DRAKO is a Mobile Location Data provider with a programmatic trading desk specializing in geolocation analytics and programmatic advertising. Our Consumer Travel History Data has helped cities, counties, and states better understand who their visitors are so that they can effectively develop and deliver advertising campaigns. We’re in a unique position to deliver enriched insight beyond traditional surveying or other data sources because of our rich dataset, proprietary modelling capabilities, and analytical capabilities.

MAIDs (Mobile Advertising IDs) are unique device identifiers associated with consenting mobile devices that can be utilized for geolocation based analyses and audiences. Drako uses MAIDs to fuel our Consumer Travel History Data utilizing our Home Location Model. The Home Location of a MAID is determined based on where that MAID is seen most frequently between the hours of 11pm and 6am (local time). Using this we are able to determine the Home Location of a user which in turn allows us to identify when and where they are travelling.

Beyond identifying that users are tourists, we can also classify them into different bins by their frequency / dwell time over their estimated number of visits. Using our data and frequency, we can identify: overnight visitors, weekend visits, short-term stays, long-term stays, or frequent holiday visitors !

Beyond Consumer Travel History Data in your defined geography alone, we are also able to provide: - Home location - Find out where your audience is coming from using our home location technology - Movement - Quantify how far users have travelled between locations. - Demographics - Discover neighborhood level characteristics such as income, ethnicity, and more - Brand index - Learn which major brands and retailers your audience is visiting the most. - Visitation index - See which destinations your visitors are visiting the most - Addressable audience - Customize your audiences for your campaigns using our analytic insights

Moreover, if you’re looking to activate your Consumer Travel History Data for advertising, we’re always able to further refine or filter your desired audience with our other Audience Data, such as: Brand visits, Geodemographics, Ticketed Event visits, Purchase Intent (in Canada), Purchase History (in USA), and more !

Data Compliance: All of our Consumer Travel History Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

The World Cities Culture Forum, established in 2012, is a leading global network of civic leaders from over 40 creative cities across six continents, representing a combined population of over 245 million. The forum fosters collaborations to place culture at the core of urban development, addressing 21st-century challenges such as climate change, affordable workspaces, cultural tourism, and diversity in public spaces. Through its Global Summit, partnerships, and programs like the Leadership Exchange Programme and Digital Dialogue Masterclasses, the forum promotes cultural integration in city planning. The World Cities Culture Report 2022 provides comprehensive open-source data on culture, including over 60 datasets from 40 cities. Contextual Data: Includes demographics such as characteristics of the overall and working-age populations (including percent who were foreign born) and of the geographical area, such as the percentage of national population living in the city and the percentage of the area devoted to parks and other public green spaces. Cultural Infrastructure: Provides counts (and rates) of various facilities and venues, including art galleries, artists' studios, rehearsal spaces, bars, bookshops, cinemas, community centers, concert halls, museums, nightclubs, libraries, video game arcades, and theatres. Participation and Tourism: Focuses on cultural participation metrics, such as cinema and theatre admissions, festival attendance, museum visits, average daily attendance at the top five art exhibits, and international tourist numbers. Creative Economy: Encompasses data on book publishing, creative industries employment, film festivals, restaurant ratings, and performances. Education: Includes statistics on public library book loans, higher education levels, international student enrollment, and specialist institutes in art and design education. The source for each number is identified within the dataset. Data users can freely download selected datasets as .csv files.

In 2023, total tourist arrivals amounted to around 12.1 million, and 4.27 million of these were visitors from abroad. Berlin is the German capital and one of the most popular tourist destinations in the country, as well as among European cities in general. Tourist magnet Berlin’s residential population was over 3.7 million people in 2022. Visitors could choose from a total of 724 travel accommodation establishments to stay at. Among these, the most represented were bed and breakfast locations, followed by hotels and vacation homes. The city recorded around 29.6 million tourist overnight stays in 2023, which was an increase compared to the year before. In addition to a thriving accommodation industry and lots of famous history, Berlin offers a lot of opportunities to enjoy nature in or not far from the city. The German capital also houses one of the most famous attractions in Europe and the world - the Berlin Wall. Berlin booming Berlin is one of the three German city-states, alongside Hamburg and Bremen. This means that in addition to being metropolitan areas, all three also have federal state status. Germany has a total of 16 federal states. Berlin as the capital is recognizable all over the world for its still visible traces of both East and West German history.

