The highest city in the world with a population of more than one million is La Paz. The Capital of Bolivia sits ***** meters above sea level, and is more than 1,000 meters higher than the second-ranked city, Quito. La Paz is also higher than Mt. Fuji in Japan, which has a height of 3,776 meters. Many of the world's largest cities are located in South America. The only city in North America that makes the top 20 list is Denver, Colorado, which has an altitude of ***** meters.

This statistic displays the countries with the greatest range between their highest and lowest elevation points. China and Nepal share the highest elevation point worldwide, which ascends to an amount of 8848 meters above sea level. Near the city Turpan Pendi, Xinjiang, China's elevation reaches *** meters below sea level.

As of 2024, Hong Kong was the city in the world with the most skyscrapers that were at least *** meters high. The next city in the ranking was Shenzhen with *** buildings exceeding that height, followed by New York City with *** buildings. Some of the other cities on the list were Dubai and Guangzhou. The Burj Khalifa in Dubai was the highest building in the world.

[From GeoData Center Home Page descriptions, "http://www.gi.alaska.edu/alaska-satellite-facility/geodata-center"]

 The GeoData Center is the browse facility for the state copy of the AHAP
 collection, which covers approximately 95% of the State of Alaska in 1:60,000
 color infrared (CIR) and 1:120,000 black and white (B&W) photography. The data
 reside in 10" film format. Approximately 70,000 frames of photography were
 acquired between 1978 and 1986.

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and warning efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Grid spacings for the DEMs range from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and modeling efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Grid spacings for the DEMs range from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).

At 20,310 feet (6.2km) above sea level, the highest point in the United States is Denali, Alaska (formerly known as Mount McKinley). The highest point in the contiguous United States is Mount Whitney, in the Sierra Nevada mountain range in California; followed by Mount Elbert, Colorado - the highest point in the Rocky Mountains. When looking at the highest point in each state, the 13 tallest peaks are all found in the western region of the country, while there is much more diversity across the other regions and territories.

Despite being approximately 6,500 feet lower than Denali, Hawaii's Mauna Kea is sometimes considered the tallest mountain (and volcano) on earth. This is because its base is well below sea level - the mountain has a total height of 33,474 feet, which is almost 4,500 feet higher than Mount Everest.

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Air quality is a critical aspect of environmental health that directly impacts the well-being of people worldwide. The quality of the air we breathe is influenced by a myriad of factors, ranging from industrial activities and vehicular emissions to natural phenomena. Examining the global scenario reveals both progress and challenges in the quest for cleaner air.

One of the primary indicators used to assess air quality is the Air Quality Index (AQI), a numerical scale that quantifies the concentration of pollutants in the air. The major pollutants considered in AQI calculations include particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3). By evaluating these pollutants, experts can gauge the potential health risks associated with breathing the air in a specific location.

Over the past few decades, there has been a growing awareness of the importance of monitoring and improving air quality globally. Numerous countries have implemented stringent regulations and adopted cleaner technologies to reduce emissions from industries and transportation. The transition to renewable energy sources has also played a crucial role in mitigating air pollution, with a focus on reducing reliance on fossil fuels.

In regions such as North America and Europe, substantial progress has been made in curbing air pollution through the implementation of stringent environmental policies and the development of advanced technologies. Air quality monitoring networks in these regions provide real-time data, allowing authorities to take prompt action when pollution levels exceed acceptable limits.

However, despite these advancements, many parts of the world continue to face severe air quality challenges. In rapidly industrializing regions, such as parts of Asia and Africa, the concentration of pollutants often surpasses safe limits, leading to adverse health effects. Urbanization and population growth contribute to increased vehicular emissions, industrial activities, and energy consumption, all of which exacerbate air pollution.

The impact of climate change further complicates the global air quality scenario. Changes in weather patterns, such as heatwaves and wildfires, can elevate pollutant levels and worsen air quality. The increasing frequency and intensity of extreme weather events pose a significant threat to the efforts aimed at improving air quality worldwide.

The COVID-19 pandemic also provided a unique opportunity to observe the effects of reduced human activities on air quality. Lockdowns and travel restrictions led to a temporary decline in pollution levels in various cities globally, offering insights into the potential benefits of sustainable practices and reduced emissions.

