Romz16/Mobility_Network_Data-Analysis

dataset_mobility_networks

Local

Sao Paulo 2017

The metropolitan region of Sao Paulo 2017 data is available at this link. It contains the numbers of trips from traffic zones in the metropolitan area of Sao Paulo, Brazil, distinguishing different transport modes, namely on foot, by car, by bicycle, using collective transport, individual and non-motorized transport. For each transport mode, there is a weighted adjacency matrix in a xlsx file.

We provide the networks built from this file in GraphML format, including geographical coordinates and scripts to plot them according to the networks generated in GraphML.

Federal District Urban Mobility Survey

Data on the Federal District Urban Mobility Survey (Pesquisa de Mobilidade Urbana do Distrito Federal) are available at this link. This survey was part of the Federal District Development Plan for Rail Transit (Plano de Desenvolvimento do Transporte Público sobre Trilhos do Distrito Federal), conducted by the Metro company of the region. The project involved various field surveys to describe urban mobility patterns within the metropolitan area of the Federal District, including 19 towns in Goiás and 3 towns in Minas Gerais.

The dataset contains activity diaries collected from the survey, detailing trips made by individuals within a working day. Each entry provides information on the origin and destination of the trip, the mode of transportation used, and details about the person making the trip. This dataset offers comprehensive insights into daily travel patterns and transportation choices within the Federal District and its neighboring areas.

Helsinki City Bike

Data on the Helsinki City Bike Network Analysis is available in this notebook. This dataset examines the Helsinki city bike system using descriptive statistics and network analysis to better understand trip patterns and the structure of the bike-sharing network.

The dataset contains information on trips made between bike stations, including the departure station ID, return station ID, latitude and longitude of the departure station, and a metric called PESO. The PESO metric was calculated by considering each trip between two stations as one person and summing all trips between the same pair of stations to determine the total weight of the edge connecting those stations.

This research models the Helsinki city bike system as a weighted graph, where bike stations are represented as nodes, and trips between stations are represented as edges weighted by the PESO metric. The analysis provides infomation of key stations in the network, the connectivity and flow of trips between stations, and overall usage patterns.

The dataset and analysis are useful for urban mobility planning, optimization of bike-sharing systems, and developing strategies to promote sustainable transportation in Helsinki.

Origin/Destination survey with mobile phone data for the metropolitan region of Belo Horizonte (RMBH)

Data on the origin/destination survey with mobile phone data for the metropolitan region of Belo Horizonte (RMBH) are available at this link. Containing a more complete explanation of all the research carried out and its attachments, where 2 periods of 20 days were researched, the first period was in November 2019 (from the 1st to the 20th) and the second period was from the 1st to the 20th of May of 2021, covering 393 zones in the metropolitan region of Belo Horizonte (RMBH) using mobile phone data to obtain the origin destination (OD) matrices, covering the cities of Baldim, Belo Horizonte, Betim, Brumadinho, Caeté, Capim Branco, Confins, Contagem, Esmeraldas, Florestal, Ibirité, Igarapé, Itaguara, Itatiaiuçu, Jaboticatubas, Juatuba, Lagoa Santa, Mário Campos, Mateus Leme, Matozinhos, Nova Lima, Nova União, Pedro Leopoldo, Raposos, Ribeirão das Neves, Rio Acima , Rio Manso, Sabará, Santa Luzia, São Joaquim de Bicas, São José da Lapa, Sarzedo, Taquaraçu de Minas and Vespasiano. The matrix expresses the daily flow of trips among all origin-destination pairs classified according to their reason, period of travel and socio-demographic profile of travelers.

Finally, this research did not generate any graph of the trips made, but in the research results several metrics used in the research can be found: reason for the trip, period of realization and sociodemographic profile of the travelers that can be used in the future to generate graphs, these graphs can be contact network and mobility network.

