Full profile of 10,000 people in the US - download here, data schema here, with more than 40 data points including - Full Name - Education - Location - Work Experience History and many more!

There are additionally 258+ Million US people profiles available, visit the LinkDB product page here.

Our LinkDB database is an exhaustive database of publicly accessible LinkedIn people and companies profiles. It contains close to 500 Million people and companies profiles globally.

Summary

This dataset contains biographical information derived from articles on English Wikipedia as it stood in early June 2024. It was created as part of the Structured Contents initiative at Wikimedia Enterprise and is intended for evaluation and research use.

The beta sample dataset is a subset of the Structured Contents Snapshot focusing on people with infoboxes in EN wikipedia; outputted as json files (compressed in tar.gz).

We warmly welcome any feedback you have. Please share your thoughts, suggestions, and any issues you encounter on the discussion page for this dataset here on Kaggle.

Data Structure

• File name: wme_people_infobox.tar.gz
• Size of compressed file: 4.12 GB
• Size of uncompressed file: 21.28 GB

Noteworthy Included Fields: - name - title of the article. - identifier - ID of the article. - image - main image representing the article's subject. - description - one-sentence description of the article for quick reference. - abstract - lead section, summarizing what the article is about. - infoboxes - parsed information from the side panel (infobox) on the Wikipedia article. - sections - parsed sections of the article, including links. Note: excludes other media/images, lists, tables and references or similar non-prose sections.

The Wikimedia Enterprise Data Dictionary explains all of the fields in this dataset.

Stats

Infoboxes - Compressed: 2GB - Uncompressed: 11GB

Infoboxes + sections + short description - Size of compressed file: 4.12 GB - Size of uncompressed file: 21.28 GB

Article analysis and filtering breakdown: - total # of articles analyzed: 6,940,949 - # people found with QID: 1,778,226 - # people found with Category: 158,996 - people found with Biography Project: 76,150 - Total # of people articles found: 2,013,372 - Total # people articles with infoboxes: 1,559,985 End stats - Total number of people articles in this dataset: 1,559,985 - that have a short description: 1,416,701 - that have an infobox: 1,559,985 - that have article sections: 1,559,921

This dataset includes 235,146 people articles that exist on Wikipedia but aren't yet tagged on Wikidata as instance of:human.

Maintenance and Support

This dataset was originally extracted from the Wikimedia Enterprise APIs on June 5, 2024. The information in this dataset may therefore be out of date. This dataset isn't being actively updated or maintained, and has been shared for community use and feedback. If you'd like to retrieve up-to-date Wikipedia articles or data from other Wikiprojects, get started with Wikimedia Enterprise's APIs

Initial Data Collection and Normalization

The dataset is built from the Wikimedia Enterprise HTML “snapshots”: https://enterprise.wikimedia.com/docs/snapshot/ and focuses on the Wikipedia article namespace (namespace 0 (main)).

Who are the source language producers?

Wikipedia is a human generated corpus of free knowledge, written, edited, and curated by a global community of editors since 2001. It is the largest and most accessed educational resource in history, accessed over 20 billion times by half a billion people each month. Wikipedia represents almost 25 years of work by its community; the creation, curation, and maintenance of millions of articles on distinct topics. This dataset includes the biographical contents of English Wikipedia language editions: English https://en.wikipedia.org/, written by the community.

Attribution

Terms and conditions

Wikimedia Enterprise provides this dataset under the assumption that downstream users will adhere to the relevant free culture licenses when the data is reused. In situations where attribution is required, reusers should identify the Wikimedia project from which the content was retrieved as the source of the content. Any attribution should adhere to Wikimedia’s trademark policy (available at https://foundation.wikimedia.org/wiki/Trademark_policy) and visual identity guidelines (ava...

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...).