The data is from:

https://simplemaps.com/data/world-cities

We're proud to offer a simple, accurate and up-to-date database of the world's cities and towns. We've built it from the ground up using authoritative sources such as the NGIA, US Geological Survey, US Census Bureau, and NASA.

Our database is:

• Up-to-date: It was last refreshed on May 11, 2025.
• Comprehensive: Over 4 million unique cities and towns from every country in the world (about 48 thousand in basic database).
• Accurate: Cleaned and aggregated from official sources. Includes latitude and longitude coordinates.
• Simple: A single CSV file, concise field names, only one entry per city.

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 number of international tourist arrivals worldwide rose sharply in 2023 compared to the previous year across all the most visited destinations in the world. Overall, France was the most visited country by inbound travelers worldwide in 2023, with *** million international tourist arrivals. Spain, the United States, and Italy followed in the ranking that year. Has global inbound tourism recovered from the impact of COVID-19? In 2023, the number of international tourist arrivals worldwide totaled approximately *** billion. While this figure represented a ** percent annual increase, it remained below the peak in inbound tourist arrivals reported in 2019, the year before the onset of the COVID-19 pandemic. That said, international tourism receipts worldwide exceeded pre-pandemic levels in 2023, peaking at *** trillion U.S. dollars. What are the most popular global regions for inbound tourism? When breaking down the number of international tourist arrivals worldwide by region, Europe has consistently reported the highest volume of inbound travelers, both before and after the impact of the health crisis. In 2023, this region alone accounted for roughly ** percent of global inbound tourist arrivals. Meanwhile, Asia and the Pacific recorded the second-highest number of inbound tourist arrivals worldwide in 2023.

Visit Britain publish data relating to international visitors to the UK. They produce the data in two formats - individual spreadsheets for each region that are updated annually, and a single spreadsheet for all regions, containing less detail but updated quarterly.

Data shows London totals for nights, visits, and spend. Data broken down by age, purpose, duration, mode and country. This data is also available from Visit Britain website, including the latest quarterly data for other regions.

All data taken from the International Passenger Survey (IPS).

Some additional data on domestic tourism can be found on the Visit Britain website, and Visit England both overnight tourism and Day visits pages.

Data on accomodation occupancy levels is also available from Visit England.

An overview of all tourism data for London can be found in this GLAE report 'Tourism in London'

Further information can be found on the London and Partners website.

Comparisons of international tourist arrivals with other world cities are produced by Euromonitor and in Mastercard's Global Destination Cities Index of 2012, 2013, 2014, and 2015.

This dataset is included in the Greater London Authority's Night Time Observatory. Click here to find out more.

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv, .tsv, and .xlsx.

Recall that CSV, TSV, and Excel files are three common formats for storing data. Three files containing data on 2019 Airbnb listings are available to you:

data/airbnb_price.csv This is a CSV file containing data on Airbnb listing prices and locations.

listing_id: unique identifier of listing price: nightly listing price in USD nbhood_full: name of borough and neighborhood where listing is located data/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

listing_id: unique identifier of listing description: listing description room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments data/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

listing_id: unique identifier of listing host_name: name of listing host last_review: date when the listing was last reviewed

The World Council on City Data (WCCD) awarded the City of Melbourne a platinum designation for its compliance with ISO 37120 (http://www.iso.org/iso/catalogue_detail?csnumber=62436), the world’s first international standard for city indicators. Reporting to the standard allows cities to compare their service delivery and quality of life to other cities globally. The City of Melbourne was one on 20 cities to, globally to help pilot this program and is one of sixteen cities to receive the highest level of accreditation (platinum). \r
Having an international standard methodology to measure city performance allows the City of Melbourne to share data about practices in service delivery, learn from other global cities, rank its results relative to those cities, and address common challenges through more informed decision making. \r
Indicators include: Fire and emergency response; Governance; Health; Recreation; Safety; Shelter; Solid Waste; Telecommunications and Innovation; Transportation; Urban Planning; Wastewater; Water and Sanitation; Economy; Education; Energy; Environment; and Finance.\r
City of Melbourne also submitted an application for accreditation, on behalf of ‘Greater Melbourne’, to the World Council on City Data and this resulted in an ‘Aspirational’ accreditation awarded to wider Melbourne. \r
A summary of Melbourne's results is available here (http://open.dataforcities.org/). Visit the World Council on City Data’s Open Data Portal to compare our results to other cities from around the world.