MTA Subway Stations

Data on MTA subway and Staten Island Railway stations is available with the most recent metadata update as of August 9, 2024, is available at this link. This dataset provides a comprehensive listing of all subway and Staten Island Railway stations, including detailed information on their locations, Station Master Reference Number (MRN), Complex MRN, and GTFS Stop ID. The dataset also includes details on the services available at each station, the type of structure in which the station is situated, and whether the station is located in Manhattan’s Central Business District (CBD). Additionally, the dataset indicates the ADA-accessibility status of each station, ensuring information on accessibility is included.

SF Bay Area Bike Share

Data on the SF Bay Area Bike Share is available here. This dataset contains anonymized bike trip data collected from August 2013 to August 2015, covering trips made using the Bay Area Bike Share system, which offers convenient, affordable bike trips around the San Francisco Bay Area. The dataset is a transformed version of the data originally released by the Bay Area Bike Share program, which also provides a real-time API for live data access.

The dataset includes key attributes such as the IDs of the starting and ending stations (start_station_id and end_station_id) and a metric called PESO. The PESO metric represents the total weight of connections between stations. Each trip between two stations was treated as one unit, and all trips between the same pair of stations were summed to calculate the total weight of the edge connecting those stations.

This dataset can be modeled as a weighted graph, where bike stations are represented as nodes and trips between stations as edges weighted by the PESO metric. The analysis focuses on identifying key stations, exploring connectivity and flow between stations, and understanding usage patterns.

The data and analysis can be used to optimize the Bay Area Bike Share system, inform urban mobility planning, and develop strategies for promoting sustainable transportation in the San Francisco Bay Area.

Mobility Uber LIMA - Perú Dataset

Data on the Mobility Uber LIMA - Perú dataset is available here. This dataset comes from a mobility startup that allows users to book rides from any point A to any point B within the city using a smartphone. The ride value is automatically calculated by the app at the time of request, taking into account the distance, estimated travel time, and current car availability (based on demand and offer balance).

The dataset includes detailed information about each ride, including start and end points, ride value, and other relevant factors such as estimated travel time. Once the ride ends, the passenger’s credit card is charged, and a percentage of the ride value is transferred to the driver's bank account. Additionally, the ride can be cancelled by either the driver or the passenger before the passenger is picked up.

In this analysis, each trip from the meeting point to the departure point is considered as one passenger, providing valuable data on the overall flow of rides within Lima, Peru. This dataset offers urban mobility patterns, demand for rides, and service efficiency, which can be used to improve the Uber ride-sharing system and inform transportation planning in the region.

Mexico City Mobility Dataset

Data on the Mexico City Mobility Dataset is available in this OSF repository. The dataset covers 456 days of mobility data, starting from January 1st, 2020, to March 31st, 2021. Each day is represented by an individual comma-separated values (csv) file, corresponding to a mobility network for that specific day. Day 1 corresponds to January 1st, 2020, and Day 456 corresponds to March 31st, 2021.

Each node in the network represents an AGEB (Geographic Area of Basic Population), as defined by the National Institute of Statistics, Geography and Informatics (INEGI). Each network file contains an edge list with three columns:

• The first column indicates the origin AGEB.
• The second column indicates the destination AGEB.
• The third column is an integer representing the edge weight, i.e., the number of trips observed between the two connected AGEBs on that specific day.

The weight of the networks has been normalized to account for daily variations in mobility. This dataset offers a detailed view of the mobility dynamics in Mexico City, which can be used for various analyses, including urban mobility studies, transportation planning, and understanding the flow of people between different regions of the city.

NY Taxi Trip Data

Data on the Taxi Trip Data is available here. This dataset represents a detailed compilation of trips made using yellow taxis in New York City. It includes a wide range of information, such as pickup and drop-off times, fare amounts, payment types, and geographic data, offering a comprehensive view into urban mobility and the economics of taxi rides within the city.

