The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.

Population of Russia

Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.

Terrestrial Connectivity is one of the main outputs of the CA Department of Fish and Wildlife’s Areas of Conservation Emphasis (ACE) project. This dataset evaluates how an area contributes to animal movement and general ecological flow. It includes information on corridors that allow for species migration, including narrow channels through highly disturbed areas which are critical for retaining the last threads of connectivity in these areas, as well as high usage areas between large, contiguous and natural landscapes which are described as intact. The ACE Ranks are used to indicate level of connectivity conservation urgency, with essential corridors and linkages emphasized with highest level scores of 4 or 5. Areas that have high connectivity, but have not been identified as having channelized areas for species corridors or habitat linkages, are given a rank 3. Large, intact regions which also contribute to connectivity but possess greater redundancy on account of their size, are given a lower rank of 2. Areas that show no opportunity for connectivity are given the lowest rank of 1.

This dataset tracks annual overall district rank from 2011 to 2022 for Downingtown Area School District

The statistic shows the largest countries in South America, based on land area. Brazil is the largest country by far, with a total area of over 8.5 million square kilometers, followed by Argentina, with almost 2.8 million square kilometers.

The average for 2021 based on 196 countries was 656013 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

About this dataset

Context

This list ranks the 16 cities in the Nome Census Area, AK by Non-Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Non-Hispanic Asian Population: This column displays the rank of cities in the Nome Census Area, AK by their Non-Hispanic Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Non-Hispanic Asian Population: The Non-Hispanic Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Non-Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Nome Census Area Non-Hispanic Asian Population: This tells us how much of the entire Nome Census Area, AK Non-Hispanic Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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The QS Rankings, renowned for its esteemed university evaluations, annually releases the QS World University Rankings. The 2024 edition comprises a dataset encompassing the top 100 universities globally, with each entry defined by 12 features.

The 'rank' feature denotes the university's position in the QS rankings, offering a quantitative representation of its standing. The 'university' column identifies the institution by name. The 'overall score' is a floating-point value derived from various contributing factors, reflecting the comprehensive evaluation undertaken by QS.

Academic reputation, an integral aspect, is quantified in the 'academic reputation' feature, while 'employer reputation' gauges the institution's standing in the professional realm. The 'faculty student ratio' is calculated by dividing the faculty count by the number of students, a metric often indicative of the learning environment's quality.

'Citations per faculty' delves into the scholarly impact, measuring the total citations received by an institution's papers over five years, normalized by faculty size. The 'international faculty ratio' and 'international students ratio' shed light on the global diversity of the academic community, capturing the proportion of foreign faculty and students.

The 'international research network' employs a formula to quantify the institution's global partnerships and collaborations. 'Employment outcomes' are assessed through a formula involving alumni impact and graduate employment indices, providing insights into the professional success of graduates.

Finally, the 'sustainability' feature evaluates an institution's commitment to environmental sciences, considering alumni outcomes and academic reputation within the field. It also examines the inclusion of climate science and sustainability in the curriculum, reflecting the growing emphasis on environmental consciousness in higher education.

In essence, this dataset encapsulates a multifaceted evaluation of universities worldwide, encompassing academic, professional, and sustainability dimensions, making it a valuable resource for individuals and institutions navigating the dynamic landscape of global higher education. VALUE FOUNDS IS HIPOTICALY data 2021

This dataset contains simulated datasets, empirical data, and R scripts described in the paper: "Li, Q. and Kou, X. (2021) WiBB: An integrated method for quantifying the relative importance of predictive variables. Ecography (DOI: 10.1111/ecog.05651)".

A fundamental goal of scientific research is to identify the underlying variables that govern crucial processes of a system. Here we proposed a new index, WiBB, which integrates the merits of several existing methods: a model-weighting method from information theory (Wi), a standardized regression coefficient method measured by ß* (B), and bootstrap resampling technique (B). We applied the WiBB in simulated datasets with known correlation structures, for both linear models (LM) and generalized linear models (GLM), to evaluate its performance. We also applied two other methods, relative sum of wight (SWi), and standardized beta (ß*), to evaluate their performance in comparison with the WiBB method on ranking predictor importances under various scenarios. We also applied it to an empirical dataset in a plant genus Mimulus to select bioclimatic predictors of species' presence across the landscape. Results in the simulated datasets showed that the WiBB method outperformed the ß* and SWi methods in scenarios with small and large sample sizes, respectively, and that the bootstrap resampling technique significantly improved the discriminant ability. When testing WiBB in the empirical dataset with GLM, it sensibly identified four important predictors with high credibility out of six candidates in modeling geographical distributions of 71 Mimulus species. This integrated index has great advantages in evaluating predictor importance and hence reducing the dimensionality of data, without losing interpretive power. The simplicity of calculation of the new metric over more sophisticated statistical procedures, makes it a handy method in the statistical toolbox.