This table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Countries visited (15 items: United States; Mexico; United Kingdom; France; ...) Travel characteristics (3 items: Visits; Nights; Spending in country).

Key information about Poland Visitor Arrivals

• Poland Visitor Arrivals recorded 18,986,700 person in Dec 2023, compared with 15,947,700 person in the previous year
• Poland Visitor Arrivals data is updated yearly, available from Dec 1995 to Dec 2023
• The data reached an all-time high of 89,118,000 person in Dec 1999 and a record low of 8,418,000 person in Dec 2020

Baltimore City Child Health

An Exploration of 2010 Birth, Prenatal Visit, Lead Exposure and Teen Birth Rates

By City of Baltimore [source]

About this dataset

This Baltimore City Child and Family Health Indicators dataset provides us with crucial information that can support the health and well-being of Baltimore City residents. It contains 13 indicators such as low birth weight, prenatal visits, teen births, and more. This data is sourced from the Maryland Department of Health & Mental Hygiene (DHMH), Baltimore Substance Abuse Systems (BSAS), theBaltimore City Health Department, and the US Census Bureau. Through this data set we can gain a better understanding of how Baltimore City citizens’ health compares to other areas and how it has changed over time. By investigating this dataset we are given an opportunity to create potential strategies for providing better care for our community. With discoveries from these indicators, together as a city we can bring about lasting change in protecting public health within Baltimore

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For more datasets, click here.

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How to use the dataset

This dataset provides valuable information about the health and wellbeing of children and families in Baltimore City in 2010. The data is organized by CSA (Census Statistical Area) and includes stats on term births, low birth weight births, prenatal visits, teen births, and lead testing. This dataset can be used to analyze trends in children's health over time as well as identify potential areas that need more attention or resources.

To use this dataset: - Read through the data dictionary to understand what each column represents.
- Choose which columns you would like to explore further.
- Filter or subset the data as you see fit then visualize it with graphs or maps to better understand how conditions vary across neighborhoods in Baltimore City.
- Consider comparing the data from this year with prior years if available for deeper analysis of changes over time.
- Look for correlations among columns that could help explain disparities between neighborhoods and create strategies for improving outcomes through policy interventions or other programs designed specifically for those areas needs

Research Ideas

• Mapping health disparities in high-risk areas to target public health interventions.
• Identifying neighborhoods in need of additional resources for prenatal care, infant care, and lead testing and create specific programs to address these needs.
• Creating an online dashboard that displays real time data on Baltimore City’s population health indicators such as birth weight, teenage pregnancies, and lead poisoning for the public to access easily

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BNIA_Child_Fam_Health_2010.csv | Column name | Description | |:---------------|:----------------------------------------------------------| | the_geom | Geometry of the Census Statistical Area (CSA) (Geometry) | | CSA2010 | Census Statistical Area (CSA) (String) | | termbir10 | Total number of term births in 2010 (Integer) | | birthwt10 | Total number of low birth weight births in 2010 (Integer) | | prenatal10 | Total number of prenatal visits in 2010 (Integer) | | teenbir10 | Total number of teen births in 2010 (Integer) | | leadtest10 | Total number of lead tests conducted in 2010 (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of Baltimore.

London's Airbnb

Airbnb listings in London

About this dataset

This dataset provides information on Airbnbs in London. Each row represents one listing, and there are a variety of columns with information on the listing, such as the name, host, price, etc.

