IE 404 : Team Not Found
The dataset provided by the Missing Migrants Project tracks the deaths of migrants, including refugees and asylum-seekers, who have perished or gone missing during the process of migration towards international destinations. It spans the years from 2014 to 2024, encompassing significant periods of global migration activity. It's important to note that these data represent minimum estimates, as many migrant deaths during migration remain unrecorded. The dataset includes information on attempted crossings of the Mediterranean Sea from 2016 to 2024, detailing arrivals in Europe, interceptions by North African and Turkish Coast Guards, as well as migrant deaths and disappearances.
Objectives of the analysis:
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Understanding Migration Patterns: Analyze trends in migrant deaths, arrivals in Europe, and interceptions by authorities to understand migration dynamics, especially in the Mediterranean.
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Identifying Risk Factors: Identify factors contributing to migrant deaths, such as routes, seasons, and demographics, to inform risk reduction strategies.
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Informing Policy and Intervention: Provide insights for policy-makers and intervention efforts to improve migration management and protect vulnerable migrants.
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Raising Awareness: Highlight the human toll of irregular migration and advocate for international cooperation and humanitarian responses to address migrant safety.
To set up and install our project on your local device, follow these steps:
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Clone the Repository: Begin by cloning the repository to your local machine. You can do this by executing the following command in your terminal or command prompt:
git clone [repository URL] -
Install Necessary Packages: Navigate to the project directory and ensure that you have all the required packages. The packages can be installed at the beginning of each Jupyter notebook.
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Set Up Environment: If there are any additional environment configurations or setups required, please refer to the project documentation or README file for instructions.
By following these steps, you should be able to successfully install and set up our project on your local machine. If you encounter any issues or have any questions, feel free to reach out to us for assistance.
[Explain how to use your project, including any commands, configuration options, or examples. Provide screenshots or GIFs if applicable.]
We welcome contributions from everyone to improve this project. To contribute, please follow these guidelines:
If you encounter a bug or issue while using the project, please ensure the bug has not already been reported by checking the existing issues. If it hasn't been reported yet, open a new issue providing detailed steps to reproduce the bug, along with any relevant information about your environment.
If you have an idea for a new feature or enhancement, feel free to submit a feature request. Before submitting, please check the existing feature requests to avoid duplicates. Describe the proposed feature clearly and provide any relevant context or use cases to help us understand its importance.
Thank you for your interest in contributing to our project! We appreciate your help in making it better for everyone.
We extend our sincere appreciation to the following individuals, organizations, and resources that have contributed to the development and success of this project:
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Hertie School: We are grateful to the Hertie School for presenting the challenge and organizing the Data4GoodFest, providing a platform for innovation and collaboration in the pursuit of social impact.
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Missing Migrants Project: Our heartfelt thanks to the Missing Migrants Project for providing the dataset, which has been instrumental in our analysis and solution development.
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Frameworks Used: We utilized several frameworks to develop this project, including Python for backend development, Shiny for interactive web applications, OpenAI for natural language processing capabilities, and the News API for gathering additional information from online news sources. Each of these tools played a crucial role in shaping our solution and enhancing its functionality.
We also draw inspiration from various sources and references throughout the development process, contributing to the creativity and effectiveness of our project.
Sweetviz. (n.d.). Retrieved from https://pypi.org/project/sweetviz/
Shiny. (n.d.). Retrieved from https://shiny.posit.co/py/
OpenAI. (n.d.). OpenAI API Documentation. Retrieved from https://platform.openai.com/docs/api-reference?lang=python
We are a team of Bachelor students from IE University, comprising Armand Hubler, Isabel De Valenzuela, Aswin Subramanian Maheswaran, Emili Khachatryan, and Riyad Mazari. Our academic pursuits span across Computer Science, Data and Business Analytics, and Business Administration.
With a shared passion for technology and social impact, we have collaborated to develop this project, leveraging our diverse skill sets and academic backgrounds. Our combined expertise in computer science, data analytics, and business administration has been instrumental in conceptualizing, designing, and implementing innovative solutions to address real-world challenges.
As aspiring professionals in our respective fields, we are committed to leveraging technology for positive change and making meaningful contributions to society. We are excited to share our project with the community and welcome feedback and collaboration opportunities to further enhance its impact.
To enrich our dataset and provide a more comprehensive understanding of migration dynamics, we have incorporated additional sources:
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Social Media Monitoring: We have implemented a social media monitoring system to gather real-time data and insights from various platforms. This allows us to capture public discourse, sentiments, and emerging trends related to migration issues.
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Official Government Reports & NGO Data: We have integrated official government reports and data from non-governmental organizations (NGOs) to complement our dataset. These authoritative sources provide valuable statistics and insights into migration patterns, challenges, and interventions.
In order to capture a more nuanced picture of migrant demographics and experiences, we have expanded our dataset to include additional variables:
- Gender, Age, Family Size, etc.: We have scraped data on gender, age, family size, and other relevant demographics to better understand the composition and needs of migrant populations. This allows us to analyze migration trends and outcomes through a more intersectional lens.
To facilitate informed decision-making and policy development, we have integrated our platform with policy tools and frameworks:
- APIs and Plugins for Policy Integration: We have developed APIs and plugins to seamlessly integrate our platform with existing policy frameworks used by governments and international organizations (IOs). This enables policymakers to access relevant data, insights, and analysis directly within their workflow, enhancing the efficacy of migration policies and interventions.
TRT World. (2023). Numbers in migration: Where the world stands in 2023. Retrieved from https://www.trtworld.com/discrimination/numbers-in-migration-where-the-world-stands-in-2023-16291722
World Bank. (n.d.). Home. Retrieved from https://data.worldbank.org/
International Committee of the Red Cross (ICRC). (n.d.). No Trace of You: Missing Migrants. Retrieved from https://www.icrc.org/en/document/no-trace-you-missing-migrants
International Organization for Migration (IOM). (n.d.). Missing Migrants. Retrieved from https://missingmigrants.iom.int/
UNHCR. (2023). Mid-year trends report 2023. Retrieved from https://www.unhcr.org/mid-year-trends-report-2023
Here are the links formatted in APA style:
ACLED. (n.d.). Conflict Index. Retrieved from https://acleddata.com/conflict-index/#:~:text=The%20ACLED%20Conflict%20Index%20assesses,collected%20for%20the%20past%20year
Our World in Data. (n.d.). War and Peace Data Explorers. Retrieved from https://ourworldindata.org/war-and-peace-data-explorers