A space–time parallel framework for fine-scale visualization of pollen levels across the Eastern United States

References

• Aguilera, F., Dhiab, A. B., Msallem, M., Orlandi, F., Bonofiglio, T., Ruiz-Valenzuela, L., García-Mozo, H. (2015). Airborne-pollen maps for olive-growing trees throughout the Mediterranean region: Spatio-temporal interpretation. Aerobiologia, 31, 421–434. doi:10.1007/s10453-015-9375-5
   Web of Science ®Google Scholar
• Aigner, W., Miksch, S., Schumann, H., & Tominski, C. (2011). Visualization of time-oriented data. London: Springer.
   Google Scholar
• Alba, F., Nieto-Lugilde, D., Comtois, P., De La Guardia, C. D., De Linares, C., & Ruiz, L. (2006). Airborne-pollen map for Olea europaea L. in eastern Andalusia (Spain) using GIS: Estimation models. Aerobiologia, 22, 107–116. doi:10.1007/s10453-006-9024-0
   Web of Science ®Google Scholar
• Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., … Tominski, C. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24, 1577–1600. doi:10.1080/13658816.2010.508043
   Web of Science ®Google Scholar
• Bleisch, S. (2012). 3D Geovisualization-definition and structures for the assessment of usefulness. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, I–2, 129–134. doi:10.5194/isprsannals-I-2-129-2012
   Google Scholar
• Brewer, C. A., Hatchard, G. W., & Harrower, M. A. (2003). ColorBrewer in print: A catalog of color schemes for maps. Cartography and Geographic Information Science, 30, 5–32. doi:10.1559/152304003100010929
   Google Scholar
• Burr, M. L., Emberlin, J. C., Treu, R., Cheng, S., & Pearce, N. E.; ISAAC Phase One Study Group. (2003). Pollen counts in relation to the prevalence of allergic rhinoconjunctivitis, asthma and atopic eczema in the International Study of Asthma and Allergies in Childhood (ISAAC). Clinical and Experimental Allergy, 33, 1675–1680. doi:10.1111/j.1365-2222.2003.01816.x
   PubMed Web of Science ®Google Scholar
• Centers for Disease Control and Prevention. (2015). Allergies and Hay fever. Retrieved from https://www.cdc.gov/nchs/fastats/allergies.htm
   Google Scholar
• Charpin, D., & Caillaud, D. (2014). Épidémiologie de l’allergie pollinique. Revue des Maladies Respiratoires, 31(4), 365–374. doi:10.1016/j.rmr.2013.12.006
   PubMedGoogle Scholar
• D’Amato, G., Cecchi, L., D’Amato, M., & Annesi-Maesano, I. (2014). Climate change and respiratory diseases. European Respiratory Review, 23(132), 161–169. doi:10.1183/09059180.00001714
   PubMedGoogle Scholar
• Dahl, Å., Galán, C., Hajkova, L., Pauling, A., Sikoparija, B., Smith, M., & Vokou, D. (2013). The onset, course and intensity of the pollen season. In M. Sofiev & K.-C. Bergmann (Eds.), Allergenic pollen (pp. 29–70). Dordrecht, Netherlands: Springer.
   Google Scholar
• DellaValle, C. T., Triche, E. W., & Bell, M. L. (2012). Spatial and temporal modeling of daily pollen concentrations. International Journal Biometeorology, 56, 183–194. doi:10.1007/s00484-011-0412-y
   PubMed Web of Science ®Google Scholar
• Delmelle, E., Jia, M., Dony, C., Casas, I., & Tang, W. (2015). Space-time visualization of dengue fever outbreaks. In P. Kanaroglou, E. Delmelle, D. Ghosh, A. Paez, & S. Elloitt (Eds.), Spatial analysis in health geography (pp. 1–20). Burlington, VT: Ashgate.
