D4 Columbia Hurricane Migration

Welcome, this is the folder repository for the D4 Hack Columbia-Hurricane-Migration team

Repository structure

This template provides the following suggested organizaiton structure for the project repository, but each project team is free to organize their repository as they see fit.

• contributors/
  Each team member can create their own folder under contributors, within which they can work on their own scripts, notebooks, and other files. Having a dedicated folder for each person helps to prevent conflicts when merging with the main branch. This is a good place for team members to start off exploring data and methods for the project.
• notebooks/
  Notebooks that are considered delivered results for the project should go in here.
• scripts/
  Code that is shared by the team should go in here (e.g. functions or subroutines). These will be files other than Jupyter Notebooks such as Python scripts (.py).
• .gitignore
  This file sets the files that will be globally ignored by git for the project. (e.g. you may want git to ignore temporary files or large data files, read more about ignoring files here)
• environment.yml
conda environment description needed to run this project.
• README.md
  Description of the project (see suggested headings below)
• model-card.md
  Description (following a metadata standard) of any machine learning models used in the project

Project or Team Name

Modeling impacts on hurricane wind gusts and storm surge on migration in the US

Provide a brief introduction describing the proposed work. Be sure to also describe what skills team members will get to learn and practice as part of this project.

This project investigates the impact of flooding and hurricanes on migration patterns in the US, utilizing migration data from the Internal Revenue Services and American Community Survey. It aims to understand how socio-economic status influences migration as a result of the impact of storm surge and wind gusts induced by hurricanes, employing advanced modeling techniques including Bayesian hierarchical models and machine learning. It also explores how early warning systems can be leveraged to reduce risks and inform adaptation strategies, providing decision-makers with long-term damage estimates to highlight the cost of inaction.

Collaborators

List all participants on the project. Here is a good space to share your personal goals for the hackweek and things you can help with.

Name	Personal goals	Can help with	Role
Fabien Cottier	Eager to learn more about predictive modeling, including machine learning and Bayesian hierarchical models	Theory, migration data, R, Python	Project Lead
Andrew Kruczkiewicz	learning about your dataset	GitHub, Jupyter, cloud computing	Team Member
Mona Hemmati	Practice leading a software project	machine learning and python (scipy, scikit-learn)	Team Member
Kytt MacManus	Improve skills with GeoAI, new ideas on DSS development, network	Python, Jupyter, Data Wrangling, Gridded Data	Team Member

The problem

Provide a few sentences describing the problem are you going to explore. If this is a technical exploration of software or data science methods, explain why this work is important in a broader context and specific applications of this work.

Data and Methods

Data

Briefly describe and provide citations for the data that will be used (size, format, how to access).

Existing methods

How would you or others traditionally try to address this problem? Provide any relevant citations to prior work.

Proposed methods/tools

What new approaches would you like to implement for addressing your specific question(s) or application(s)?

Will your project use machine learning methods? If so, we invite you to create a model card!

Additional resources or background reading

Optional: links to manuscripts or technical documents providing background information, context, or other relevant information.

Project goals and tasks

Project goals

List the specific project goals or research questions you want to answer. Think about what outcomes or deliverables you'd like to create (e.g. a series of tutorial notebooks demonstrating how to work with a dataset, results of an analysis to answer a science question, an example of applying a new analysis method, or a new python package).

• Build an initial predictive model of migration as a results of hurricane wind gusts for demonstration and evaluation purposes
• (ideally) Validate model against external data sources on migration (possibly based on feedbacks from other D4 participants)

• Understanding how a predictive model of migration may support decision-makers. Producing output/tool whose effectiveness may be evaluated by decision-makers

Tasks

What are the individual tasks or steps that need to be taken to achieve each of the project goals identified above? What are the skills that participants will need or will learn and practice to complete each of these tasks? Think about which tasks are dependent on prior tasks, or which tasks can be performed in parallel.

• Task 1 (all team members will learn to use GitHub)
• Task 2 (team members will use the scikit-learn python library)
  • Task 2a (assigned to team member A)
  • Task 2b (assigned to team member B)
• Task 3
• ...

Project Results

Use this section to briefly summarize your project results. This could take the form of describing the progress your team made to answering a research question, developing a tool or tutorial, interesting things found in exploring a new dataset, lessons learned for applying a new method, personal accomplishments of each team member, or anything else the team wants to share.

You could include figures or images here, links to notebooks or code elsewhere in the repository (such as in the notebooks folder), and information on how others can run your notebooks or code.