An Adaptive Spatiotemporal Sampling Method for Optimal Environmental Sensor Placement
Bibliographic Data
| ID | 3776048 |
|---|---|
| Authors | Junfeng Gu (China University of Geosciences), Qingfeng Guan (0000-0002-7392-3709, China University of Geosciences, corresponding author), Yuanyuan Li (0009-0009-3256-4197, Henan Academy of Sciences) |
| Year | 2025 |
| Volume | 115 |
| Issue | 9 |
| Pages | 1-19 |
| Publication date | 2025-07-16 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the American Association of Geographers (JOURNAL) |
| Journal identifiers | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2025.2522841 |
| OpenAlex | W4412478539 |
| Language | EN |
| References cited | 59 |
The optimal placement of sensors is crucial for the maximization of resource utilization in environmental monitoring. The location optimization methods for maximal coverage are not suitable for applications that aim to predict the values of environmental variables at locations where no sensor is placed. For such a task, spatial sampling strategies have been developed to enhance the prediction accuracy by optimal placement of a limited number of sensors. These sensor placement strategies, however, primarily focus on the spatial variation of an environmental variable when selecting sampling locations, neglecting the variable’s spatiotemporal heterogeneity, leading to inadequate spatiotemporal representativeness and limited prediction accuracies at different times. This study proposes a novel adaptive spatiotemporal sampling (ASTS) framework for sensor placement by combining the spatiotemporal variation features of the environmental variable with an adaptive sampling principle. The framework employs the integrated nested Laplace approximation for stochastic partial differential equations (INLA-SPDE) to capture the spatiotemporal variation and uses the adaptive sampling design principle to optimally place a predetermined number of sensors to maximize the prediction accuracy over space and time. The efficacy of the ASTS was validated using a PM2.5 data set from Wuhan, China. The ASTS framework reduced prediction root mean square error by 26.11 percent and computational time by 47.83 percent compared to methods focusing only on spatial variation and genetic algorithm combined with INLA-SPDE, respectively. The ASTS framework can be used to establish a new sensor placement plan or enhance existing networks by optimally adding sensors, ensuring high prediction accuracy across geographic locations and time periods
Adaptive sampling · Computer vision · Environmental planning · Environmental resource management · Geography · Monte Carlo method · Statistics · Air Quality Monitoring and Forecasting · Computer Science · Environmental Science · Mathematics · Odor and Emission Control Technologies · Water Quality Monitoring Technologies
Spatial and Spatio‐temporal Bayesian Models with R‐Inla
An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields
Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China
Identification of Key Determinants Influencing Spatiotemporal Heterogeneity of Urban Resilience
Spatio-temporal heterogeneity and influencing factors in the synergistic enhancement of urban ecological resilience
A new approach for determining optimal placement of PM 2.5 air quality sensors
Using green infrastructure to improve urban air quality (GI4AQ)
The sensor desert quandary
Geospatial Estimation of Individual Exposure to Air Pollutants
The Validity and Usefulness of Laws in Geographic Information Science and Geography
Optimizing for Equity
A Computational Framework for Preserving Privacy and Maintaining Utility of Geographically Aggregated Data
| Citation velocity | historical |
|---|---|
| Highly cited | No |