TAPS: Target-Aware Permanent Sampling for Graph Diffusion
Abstract
Permanent air-quality networks are expensive to install and maintain, yet many decisions depend on one future regional exposure rather than the complete pollution field. We introduce Target-Aware Permanent Sampling (TAPS), a graph-diffusion framework for selecting permanent locations observed repeatedly over time, that can reduce unnecessary sensor installations, maintenance, and cost while preserving the information needed for future air-quality decisions, subject to validation with the estimator that will use the network. We formulate the permanent space-time sampling problem and show that, in the noiseless model, recovery of one prescribed target can require less information than full-state identification, for which we give an explicit lower bound on the permanent-location count. We also derive a greedy rule whose marginal gain factors into raw target response and a finite-update correction. We evaluate TAPS on regulatory air-quality networks in California, Canada, and England. In each case the prescribed regional target becomes numerically recoverable at permanent-location budgets well below those required to identify the retained state, and TAPS attains lower regularized target risk than target-weight, geometric, and design-based placements. A blind prospective study further shows that the criterion can be applied before candidate sites have any ground-monitor history, using exogenous environmental covariates alone.
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BibTeX
@misc{lin2026taps,
title = {{TAPS: Target-Aware Permanent Sampling for Graph Diffusion}},
author = {Michelle Lin and Laura P. Schaposnik},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.37247},
eprint = {2609.37247},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.37247},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - TAPS: Target-Aware Permanent Sampling for Graph Diffusion
AU - Michelle Lin
AU - Laura P. Schaposnik
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.37247
UR - https://arxiv.org/abs/2609.37247
AB - Permanent air-quality networks are expensive to install and maintain, yet many decisions depend on one future regional exposure rather than the complete pollution field. We introduce Target-Aware Permanent Sampling (TAPS), a graph-diffusion framework for selecting permanent locations observed repeatedly over time, that can reduce unnecessary sensor installations, maintenance, and cost while preserving the information needed for future air-quality decisions, subject to validation with the estimator that will use the network. We formulate the permanent space-time sampling problem and show that, in the noiseless model, recovery of one prescribed target can require less information than full-state identification, for which we give an explicit lower bound on the permanent-location count. We also derive a greedy rule whose marginal gain factors into raw target response and a finite-update correction. We evaluate TAPS on regulatory air-quality networks in California, Canada, and England. In each case the prescribed regional target becomes numerically recoverable at permanent-location budgets well below those required to identify the retained state, and TAPS attains lower regularized target risk than target-weight, geometric, and design-based placements. A blind prospective study further shows that the criterion can be applied before candidate sites have any ground-monitor history, using exogenous environmental covariates alone.
ER -