This data contains information about people involved in a crash and if any injuries were sustained. This dataset should be used in combination with the traffic Crash and Vehicle dataset. Each record corresponds to an occupant in a vehicle listed in the Crash dataset. Some people involved in a crash may not have been an occupant in a motor vehicle, but may have been a pedestrian, bicyclist, or using another non-motor vehicle mode of transportation. Injuries reported are reported by the responding police officer. Fatalities that occur after the initial reports are typically updated in these records up to 30 days after the date of the crash. Person data can be linked with the Crash and Vehicle dataset using the “CRASH_RECORD_ID” field. A vehicle can have multiple occupants and hence have a one to many relationship between Vehicle and Person dataset. However, a pedestrian is a “unit” by itself and have a one to one relationship between the Vehicle and Person table. The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. The crash data published on the Chicago data portal mostly follows the data elements in SR1050 form. The current version of the SR1050 instructions manual with detailed information on each data elements is available here. Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

American Time Use Survey: Daily Activities

Americans' Daily Activities: Education, Employment, Gender, and Leisure Time

By Throwback Thursday [source]

About this dataset

The American Time Use Survey dataset provides comprehensive information on how individuals in America allocate their time throughout the day. It includes various aspects of daily activities such as education level, age, employment status, gender, number of children, weekly earnings and hours worked. The dataset also includes data on specific activities individuals engage in like sleeping, grooming, housework, food and drink preparation, caring for children, playing with children, job searching, shopping and eating and drinking. Additionally it captures time spent on leisure activities like socializing and relaxing as well as engaging in specific hobbies such as watching television or golfing. The dataset also records the amount of time spent volunteering or running for exercise purposes.

Each entry is organized based on categorical variables such as education level (ranging from lower levels to higher degrees), age (capturing different age brackets), employment status (including employed full-time or part-time), gender (male or female) and the number of children an individual has. Furthermore it provides information regarding an individual's weekly earnings and hours worked.

This extensive dataset aims to provide insights into how Americans prioritize their time across various aspects of their lives. Whether it be focusing on work-related tasks or indulging in recreational activities,it offers a comprehensive look at the allocation of time among different demographic groups within American society.

This dataset can be used for understanding trends in daily activity patterns across demographics groups over multiple years without directly referencing specific dates

How to use the dataset

How to use this dataset: American Time Use Survey - Daily Activities

Welcome to the American Time Use Survey dataset! This dataset provides valuable information on how Americans spend their time on a daily basis. Here's a guide on how to effectively utilize this dataset for your analysis:

• Familiarize yourself with the columns:

  • Education Level: The level of education attained by the individual.
  • Age: The age of the individual.
  • Age Range: The age range the individual falls into.
  • Employment Status: The employment status of the individual.
  • Gender: The gender of the individual.
  • Children: The number of children that an individual has.
  • Weekly Earnings: The amount of money earned by an individual on a weekly basis.
  • Year: The year in which the data was collected.
  • Weekly Hours Worked: The number of hours worked by an individual on a weekly basis.

• Identify variables related to daily activities: This dataset provides information about various daily activities undertaken by individuals. Some important variables related to daily activities include:

  • Sleeping
  • Grooming
  • Housework
  • Food & Drink Prep
  • Caring for Children
  • Playing with Children
  • Job Searching …and many more!

• Analyze time spent on different activities: This dataset includes numerical values representing time spent in minutes for specific activities such as sleeping, grooming, housework, food and drink preparation, etc. You can use this data to analyze and compare how different groups of individuals allocate their time throughout the day.

• Explore demographic factors: In addition to daily activities, this dataset also includes columns such as education level, age range, employment status, gender, and number of children. You can cross-reference these demographic factors with activity data to gain insights into how different population subgroups spend their time differently.

• Identify trends and patterns: You can use this dataset to identify trends and patterns in how Americans allocate their time over the years. By analyzing data from different years, you may discover changes in certain activities and how they relate to demographic factors or societal shifts.

• Visualize the data: Creating visualizations such as bar graphs, line plots, or pie charts can provide a clear representation of how time is allocated for different activities among various groups of individuals. Visualizations help in understanding the distribution of time spent on different activities and identifying any significant differences or similarities across demographics.

Remember that each column represents a specific variable, whi...

Population Mid-year Estimates from the Office for National Statistics (ONS). These are the official estimates of the resident population in Lincolnshire. ONS uses information from the census and other data to produce these official mid-year population estimates every year between each census. These figures show how many people live in each local area and the population age-sex structure. This data is updated annually. Although the ONS data shows exact numbers, they are estimates so some rounding should be applied. For current Armed forces populations, two Ministry of Defence links are also shown below. The ONS 2021 Census link has Veterans data. Population Projections data sourced from ONS is also available on this platform. The Source link shown below is to the ONS Nomis website. It has user-friendly data query tools for a broad range of ONS and other datasets from official sources.

