arXiv subjects

Daniel Casey

Publications and source records attributed to Daniel Casey.

3 recordsLinked to original sources

arXiv2026

Estimating Residential Displacement in the Central Puget Sound Region using Household Survey Data

Housing instability is a persistent challenge faced by households in cities across the United States. In worst-case scenarios, households are displaced from their residences and forced to start anew. In an effort to mitigate the harms of residential displacement, local policymakers have an interest in monitoring residential displacement within their communities. In this work, we propose a new strategy to estimate sub-county residential displacement within the Central Puget Sound Region using data from three household survey programs. We first estimate residential displacement between 2016-2023 from a local household travel survey using a Bayesian spatiotemporal model, and poststratify with data from the American Community Survey. We then benchmark these estimates to the American Housing Survey to ensure consistency across sources. The results reveal east-west and north-south differences in residential displacement rates within the region as well as a temporary moderation of displacement in the 2020-2021 cohort of movers. Our estimates are publicly available for interested stakeholders to further study trends in residential displacement in the Central Puget Sound Region, and our methodology is transportable to other jurisdictions with similar data contexts.

stat.AP↗

arXiv2026

A joint diffusion approach to multi-modal inference in inertial confinement fusion

A combination of physics-based simulation and experiments has been critical to achieving ignition in inertial confinement fusion (ICF). Simulation and experiment both produce a mixture of scalar and images outputs, however only a subset of simulated data are available experimentally. We introduce a generative framework, called JointDiff, which enables predictions of conditional simulation input and output distributions from partial, multi-modal observations. The model leverages joint diffusion to unify forward surrogate modeling, inverse inference, and output imputation into one architecture. We train our model on a large ensemble of three-dimensional Multi-Rocket Piston simulations and demonstrate high accuracy, statistical robustness, and transferability to experiments performed at the National Ignition Facility (NIF). This work establishes JointDiff as a flexible generative surrogate for multi-modal scientific tasks, with implications for understanding diagnostic constraints, aligning simulation to experiment, and accelerating ICF design.

physics.plasm-ph↗

arXiv2014

Creation of a high spatiotemporal resolution global database of continuous mangrove forest cover for the 21st Century (CGMFC-21)

The goal of this research is to provide high resolution local, regional, national and global estimates of annual mangrove forest area from 2000 through to 2012. To achieve this we synthesize the Global Forest Change database, the Terrestrial Ecosystems of the World database, and the Mangrove Forests of the World database to extract mangrove forest cover at high spatial and temporal resolutions. We then use the new database to monitor mangrove cover at the global, national and protected area scales. Countries showing relatively high amounts of mangrove loss include Myanmar, Malaysia, Cambodia, Indonesia, and Guatemala. Indonesia remains by far the largest mangrove-holding nation, containing between 26 percent and 29 percent of the global mangrove inventory with a deforestation rate of between 0.26 percent and 0.66 percent annually. Global mangrove deforestation continues but at a much reduced rate of between 0.16 percent and 0.39 percent annually. Southeast Asia is a region of concern with mangrove deforestation rates between 3.58 percent and 8.08 percent during the analysis period, this in a region containing half of the entire global mangrove forest inventory. The global mangrove deforestation pattern from 2000 to 2012 is one of decreasing rates of deforestation, with many nations essentially stable, with the exception of the largest mangrove-holding region of Southeast Asia. We provide a standardized global spatial dataset that monitors mangrove deforestation globally at high spatiotemporal resolutions, covering 99 percent of all mangrove forests. These data can be used to drive the mangrove research agenda particularly as it pertains to improved monitoring of mangrove carbon stocks and the establishment of baseline local mangrove forest inventories required for payment for ecosystem service initiatives.

physics.geo-ph↗