This dataset could be used to study patterns in Airbnb pricing, to understand how Airbnbs are being used in London, or to compare different neighborhoods in London

How to use the dataset

If you're looking for information on Airbnbs in London, this dataset is a great place to start. It provides information on the listings and reviews for Airbnb in the city of London.

Airbnb is a popular vacation rental platform that allows travelers to find and book accommodations around the world. With over 3 million listings in more than 65,000 cities, Airbnb has something for everyone.

London is one of the most popular tourist destinations in the world, and Airbnb offers a unique way to experience the city. With so many different neighborhoods to choose from, there's an Airbnb listing for everyone.

This dataset includes information on the listing price, minimum nights required, number of reviews, and more. With this data, you can begin to understand how people are using Airbnb in London and what factors affect pricing. So whether you're looking for a place to stay during your next trip or just curious about how Airbnb is being used in different cities, this dataset is for you!

Research Ideas

• If there's a relationship between the price per listing and how long it is available on Airbnb, this could be used to recommend lower prices for listings that are unlikely to stay booked for very long periods of time.
• There might be a relationship between the number of reviews per month and the calculated host listings count. If there is, this information could be used to help improve customer satisfaction by either recommending that hosts with lots of listings receive more reviews or that they stagger their listing availabilities so that they can provide better service.
• The neighbourhood data could be used to cluster listings into areas with similar characteristics, which would then allow customers to easily find similar listings in different areas of the city based on their preferences

Acknowledgements

This dataset is brought to you by Kelly Garrett. If you use it in your research, please cite her Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: listings.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------| | name | The name of the listing. (String) | | host_name | The name of the host. (String) | | neighbourhood_group | The neighbourhood group the listing is in. (String) | | latitude | The latitude of the listing. (Float) | | longitude | The longitude of the listing. (Float) | | room_type | The type of room. (String) | | price | The price of the listing. (Integer) | | minimum_nights | The minimum number of nights required to stay at the listing. (Integer) | | number_of_reviews | The number of reviews for the listing. (Integer) | | last_review | The date of the last review. (Date) | | reviews_per_month | The number of reviews per month. (Float) | | calculated_host_listings_count | The number of listings the host has. (Integer) | | availability_365 | The number of days the listing is available in a year. (Integer) |

File: reviews.csv | Column name | Description | |:----------------|:--------------------------------------| | last_review | The date of the last review. (String) |

File: neighbourhoods.csv | Column...

This dataset represents simulated sales data for an electronics shop operating in the United States from 2024 (January to November). It is designed for individuals who want to practice data analysis, visualization, and machine learning techniques. The dataset reflects real-world sales scenarios, including various products, customer information, order statuses, and sales channels. It is ideal for learning and experimenting with data analytics, business insights, and visualization tools like Power BI, Tableau, or Python libraries.

Dataset Features ProductID: Unique identifier for each product. ProductName: Name of the electronic product (e.g., Phone, Laptop, Drone). ProductPrice: The price of the product is in USD. OrderedQuantity: Number of units ordered by the customer. OrderStatus: Status of the order (e.g., Delivered, In Process, On Hold, Canceled). CustomerName: Name of the customer who placed the order. State: State of the customer in the United States (e.g., California, Texas). City: City of the customer within the state. Latitude & Longitude: Geographic coordinates of the customer's location for mapping purposes. OrderChannel: Channel through which the order was placed (e.g., Website, Phone, Physical Store, Social Media). OrderDate: Date of the order (range: January 1, 2024, to November 30, 2024).

Potential Use Cases

Exploratory Data Analysis (EDA): Analyze sales trends across months, states, or product categories. Identify the most popular sales channels or products. Examine the distribution of order statuses.

Data Visualization: Create dashboards to visualize sales performance, customer demographics, and geographic distribution. Plot order locations on a map using latitude and longitude.

Machine Learning: Predict future sales trends using historical data. Classify order statuses based on product and order details. Cluster customers based on purchase behavior or location.

Business Insights: Analyze revenue contributions from different states or cities. Understand customer preferences across product categories.

Technical Details File Format: Excel (with a .xlsx extension) Number of Rows: 11000 Period: January 1, 2024, to November 30, 2024 Simulated Data: The data is entirely synthetic and does not represent real customers or transactions.