Here are a few use cases for this project:

1. Crowd Control: Use the "people_counting" model for monitoring and managing crowd sizes at public events such as concerts, sports events, or festivals. The model can provide real-time data on the number of people present, which can be used to control entry and enforce safety standards.

2. Retail Analysis: The model can be used in retail settings to monitor customer traffic in stores. Understanding peak hours and days can help management make decisions about staffing, product placement, and sales promotions.

3. Public Safety: Implement the model in surveillance systems of urban areas to detect overcrowded zones or unusual gathering which may indicate a potential public safety issue such as protests or accidents.

4. Transportation Planning: Use the model in public transportation planning. By counting the number of people on different routes or at different times, planners can effectively manage routes and schedules.

5. Smart Building Management: Implement the model in commercial and residential buildings to monitor occupancy rates and usage patterns. This data can be used for optimizing energy management and improving facilities management.

Lebanon Number Data is a list of phone numbers that you can filter in many ways. You can filter by gender, age, or even relationship status. To contact young people, filter the list to show only numbers from that age group. This helps you connect with the right individual quickly. The list also follows GDPR rules, which means it protects people’s privacy. Furthermore, we regularly update the Lebanon Number Data for clarity. It removes invalid numbers, saving time by avoiding outdated contact details. This feature keeps the list fresh and up-to-date and makes your work more efficient. With Lebanon contact data, you can trust accurate, up-to-date details and filter them to meet your needs. Lebanon phone data is a collection of phone numbers that is 100% correct and valid. The companies that provide this data check every number carefully to make sure it works. So, when you use this cellphone data, you don’t have to worry about the wrong numbers. If, for some reason, a number doesn’t work, you get a replacement guarantee. This means, if a number is invalid, they will give you a new dialing number at no extra cost. Moreover, Lebanon phone data comes with all phone number subscribers’ permission. This means the people who own the numbers have agreed to share their information. It’s very important to have this permission because it keeps you out of legal trouble. Using this database without the customer’s permission can be problematic, but this data is safe and secure. Lebanon phone number list is a collection of phone numbers of people living in Lebanon. This list is very helpful for businesses that need to contact people in Lebanon. The information comes from reliable sources, such as government records, websites, and phone service providers. You can even check the URLs where the data came from. This ensures that the phone numbers are accurate. Also, if you need help, 24/7 support is available. Also, the Lebanon phone number list follows the opt-in rule. Number owners know that others use their info, making it safe to use the data. You won’t face any trouble, and it respects people’s privacy. Using the Lebanon contact number list from our List to Data website, you can confidently connect with the right people.

Dataset Overview

This dataset contains 417,937 biographical records of notable individuals, extracted from Wikidata using SPARQL queries via the Wikidata Query Service.

Key Selection Criteria:

• Timeframe: Individuals born in the 20th or 21st century (1901–present).
• Country of Birth: Entries must include the country_of_birth.
• Photo Availability: Each entry includes an associated image (image_url is mandatory).
• Profession Filter: Focused on individuals with occupations categorized in occupation_groups.csv (Science & Academia, Arts & Culture, Public Figures, Sports, Business).

Column Descriptions

Notes

The data may contain some number of inaccuracies, due to inconsistencies or errors in the original Wikidata entries. This can sometimes be seen in date fields, especially date_of_image.

Source and Licensing Notes

• All data in this dataset was derived from Wikidata. Wikidata content is available under the CC0 1.0 license.
• Images linked in image_url and signature_url are hosted on Wikimedia Commons and may have individual licenses (e.g., CC BY-SA, Public Domain). Please check the license terms on the source page before using.

