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 2020, Ifrane was the highest city in Morocco, with an altitude of ***** meters. Midelt and Errachidia followed, as they were ***** meters and ***** meters above sea level, respectively. In contrast, the areas of Tétouan and Kénitra each recorded the lowest altitude in the country.

The United States has an average elevation of roughly 2,500 feet (763m) above sea level, however there is a stark contrast in elevations across the country. Highest states Colorado is the highest state in the United States, with an average elevation of 6,800 feet (2,074m) above sea level. The 10 states with the highest average elevation are all in the western region of the country, as this is, by far, the most mountainous region in the country. The largest mountain ranges in the contiguous western states are the Rocky Mountains, Sierra Nevada, and Cascade Range, while the Appalachian Mountains is the longest range in the east - however, the highest point in the U.S. is Denali (Mount McKinley), found in Alaska. Lowest states At just 60 feet above sea level, Delaware is the state with the lowest elevation. Delaware is the second smallest state, behind Rhode Island, and is located on the east coast. Larger states with relatively low elevations are found in the southern region of the country - both Florida and Louisiana have an average elevation of just 100 feet (31m) above sea level, and large sections of these states are extremely vulnerable to flooding and rising sea levels, as well as intermittent tropical storms.

STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data is the remotely sensed trace gas data for the NASA Gulfstream III aircraft taken by the High Altitude Lidar Observatory (HALO) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.

IntroductionClimate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.Back to topMethods and QualifiersThis map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).Areas using lidar-based elevation data: U.S. coastal states except AlaskaElevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)). Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago. CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.Warning for areas using other elevation data (all other areas)Areas of this map not listed above use elevation data on a roughly 90-meter horizontal resolution grid derived from NASA’s Shuttle Radar Topography Mission (SRTM). SRTM provides surface elevations, not bare earth elevations, causing it to commonly overestimate elevations, especially in areas with dense and tall buildings or vegetation. Therefore, the map under-portrays areas that could be submerged at each water level, and exposure is greater than shown (Kulp and Strauss, 2016). However, SRTM includes error in both directions, so some areas showing exposure may not be at risk.SRTM data do not cover latitudes farther north than 60 degrees or farther south than 56 degrees, meaning that sparsely populated parts of Arctic Circle nations are not mapped here, and may show visual artifacts.Areas of this map in Alaska use elevation data on a roughly 60-meter horizontal resolution grid supplied by the U.S. Geological Survey (USGS). This data is referenced to a vertical reference frame from 1929, based on historic sea levels, and with no established conversion to modern reference frames. The data also do not take into account subsequent land uplift and subsidence, widespread in the state. As a consequence, low confidence should be placed in Alaska map portions.Flood control structures (U.S.)Levees, walls, dams or other features may protect some areas, especially at lower elevations. Levees and other flood control structures are included in this map within but not outside of the U.S., due to poor and missing data. Within the U.S., data limitations, such as an incomplete inventory of levees, and a lack of levee height data, still make assessing protection difficult. For this map, levees are assumed high and strong enough for flood protection. However, it is important to note that only 8% of monitored levees in the U.S. are rated in “Acceptable” condition (ASCE). Also note that the map implicitly includes unmapped levees and their heights, if broad enough to be effectively captured directly by the elevation data.For more information on how Surging Seas incorporates levees and elevation data in Louisiana, view our Louisiana levees and DEMs methods PDF. For more information on how Surging Seas incorporates dams in Massachusetts, view the Surging Seas column of the web tools comparison matrix for Massachusetts.ErrorErrors or omissions in elevation or levee data may lead to areas being misclassified. Furthermore, this analysis does not account for future erosion, marsh migration, or construction. As is general best practice, local detail should be verified with a site visit. Sites located in zones below a given water level may or may not be subject to flooding at that level, and sites shown as isolated may or may not be be so. Areas may be connected to water via porous bedrock geology, and also may also be connected via channels, holes, or passages for drainage that the elevation data fails to or cannot pick up. In addition, sea level rise may cause problems even in isolated low zones during rainstorms by inhibiting drainage.ConnectivityAt any water height, there will be isolated, low-lying areas whose elevation falls below the water level, but are protected from coastal flooding by either man-made flood control structures (such as levees), or the natural topography of the surrounding land. In areas using lidar-based elevation data or CoastalDEM (see above), elevation data is accurate enough that non-connected areas can be clearly identified and treated separately in analysis (these areas are colored green on the map). In the U.S., levee data are complete enough to factor levees into determining connectivity as well.However, in other areas, elevation data is much less accurate, and noisy error often produces “speckled” artifacts in the flood maps, commonly in areas that should show complete inundation. Removing non-connected areas in these places could greatly underestimate the potential for flood exposure. For this reason, in these regions, the only areas removed from the map and excluded from analysis are separated from the ocean by a ridge of at least 20 meters (66 feet) above the local high tide line, according to the data, so coastal flooding would almost certainly be impossible (e.g., the Caspian Sea region).Back to topData LayersWater Level | Projections | Legend | Social Vulnerability | Population | Ethnicity | Income | Property | LandmarksWater LevelWater level means feet or meters above the local high tide line (“Mean Higher High Water”) instead of standard elevation. Methods described above explain how each map is generated based on a selected water level. Water can reach different levels in different time frames through combinations of sea level rise, tide and storm surge. Tide gauges shown on the map show related projections (see just below).The highest water levels on this map (10, 20 and 30 meters) provide reference points for possible flood risk from tsunamis, in regions prone to them.