GIC created the habitat cores model using the National Land Cover Database (NLCD) 2019 land cover data (the most recent land cover available when this project began). The NLCD provides nation-wide data on land cover and land cover change at the Landsat Thematic Mapper (TM) 30-meter resolution (30 x 30 meter pixels of analysis) and is appropriate for mapping rural landscapes.

To be considered a habitat core, the native landscape must encompass more than 100 acres of intact area. This acreage standard is based on studies evaluating the minimum acreage for terrestrial species to survive and thrive. For example, interior forest dwelling birds such as cerulean warblers need 100 acres of interior forest habitat for adequate foraging and nesting habitats. Large, intact forest cores are less impacted by disturbances and can better support area-sensitive and extinction-prone species because they retain larger populations, and their habitat is less likely to degrade through time (Ewers et al 2006). Forest fragments or woodlands less than 100 acres (known as patches) were also mapped to aid in identification of corridors or pathways for species to migrate across the landscape. These fragments, while not ideal habitat for larger species, can provide quality refugia for some species. Fragments can act also act as stepping stones, allowing species to move across the landscape while minimizing their exposure to predators and other disturbances. Such 2019 NLCD landcover types as forests and wetlands were then evaluated to determine their intactness by identifying features that fragment them, such as roads, buildings, transmission corridors, large rivers, and so on. These features bisect the landscape into smaller units (see maps). If an area is bisected too often, it does not contain a large enough habitat area to support interior nesting species and thus is too small to function as a habitat core.

To ensure that there is enough interior habitat, GIC’s analysts first subtract (clip out) the outer edge for a distance of 300 feet to ensure that potentially disturbed area is not counted as interior habitat. Edge areas are more likely to contain invasive species, suffer from wind impacts leading to dryness and blowdowns, and opportunistic predators such as domestic cats and dogs. In the final map of intact habitats, this edge area is added back in, but does not count towards the 100-acre minimum core size.The next step in the process is to divide the acreage into quintiles or “natural breaks.” This sorts the cores by size, which is the most important element for contributing to species abundance – bigger landscapes can generally support more species. However, there are other landscape factors that contribute to species abundance such as surface waters. Thus, in addition to geometry and extent, habitat cores are ranked based additional environmental attributes. Assigning attributes to each core allows for the identification and prioritization of specific high-quality and high-value habitat during strategy development. Not all habitats will be protected and resources for management or conservation are usually limited. Ranking habitat cores by their quality allows land-use planners, agency officials, and landowners or site managers to prioritize specific landscapes that provide the highest value for species.

The rankings use landscape-based environmental and ecological attributes. Examples of environmental attribute data used to rank cores include the number of wetlands found within a core; the presence of rare, threatened or endangered species; species richness; soil diversity; the length of stream miles; and topography. These factors all influence the diversity of plants, insects, animals and other biota within a forest or even a wetland core. Core Ranking is represented in the Habitat Core layer. To access it, download the Habitat Core Layer and view the “Score Weight” attribute field.

In 2023, New York led the ranking of the largest built-up urban areas worldwide, with a land area of ****** square kilometers. Boston-Providence and Tokyo-Yokohama were the second and third largest megacities globally that year.