   Google Scholar
• Delmelle, E., Dony, C., Casas, I., Jia, M., & Tang, W. (2014). Visualizing the impact of space-time uncertainties on dengue fever patterns. International Journal of Geographical Information Science, 28(5), 1107–1127. doi:10.1080/13658816.2013.871285
   Web of Science ®Google Scholar
• Delmelle, E. M. (2014). Spatial sampling. In M. M. Fisher & P. Nijkamp (Eds.), Handbook of regional science (pp. 1385–1399). Berlin: Springer.doi:10.1007/978-3-642-23430-9_73
   Google Scholar
• Demšar, U., & Virrantaus, K. (2010). Space–Time density of trajectories: Exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24, 1527–1542. doi:10.1080/13658816.2010.511223
   Web of Science ®Google Scholar
• Ding, Y., & Densham, P. J. (1996). Spatial strategies for parallel spatial modelling. International Journal of Geographical Information Systems, 10, 669–698. doi:10.1080/02693799608902104
   Web of Science ®Google Scholar
• Eaglin, T., Cho, I., & Ribarsky, W. (2017, January). Space-time kernel density estimation for real-time interactive visual analytics. Proceedings of the 50th Hawaii International Conference on System Sciences, Waikoloa, HI. doi:10.24251/HICSS.2017.000
   Google Scholar
• Fang, T. B., & Lu, Y. (2011). Constructing a near real-time space-time cube to depict urban ambient air pollution scenario. Transactions in GIS, 15, 635–649. doi:10.1007/s00477-016-1274-y
   Google Scholar
• Fouedjio, F. (2017). Second-order non-stationary modeling approaches for univariate geostatistical data. Stochastic Environmental Research and Risk Assessment, 31, 1887–1906.doi:10.1111/j.1467-9671.2011.01283.x
   Google Scholar
• Gabriel, E. (2014). Estimating second-order characteristics of inhomogeneous spatio-temporal point processes: Influence of edge correction methods and intensity estimates. Methodology and Computing in Applied Probability, 16, 411–431. doi:10.1007/s11009-013-9358-3
   Web of Science ®Google Scholar
• Garcia-Mozo, H., Galan, C., & Vazquez, L. (2006). The reliability of geostatistic interpolation in olive field floral phenology. Aerobiologia, 22, 95. doi:10.1007/s10453-006-9026-y
   Web of Science ®Google Scholar
• Guo, L., Lei, L., Zeng, Z.-C., Zou, P., Liu, D., & Zhang, B. (2015). Evaluation of spatio-temporal variogram models for mapping XCO2 using satellite observations: A case study in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1), 376–385. doi:10.1109/JSTARS.2014.2363019
   Web of Science ®Google Scholar
• Haas, T. C. (1990). Kriging and automated variogram modeling within a moving window. Atmospheric Environment. Part A. General Topics, 24(7), 1759–1769. doi:10.1016/0960-1686(90)90508-K
   Web of Science ®Google Scholar
• Haase, P. (1995). Spatial pattern analysis in ecology based on Ripley’s K‐function: Introduction and methods of edge correction. Journal of Vegetation Science, 6(4), 575–582. doi:10.2307/3236356
   Web of Science ®Google Scholar
• Hägerstrand, T. (1970). What about people in regional science? Papers in Regional Science, 24(1), 6–21. doi:10.1111/j.1435-5597.1970.tb01464.x
   Google Scholar
• Hohl, A., Delmelle, E., Tang, W., & Casas, I. (2016a). Accelerating the discovery of space-time patterns of infectious diseases using parallel computing. Spatial and Spatio-Temporal Epidemiology, 19, 10–20. doi:10.1016/j.sste.2016.05.002
   PubMed Web of Science ®Google Scholar
• Hohl, A., Casas, I., Delmelle, E. M., & Tang, W. (2016b). Hybrid indexing for parallel analysis of spatiotemporal point patterns. Proceedings of the 9th international conference on geographic information science. doi:10.21433/B3114824R3WG
   Google Scholar