The dataset allows for the analysis of various aspects of taxi usage, including temporal variations in demand, geographic movement patterns, and fare trends. It is particularly valuable for understanding transportation behavior in New York City, the relationship between different factors such as trip duration and fare, and the role of taxis in the city's overall transportation network.

This dataset can be used for a variety of applications, including urban transportation planning, fare structure analysis, studying traffic patterns, and exploring the dynamics of taxi services in relation to city-wide events and trends.

Regional

COVID-19 US Mobility Flows

The COVID-19 US Mobility Flows dataset is available in the GitHub repository. This dataset provides dynamic human mobility patterns across the United States during the COVID-19 pandemic, with a focus on population flows between different geographic areas.

The data tracks population movements at three geographic scales: census tract, county, and state. Using anonymous mobile phone data from SafeGraph, the dataset computes and infers daily and weekly origin-to-destination (O-D) population flows. Each entry in the dataset represents the movement of people between a specific origin and destination, with associated geographic coordinates and the number of individuals making the trip.

This dataset is crucial for understanding how human mobility patterns changed in response to public health interventions such as stay-at-home orders during the pandemic. By analyzing these flows, public health officials and researchers can gain insights into the effectiveness of containment measures, monitor the spread of the virus, and study how individuals adjusted their behaviors in response to the crisis.

The dataset starts from January 1st, 2019, and offers a high spatiotemporal resolution, making it an invaluable tool for monitoring epidemic dynamics and informing public health policy. Its high correlation with other openly available data sources reinforces the dataset’s reliability, making it a key resource for analyzing human behavior during the pandemic. Additionally, this up-to-date O-D flow data supports various social sensing and transportation applications beyond epidemic monitoring.

Flights in India Dataset

The Flights in India dataset provides detailed information about non-stop flights operating between major airports in India, specifically from 14th February 2022 to 28th February 2022. The data was collected on 5th February 2022 and includes flights with Economy Cabin Class only.

The dataset covers flights operating at the following airports:

• Mumbai: Chhatrapati Shivaji International Airport (BOM)
• Delhi: Indira Gandhi International Airport (DEL)
• Bengaluru: Kempegowda International Airport (BLR)
• Hyderabad: Rajiv Gandhi International Airport (HYD)
• Kolkata: Netaji Subhas Chandra Bose Airport (CCU)
• Chennai: Chennai Airport (MAA)

This dataset includes various flight details such as the origin and destination airports, flight timings, and other relevant information for the listed airports within the given timeframe. It offers domestic air travel patterns, flight availability, and the connectivity between key cities in India. This dataset can be useful for analyzing trends in air travel demand, optimizing flight schedules, and studying patterns in regional connectivity within India’s aviation network.

Indian Trains Dataset

The Indian Trains dataset provides a comprehensive collection of details about trains operating across India, including their schedules, stations, and other relevant information. The data offers the vast railway network of India, capturing essential aspects of train travel, such as departure and arrival times, train numbers, and the stations served.

The dataset includes information on various types of trains and their routes, enabling analysis of travel patterns, regional connectivity, and the efficiency of the Indian railway system. It can be used to study the scheduling of trains, station-wise performance, and the volume of train traffic between different regions.

This dataset is valuable for transportation analysts, urban planners, and anyone interested in the operation and optimization of India's railway system. By providing a snapshot of the Indian train network, it helps in understanding the key components of the country's transportation infrastructure.

Brazil road and waterway connections IBGE 2016

Data on road and waterway connections are available at this link. Containing municipalities not subject to search: contains a list of municipalities whose links were not raised due to that there is no public transport or that the agents responsible for transport do not correspond to the requirements of regularity necessary for the collection. Also contains a brief description of how transportation is carried out in the municipalities - centrality indexes: contains two tables. The first presents indexes of proximity, intermediation and degree, for the set of municipalities surveyed (5,386), calculated from network analysis using graph theory. The second presents the centrality index and the average cost by time for 244 higher ranking urban centers (from sub-regional centers above), from REGIC 2007. - Database - road and waterway connections 2016: It constitutes the core of the research, containing the pairs of links aggregated between municipalities. Frequencies were aggregated, round trip, as well as redundant sections, for each pair of municipalities connected by transport lines. Considering the costs and minimum travel time.

