This dataset is a .csv file containing central latitude and longitude points for all countries around the globe

This netCDF file provides ISO country codes at 0.1 degrees spatial resolution. Oceans and undefined territories are identified with a fictional ISO code. The situation reported may not include more recent (post-2000) historical changes.

Based on an elaboration of the `countries` software tool, available on github (see countries fork).

The work is provided 'as is', without guarantees or conditions of any kind.

This dataset is about countries per year in Central America and has 512 rows. It features 4 columns: date, country, latitude, and longitude. The preview is ordered by date (descending).

This dataset is about cities in the United States. It has 4,171 rows. It features 4 columns: country, latitude, and longitude.

Analysis of ‘Latitude and Longitude for Every Country and State’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/paultimothymooney/latitude-and-longitude-for-every-country-and-state on 28 January 2022.

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

Content

GPS coordinates for every world country and every USA state.

Columns = [country-code,latitude,longitude,country,usa-state-code,usa-state-latitude,usa-state-longitude,usa-state]

Acknowledgements

Original source of data was https://developers.google.com/public-data/docs/canonical/countries_csv and https://developers.google.com/public-data/docs/canonical/states_csv. Data was originally released under a Creative Commons 4.0 license.

Photo by Марьян Блан | @marjanblan on Unsplash

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

This dataset is about cities in the United Kingdom. It has 861 rows. It features 5 columns: country, population, latitude, and longitude.

This dataset is about cities in Vietnam. It has 65 rows. It features 5 columns: country, population, latitude, and longitude.

State-Location-Coordinates

Contains

• 240 + country's latitude and longitude.
• 2.7k+ state's latitude and longitude.

All data was prepared using geopy.

This dataset is about museums in the United States. It has 8 rows. It features 5 columns: country, visitors, latitude, and longitude.

Dataset

This dataset was created by Kiran Reddy

Contents

This Dataset contains year, month, origin time, state, country and location wise Latitude, Longitude, Depth and Magnitude of Earthquake events occured in India and its surrounding countries namely Afghanistan, Bangladesh, Bhutan, Kyrgyzstan, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Seychelles, Tajikistan, Uzbekistan, China, Sri Lanka, Turkmenistan and Thailand

Notes: origin_time is India Standard Time (IST) Time Zone

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

This dataset is about cities in Luxembourg. It has 425 rows. It features 5 columns: country, population, latitude, and longitude.

This paper presents the results of a Secchi depth data mining study for the North Sea - Baltic Sea region. 40,829 measurements of Secchi depth were compiled from the area as a result of this study. 4.3% of the observations were found in the international data centers [ICES Oceanographic Data Center in Denmark and the World Ocean Data Center A (WDC-A) in the USA], while 95.7% of the data was provided by individuals and ocean research institutions from the surrounding North Sea and Baltic Sea countries. Inquiries made at the World Ocean Data Center B (WDC-B) in Russia suggested that there could be significant additional holdings in that archive but, unfortunately, no data could be made available. The earliest Secchi depth measurement retrieved in this study dates back to 1902 for the Baltic Sea, while the bulk of the measurements were gathered after 1970. The spatial distribution of Secchi depth measurements in the North Sea is very uneven with surprisingly large sampling gaps in the Western North Sea. Quarterly and annual Secchi depth maps with a 0.5° x 0.5° spatial resolution are provided for the transition area between the North Sea and the Baltic Sea (4°E-16°E, 53°N-60°N).

Dataset

This dataset was created by Jon Scheaffer

Released under CC0: Public Domain

Contents

The ESS-DIVE location metadata reporting format provides instructions and templates for reporting a minimum set of metadata for discrete point locations in geographic space represented by x, y, and z coordinates. This format was created based on a need for earth and environmental science researchers to more consistently provide metadata about locations where they conduct studies. To create the format, we incorporated elements from ESS-DIVE’s community reporting formats as well as 12 additional data standards or other data resources (e.g., databases, data systems, or repositories). In the template, we ask researchers to indicate unique locations using Location IDs and indicate hierarchies of locations through parent location IDs. We also provide additional optional fields for researchers to indicate how they measured the point location and the date and time that the location was first used as a research site This dataset contains support documentation for the reporting format (README.md and instructions.md), a terminology guide (guide.md), a crosswalk indicating how this reporting format relates to existing standards and data resources (Location_metadata_crosswalk.csv), a data dictionary (dd.csv), file-level metadata (flmd.csv), and the location metadata templates in both CSV (Location_metadata_template.csv) and Excel formats (Location_metadata_template.xlsx).

universities in United States. name, type, date founded, city, administrative división, continent, Country, latitude, longitude, number of Students, Website, employees

Context

The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Source: https://en.wikipedia.org/wiki/2019%E2%80%9320_coronavirus_pandemic.

Content

Coronavirus COVID-19 confirmed cases, deaths, mortality rate, country, latitude, and longitude.