• Hohl, A., Delmelle, E. M., & Tang, W. (2015). Spatiotemporal domain decomposition for massive parallel computation of space-time kernel density. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-4/W2(4), 7–11. doi:10.5194/isprsannals-II-4-W2-7-2015
   Google Scholar
• Hu, H., & Shu, H. (2015). An improved coarse-grained parallel algorithm for computational acceleration of ordinary Kriging interpolation. Computers & Geosciences, 78, 44–52. doi:10.1016/j.cageo.2015.02.011
   Web of Science ®Google Scholar
• Huang, F., Liu, D., Tan, X., Wang, J., Chen, Y., & He, B. (2011). Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS. Computers & Geosciences, 37(4), 426–434. doi:10.1016/j.cageo.2010.05.024
   Web of Science ®Google Scholar
• Jochner, S. C., Sparks, T. H., Estrella, N., & Menzel, A. (2012). The influence of altitude and urbanisation on trends and mean dates in phenology (1980–2009). International Journal of Biometeorology, 56(2), 387–394. doi:10.1007/s00484-011-0444-3
   PubMed Web of Science ®Google Scholar
• Kwan, M. P. (2004). GIS methods in time‐geographic research: Geocomputation and geovisualization of human activity patterns. Geografiska Annaler: Series B, Human Geography, 86(4), 267–280. doi:10.1111/j.0435-3684.2004.00167.x
   Google Scholar
• Kyriakidis, P. C., & Journel, A. G. (1999). Geostatistical space–Time models: A review. Mathematical Geology, 31, 651–684. doi:10.1023/A:1007528426688
   Google Scholar
• León Ruiz, E. J., García Mozo, H., Domínguez Vilches, E., & Galán, C. (2012). The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) Link. The Scientific World Journal, 2012, 1–19. 624247. doi:10.1100/2012/624247.
   Google Scholar
• Levetin, E., & Van de Water, P. K. (2003). Pollen count forecasting. Immunology and Allergy Clinics, 23(3), 423–442. doi:10.1016/S0889-8561(03)00019-5
   Google Scholar
• Levoy, M. (1988). Display of surfaces from volume data. IEEE Computer Graphics and Applications, 8, 29–37. doi:10.1109/38.511
   Web of Science ®Google Scholar
• Li, L., Losser, T., Yorke, C., & Piltner, R. (2014). Fast inverse distance weighting-based spatiotemporal interpolation: A web-based application of interpolating daily fineparticulate matter PM2.5 in the contiguous U.S. Using parallel programming and k-d tree. International Journal of Environmental Research and Public Health, 11, 9101–9141. doi:10.3390/ijerph110909101
   PubMed Web of Science ®Google Scholar
• Miller, H. J. (2005). A measurement theory for time geography. Geographical Analysis, 37(1), 17–45. doi:10.1111/j.1538-4632.2005.00575.x
   Web of Science ®Google Scholar
• Nakaya, T., & Yano, K. (2010). Visualising crime clusters in a space‐time cube: An exploratory data‐analysis approach using space‐time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223–239. doi:10.1111/j.1467-9671.2010.01194.x
   Web of Science ®Google Scholar
• Nowosad, J. (2016). Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus and Betula. International Journal of Biometeorology, 60(6), 843–855. doi:10.1007/s00484-015-1077-8
   PubMed Web of Science ®Google Scholar
• Nowosad, J., Stach, A., Kasprzyk, I., Weryszko-Chmielewska, E., Piotrowska-Weryszko, K., Puc, M., ... Majkowska-Wojciechowska, B. (2016). Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count. Aerobiologia, 32(3), 453–468. doi:10.1007/s10453-015-9418-y
   PubMed Web of Science ®Google Scholar
• Pebesma, E. J., De Jong, K., & Briggs, D. (2007). Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: An air quality example. International Journal of Geographical Information Science, 21(5), 515–527. doi:10.1080/13658810601064009
   Web of Science ®Google Scholar
• Pfister, G. F. (2001). An introduction to the infiniband architecture. High Performance Mass Storage and Parallel I/O, 617–632.