We provide the networks built from these files in GraphML format, including geographic coordinates and scripts to plot them according to the networks generated in GraphML.

Intermunicipal travel networks of Mexico during the COVID-19 pandemic

Data on intermunicipal travel networks of Mexico during the COVID-19 pandemic are available at this link. Containing a more complete explanation about all the research carried out, where a public dataset of 731 intercity networks of origin-destination in Mexico were used, each network is represented for each day during the period 2020-2021, describing the patterns of trips made between municipalities in Mexico in the period 2020-2021, using anonymized mobile location data. In which characteristic changes associated with factors such as COVID-19 restrictions and population size were observed. The nodes represent municipalities (third-level administrative division) or official metropolitan areas. These are weighted and directed networks, where the weight of the edge ( i , j ) is equal to the total number of observed trips from node i to node j normalized by the different number of mobile devices we registered that day. The dataset with these 731 networks is freely available in an OSF repository this link.

Finally, nine plotted dates were made available to represent different important events during the evolution of the pandemic in Mexico, as well as explanations of the necessary changes for each plot.

Matriz Origem/Destino de Passageiros com Base em Big Data de Telefonia Móvel - MPOR OD

Os dados da Matriz Origem/Destino de Passageiros com base em Big Data de Telefonia Móvel estão disponíveis no portal de dados abertos do governo aqui. Este conjunto de dados oferece informações detalhadas sobre os deslocamentos multimodais de passageiros, capturados e classificados em duas categorias principais: "aéreo" e "não aéreo".

A Matriz Origem/Destino foi construída com base em dados de telefonia móvel, representando o movimento de pessoas entre Unidades Territoriais de Planejamento (UTPs), municípios e aeroportos. As informações sobre o fluxo de passageiros entre diferentes pontos do território são importantes para a análise da mobilidade urbana e intermunicipal, além de fornecer dados essenciais para o planejamento e gestão de infraestruturas de transporte, como aeroportos e rodoviárias.

Esses dados podem ser usados para entender padrões de deslocamento de passageiros, analisar a conectividade entre diferentes regiões e auxiliar na formulação de políticas públicas relacionadas à mobilidade urbana e transporte multimodal.

The Temporal Network of Mobile Phone Users in Changchun Municipality, Northeast China

The dataset titled The Temporal Network of Mobile Phone Users in Changchun Municipality, Northeast China is available on Figshare. This dataset provides detailed information on the mobile phone usage patterns of individuals in Changchun Municipality, which is located in Northeast China. It captures the temporal aspects of mobile phone network interactions, shedding light on how users interact over time within a specific urban area.

The data tracks mobile phone users' spatial and temporal movement, creating a dynamic view of the interactions between users throughout the day. This dataset is particularly useful for studying urban mobility, social interactions, and communication patterns in a highly populated urban center.

Key features of the dataset include:

• Temporal interactions: Captures user interactions at different times of day, allowing for analysis of patterns such as peak communication hours, changes in behavior, and network dynamics.
• Geospatial data: Provides insights into how mobile phone users are distributed geographically across Changchun and how their movements correlate with communication activities.
• User network: Builds a temporal network, representing how individuals' mobile phone usage connects them within a broader social context.

This dataset can be valuable for researchers studying urban mobility, social dynamics, and communication networks, as well as for applications in the fields of transportation planning, epidemic modeling, and social behavior analysis. By examining the interactions between mobile phone users in this specific region, one can better understand human behavior in the context of modern communication technologies.