Uruguay number dataset has accurate numbers attached with verified through our team. Most importantly, these cell phone numbers belong to active users only. In fact, these specialties make it a valuable marketing help. if your business is new or old, you can increase your reach and connect to a large audience with this directory. Yet, you will find many people who have an attraction to your products and will purchase from you. The Uruguay number dataset will support you make your brand more renowned. In general, by becoming a known brand in the market, you can grow your brand value significantly. In other words, the contacts on this mobile number list are active and real. As a result, you will benefit greatly if you purchase this cheap but valuable database. Uruguay phone data can be a great solution for telemarketing. Anyone can use the contact lead to reach various people all over the country. Uruguay phone data allows you to give product details with your messages to make them more appealing and reliable. Your product quality and content will increase the awareness of the interested client. Our team gives the number lead in an Excel and CSV file. Uruguay phone data is an opt-in and permission-based contact list. Even, with a cheap yet fresh list like ours, your SMS marketing will be more influential. People can now relate to your business more after you successfully utilize this library from our website. So, order this number package now from List To Data to promote your goods and services everywhere inside the country. Uruguay phone number list is a massive database. However, many people will show interest in your products and services. We promise you sincere service and active support, we create it following GDPR rules. Our special team will solve the issue for you, thus you don’t have to worry about not obtaining the worth of your money. Further, the Uruguay phone number list will aid your business in many new ways. On the other hand, no one wants to miss out on such a huge and versatile audience in Uruguay. To this end, purchasing this contact list will be a gem for any business any day.

Population Mid-year Estimates from the Office for National Statistics (ONS). These are the official estimates of the resident population in Lincolnshire. ONS uses information from the census and other data to produce these official mid-year population estimates every year between each census. These figures show how many people live in each local area and the population age-sex structure. This data is updated annually. Although the ONS data shows exact numbers, they are estimates so some rounding should be applied. For current Armed forces populations, two Ministry of Defence links are also shown below. The ONS 2021 Census link has Veterans data. Population Projections data sourced from ONS is also available on this platform. The Source link shown below is to the ONS Nomis website. It has user-friendly data query tools for a broad range of ONS and other datasets from official sources.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

The database for this study (Briganti et al. 2018; the same for the Braun study analysis) was composed of 1973 French-speaking students in several universities or schools for higher education in the following fields: engineering (31%), medicine (18%), nursing school (16%), economic sciences (15%), physiotherapy, (4%), psychology (11%), law school (4%) and dietetics (1%). The subjects were 17 to 25 years old (M = 19.6 years, SD = 1.6 years), 57% were females and 43% were males. Even though the full dataset was composed of 1973 participants, only 1270 answered the full questionnaire: missing data are handled using pairwise complete observations in estimating a Gaussian Graphical Model, meaning that all available information from every subject are used.

The feature set is composed of 28 items meant to assess the four following components: fantasy, perspective taking, empathic concern and personal distress. In the questionnaire, the items are mixed; reversed items (items 3, 4, 7, 12, 13, 14, 15, 18, 19) are present. Items are scored from 0 to 4, where “0” means “Doesn’t describe me very well” and “4” means “Describes me very well”; reverse-scoring is calculated afterwards. The questionnaires were anonymized. The reanalysis of the database in this retrospective study was approved by the ethical committee of the Erasmus Hospital.

Size: A dataset of size 1973*28

Number of features: 28

Ground truth: No

Type of Graph: Mixed graph

The following gives the description of the variables:

Feature FeatureLabel Domain Item meaning from Davis 1980

001 1FS Green I daydream and fantasize, with some regularity, about things that might happen to me.

002 2EC Purple I often have tender, concerned feelings for people less fortunate than me.

003 3PT_R Yellow I sometimes find it difficult to see things from the “other guy’s” point of view.

004 4EC_R Purple Sometimes I don’t feel very sorry for other people when they are having problems.

005 5FS Green I really get involved with the feelings of the characters in a novel.

006 6PD Red In emergency situations, I feel apprehensive and ill-at-ease.

007 7FS_R Green I am usually objective when I watch a movie or play, and I don’t often get completely caught up in it.(Reversed)

008 8PT Yellow I try to look at everybody’s side of a disagreement before I make a decision.

009 9EC Purple When I see someone being taken advantage of, I feel kind of protective towards them.

010 10PD Red I sometimes feel helpless when I am in the middle of a very emotional situation.

011 11PT Yellow sometimes try to understand my friends better by imagining how things look from their perspective

012 12FS_R Green Becoming extremely involved in a good book or movie is somewhat rare for me. (Reversed)

013 13PD_R Red When I see someone get hurt, I tend to remain calm. (Reversed)

014 14EC_R Purple Other people’s misfortunes do not usually disturb me a great deal. (Reversed)

015 15PT_R Yellow If I’m sure I’m right about something, I don’t waste much time listening to other people’s arguments. (Reversed)

016 16FS Green After seeing a play or movie, I have felt as though I were one of the characters.