Why Use This Dataset? This dataset is tailored for individuals and students interested in: Building their data analysis and visualization skills. Learning how to work with real-world-like business datasets. Practicing machine learning with structured data. Acknowledgment This dataset was generated to mimic real-world sales data scenarios for educational and research purposes. Feel free to use it for learning and projects, and share your insights with the community!

In 2023, the most visited state museum in Berlin was the Pergamon Museum, with over 700,000 visits. That year, the Neues Museum and the Neue Nationalgalerie followed in the ranking, recording around 661,000 and 648,000 visits, respectively.

A global self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries. All geospatial data is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Global Zip Code Database (Geospatial data)

• Address capture and validation

• Map and visualization

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Data export methodology

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

Product Features

• Fully and accurately geocoded

• Administrative areas with a level range of 0-4

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

For additional insights, you can combine the map data with:

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Times

Why do companies choose our location databases

• Enterprise-grade service

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

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

This city boundary shapefile was extracted from Esri Data and Maps for ArcGIS 2014 - U.S. Populated Place Areas. This shapefile can be joined to 500 Cities city-level Data (GIS Friendly Format) in a geographic information system (GIS) to make city-level maps.

Our location data powers the most advanced address validation solutions for enterprise backend and frontend systems.

A global, standardized, self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries.

All geospatial data for address data validation is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Address Validation at Zip Code Level Database (Geospatial data)

• Address capture and address validation

• Address autocomplete

• Address verification

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Product Features

• Dedicated features to deliver best-in-class user experience

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

Data export methodology

Our location data packages are offered in variable formats, including .csv. All geospatial data for address validation are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why do companies choose our location databases

• Enterprise-grade service

• Full control over security, speed, and latency

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

• Seamlessly integrated into your software

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

Context

I really get fascinated by good quality food being served in the restaurants and would like to help community find the best cuisines around their area

Content

Zomato API Analysis is one of the most useful analysis for foodies who want to taste the best cuisines of every part of the world which lies in their budget. This analysis is also for those who want to find the value for money restaurants in various parts of the country for the cuisines. Additionally, this analysis caters the needs of people who are striving to get the best cuisine of the country and which locality of that country serves that cuisines with maximum number of restaurants.♨️

For more information on Zomato API and Zomato API key • Visit : https://developers.zomato.com/api#headline1 • Data Collection: https://developers.zomato.com/documentation

Data Fetching the data: • Data has been collected from the Zomato API in the form of .json files(raw data) using the url=https://developers.zomato.com/api/v2.1/search?entity_id=1&entity_type=city&start=1&count=20 • Raw data can be seen here

Data Collection: Data collected can be seen as a raw .json file here

Data Storage: The collected data has been stored in the Comma Separated Value file Zomato.csv. Each restaurant in the dataset is uniquely identified by its Restaurant Id. Every Restaurant contains the following variables:

• Restaurant Id: Unique id of every restaurant across various cities of the world • Restaurant Name: Name of the restaurant • Country Code: Country in which restaurant is located • City: City in which restaurant is located • Address: Address of the restaurant • Locality: Location in the city • Locality Verbose: Detailed description of the locality • Longitude: Longitude coordinate of the restaurant's location • Latitude: Latitude coordinate of the restaurant's location • Cuisines: Cuisines offered by the restaurant • Average Cost for two: Cost for two people in different currencies 👫 • Currency: Currency of the country • Has Table booking: yes/no • Has Online delivery: yes/ no • Is delivering: yes/ no • Switch to order menu: yes/no • Price range: range of price of food • Aggregate Rating: Average rating out of 5 • Rating color: depending upon the average rating color • Rating text: text on the basis of rating of rating • Votes: Number of ratings casted by people

Acknowledgements

I would like to thank Zomato API for helping me collecting data

Inspiration

Data Processing has been done on the following categories: Currency City Location Rating Text

CLCA codeThe City Local Clustering Algorithm (CLCA) code is in FORTRAN 90 and it was implemented with OpenMP for parallel programming. We also provide the compilation and run script.clca_codes.zip