Pakistan Cities Network Analysis

The Pakistan Cities Network dataset offers valuable infomation about urbanization, corridor development, and the interconnectivity of cities across Pakistan. The dataset is focused on understanding the dynamics of urban networks, which are increasingly seen as multi-centric regions extending beyond traditional city limits into suburban and transitional areas. These urban corridors are often linked to economic growth, with cities forming clusters connected by transport routes.

Key urban corridors in Pakistan, such as the Faisalabad Industrial Estate Development and Management Company (FIEDMC), aim to connect megacities with industrial hubs and ports, driving regional economic development. This dataset can be used to analyze the interconnections between cities and the role of transport routes in facilitating economic activities.

The importance of analyzing city networks can be seen in several contexts:

• Urban Planning and Development: Understanding the connectivity between cities helps urban planners make informed decisions about infrastructure, transportation systems, and resource allocation, leading to more efficient and sustainable urban areas.
• Economic Growth: City networks are often built around trade, industry, and business. By analyzing these networks, it is possible to identify key economic hubs and trade routes, which are vital for boosting economic growth and development.
• Environmental Impact: Understanding city networks helps to assess the environmental consequences of urbanization. This knowledge can be used to develop strategies for pollution control, waste management, and climate change mitigation.
• Social and Cultural Exchange: Cities that are part of a network often share cultural and social connections. Analyzing these relationships can promote cultural exchange and help address social issues across multiple urban areas.
• Emergency Response and Public Safety: In emergencies, city networks play a crucial role in coordinating responses. A clear understanding of these networks enables faster and more efficient disaster management, which can save lives and reduce damages.
• Public Health: City networks are key to managing public health crises. By analyzing how cities are connected, it is possible to better manage the spread of diseases, allocate healthcare resources, and develop public health strategies.
• Technological Innovation: Cities are often hubs of innovation. Analyzing city networks helps understand how new technologies spread and how environments that support technological advancement can be fostered.

In conclusion, the analysis of city networks in Pakistan is critical for making informed decisions that affect urban development, economic growth, environmental sustainability, cultural exchange, public safety, public health, and technological progress. This dataset provides valuable data for decision-making processes that will enhance the quality of life for millions of people in urban areas.

Spectus Origin-Destination Derived Mobility Data - United Kingdom

The Spectus Origin-Destination Derived Mobility Data provides valuable datas about the mobility patterns of individuals across the United Kingdom. The data is derived from mobile phone GPS information, collected by Spectus, a mobility and location data provider. This data is GDPR-compliant, de-identified, and sourced from opted-in users who have provided informed consent for their anonymized data to be used for research purposes.

The dataset includes aggregated daily counts of journeys made between local authorities (2021 boundaries) in the UK. It covers mobility data collected between 2019 and the end of 2021, offering a rich view of movement patterns during that period.

Key features of the dataset:

• Origin-Destination Data: The dataset captures journeys between local authorities, including intra-local-authority flows, reflecting how people travel between regions within the UK.
• Z-values: The journey counts are presented as rounded z-values, which represent the relative frequencies of the flows between origin-destination pairs. This allows for the analysis of mobility patterns and travel volumes between different areas.
• Journey Count Decile: Data is organized into deciles, providing an overview of the distribution of journey counts across different origin-destination pairs.
• Methodology: The data profile outlines the methodology used to process and aggregate the raw data, ensuring that the results reflect the actual travel patterns within the defined geographic boundaries.

This dataset can be used for various purposes, including transportation planning, urban mobility analysis, economic studies, and public health research. By understanding the flow of people between different regions, researchers and policymakers can develop more informed strategies to improve transportation systems, manage infrastructure, and promote regional development.

For further details on the methodology and data specifics, refer to the data profile.

Public transportation spain

Data on public transportation in Spain, specifically focusing on high-speed rail ticket pricing, is available at this link. The dataset covers approximately 30 days of data, totaling around 2.5 million rows, providing an in-depth analysis of ticket pricing and travel patterns in Spain's high-speed rail network.This dataset is particularly valuable for analyzing pricing trends and travel behavior in Spain's high-speed rail network. Researchers and analysts can use these data to identify pricing patterns, compare different train types and classes, and understand how factors such as the start and end dates of trips influence ticket prices. Additionally, the dataset allows for exploration of how origin and destination affect pricing and passenger choices regarding train type and class.

