All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

The Human Mortality Database (HMD) was created to provide detailed mortality and population data to researchers, students, journalists, policy analysts, and others interested in the history of human longevity. The project began as an outgrowth of earlier projects in the Department of Demography at the University of California, Berkeley, USA, and at the Max Planck Institute for Demographic Research in Rostock, Germany (see history). It is the work of two teams of researchers in the USA and Germany (see research teams), with the help of financial backers and scientific collaborators from around the world (see acknowledgements).

The French Institute for Demographic Studies (INED) has also supported the further development of the database in recent years.

People Data Labs is an aggregator of B2B person and company data. We source our globally compliant person dataset via our "Data Union".

The "Data Union" is our proprietary data sharing co-op. Customers opt-in to sharing their data and warrant that their data is fully compliant with global data privacy regulations. Some data sources are provided as a one time dump, others are refreshed every time we do a new data build. Our data sources come from a variety of verticals including HR Tech, Real Estate Tech, Identity/Anti-Fraud, Martech, and others. People Data Labs works with customers on compliance based topics. If a customer wishes to ensure anonymity, we work with them to anonymize the data.

Our person data has over 100 fields including resume data (work history, education), contact information (email, phone), demographic info (name, gender, birth date) and social profile information (linkedin, github, twitter, facebook, etc...).

The data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards: Immigrants, Refugees, Muslims, Hispanics, Venezuelans News Media Consumption Trust in News Media and Societal Institutions Frequency and Valence of Intergroup Contact Realistic and Symbolic Intergroup Threat Right-wing Authoritarianism Social Dominance Orientation Political Efficacy Personality Characteristics Perceived COVID-threat, and Socio-demographic Characteristics For the adult population aged 25 to 65 in seven European countries: Austria Belgium Germany Hungary Italy Spain Sweden And for ages ranged from 18 to 65 for: United States of America Colombia The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections.

Germany DE: Mortality Rate: Adult: Male: per 1000 Male Adults data was reported at 85.005 Ratio in 2020. This records an increase from the previous number of 83.621 Ratio for 2019. Germany DE: Mortality Rate: Adult: Male: per 1000 Male Adults data is updated yearly, averaging 111.806 Ratio from Dec 1990 (Median) to 2020, with 31 observations. The data reached an all-time high of 158.105 Ratio in 1990 and a record low of 83.621 Ratio in 2019. Germany DE: Mortality Rate: Adult: Male: per 1000 Male Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Health Statistics. Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.;(1) United Nations Population Division. World Population Prospects: 2024 Revision. (2) HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.;Weighted average;

The main variables for this Location dataset are: - Pedestrian influx - Vehicle flow - Resident population - Income level - Business concentration.

Also, the model is enriched with information on the population interested in a specific topic (Like retail store location data), measured from the interaction of users in social networks (Consumer behavior data).

All the variables evaluated in the model are at the spatial grid level, to which it is possible to add existing points of sale and their respective revenue. This additional information makes it possible to estimate the billing of an additional Point of Sale in the best areas identified to locate a specific type of business.

Why should you trust PREDIK Data-Driven? In 2023, we were listed as Datarade's top providers. Why? Our solutions for location data, consumer behavior data, and store location data adapt according to the specific needs of companies. Also, PREDIK methodology focuses on the client and the necessary elements for the success of their projects.

A global database that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date population trends for market research, ad targeting, and sales territory mapping.

Self-hosted population dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The geodemographic data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Demographic/Geodemographic data)

• Ad targeting

• Market Intelligence

• Customer analytics

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

Geographic data export methodology

Our location data packages are offered in CSV format. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our location databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

LinkDB is an exhaustive dataset of publicly accessible LinkedIn people and companies, containing close to 500M people & companies profiles by region.

LinkDB is updated up to millions of profiles daily at the point of purchase. Post-purchase, you can keep LinkDB updated quarterly for a nominal fee.

Data is shipped in Parquet file format, Apache Parquet, a column-oriented data file format.

All our data and procedures are in place that meet major legal compliance requirements such as GDPR, CCPA. We help you be compliant too.