Last updated 3/11/2020. Data is preliminary and will be much more accurate in coming months as more cases are reported.

Disclaimer: Data will be more accurate as more data comes in

Acknowledgements

Banner photo by Adhy Savala on Unsplash.

Data generated from the notebook https://www.kaggle.com/paultimothymooney/does-latitude-impact-the-spread-of-covid-19 using data from https://www.kaggle.com/paultimothymooney/latitude-and-longitude-for-every-country-and-state and https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset, all of which were released under open data licenses.

The international, interdisciplinary biodiversity research project BIOTA AFRICA initiated a standardized biodiversity monitoring network along climatic gradients across the African continent. Due to an identified lack of adequate monitoring designs, BIOTA AFRICA developed and implemented the standardized BIOTA Biodiversity Observatories, that meet the following criteria (a) enable long-term monitoring of biodiversity, potential driving factors, and relevant indicators with adequate spatial and temporal resolution, (b) facilitate comparability of data generated within different ecosystems, (c) allow integration of many disciplines, (d) allow spatial up-scaling, and (e) be applicable within a network approach. A BIOTA Observatory encompasses an area of 1 km2 and is subdivided into 100 1-ha plots. For meeting the needs of sampling of different organism groups, the hectare plot is again subdivided into standardized subplots, whose sizes follow a geometric series. To allow for different sampling intensities but at the same time to characterize the whole square kilometer, the number of hectare plots to be sampled depends on the requirements of the respective discipline. A hierarchical ranking of the hectare plots ensures that all disciplines monitor as many hectare plots jointly as possible. The BIOTA Observatory design assures repeated, multidisciplinary standardized inventories of biodiversity and its environmental drivers, including options for spatial up- and downscaling and different sampling intensities. BIOTA Observatories have been installed along climatic and landscape gradients in Morocco, West Africa, and southern Africa. In regions with varying land use, several BIOTA Observatories are situated close to each other to analyze management effects.

Analysis of ‘Geographical origin of music’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yadhua/geographical-origin-of-music on 28 January 2022.

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

Context

The dataset was built from a personal collection of 1059 tracks covering 33 countries/area. The music used is traditional, ethnic or `world' only, as classified by the publishers of the product on which it appears. Any Western music is not included because its influence is global - what we seek are the aspects of music that most influence location. Thus, being able to specify a location with strong influence on the music is central.

Content

The geographical location of origin was manually collected the information from the CD sleeve notes, and when this information was inadequate we searched other information sources. The location data is limited in precision to the country of origin.

The country of origin was determined by the artist's or artists' main country/area of residence. Any track that had ambiguous origin is not included. We have taken the position of each country's capital city (or the province of the area) by latitude and longitude as the absolute point of origin.

The program MARSYAS[1] was used to extract audio features from the wave files. We used the default MARSYAS settings in single vector format (68 features) to estimate the performance with basic timbal information covering the entire length of each track. No feature weighting or pre-filtering was applied. All features were transformed to have a mean of 0, and a standard deviation of 1. We also investigated the utility of adding chromatic attributes. These describe the notes of the scale being used. This is especially important as a distinguishing feature in geographical ethnomusicology. The chromatic features provided by MARSYAS are 12 per octave - Western tuning, but it may be possible to tell something from how similar to or different from Western tuning the music is.

The dataset is present in two files, where each file corresponds to a different feature sets.

Both files contain the audio features of 1059 tracks.

In the 'default_features_1059_tracks.txt' file, the first 68 columns are audio features of the track, and the last two columns are the origin of the music, represented by latitude and longitude.

In the 'default_plus_chromatic_features_1059_tracks.txt' file, the first 116 columns are audio features of the track, and the last two columns are the origin of the music.

The original dataset was absolutely raw. The datasets I have attached have been provided with columns titled Features(1 to 68 and 116).

I have also added a csv containing the list of countries along with their latitude and longitude coordinates. The latitude and longitude values are present in N,S,E,W format which can be translated to N:+,S:-,E:+,W:-

The description of music collection and audio features can be found in:

Fang Zhou, Claire Q and Ross. D. King Predicting the Geographical Origin of Music, ICDM, 2014

Acknowledgements

I had obtained the dataset from the UCI machine learning repository. The link is provided below. https://archive.ics.uci.edu/ml/datasets/Geographical+Original+of+Music

Creators: Fang Zhou (fang.zhou '@' nottingham.edu.cn) The University of Nottinghan, Ningbo, China

Donors of the Dataset: Fang Zhou (fang.zhou '@' nottingham.edu.cn) Claire Q (eskoala '@' gmail.com) Ross D. King (ross.king '@' manchester.ac.uk)

The following citation is requested if you use the dataset:

Fang Zhou, Claire Q and Ross. D. King Predicting the Geographical Origin of Music, ICDM, 2014

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