects. This is one of two coarse resolution Socioeconomic data sets included in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, the other being the Gridded Population of the World (GPW), also produced by CIESIN.

The Smokies ATBI, a project of Discover Life in America, seeks to catalog the estimated 60,000-80,000 species of living organisms in Great Smoky Mountains National Park. A brainchild of renowned ecologist Dan Janzen, the first ATBI was supposed to take place in the rainforests of northwest Costa Rica. Due to bureaucratic difficulties, however, the location was changed to the Great Smoky Mountains National Park.The idea behind an All Taxa Biodiversity Inventory (ATBI) is simple. If we want to be good stewards of our environment and keep the world around us healthy and vibrant, we need to understand the web of biodiversity. The information we need—how many species live in an environment, what jobs these species do and how they interact with each other—is largely unknown.Although most explanations of an ATBI stress that it is an effort to identify all the species of life in a given area, that is not its only goal. An ATBI is more than just a checklist of species names. It is a complex and living database of species locations, habitats, genetic diversity, population density, symbiotic relationships and predator-prey interactions. It is a cooperative effort between expert scientists specializing in all different forms of life. It is a way to discover new species in need of protection, identify new threats in time to act and understand how to protect a complex and valuable ecosystem like the Smoky Mountains.The corresponding Integration of Resource Management Applications (IRMA) NPS Data Store reference is Great Smoky Mountains National Park All Taxa Biodiversity Index Plots.

BackgroundQuercus oleoides Cham. and Schlect., tropical live oak, is a species of conservation importance in its southern range limit of northwestern Costa Rica. It occurs in high-density stands across a fragmented landscape spanning a contrasting elevation and precipitation gradient. We examined genetic diversity and spatial genetic structure in this geographically isolated and genetically distinct population. We characterized population genetic diversity at 11 nuclear microsatellite loci in 260 individuals from 13 sites. We monitored flowering time at 10 sites, and characterized the local environment in order to compare observed spatial genetic structure to hypotheses of isolation-by-distance and isolation-by-environment. Finally, we quantified pollen dispersal distances and tested for local adaptation through a reciprocal transplant experiment in order to experimentally address these hypotheses.ResultsHigh genetic diversity is maintained in the population and the genetic variation is significantly structured among sampled sites. We identified 5 distinct genetic clusters and average pollen dispersal predominately occurred over short distances. Differences among sites in flowering phenology and environmental factors, however, were not strictly associated with genetic differentiation. Growth and survival of upland and lowland progeny in their native and foreign environments was expected to exhibit evidence of local adaptation due to the more extreme dry season in the lowlands. Seedlings planted in the lowland garden experienced much higher mortality than seedlings in the upland garden, but we did not identify evidence for local adaptation.ConclusionOverall, this study indicates that the Costa Rican Q. oleoides population has a rich population genetic history. Despite environmental heterogeneity and habitat fragmentation, isolation-by-distance and isolation-by-environment alone do not explain spatial genetic structure. These results add to studies of genetic structure by examining a common, tropical tree over multiple habitats and provide information for managers of a successional forest in a protected area.

