Manufactured housing and climate mobility
Source: https://ugresearch.osu.edu/research-postings/manufactured-housing-and-climate-mobility Parent: https://ugresearch.osu.edu/research-positions
Field of Study:
data analytics, climate adaptation, disaster resilience, manufactured housing
Department:
Civil, Environmental & Geodetic Engineering
Rank of Student:
Junior or senior
Desired Majors:
Computer science or engineering with coding experience
Hours per Week:
10
Compensation Type:
Academic Credit,
Salary / Stipend
Contact:
Professor Kelsea Best- best.309@osu.edu
Private
Public
Project Description
Manufactured (and mobile) housing, or housing that is wholly prefabricated elsewhere and then transported to the installation site, provides permanent housing for approximately 20 million individuals in the United States, especially low-income and rural households. Manufactured housing is generally highly vulnerable to damage from natural hazards including hurricanes, earthquakes, and wildfires, and also poor at providing insulation from temperature extremes. As a large and particularly vulnerable population, it is important to better understand how residents of manufactured housing make decisions about moving or staying in high disaster risk areas. Critical questions remain about mobility for manufactured housing residents including (1) How does the rate of migration from manufactured housing compare with the rate of migration for other residents within a county?; (2) How do the rates of migration vary for manufactured housing residents versus other residents after a natural hazard event, and does the type of hazard matter?; and (3) When manufactured housing residents do migrate, where do they go and how do their destinations compare to their origins in terms of affordability and hazard exposure? \ \ To address these questions, we use data from DataAxel on residential mobility. The student researcher will help with data processing and analysis to begin to address our research questions.
Additional Information
I am flexible in terms of hourly salary or academic credit in the Spring semester.
Required Applicant Information
Please provide a CV and a description of any specific experience with data analytics/ working with large datasets
Required or Desired Skills
Proficiency in R or Python for data analytics, comfort and experience working with very large data, experience using the Ohio Super Computer, curiosity, willingness to learn, resilience to overcome challenges
Faculty Member Lead:
Kelsea Best
Starting Semester:
Spring
Length of Project (in semesters):
2