According to INSPIRE transformed development plan “Behind the high altitude path (original)” of the city of Nürtingen based on an XPlanung dataset in version 5.0.

Overview

The Songdo Vision dataset provides high-resolution (4K, 3840×2160 pixels) RGB images annotated with categorized axis-aligned bounding boxes (BBs) for vehicle detection from a high-altitude bird’s-eye view (BeV) perspective. Captured over Songdo International Business District, South Korea, this dataset consists of 5,419 annotated video frames, featuring approximately 300,000 vehicle instances categorized into four classes:

Car (including vans and light-duty vehicles)

Bus

Truck

Motorcycle

This dataset can serve as a benchmark for aerial vehicle detection, supporting research and real-world applications in intelligent transportation systems, traffic monitoring, and aerial vision-based mobility analytics. It was developed in the context of a multi-drone experiment aimed at enhancing geo-referenced vehicle trajectory extraction.

⚠️ Important: If you use this dataset in your work, please cite the following reference [1]:

Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery, arXiv preprint arXiv:2411.02136.

(Note: This manuscript shall be replaced by the published version once available.)

Motivation

Publicly available datasets for aerial vehicle detection often exhibit limitations such as:

Non-BeV perspectives with varying angles and distortions

Inconsistent annotation quality, with loose or missing bounding boxes

Lower-resolution imagery, reducing detection accuracy, particularly for smaller vehicles

Lack of annotation detail, especially for motorcycles in dense urban scenes with complex backgrounds

To address these challenges, Songdo Vision provides high-quality human-annotated bounding boxes, with machine learning assistance used to enhance efficiency and consistency. This ensures accurate and reliable ground truth for training and evaluating detection models.

Dataset Composition

The dataset is randomly split into training (80%) and test (20%) subsets:

Subset Images Car Bus Truck Motorcycle Total Vehicles

Train 4,335 195,539 7,030 11,779 2,963 217,311

Test 1,084 49,508 1,759 3,052 805 55,124

A subset of 5,274 frames was randomly sampled from drone video sequences, while an additional 145 frames were carefully selected to represent challenging cases, such as motorcycles at pedestrian crossings, in bicycle lanes, near traffic light poles, and around other distinctive road markers where they may blend into the urban environment.

Data Collection

The dataset was collected as part of a collaborative multi-drone experiment conducted by KAIST and EPFL in Songdo, South Korea, from October 4–7, 2022.

A fleet of 10 drones monitored 20 busy intersections, executing advanced flight plans to optimize coverage.

4K (3840×2160) RGB video footage was recorded at 29.97 FPS from altitudes of 140–150 meters.

Each drone flew 10 sessions per day, covering peak morning and afternoon periods.

The experiment resulted in 12TB of 4K raw video data.

More details on the experimental setup and data processing pipeline are available in [1].

