Scholars@Duke
Zach Calhoun

Selected Publications

Neighborhood Environmental and Contextual Factors Improve Prediction of Childhood Body Mass Index: An Application of Novel Graph Neural Networks.

Journal Article AJE Adv · September 24, 2025 Childhood obesity is a major risk factor for adult cardiovascular disease. Current obesity-prediction models were not developed in diverse populations and do not include heterogeneous social, environmental, and climate factors that may impact body mass ind ... Full text Link to item Cite

Big, noisy data: how scalable Gaussian processes can leverage personal weather stations to improve spatiotemporal coverage of urban climate networks

Other · May 21, 2025 Urban temperature varies dramatically across space and time, yet capturing this variability requires a dense, reliable sensor network—something that is rarely available in practice. Spatiotemporal gaps in data coverage make it difficult to connect ... Full text Cite

Refining Citizen Climate Science: Addressing Preferential Sampling for Improved Estimates of Urban Heat

Journal Article Environmental Science and Technology Letters · August 13, 2024 Studies of urban heat are often limited by their ability to measure air temperature; data are collected either at a few locations over time or at many locations at one point in time. Citizen science approaches to observing temperature provide a way to over ... Full text Cite

Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference.

Journal Article Scientific reports · January 2024 The urban heat island effect causes increased heat stress in urban areas. Cool roofs and urban greening have been promoted as mitigation strategies to reduce this effect. However, evaluating their efficacy remains a challenge, as potential temperature redu ... Full text Open Access Cite

Self-Supervised Encoders Are Better Transfer Learners in Remote Sensing Applications

Journal Article Remote Sensing · November 1, 2022 Transfer learning has been shown to be an effective method for achieving high-performance models when applying deep learning to remote sensing data. Recent research has demonstrated that representations learned through self-supervision transfer better than ... Full text Cite