Marriage and Statistics through Space and Time

Project Summary

Over ten weeks, Mathematics/Economics majors Khuong (Lucas) Do and Jason Law joined forces with Analytical Political Economy Masters student Feixiao Chen to analyze the spati-temporal distribution of birth addresses in North Carolina. The goal of the project was to understand how/whether the distributions of different demographic categories (white/black, married/unmarried, etc.) differed, and how these differences connected to a variety of socioeconomic indicators.

Themes and Categories
Interdisciplinary Inquiry, Public Policy
Data+
Year
2017
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Project Results: Working with over 2 million births from the North Carolina Birth Records Database, as well as block-group level socioeconomic indicators from the Census and American Community Survey, the team used spatial-statistics techniques to find 'hotspots' corresponding to clusters of births in specific categories. They then showed significant connections between socioeconomic indicators and cluster membership. Finally, they employed cutting-edge machine-learning techniques to quantify the distributional differences between births in different demographic categories.

Partially funded by the Sanford School of Public Policy

Click here for the Executive Summary

Faculty Leads: Christina Gibson-Davis, Paul Bendich

Project Manager: Lizzy Huang

Related People

Related Projects

Getting Granular on Social Determinants of Health

Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. Applied methods will include machine learning, risk prediction, and assessment of bias. This Data+ project is sponsored by the Forge, Duke's center for actionable data science.

Project Leads: Shelly Rusincovitch, Ricardo Henao, Azalea Kim

Project Manager: Austin Talbot

Investigating oil and gas production in the United Kingdom

Producing oil and gas in the North Sea, off the coast of the United Kingdom, requires a lease to extract resources from beneath the ocean floor and companies bid for those rights. This team will consult with professionals at ExxonMobil to understand why these leases are acquired and who benefits. This requires historical data on bid history to investigate what leads to an increase in the number of (a) leases acquired and (b) companies participating in auctions. The goal of this team is to create a well-structured dataset based on company bid history from the U.K. Oil and Gas Authority; data which will come from many different file structures and formats (tabular, pdf, etc.). The team will curate these data to create a single, tabular database of U.K. bid history and work programs.

Project Lead: Kyle Bradbury

Project Manager: Artem Streltsov

On the Shelf: exploring oil and gas production in the Gulf of Mexico

Producing oil and gas in the Gulf of Mexico requires rights to extract these resources from beneath the ocean floor and companies bid into the market for those rights. The top bids are sometimes significantly larger than the next highest bids, but it’s not always clear why this differential exists and some companies seemingly overbid by large margins. This team will consult with professionals at ExxonMobil to curate and analyze historical bid data from the Bureau of Ocean Energy Management that contains information on company bid history, infrastructure, wells, and seismic survey data as well as data from the companies themselves and geopolitical events. The stretch goal of the team will be to see if they can uncover the rationale behind historic bidding patterns. What do the highest bidders know that other bidders do not (if anything)? What characteristics might incentivize overbidding to minimize the risk of losing the right to produce (i.e. ambiguity aversion)?

Project Lead: Kyle Bradbury

Project Manager: Hyeongyul Roh