Bounding Box Annotations & Formats

Annotations were generated using a semi-automated object detection annotation process in Azure ML Studio, leveraging machine learning-assisted bounding box detection with human verification to ensure precision.

Each annotated frame includes categorized, axis-aligned bounding boxes, stored in three widely-used formats:

1. COCO JSON format

Single annotation file per dataset subset (i.e., one for training, one for testing).

Contains metadata such as image dimensions, bounding box coordinates, and class labels.

Example snippet:

{ "images": [{"id": 1, "file_name": "0001.jpg", "width": 3840, "height": 2160}], "annotations": [{"id": 1, "image_id": 1, "category_id": 2, "bbox": [500, 600, 200, 50], "area": 10000, "iscrowd": 0}], "categories": [ {"id": 1, "name": "car"}, {"id": 2, "name": "bus"}, {"id": 3, "name": "truck"}, {"id": 4, "name": "motorcycle"} ] }

1. YOLO TXT format

One annotation file per image, following the format:

Bounding box values are normalized to [0,1], with the origin at the top-left corner.

Example snippet:

0 0.52 0.63 0.10 0.05 # Car bounding box 2 0.25 0.40 0.15 0.08 # Truck bounding box

1. Pascal VOC XML format

One annotation file per image, structured in XML.

Contains image properties and absolute pixel coordinates for each bounding box.

Example snippet:

0001.jpg 384021603

car
500600600650

File Structure

The dataset is provided as two compressed archives:

1. Training Data (train.zip, 12.91 GB)

train/ │── coco_annotations.json # COCO format │── images/ │ ├── 0001.jpg │ ├── ... │── labels/ │ ├── 0001.txt # YOLO format │ ├── 0001.xml # Pascal VOC format │ ├── ...

1. Testing Data (test.zip, 3.22 GB)

test/ │── coco_annotations.json │── images/ │ ├── 00027.jpg │ ├── ... │── labels/ │ ├── 00027.txt │ ├── 00027.xml │ ├── ...

Additional Files

README.md – Dataset documentation (this description)

LICENSE.txt – Creative Commons Attribution 4.0 License

names.txt – Class names (one per line)

data.yaml – Example YOLO configuration file for training/testing

Citation & Attribution

Preferred Citation:

If you use Songdo Vision for any purpose, whether in academic research, commercial applications, open-source projects, or benchmarking efforts, please cite our accompanying manuscript [1]:

Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery, arXiv preprint arXiv:2411.02136.

(Note: This manuscript shall be replaced by the published version once available.)

Note: Although Zenodo automatically provides a formal dataset citation (shown below), we kindly request that you reference the manuscript as the primary source of this work.

Dataset Citation (for archival purposes):

Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City (v1). Zenodo. DOI: 10.5281/zenodo.13828408.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

[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.

This dataset provides information about the number of properties, residents, and average property values for Mountain Top Lane cross streets in Park City, UT.

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 a variety of vertical datums and horizontal datum of World Geodetic System of 1984 (WGS84). Cell size for the DEMs ranges 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.

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.

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).

This horizontal bar chart displays urban population living in areas where elevation is below 5 meters (% of total population) by capital city using the aggregation average, weighted by population in Western Africa. The data is about countries.

About this dataset

Context

The dataset tabulates the Mountain City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Mountain City. The dataset can be utilized to understand the population distribution of Mountain City by age. For example, using this dataset, we can identify the largest age group in Mountain City.

Key observations

The largest age group in Mountain City, TN was for the group of age 60 to 64 years years with a population of 288 (12.66%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Mountain City, TN was the 15 to 19 years years with a population of 24 (1.05%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Mountain City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Mountain City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for Mountain City Population by Age. You can refer the same here

This horizontal bar chart displays urban population living in areas where elevation is below 5 meters (% of total population) by capital city using the aggregation average, weighted by population in Turkey. The data is about countries per year.

This horizontal bar chart displays urban population living in areas where elevation is below 5 meters (% of total population) by capital city using the aggregation average, weighted by population in Southern Asia. The data is about countries.

This horizontal bar chart displays urban population living in areas where elevation is below 5 meters (% of total population) by capital city using the aggregation average, weighted by population in Micronesia. The data is about countries.