Global

Fligths in Brazil

Data on all flights tracked by the National Civil Aviation Agency (ANAC) in Brazil from January 2015 to August 2017 are available at this link. The dataset contains detailed records of every flight during this period, including information on the origin and destination airports, the airline operating the flight, flight numbers, scheduled departure and arrival times, and the duration of each flight. Additionally, the dataset includes the state (UF) where each airport is located, with airports outside Brazil marked as N/I. The status of each flight, such as whether it was on-time, delayed, or canceled, is also recorded. In cases where specific flight details were unidentified, they are labeled as "Nao Identificado". Although the dataset is in Portuguese, a translation for the columns and other relevant terms is provided to facilitate its use.

Finally, this dataset offers a comprehensive foundation for analyzing air travel patterns and operational performance across Brazil during the 2015-2017 period.

Air Traffic Networks

Data on air traffic networks provided by the National Civil Aviation Agency (ANAC) is updated bi-monthly, on the 19th and 24th of each month,are available at this link. As a result, the update date may not align with other statistical data publications, potentially leading to minor discrepancies in values when compared to other sources.The information published aims to bridge the gap between society and ANAC through transparency and the promotion of knowledge production both within and outside the agency.

It is important to note that the files provided contain raw data. External users generating reports from this data may obtain results that significantly differ from the official figures released by ANAC due to applied filters or the specific objectives of the reports. Users are advised to carefully review the dataset, variable descriptions, and data collection methodology to ensure the technical accuracy of any derived work.

2015 Flight Delays

The 2015 Flight Delays dataset, provided by the U.S. Department of Transportation (DOT) Bureau of Transportation Statistics, tracks the on-time performance of domestic flights operated by large air carriers in the United States. This dataset contains detailed information on the delays, cancellations, diversions, and on-time performances of flights throughout the year 2015.

Context: The U.S. DOT compiles summary data on flight status for domestic flights, which includes information on the number of on-time, delayed, canceled, and diverted flights. This data is a valuable resource for understanding the operational efficiency of air travel, helping to identify trends in delays and cancellations.

Key components of the dataset:

• Flight Delays: The dataset includes detailed records of flight delays, helping to analyze the factors influencing the timing of flight arrivals and departures.
• Cancellations and Diversions: Information on canceled and diverted flights is included, offering into operational disruptions and how airlines respond to various challenges.
• On-time Performance: The dataset contains data on on-time flights, providing a benchmark for evaluating the punctuality of different carriers and airports.

This dataset is valuable for analyzing factors contributing to flight delays, the effectiveness of airline operations, and trends in the U.S. air travel industry in 2015. Researchers, airlines, and policymakers can utilize this information to improve operational efficiency, manage disruptions, and enhance customer experiences.

For further details and to explore the dataset, visit the Kaggle page.

Synthetic European Road Flow Data Based on ETISplus

This dataset provides estimated European truck traffic flows between 1,675 regions across Europe. It is based on the publicly available ETISplus project from 2010, which collected Europe-wide freight volumes and calibrated the resulting origin-destination (O-D) matrices with real-world traffic flows.

The dataset has been updated with current Eurostat data to provide more accurate and up-to-date traffic flow estimates. The ETISplus project and the subsequent updates enable detailed into freight transportation patterns across Europe, which can be used for transportation planning, infrastructure development, and economic analysis.

Key features of the dataset:

• 1,675 Regions: The dataset covers 1,675 regions across Europe, representing a broad scope of freight movement.
• Origin-Destination Matrices: The dataset includes O-D matrices that detail the estimated truck traffic between various regions, based on freight volumes and traffic data.
• Eurostat Updates: The truck traffic data from the original ETISplus project has been updated using current Eurostat data, ensuring the dataset reflects recent transportation trends.
• Freight Volumes: The dataset helps track freight transport volumes, which are critical for analyzing economic activity, supply chains, and transport infrastructure needs in Europe.