Этот набор данных содержит показатели плотности населения и площади застройки в населенных пунктах и городах. Источники данных и методология Поверхности FUA и городов рассчитываются на основе шейп-файлов FUA и городов с использованием инструментов ГИС. При построении данных используются сетки застроенной поверхности СГС (Европейская комиссия, пакет данных GHSL 2023). Данные о численности населения получены из набора данных Население в разбивке по возрасту и полу - населенные пункты и города. Определение населенных пунктов и городов ОЭСР в сотрудничестве с ЕС разработала согласованное определение функциональных городских зон (FUAS), чтобы отразить экономическую и функциональную доступность городов на основе ежедневных поездок на работу (ОЭСР, 2012). FUA состоят из: города, который определяется городскими центрами по степени урбанизации и адаптируется к ближайшим местным административным единицам для определения города. Зона пригородного сообщения – включает все районы, где в городе работает не менее 15% занятых жителей. Процесс определения границ включает: Присвоение этому FUA муниципалитетов, окруженных одним FUA. За исключением несмежных муниципалитетов. В этом определении указаны 1 285 населенных пунктов и 1 402 города во всех странах - членах ОЭСР, за исключением Коста-Рики и трех присоединяющихся стран. Приведите этот набор данных База данных ОЭСР по регионам, городам и локальным районам (Численность населения и плотность застройки - города и центральные районы), http://oe.cd/geostats Дополнительная информация По любым вопросам или комментариям, пожалуйста, пишите по адресу RegionStat@oecd.orgСтатистику FUA и города можно более подробно изучить с помощью интерактивного Регионы ОЭСР и Статистический атлас городов Веб-инструмент. Этот набор данных содержит показатели плотности населения и площади застройки в крупных городах. Источники данных и методология FUA и городские поверхности рассчитываются на основе шейп-файлов FUA и городских городов с использованием инструментов ГИС. При построении данных используются сетки застроенной поверхности СГС (Европейская комиссия, пакет данных GHSL 2023). Данные о численности населения получены из набора данных Население в разбивке по возрасту и полу - населенные пункты и города. Определение населенных пунктов и городов ОЭСР в сотрудничестве с ЕС разработала согласованное определение функциональных городских зон (FUAS), чтобы отразить экономический и функциональный охват городов на основе ежедневных поездок на работу (ОЭСР, 2012). FUA состоят из: города, который определяется городскими центрами по степени урбанизации и адаптируется к ближайшим местным административным единицам для определения города. Зона общественного пользования – включает в себя все районы, где в городе работает не менее 15% занятых жителей. Процесс определения границ включает: Присвоение этому FUA муниципалитетов, окруженных одним FUA. За исключением несмежных муниципалитетов. В этом определении указаны 1 285 населенных пунктов и 1 402 города во всех странах - членах ОЭСР, за исключением Коста-Рики и трех присоединяющихся стран. Приведите этот набор данных База данных ОЭСР по регионам, городам и локальным районам (Численность населения и плотность застройки - города и центральные районы), http://oe.cd/geostats Дополнительная информация По любым вопросам или комментариям, пожалуйста, пишите по адресу RegionStat@oecd.orgСтатистику FUA и города можно более подробно изучить с помощью интерактивного Регионы ОЭСР и Статистический атлас городов Веб-инструмент. This dataset provides indicators of population density and built-up area in FUAs and cities. Data sources and methodology FUA and city surfaces are calculated from FUA and city shapefiles, using GIS tools. Built-up data uses GHS built-up surface grids (European Commission, GHSL Data Package 2023). Population counts are extracted from the Population by age and sex - FUAs and cities dataset. Defining FUAs and cities The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns (OECD, 2012). FUAs consist of: A city – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city. A commuting zone – including all local areas where at least 15% of employed residents work in the city. The delineation process includes: Assigning municipalities surrounded by a single FUA to that FUA. Excluding non-contiguous municipalities. The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries. Cite this dataset OECD Regions, cities and local areas database (Population and built-up density - Cities and FUAs), http://oe.cd/geostats Further information For any question or comment, please write to RegionStat@oecd.orgFUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool. This dataset provides indicators of population density and built-up area in FUAs and cities. Data sources and methodology FUA and city surfaces are calculated from FUA and city shapefiles, using GIS tools. Built-up data uses GHS built-up surface grids (European Commission, GHSL Data Package 2023). Population counts are extracted from the Population by age and sex - FUAs and cities dataset. Defining FUAs and cities The OECD, in cooperation with the EU, has developed a harmonised definition of functional urban areas (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns (OECD, 2012). FUAs consist of: A city – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city. A commuting zone – including all local areas where at least 15% of employed residents work in the city. The delineation process includes: Assigning municipalities surrounded by a single FUA to that FUA. Excluding non-contiguous municipalities. The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries. Cite this dataset OECD Regions, cities and local areas database (Population and built-up density - Cities and FUAs), http://oe.cd/geostats Further information For any question or comment, please write to RegionStat@oecd.orgFUA and City Statistics can be further explored with the interactive OECD Regions and Cities Statistical Atlas web-tool.