A collection of population life tables covering a multitude of countries and many years. Most of the HLD life tables are life tables for national populations, which have been officially published by national statistical offices. Some of the HLD life tables refer to certain regional or ethnic sub-populations within countries. Parts of the HLD life tables are non-official life tables produced by researchers. Life tables describe the extent to which a generation of people (i.e. life table cohort) dies off with age. Life tables are the most ancient and important tool in demography. They are widely used for descriptive and analytical purposes in demography, public health, epidemiology, population geography, biology and many other branches of science. HLD includes the following types of data: * complete life tables in text format; * abridged life tables in text format; * references to statistical publications and other data sources; * scanned copies of the original life tables as they were published. Three scientific institutions are jointly developing the HLD: the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, the Department of Demography at the University of California at Berkeley, USA and the Institut national d''��tudes d��mographiques (INED) in Paris, France. The MPIDR is responsible for maintaining the database.

1. Specifications Data size : 200,000 ID

Population distribution : race distribution: Asians, Caucasians, black people; gender distribution: gender balance; age distribution: from child to the elderly, the young people and the middle aged are the majorities

Collection environment : indoor scenes, outdoor scenes

Collection diversity : various postures, expressions, light condition, scenes, time periods and distances

Collection device : iPhone, android phone, iPad

Collection time : daytime,night

Image Parameter : the video format is .mov or .mp4, the image format is .jpg

Accuracy : the accuracy of actions exceeds 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go machine learning (ML) data supports instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

People data is our proprietary mobile user dataset that links anonymous IDs to multiple attributes related to demographics, device ownership, audience segments, key locations, and more. This enables our partner brands to get a holistic view of a consumer based on their persona and be able to instantly gain actionable insights.

People Data Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as user demographics,anonymous id, device details, location, affluence, interests, traveled countries, and so on.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly/quarterly).

Business Needs: Consumer Insights: Gain a complete 360-degree view of the customer to detect behavioral changes, assess patterns, and forecast business effects. Data Enrichment; Leverage O2O consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment. Sales Forecasting: Analyze consumer behavior to predict sales and monitor performance of investments Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas.

Data Attributes: anonymous id id_type Age Gender Carrier Make Model OS os_version home_country Home_geohash Work_geohash Device_price Device_age Affluence Brands_visited Place_categories Geo_behaviour Interests travelled_countries

Costa Rica CR: Mortality Rate: Adult: Male: per 1000 Male Adults data was reported at 111.434 Ratio in 2023. This records a decrease from the previous number of 125.344 Ratio for 2022. Costa Rica CR: Mortality Rate: Adult: Male: per 1000 Male Adults data is updated yearly, averaging 128.556 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 200.781 Ratio in 1960 and a record low of 111.434 Ratio in 2023. Costa Rica CR: Mortality Rate: Adult: Male: per 1000 Male Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Social: Health Statistics. Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.;(1) United Nations Population Division. World Population Prospects: 2024 Revision. (2) HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.;Weighted average;

1. Specifications Product : Biometric Data

Data size : 200,000 ID

Race distribution : black people, Caucasian people, brown(Mexican) people, Indian people and Asian people

Gender distribution : gender balance

Age distribution : young, midlife and senior

Collecting environment : including indoor and outdoor scenes

Data diversity : different face poses, races, ages, light conditions and scenes Device : cellphone

Data format : .jpg/png

Accuracy : the accuracy of labels of face pose, race, gender and age are more than 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go Biometric Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

Street Noise-Level Dataset

Silencio’s Street Noise-Level Dataset offers unique access to hyper-local, real-world noise exposure data across more than 200 countries. Built from over 35 billion datapoints, collected via our mobile app and enriched with AI-powered interpolation, this dataset delivers detailed average noise levels (dBA) at the street and neighborhood level.

Chronic noise exposure is a growing public health concern linked to stress, cardiovascular risks, sleep disorders, and reduced quality of life — all of which are increasingly relevant for public health studies, insurance risk modeling, and wellness program design. Silencio’s data allows buyers to quantify environmental noise exposure and incorporate it into risk assessments, premium modeling, urban health studies, and wellness product development.

In addition to objective noise measurements, Silencio provides access to the world’s largest noise complaint database, offering complementary subjective insights directly from communities, enabling more precise correlations between noise exposure and health outcomes.

Data is available as: • CSV exports • S3 bucket delivery • High-resolution maps, perfect for health impact assessments, research publications, or integration into insurance models.