This dataset contains the code for Relevance and Redundancy ranking; a an efficient filter-based feature ranking framework for evaluating relevance based on multi-feature interactions and redundancy on mixed datasets.Source code is in .scala and .sbt format, metadata in .xml, all of which can be accessed and edited in standard, openly accessible text edit software. Diagrams are in openly accessible .png format.Supplementary_2.pdf: contains the results of experiments on multiple classifiers, along with parameter settings and a description of how KLD converges to mutual information based on its symmetricity.dataGenerator.zip: Synthetic data generator inspired from NIPS: Workshop on variable and feature selection (2001), http://www.clopinet.com/isabelle/Projects/NIPS2001/rar-mfs-master.zip: Relevance and Redundancy Framework containing overview diagram, example datasets, source code and metadata. Details on installing and running are provided below.Background. Feature ranking is benfiecial to gain knowledge and to identify the relevant features from a high-dimensional dataset. However, in several datasets, few features by themselves might have small correlation with the target classes, but by combining these features with some other features, they can be strongly correlated with the target. This means that multiple features exhibit interactions among themselves. It is necessary to rank the features based on these interactions for better analysis and classifier performance. However, evaluating these interactions on large datasets is computationally challenging. Furthermore, datasets often have features with redundant information. Using such redundant features hinders both efficiency and generalization capability of the classifier. The major challenge is to efficiently rank the features based on relevance and redundancy on mixed datasets. In the related publication, we propose a filter-based framework based on Relevance and Redundancy (RaR), RaR computes a single score that quantifies the feature relevance by considering interactions between features and redundancy. The top ranked features of RaR are characterized by maximum relevance and non-redundancy. The evaluation on synthetic and real world datasets demonstrates that our approach outperforms several state of-the-art feature selection techniques.# Relevance and Redundancy Framework (rar-mfs) rar-mfs is an algorithm for feature selection and can be employed to select features from labelled data sets. The Relevance and Redundancy Framework (RaR), which is the theory behind the implementation, is a novel feature selection algorithm that - works on large data sets (polynomial runtime),- can handle differently typed features (e.g. nominal features and continuous features), and- handles multivariate correlations.## InstallationThe tool is written in scala and uses the weka framework to load and handle data sets. You can either run it independently providing the data as an .arff or .csv file or you can include the algorithm as a (maven / ivy) dependency in your project. As an example data set we use heart-c. ### Project dependencyThe project is published to maven central (link). To depend on the project use:- maven xml de.hpi.kddm rar-mfs_2.11 1.0.2 - sbt: sbt libraryDependencies += "de.hpi.kddm" %% "rar-mfs" % "1.0.2" To run the algorithm usescalaimport de.hpi.kddm.rar._// ...val dataSet = de.hpi.kddm.rar.Runner.loadCSVDataSet(new File("heart-c.csv", isNormalized = false, "")val algorithm = new RaRSearch( HicsContrastPramsFA(numIterations = config.samples, maxRetries = 1, alphaFixed = config.alpha, maxInstances = 1000), RaRParamsFixed(k = 5, numberOfMonteCarlosFixed = 5000, parallelismFactor = 4))algorithm.selectFeatures(dataSet)### Command line tool- EITHER download the prebuild binary which requires only an installation of a recent java version (>= 6) 1. download the prebuild jar from the releases tab (latest) 2. run java -jar rar-mfs-1.0.2.jar--help Using the prebuild jar, here is an example usage: sh rar-mfs > java -jar rar-mfs-1.0.2.jar arff --samples 100 --subsetSize 5 --nonorm heart-c.arff Feature Ranking: 1 - age (12) 2 - sex (8) 3 - cp (11) ...- OR build the repository on your own: 1. make sure sbt is installed 2. clone repository 3. run sbt run Simple example using sbt directly after cloning the repository: sh rar-mfs > sbt "run arff --samples 100 --subsetSize 5 --nonorm heart-c.arff" Feature Ranking: 1 - age (12) 2 - sex (8) 3 - cp (11) ... ### [Optional]To speed up the algorithm, consider using a fast solver such as Gurobi (http://www.gurobi.com/). Install the solver and put the provided gurobi.jar into the java classpath. ## Algorithm### IdeaAbstract overview of the different steps of the proposed feature selection algorithm:https://github.com/tmbo/rar-mfs/blob/master/docu/images/algorithm_overview.png" alt="Algorithm Overview">The Relevance and Redundancy ranking framework (RaR) is a method able to handle large scale data sets and data sets with mixed features. Instead of directly selecting a subset, a feature ranking gives a more detailed overview into the relevance of the features. The method consists of a multistep approach where we 1. repeatedly sample subsets from the whole feature space and examine their relevance and redundancy: exploration of the search space to gather more and more knowledge about the relevance and redundancy of features 2. decude scores for features based on the scores of the subsets 3. create the best possible ranking given the sampled insights.### Parameters| Parameter | Default value | Description || ---------- | ------------- | ------------|| m - contrast iterations | 100 | Number of different slices to evaluate while comparing marginal and conditional probabilities || alpha - subspace slice size | 0.01 | Percentage of all instances to use as part of a slice which is used to compare distributions || n - sampling itertations | 1000 | Number of different subsets to select in the sampling phase|| k - sample set size | 5 | Maximum size of the subsets to be selected in the sampling phase|