To address the challenges associated with global air quality, international collaboration and information exchange are crucial. Initiatives like the World Air Quality Index project provide a platform for sharing real-time air quality data from around the world, fostering a collective effort to combat air pollution.

In conclusion, the state of global air quality is a multifaceted issue that demands continuous attention and concerted efforts. While some regions have made significant strides in improving air quality through regulatory measures and technological advancements, others face persistent challenges due to rapid industrialization and urbanization. The ongoing impact of climate change and unforeseen events, such as pandemics, further underscore the need for a coordinated, global approach to safeguarding air quality and, consequently, the health of our planet and its inhabitants.

The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). In some areas, the surveying was accomplished using airboats. Because access was a logistical problem with airboats, the USGS developed a helicopter-based instrument known as the Airborne Height Finder (AHF). All subsequent data collection used the AHF. Data were collected from the Loxahatchee National Wildlife Refuge, south through the Water Conservation Areas (1A, 2A, 2B, 3A, and 3B), Big Cypress National Park, the Everglades National Park, to the Florida Bay. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html . The work was performed for Everglades ecosystem restoration purposes.

 The data are from regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services were also rendered to provide vertical reference points for numerous water level gauges. Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) were collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.

This dataset was developed by KTH-dESA and describes settlement patterns relating to electrification in Madagascar. Using the Open Source Spatial Electrification Tool three attributes have been assigned to the settlements retrieved from the Madagascar High Resolution Settlement Layer developed by Facebook Connectivity Lab and CIESIN [1]. The three attributes are as follows: Urban or rural status. The urban cutoff level, i.e. the minimum population density per square kilometer, has been calculated so that the urban population matches the official statistics of 35 % in 2015 [2]. The urban cutoff level was calculated to be 683 people/km2, meaning that all settlements above this value are considered urban. The number of households in the settlements by 2030. Based on the urban or rural status the future population for the settlements have been estimated by applying a population growth rate to match future population projections according to [3] and [4]. The number of households 2030 have then been calculated using the epected urban and rural household sizes by 2030 of 3.7 and 4.4 people per household respectively [5]. Modeled household electrification status in 2015 (1 if the household in the cell are considered electrified by the national grid, 2 if electrified by mini-grids and 0 if non-electrified). The algorithm in OnSSET determines which household are likely to be electrified in 2015 to match the current electrification rate of 15% [6], based on meeting certain conditions for night-time light (NTL), population density and distance to the grid and roads. For Madagascar the settlements were calculated to be electrified by the national grid (RI Antananarico, RI Toamasina and RI Fianarantsoa) if they a) where within 5 km from the grid and had a minimum population density of 2287 people/km2 or minimum NTL of 60 or b) within 10 km from the grid and had a minimum population density of 10000 people/km2 or by mini-grids if they c) had a population density above 3882 people/km2 and minimum NTL of 5 or maximum 20 kilometers to major roads. [1] Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University (2016). High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe https://energydata.info/dataset/madagascar-high-resolution-settlement-layer-2015 [2] United Nations - Economic Commission for Africa. The Demographic Profile of African Countries. (2016). [3] United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2014 Revision. (2014). [4] Unicef - division of data, research and policy. Generation 2030 | Africa. (2014). [5] Mentis, D. et al. Lighting the World: the first application of an open source, spatial electrification tool (OnSSET) on Sub-Saharan Africa. Environmental Research Letters. Vol. 12, nr 8. (2017). [6] USAID. Power Africa in Madagascar | Power Africa | U.S. Agency for International Development. Available at: https://www.usaid.gov/powerafrica/madagascar. (2017).

The U.S. Geological Survey (USGS) is coordinating the aquisition of high accuracy elevation data. Three formats of the data are available for each data set: .cor files which contain complete lists of Global Positioning System point files, .asc files which are the same as the .cor files but have been reformatted to process into ARC/INFO coverages, and .e00 files which are the ARC/INFO coverages. The files are available in the same 7.5- by 7.5-minute coverages as USGS quadrangles. The elevation data is collected on a 400 by 400 meter grid. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88).

 This project is performing regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services are also being rendered to provide vertical reference points for numerous water level gauges.

 Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) are being collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.

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.