We provide both historical and real-time data. An API is currently in development, and we welcome custom requests and early access partnerships.

Fully anonymized and GDPR-compliant, our dataset is ready to enhance health-focused research, insurance underwriting, and product innovation.

Chad TD: Mortality Rate: Adult: Male: per 1000 Male Adults data was reported at 379.573 Ratio in 2023. This records a decrease from the previous number of 386.516 Ratio for 2022. Chad TD: Mortality Rate: Adult: Male: per 1000 Male Adults data is updated yearly, averaging 420.460 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 575.365 Ratio in 1987 and a record low of 379.573 Ratio in 2023. Chad TD: Mortality Rate: Adult: Male: per 1000 Male Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Health Statistics. Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.;(1) United Nations Population Division. World Population Prospects: 2024 Revision. (2) HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.;Weighted average;

Colombia CO: Mortality Rate: Adult: Male: per 1000 Male Adults data was reported at 122.668 Ratio in 2023. This records a decrease from the previous number of 141.300 Ratio for 2022. Colombia CO: Mortality Rate: Adult: Male: per 1000 Male Adults data is updated yearly, averaging 244.089 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 306.603 Ratio in 1960 and a record low of 122.668 Ratio in 2023. Colombia CO: Mortality Rate: Adult: Male: per 1000 Male Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Health Statistics. Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.;(1) United Nations Population Division. World Population Prospects: 2024 Revision. (2) HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.;Weighted average;

Street Noise-Level Dataset — Health & Insurance Applications

Silencio’s Street Noise-Level Dataset offers health organizations, insurance companies, and wellness researchers unique access to hyper-local, real-world noise exposure data across more than 200 countries. Built from over 35 billion datapoints, collected via our mobile app and enriched with AI-powered interpolation, this dataset delivers detailed average noise levels (dBA) at the street and neighborhood level.

Chronic noise exposure is a growing public health concern linked to stress, cardiovascular risks, sleep disorders, and reduced quality of life — all of which are increasingly relevant for public health studies, insurance risk modeling, and wellness program design. Silencio’s data allows insurance and health organizations to quantify environmental noise exposure and incorporate it into risk assessments, premium modeling, urban health studies, and wellness product development.

In addition to objective noise measurements, Silencio provides access to the world’s largest noise complaint database, offering complementary subjective insights directly from communities, enabling more precise correlations between noise exposure and health outcomes.

Data is available as: • CSV exports • S3 bucket delivery • High-resolution maps, perfect for health impact assessments, research publications, or integration into insurance models.

We provide both historical and real-time data. An API is currently in development, and we welcome custom requests and early access partnerships.

Fully anonymized and GDPR-compliant, our dataset is ready to enhance health-focused research, insurance underwriting, and product innovation.

Belgium BE: Mortality Rate: Adult: Male: per 1000 Male Adults data was reported at 76.294 Ratio in 2023. This records a decrease from the previous number of 77.708 Ratio for 2022. Belgium BE: Mortality Rate: Adult: Male: per 1000 Male Adults data is updated yearly, averaging 137.643 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 209.462 Ratio in 1960 and a record low of 76.294 Ratio in 2023. Belgium BE: Mortality Rate: Adult: Male: per 1000 Male Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belgium – Table BE.World Bank.WDI: Social: Health Statistics. Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.;(1) United Nations Population Division. World Population Prospects: 2024 Revision. (2) HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.;Weighted average;

1. Specifications Data size : 200,000 ID

Race distribution : Asians, Caucasians, black people

Gender distribution : gender balance

Age distribution : ranging from teenager to the elderly, the middle-aged and young people are the majorities

Collecting environment : including indoor and outdoor scenes

Data diversity : different shooting heights, different ages, different light conditions, different collecting environment, clothes in different seasons, multiple human poses

Device : cameras

Data format : the data format is .jpg/mp4, the annotation file format is .json, the camera parameter file format is .json, the point cloud file format is .pcd

Accuracy : based on the accuracy of the poses, the accuracy exceeds 97%;the accuracy of labels of gender, race, age, collecting environment and clothes are more than 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go machine learning (ML) data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at hhttps://www.nexdata.ai/datasets/computervision?source=Datarade

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!