The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

Graph and download economic data for Refugee Population by Country or Territory of Asylum for Small States (SMPOPREFGSST) from 1990 to 2023 about refugee, small, World, and population.

This dataset provides values for SMALL BUSINESS SENTIMENT reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2022, Australia's installed small hydropower capacity reached *** megawatts, leading the small hydropower generation among other countries in Oceania. New Zealand trailed in second, with approximately *** megawatts during the same year.

The data and programs replicate tables and figures from "Export Conditions in Small Countries and their Effects on Domestic Markets", by Alfaro and Warzynski. Please see the ReadMe file for additional details.

Development plan ‘Kurzländ’ of the municipality of Heiningen transformed according to INSPIRE based on an XPlanung data set in version 5.0.

There were forecast to be approximately ******* small businesses that employ between ** and ** employees operating in the non-financial business economy of ******* in 2024, by far the most of any other country in the European Union.

Use this regional model layer when performing analysis within a single continent. This layer displays a single global land cover map that is modeled by region for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Italy's installed small hydropower capacity in 2022 stood at approximately ***** megawatts, making it the leading European country. In contrast, Molvoda, Denmark, and Estonia were among the nations with the lowest installed small hydropower capacity in 2022.

World Countries is a detailed layer of country level boundaries which is best used at large scales (e.g. below 1:2m scale). For a more generalized layer to use at small-to-medium scales, refer to the World Countries (Generalized) layer. It has been designed to be used as a layer that can be easily edited to fit a users needs and view of the political world. Included are attributes for name and ISO codes, along with continent information. Particularly useful are the Land Type and Land Rank fields which separate polygons based on their areal size. These attributes are useful for rendering at different scales by providing the ability to turn off small islands which may clutter small scale views.This dataset represents the world countries as they existed in January 2015.

Visitor Arrivals: By Country: North East Asia: Other data was reported at 1,390.000 Movement in Feb 2025. This records an increase from the previous number of 1,320.000 Movement for Jan 2025. Visitor Arrivals: By Country: North East Asia: Other data is updated monthly, averaging 300.000 Movement from Jan 1991 (Median) to Feb 2025, with 410 observations. The data reached an all-time high of 2,230.000 Movement in Aug 2019 and a record low of 0.000 Movement in Oct 2021. Visitor Arrivals: By Country: North East Asia: Other data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.Q006: Visitor Arrivals: Short Term: by Countries. [COVID-19-IMPACT]

The coastal zones of Small Island States are hotspots of human habitation and economic endeavour. In the Pacific region, as elsewhere, there are large gaps in understandings of the exposure and vulnerability of people in coastal zones. The 22 Pacific Countries and Territories (PICTs) are poorly represented in global analyses of vulnerability to seaward risks. We combine several data sources to estimate populations to zones 1, 5 and 10 km from the coastline in each of the PICTs. Regional patterns in the proximity of Pacific people to the coast are dominated by Papua New Guinea. Overall, ca. half the population of the Pacific resides within 10 km of the coast but this jumps to 97% when Papua New Guinea is excluded. A quarter of Pacific people live within 1 km of the coast, but without PNG this increases to slightly more than half. Excluding PNG, 90% of Pacific Islanders live within 5 km of the coast. All of the population in the coral atoll nations of Tokelau and Tuvalu live within a km of the ocean. Results using two global datasets, the SEDAC-CIESIN Gridded Population of the World v4 (GPWv4) and the Oak Ridge National Laboratory Landscan differed: Landscan under-dispersed population, overestimating numbers in urban centres and underestimating population in rural areas and GPWv4 over-dispersed the population. In addition to errors introduced by the allocation models of the two methods, errors were introduced as artefacts of allocating households to 1 km x 1 km grid cell data (30 arc–seconds) to polygons. The limited utility of LandScan and GPWv4 in advancing this analysis may be overcome with more spatially resolved census data and the inclusion of elevation above sea level as an important dimension of vulnerability.

World Countries provides a detailed basemap layer for the countries of the world. This layer has been designed to be used as a basemap and includes fields for official names and country codes, along with fields for continent and display. Particularly useful are the fields LAND_TYPE and LAND_RANK that separate polygons based on their size. These fields are helpful for rendering at different scales by providing the ability to turn off small islands that may clutter small-scale (zoomed out) views. The sources of this dataset are Esri, Garmin, U.S. Central Intelligence Agency (The World Factbook), and International Organization for Standardization (ISO). This layer was published in October 2024. It is updated every 12-18 months or as significant changes occur.

Hi guys, This is a portion of the very small dataset I preprocessed.

