Field of Study:
data analytics, climate adaptation, sea level rise, transportation
Department:
Civil, Environmental & Geodetic Engineering
Rank of Student:
Junior or senior
Desired Majors:
Computer science or engineering with coding experience
Hours per Week:
7
Compensation Type:
Salary / Stipend
Application Deadline:
Contact:
Professor Kelsea Best- best.309@osu.edu
Private
Public
Project Description
We are seeking a motivated undergraduate researcher to support a project examining how sea-level rise, coastal flooding, and hurricanes may disrupt access to essential services and community function in coastal Louisiana. The project aims to move beyond traditional monetary measures of disaster impacts by evaluating how hazards affect people's ability to reach critical services such as healthcare facilities by quantifying spillover effects on surrounding communities whose access may be disrupted. The broader goal is to support more equitable approaches to natural-hazard risk mitigation and climate adaptation.
The undergraduate researcher will contribute to several stages of the project. Initial work will focus on developing and validating a spatial dataset of essential-service locations, particularly healthcare services and specialized healthcare functions. The researcher may also help link service-location data, including OpenStreetMap and other facility datasets, with a building-level structure inventory used for hazard analysis. As the project progresses, the researcher will assist with running computational scenarios on a high-performance computing (HPC) cluster to estimate disruption across a wide range of possible hazard and recovery conditions.
This is a part-time research position requiring approximately 6–8 hours per week. The position provides an opportunity to develop experience in spatial data analysis, Python, GIS, high-performance computing, and natural-hazard resilience research.
Key Responsibilities:
Compile and validate essential-service location data, including healthcare specialties and screening services.
Match service locations and OpenStreetMap nodes with building-level structure inventory data using GIS.
Clean, organize, and document spatial datasets from multiple sources.
Adapt Python scripts for different hazard, disruption, and recovery scenarios.
Run large-scale scenario analyses on high-performance computing cluster.
Perform quality-control checks on datasets and model outputs.
Create maps, tables, and figures to communicate results clearly.
The undergraduate researcher will contribute to several stages of the project. Initial work will focus on developing and validating a spatial dataset of essential-service locations, particularly healthcare services and specialized healthcare functions. The researcher may also help link service-location data, including OpenStreetMap and other facility datasets, with a building-level structure inventory used for hazard analysis. As the project progresses, the researcher will assist with running computational scenarios on a high-performance computing (HPC) cluster to estimate disruption across a wide range of possible hazard and recovery conditions.
This is a part-time research position requiring approximately 6–8 hours per week. The position provides an opportunity to develop experience in spatial data analysis, Python, GIS, high-performance computing, and natural-hazard resilience research.
Key Responsibilities:
Compile and validate essential-service location data, including healthcare specialties and screening services.
Match service locations and OpenStreetMap nodes with building-level structure inventory data using GIS.
Clean, organize, and document spatial datasets from multiple sources.
Adapt Python scripts for different hazard, disruption, and recovery scenarios.
Run large-scale scenario analyses on high-performance computing cluster.
Perform quality-control checks on datasets and model outputs.
Create maps, tables, and figures to communicate results clearly.
Required Applicant Information
Please provide a CV and statement of coding experience.
Required or Desired Skills
Interest in natural hazards, climate adaptation, infrastructure resilience, healthcare accessibility, GIS, or spatial data analysis.
Familiarity with at least one programming language, preferably Python or R.
Familiarity with at least one GIS or visualization platform, such as ArcGIS, QGIS, Tableau, or a similar tool.
Strong attention to detail and willingness to work with large and sometimes imperfect real-world datasets.
Familiarity with at least one programming language, preferably Python or R.
Familiarity with at least one GIS or visualization platform, such as ArcGIS, QGIS, Tableau, or a similar tool.
Strong attention to detail and willingness to work with large and sometimes imperfect real-world datasets.
Faculty Member Lead:
Kelsea Best
Starting Semester:
Autumn
Length of Project (in semesters):
2