Data set from the "table poléométrique" made in 1782 by Charles de Fourcroy. It is a data set where 109 towns in France territory are described from a morphology point of view (urban areas). Data set is based on annex of the article published by François de Dainville in Population (Grandeur et population des villes au XVIIIe siècle, Population, 1958, 13(3), pp. 459-480).

This work takes place within a PhD in archaeology and geography - UMR 8504 Géographie-cités, Université Paris I - Panthéon-Sorbonne.

The Austin Parks and Recreation System's ranking on the Trust for Public Land ParkScore Index. This index ranks the park systems of the 100 largest cities in the U.S. based on park acreage, park size, park funding, park access, and a variety of other factors. This data set supports HE.C.2 of SD23. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/Austin-s-ParkScore-Ranking-absolute-score-and-rank/rnwr-4s4u/ *If a cell is blank, that means PARD did not have a response for that year or TPL removed the question for that

The SEO rank tracker tool market is experiencing robust growth, driven by the increasing importance of search engine optimization (SEO) for businesses of all sizes. The market, estimated at $2 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising complexity of SEO algorithms necessitates sophisticated tools to monitor keyword rankings, track competitor performance, and optimize website visibility. Secondly, the growing adoption of digital marketing strategies by businesses across various industries is driving demand for effective SEO solutions. Thirdly, the continuous evolution of search engine algorithms requires constant monitoring and adjustments, thus creating a need for real-time rank tracking capabilities offered by these tools. Finally, the increasing availability of affordable and user-friendly SEO rank tracking software is making this technology accessible to a wider range of businesses, regardless of their size or technical expertise. The market is segmented by tool features (e.g., keyword tracking, competitor analysis, rank monitoring across different search engines), pricing tiers (ranging from free plans to enterprise-level solutions), and user type (ranging from individual SEO professionals to large marketing agencies). Key players, including Semrush, Ahrefs, Moz Pro, and others, are competing based on their feature sets, data accuracy, reporting capabilities, and customer support. However, the market also faces some challenges, such as the continuous evolution of search engine algorithms and the need for tools to adapt quickly. Furthermore, the market is seeing increased competition from new entrants offering innovative features and competitive pricing models. Despite these challenges, the long-term outlook remains positive due to the sustained importance of SEO and the evolving needs of businesses in the digital landscape.

The Austin Parks and Recreation System's ranking on the Trust for Public Land ParkScore Index. This index ranks the park systems of the 100 largest cities in the U.S. based on park acreage, park size, park funding, park access, and a variety of other factors. The three factors that make up ParkScore all reflect quality: good park systems need adequate acreage, services and investment, and access. For this metric and visual, lower scores are better.

Ranking test set contains the high-quality responses that selected by some baselines, and their correlation with the conversation context are carefully annotated by 8 professional annotators (the average annotation scores are saved for ranking). For ranking test set, the metrics should be NDCG@3 and NDCG@5, since the correlation scores are provided. More details are available in the Appendix of the paper.

About this dataset

Context

This list ranks the 7 cities in the Dillingham Census Area, AK by Hispanic Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Black Population: This column displays the rank of cities in the Dillingham Census Area, AK by their Hispanic Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Black Population: The Hispanic Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Dillingham Census Area Hispanic Black Population: This tells us how much of the entire Dillingham Census Area, AK Hispanic Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This dataset tracks annual overall school rank from 2010 to 2022 for Fox Chapel Area High School

Graph and download economic data for Delinquency Rate on Credit Card Loans, Banks Not Among the 100 Largest in Size by Assets (DRCCLOBS) from Q1 1991 to Q1 2025 about credit cards, delinquencies, assets, loans, banks, depository institutions, rate, and USA.

This dataset tracks annual overall district rank from 2011 to 2022 for Mckeesport Area School District