This dataset provides country code, postal code, latitude, longitude, as well as names of state, county/province, community etc. for all countries where the data is available.

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.

Every single contact from our geographical databased with 270 million+ companies comes directly from local sources that you can trust and are GDPR proof. These sources include chamber of commerces, market surveys, business listings, directories, magazines, public records, websites, conferences, telephone directories, publishers, social media and commercial partnerships. All our e-mail data is verified by automated processes and human eyes on a ongoing basis. Have an edge against your competition with our comprehensive B2B company database. Ask us for a quote!

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 the United States. It has 4,171 rows. It features 7 columns including country, population, latitude, and longitude.

Context

The dataset consists of the Latitudes and Longitudes of all the countries of the world. This might come handy for visualizing geographical data.

An outline map showing the coastline, boundaries and major lakes and rivers for Canada and nearby countries. Included are the locations of capitals and selected places, and major latitude and longitude lines (the graticule).

(:unav)...........................................

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

Latitude and longitude is a gridded coordinate system across the surface of Earth that allows you to pinpoint exact location. Latitude marks how far north or south of the equator (zero degrees) one is while longitude determines how far east or west something or someone is from the prime meridian (zero degrees), today located in Greenwich, London, United Kingdom. Greenwich has not always been the agreed-upon prime meridian. In the 18th century, most European countries chose a location unique to them and built their maps off of that reference point. It was not until 1884, when 22 countries met in Washington, D.C., and voted the Greenwich meridian as the international standard. Other key points of latitude are the Tropic of Cancer (23°27’ N), Tropic of Capricorn (23°27’ S), the Arctic Circle (66°30’ N), and the Antarctic Circle (66°30’ S). The Tropic of Cancer, located in the northern hemisphere, is the point on Earth that receives the most direct sunlight around June 21 as the north pole tilts toward the sun. This latitude is mirrored by the Tropic of Capricorn, in the southern hemisphere, which receives the most direct sunlight around December 21 when the south pole is tilted toward the sun. The Arctic Circle, which surrounds the North Pole, marks the point where the sun does not set around June 21 or rise around December 21. Likewise, the Antarctic Circle, near the South Pole, is the location where the sun does not set around December 21 or rise around June 21.Explore this map to find out the latitude and longitude where you are today.

Initially, the temperature dataset is provided with specific coordinates in terms of longitude and latitude. To tailor this data to each country, we utilize geographical boundaries as defined by the World Bank. The method involves trimming the global temperature dataset to match the exact geographical shape of each country. To correct for potential distortions caused by the Earth's curvature on a flat map, we apply a latitude-based weighting. This step is essential for maintaining accuracy, especially in high-latitude regions where distortion is more pronounced. The result of this process is a latitude-weighted average temperature for each nation.

It's important to note, however, that due to the resolution constraints of the Copernicus dataset, this methodology might not be as effective for countries with very small landmasses. In these cases, the process may not yield reliable data.

The derived 2-meter temperature readings for each country are calculated based on administrative borders, encompassing all land surface types within these defined areas. As a result, temperatures over oceans and seas are not included in these averages, focusing the data primarily on terrestrial environments.

Global temperature averages and anomalies are calculated over all land and ocean surfaces.

The temperature anomaly is calculated by comparing the average surface temperature of a specific time period (e.g., a particular year or month) to the mean surface temperature of the same period from 1991 to 2020.

When calculating anomalies for each country, the average surface temperature of a given year or month is compared to the 1991-2020 mean temperature for that specific country.

The reason for using the 1991-2020 period as the reference mean is that it is the standard reference period used by our data source, the Copernicus Climate Change Service. This period is also adopted by the UK Met Office. This approach ensures consistency in identifying climate variations over time.

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.

Luxembourgish country border expressed as a CSV list of 5000 coordinates: First list entry contains northmost coordinates. Last list entry (row 5001) is identical to first entry. List sequence follows border in a clockwise way. All coordinates have a precision of seven decimal digits. Data was manually derived from Apple Maps, thus not representing legal/official border data.

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

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

Context

Can you tell geographical stories about the world using data science?

Content

World countries with their corresponding continents , official english names, official french names, Dial,ITU,Languages and so on.

Acknowledgements

This data was gotten from https://old.datahub.io/

Inspiration

Exploration of the world countries: - Can we graphically visualize countries that speak a particular language? - We can also integrate this dataset into others to enhance our exploration. - The dataset has now been updated to include longitude and latitudes of countries in the world.

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