017 17PD Red Being in a tense emotional situation scares me.

018 18EC_R Purple When I see someone being treated unfairly, I sometimes don’t feel very much pity for them. (Reversed)

019 19PD_R Red I am usually pretty effective in dealing with emergencies. (Reversed)

020 20FS Green I am often quite touched by things that I see happen.

021 21PT Yellow I believe that there are two sides to every question and try to look at them both.

022 22EC Purple I would describe myself as a pretty soft-hearted person.

023 23FS Green When I watch a good movie, I can very easily put myself in the place of a leading character.

024 24PD Red I tend to lose control during emergencies.

025 25PT Yellow When I’m upset at someone, I usually try to “put myself in his shoes” for a while.

026 26FS Green When I am reading an interesting story or novel, I imagine how I would feel if the events in the story were happening to me.

027 27PD Red When I see someone who badly needs help in an emergency, I go to pieces.

028 28PT Yellow Before criticizing somebody, I try to imagine how I would feel if I were in their place

More information about the dataset is contained in empathy_description.html file.

About this dataset

Context

The dataset tabulates the Maryland 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 Maryland. The dataset can be utilized to understand the population distribution of Maryland by age. For example, using this dataset, we can identify the largest age group in Maryland.

Key observations

The largest age group in Maryland was for the group of age 35 to 39 years years with a population of 429,168 (6.95%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Maryland was the 80 to 84 years years with a population of 113,210 (1.83%). 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 Maryland is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Maryland 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 Maryland Population by Age. You can refer the same here

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This dataset contains crash information from the last five years to the current date. The data is based on the National Incident Based Reporting System (NIBRS). The data is dynamic, allowing for additions, deletions and modifications at any time, resulting in more accurate information in the database. Due to ongoing and continuous data entry, the numbers of records in subsequent extractions are subject to change.About Crash DataThe Cary Police Department strives to make crash data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. As the data is updated on this site there will be instances of adding new incidents and updating existing data with information gathered through the investigative process.Not surprisingly, crash data becomes more accurate over time, as new crashes are reported and more information comes to light during investigations.This dynamic nature of crash data means that content provided here today will probably differ from content provided a week from now. Likewise, content provided on this site will probably differ somewhat from crime statistics published elsewhere by the Town of Cary, even though they draw from the same database.About Crash LocationsCrash locations reflect the approximate locations of the crash. Certain crashes may not appear on maps if there is insufficient detail to establish a specific, mappable location.

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The annual bike and pedestrian count is a volunteer data collection effort each fall that helps the City understand where and how many people are biking and walking in Somerville, and how those numbers are changing over time. This program has been taking place each year since 2010. Counts are collected Tuesday, Wednesday, or Thursday for one hour in the morning and evening using a “screen line” method, whereby cyclists and pedestrians are counted as they pass by an imaginary line across the street and sidewalks. Morning count sessions begin between 7:15 and 7:45 am, and evening count sessions begin between 4:45 and 5:15 pm. Bike counts capture the number of people riding bicycles, so an adult and child riding on the same bike would be counted as two counts even though it is only one bike. Pedestrian counts capture people walking or jogging, people using a wheelchair or assistive device, children in strollers, and people using other micro-mobility devices, such as skateboards, scooters, or roller skates. While the City and its amazing volunteers do their best to collect accurate and complete data each year and the City does quality control to catch clear errors, it is not possible to ensure 100% accuracy of the data and not all locations have been counted every year of the program. There are also several external factors impacting counts that are not consistent year-to-year, such as nearby construction and weather. For these reasons, the counts are intended to be used to observe high-level trends across the city and at count locations, and not to extrapolate that biking and walking in Somerville has changed by a specific percentage or number. Data in this dataset are available at the location count level. To request data at the movement level, please contact transportation@somervillema.gov.

This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix.

The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (30 Mins in this case) from that record's location.

The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents.

The dataset provides 72762 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses.

A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km

Higher resolutions or alternate geographies are available on request.

More information on the h3 system is available here: https://eng.uber.com/h3/

WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography.

More information on the WorldPop data is available here: https://www.worldpop.org/

One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the Spanish population within 4 hours by Truck and show only the areas which do exhibit this characteristic.

Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.