This dataset is useful for researchers, transportation planners, and policymakers working on freight transport modeling, infrastructure projects, or studies on the economic impacts of transportation networks.

For further details, access the dataset on Mendeley Data.

COVID-19 US County-to-County Mobility Flows

This dataset provides a multiscale dynamic human mobility flow across the United States, helping to assess the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. It contains daily and weekly origin-to-destination (O-D) population flows, inferred from millions of anonymous mobile phone users' visit trajectories, provided by SafeGraph.

The data starts from March 1st, 2020, and is regularly updated. It covers mobility at three geographic scales: census tract, county, and state. The dataset offers high-resolution spatiotemporal data, capturing human mobility changes and spatial interaction patterns over time. The data has been cross-validated with openly available sources, showing a high correlation and demonstrating the reliability of the dataset.

Key features of the dataset:

• Geographic Scales: The mobility data is aggregated and inferred at three levels: census tract, county, and state.
• Time Resolution: It provides both daily and weekly dynamic mobility flows, giving into short-term and long-term mobility patterns.
• Impacts of COVID-19 Interventions: The dataset is particularly valuable for understanding the effects of COVID-19-related restrictions, like stay-at-home orders, on human movement patterns.
• Applications: This dataset can be used to monitor epidemic spreading dynamics, inform public health policy, and analyze human behavior changes during a public health crisis. It also supports other applications in transportation, social sensing, and mobility studies.

For more details and to access the dataset, visit the GitHub page.

Global Transnational Mobility Dataset

This dataset provides estimates of country-to-country cross-border human mobility, or "trips," based on global statistics on tourism and air passenger traffic. The data, covering over 15 billion estimated trips, spans the years 2011 to 2016. It is derived from two major sources that have been adjusted and merged to create a comprehensive and accurate representation of global mobility.

The work was jointly conducted by the European Commission Knowledge Centre on Migration and Demography (KCMD) and the European University Institute (EUI) Migration Policy Centre (MPC) as part of the Global Mobilities Project (GMP). This dataset serves as an invaluable tool for analyzing global migration trends, the movement of people, and the interconnectedness of countries through travel.

Key Features:

• Coverage: Includes estimates for more than 15 billion trips across countries worldwide between 2011 and 2016.
• Sources: Data derived from global tourism and air passenger traffic statistics.
• Adjustment & Merging: The two sources are adjusted and merged to ensure the most accurate reflection of cross-border mobility.
• Partnership: The dataset is the result of collaboration between the European Commission Knowledge Centre on Migration and Demography (KCMD) and the European University Institute (EUI) Migration Policy Centre (MPC).

For more details and to access the dataset, visit the data access page.

Taban Airlines Flight Dataset

This dataset contains detailed information about the flights operated by Taban Airlines. Each entry in the dataset corresponds to a specific flight, including various attributes such as a unique Flight ID for identification, the origin and destination airports, and the flight number. Additionally, the dataset includes the type of airplane used for each flight, along with the flight status, which indicates whether the flight has departed, landed, or been cancelled. It also contains both the scheduled and actual times for each flight, providing a comparison between planned and real-time performance.

For more information and to access the dataset, visit the Taban Airlines Flight Dataset on Kaggle.

Trainline_eu - A Database of European Train Stations

This repository contains a comprehensive database of train stations across Europe, used by Trainline EU to identify stations from various train operators within the continent. Trainline EU offers ticket sales for a wide range of train operators, and this dataset plays a crucial role in accurately identifying and managing the stations involved in these services.

The dataset includes detailed information to help streamline station identification and ensure smooth operations for travelers using Trainline EU's platform.

For more information, visit the Stations Database on GitHub.