A space–time parallel framework for fine-scale visualization of pollen levels across the Eastern United States. Issue 5 (3rd September 2019)

• Record Type: Journal Article
• Authors: Desjardins, Michael R.; Hohl, Alexander; Griffith, Adam; Delmelle, Eric
• Is Part Of: Cartography and geographic information science. Volume 46:Issue 5(2019)
• Page Start: 428
• Page End: 440
• Publication Date: 2019-09-03
• Subjects: Geographic information science -- space–time interpolation -- pollen -- 3D visualization -- parallel computing; Cartography -- Periodicals; Geographic information systems -- Periodicals; 526
• DOI: 10.1080/15230406.2018.1515664
• Languages: English
• ISSNs: 1523-0406

Abstract

ABSTRACT: Allergic rhinitis (hay fever) resulting from seasonal pollen affects 15–30% of the population in the United States, and can exacerbate several related conditions, including asthma, atopic eczema, and allergic conjunctivitis. Timely monitoring, accurate prediction, and visualization of pollen levels are critical for public health prevention purposes, such as limiting outdoor exposure or physical activity. The low density of pollen detecting stations and complex movement of pollen represent a challenge for accurate prediction and modeling. In this paper, we reconstruct the dynamics of pollen variation across the Eastern United States for 2016 using space–time interpolation. Pollen levels were extracted according to a stratified spatial sampling design, augmented by additional samples in densely populated areas. These measurements were then used to estimate the space–time cross-correlation, inferring optimal spatial and temporal ranges to calibrate the space–time interpolation. Given the computational requirements of the interpolation algorithm, we implement a spatiotemporal domain decomposition algorithm, and use parallel computing to reduce the computational burden. We visualize our results in a 3D environment to identify the seasonal dynamics of pollen levels. Our approach is also portable to analyze other large space–time explicit datasets, such as air pollution, ash clouds, and precipitation.