My first dataset project may be small and humble, but I hope it can serve as a simple briefing material or provide insights to others. In this dataset, I have included Air Quality Index (AQI) values for nitrogen, ozone, and PM2.5, along with Gross Domestic Product (GDP) per capita. During the process of merging AQI and GDP, there was significant data loss due to handling NaN values.

When I examined the correlation between PM2.5 and GDP, I found that most countries with lower GDP values, except for one, had AQI values exceeding the standard index of 50. However, in countries with higher GDP, only three countries surpassed this threshold. For ozone, I observed a similar pattern in both lower and higher GDP countries. As for nitrogen dioxide, countries with higher GDP tended to show slightly higher AQI values compared to those with lower GDP.

For PM2.5, I speculated that it could be influenced by various factors such as infrastructure, neighboring countries, and desert conditions.

On the other hand, the contrasting trend for nitrogen dioxide could be attributed to its origin from high-temperature combustion processes in facilities like vehicles and power plants, more common in countries with more of such industries.

I hope you all can combine this data with other datasets to derive even more fascinating results! 😃😃😃

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14165116%2Fd8f4fcafd138cb838d228af4697edc52%2F2023-07-22%20%204.39.25.png?generation=1690011635518978&alt=media" alt="">

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European Small Household Appliances Waste Collected from Households by Country, 2023 Discover more data with ReportLinker!

This dataset provides information about the number of properties, residents, and average property values for Little Country Road cross streets in Ward, SC.

Worldwide, the male population is slightly higher than the female population. As of 2024, the country with the highest percentage of men was Qatar, with only slightly more than *********** of the total population being women. The United Arab Emirates followed with ** percent. Different factors can influence the gender distribution in a population, such as life expectancy, the sex ratio at birth, and immigration. For instance, in Qatar, the large share of males is due to the high immigration flows of male labor in the country.

This dataset provides values for SMALL BUSINESS SENTIMENT reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The average for 2023 based on 97 countries was 72.95 percent. The highest value was in Zimbabwe: 3409.7 percent and the lowest value was in Haiti: 0 percent. The indicator is available from 1970 to 2023. Below is a chart for all countries where data are available.

Abstract

Since its inception in the mid-1990s, the Multiple Indicator Cluster Surveys programme, known as MICS, has become the largest source of statistically sound and internationally comparable data on children and women worldwide. In countries as diverse as Bangladesh, Thailand, Fiji, Qatar, Cote d’Ivoire, Turkmenistan and Argentina, trained fieldwork teams conduct face-to-face interviews with household members on a variety of topics – focusing mainly on those issues that directly affect the lives of children and women. MICS is an integral part of plans and policies of many governments around the world, and a major data source for more than 30 Sustainable Development Goals (SDGs) indicators. The MICS programme continues to evolve with new methodologies and initiatives, including MICS Plus, MICS Link, MICS GIS and the MICS Tabulator.

Geographic coverage

Thailand (Bangkok Small Community) The majority of MICS surveys are designed to be representative at the national level. Sample sizes are sufficient to generate robust data at the regional and provincial levels, and for urban and rural areas. In MICS5, subnational surveys, covering specific population groups (such as the Roma surveys in Eastern Europe) or specific geographical areas (such as the Nalaikh District in Mongolia) within countries were also conducted.

Analysis unit

Household, Individual

Sampling procedure

The sample for the Multiple Indicator Cluster Survey (MICS) was designed to provide estimates on a large number of indicators on the situation of children and women at the national level, for areas of residence, and for geographical locations, such as regions, governorates, or districts. A multi-stage, stratified cluster sampling approach was typickly used for the selection of the survey sample. MICS5 surveys are not self-weighting. For reporting national level results, sample weights were used. A more detailed description of the sample design can be found in Appendix A of Final Report.

Mode of data collection

Face-to-face [f2f]

Research instrument

MICS questionnaires were designed by implementing agencies, typically the National Statistical Offices. In each country, MICS questionnaires were based on an assessment of the country’s data needs. The starting point were the standard MICS questionnaires designed by UNICEF’s Global MICS Team, in close coordination with experts, development partners and other international survey programmes. Countries chose from the MICS modules in the standard MICS questionnaires. UNICEF’s MICS experts supported implementing agencies to customize the questionnaires, as required, to the national setting. All survey activities, from sample and survey design, to fieldwork and report writing are carried out by the implementing agencies – with continuous technical support from UNICEF. The fifth round of MICS included four model questionnaires: (1) the Household Questionnaire, (2) the Questionnaire for Individual Women age 15-49 years, (3) the Questionnaire for Individual Men age 15-49 years, and (4) the Questionnaire for Children Under Five. The flexible, modular nature of MICS questionnaires makes it easy to remove modules which may not be relevant, and modules for which there is already good quality data from other sources.

Refer to tools page on mics.unicef.org for more detailed information on the flow of questionnaires and contents of the modules.