Data Source: This dataset was compiled from multiple data sources

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At 282 feet below sea level, Death Valley in the Mojave Desert, California is the lowest point of elevation in the United States (and North America). Coincidentally, Death Valley is less than 85 miles from Mount Whitney, the highest point of elevation in the mainland United States. Death Valley is one of the hottest places on earth, and in 1913 it was the location of the highest naturally occurring temperature ever recorded on Earth (although some meteorologists doubt its legitimacy). New Orleans Louisiana is the only other state where the lowest point of elevation was below sea level. This is in the city of New Orleans, on the Mississippi River Delta. Over half of the city (up to two-thirds) is located below sea level, and recent studies suggest that the city is sinking further - man-made efforts to prevent water damage or flooding are cited as one reason for the city's continued subsidence, as they prevent new sediment from naturally reinforcing the ground upon which the city is built. These factors were one reason why New Orleans was so severely impacted by Hurricane Katrina in 2005 - the hurricane itself was one of the deadliest in history, and it destroyed many of the levee systems in place to prevent flooding, and the elevation exacerbated the damage caused. Highest low points The lowest point in five states is over 1,000 feet above sea level. Colorado's lowest point, at 3,315 feet, is still higher than the highest point in 22 states or territories. For all states whose lowest points are found above sea level, these points are located in rivers, streams, or bodies of water.

Dataset Name: City Happiness Index

Dataset Description:

This dataset and the related codes are entirely prepared, original, and exclusive by Emirhan BULUT. The dataset includes crucial features and measurements from various cities around the world, focusing on factors that may affect the overall happiness score of each city. By analyzing these factors, we aim to gain insights into the living conditions and satisfaction of the population in urban environments.

The dataset consists of the following features:

• City: Name of the city.
• Month: The month in which the data is recorded.
• Year: The year in which the data is recorded.
• Decibel_Level: Average noise levels in decibels, indicating the auditory comfort of the citizens.
• Traffic_Density: Level of traffic density (Low, Medium, High, Very High), which might impact citizens' daily commute and stress levels.
• Green_Space_Area: Percentage of green spaces in the city, positively contributing to the mental well-being and relaxation of the inhabitants.
• Air_Quality_Index: Index measuring the quality of air, a crucial aspect affecting citizens' health and overall satisfaction.
• Happiness_Score: The average happiness score of the city (on a 1-10 scale), representing the subjective well-being of the population.
• Cost_of_Living_Index: Index measuring the cost of living in the city (relative to a reference city), which could impact the financial satisfaction of the citizens.
• Healthcare_Index: Index measuring the quality of healthcare in the city, an essential component of the population's well-being and contentment.

With these features, the dataset aims to analyze and understand the relationship between various urban factors and the happiness of a city's population. The developed Deep Q-Network model, PIYAAI_2, is designed to learn from this data to provide accurate predictions in future scenarios. Using Reinforcement Learning, the model is expected to improve its performance over time as it learns from new data and adapts to changes in the environment.

List of the top five cities with the highest ECI values.

The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery.

South America was home to the world's highest altitude soccer stadiums in 2020. At the top of the list was Daniel Alcides Carrión stadium, located at ***** meters above sea level (MASL), in the Peruvian city of Cerro del Pasco. It hosts matches during the Copa Perú, a regional football tournament. Also surpassing the ************* meters of altitude, the municipal stadium of El Alto ranked second that year, followed by Víctor Agustín stadium, at ***** MASL. Both of these stadiums are located in Bolivia, which is also home to the highest altitude soccer stadium in a capital city &#8211 Hernando Siles stadium, in La Paz.

In China, the share of the population that earned at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms was *** percent. Shenzhen topped the list with the highest share of the upper or high-class category consumers, at over ** percent.

New York was one of the cities worldwide with the highest construction costs for high rise offices as of the third quarter of 2023. The average price of building a high rise office in London amounted to over ***** U.S. dollars per square meter, while the cost for that same type of building in Johannesburg was *** U.S. dollars per square meter. The cost of multi-unit high rise buildings were also the most expensive in New York.

In the Middle East, the share of the population that earned at least the equivalent of the highest 10 percent of global income earners as of 2022 in purchasing power parity (PPP) terms was **** percent. Dubai in the UAE topped the list with the highest share of the upper or high-class category consumers in selected Middle Eastern cities, at **** percent.