   Google Scholar
• Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-validation. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of database systems (pp. 532–538). New York: Springer.
   Google Scholar
• Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society. Series B (Methodological), 172–212. https://www.jstor.org/stable/2984796
   Google Scholar
• Rojo, J., & Pérez-Badia, R. (2015). Spatiotemporal analysis of olive flowering using geostatistical techniques. Science of the Total Environment, 505, 860–869. doi:10.1016/j.scitotenv.2014.10.022
   PubMed Web of Science ®Google Scholar
• Sagl, G., Delmelle, E., & Delmelle, E. (2014). Mapping collective human activity in an urban environment based on mobile phone data. Cartography and Geographic Information Science, 41(3), 272–285. doi:10.1080/15230406.2014.888958
   Web of Science ®Google Scholar
• Sahu, S. K. (2012). Hierarchical Bayesian models for space–time air pollution data. In C. R. Rao & V. G. Rao (Eds.), Handbook of Statistics (Vol. 30, pp. 477–495). Oxford: Elsevier.
   Google Scholar
• Sahu, S. K., Gelfand, A. E., & Holland, D. M. (2007). High-resolution space–Time ozone modeling for assessing trends. Journal of the American Statistical Association, 102(480), 1221–1234. doi:10.1198/016214507000000031
   PubMed Web of Science ®Google Scholar
• Shaddick, G., & Wakefield, J. (2002). Modelling daily multivariate pollutant data at multiple sites. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(3), 351–372. doi:10.1111/1467-9876.00273
   Web of Science ®Google Scholar
• Shepherd, I. D. (2008). Travails in the third dimension: A critical evaluation of three-dimensional geographical visualization. In M. Dodge, M. McDerby, & M. Turner (Eds.), Geographic visualization: Concepts, tools and applications (pp. 199–222). Chichester, UK: Wiley.
   Google Scholar
• Sherman, M. (2011). Spatial statistics and spatio-temporal data: Covariance functions and directional properties. Hoboken, NJ: Wiley.
   Google Scholar
• Siska, P., Bryant, V. M., & Hung, I. (2006). Geospatial analysis of southern pine biome and pollen distribution patterns in Southeastern United States. Geograficky Casopis Slovenskej Akademie Vied, 58(4), 239.
   Google Scholar
• Siska, P. P., Hung, I., & Bryant Jr, V. M. (2012). the mapping of composite pollen from point sampled data and cartographic generalization. Papers of the Applied Geography Conferences, 35, 191–200. Preprint available at: https://scholarworks.sfasu.edu/spatialsci/44/
   Google Scholar
• Smith, R. L., Kolenikov, S., & Cox, L. H. (2003). Spatiotemporal modeling of PM2. 5 data with missing values. Journal of Geophysical Research: Atmospheres, 108(D24). doi:10.1029/2002JD002914
   Web of Science ®Google Scholar
• St. John, M., Cowen, M. B., Smallman, H. S., & Oonk, H. M. (2001). The use of 2D and 3D displays for shape-understanding versus relative-position tasks. Human Factors, 43(1), 79–98. doi:10.1518/001872001775992534
   PubMed Web of Science ®Google Scholar
• Thakur, S., & Hanson, A. J. (2010). A 3D visualization of multiple time series on maps. In Information Visualisation (IV), 2010 14th International Conference (pp. 336–343). Piscataway, NJ: IEEE. doi: 10.1109/IV.2010.54
   Google Scholar
• Tomczak, M. (1998). Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW)-cross-validation/jackknife approach. Journal of Geographic Information and Decision Analysis, 2(2), 18–30.
   Google Scholar
• van Kreveld, M. (2008). Working group V — Visualization — Position paper: 3D geo-visualization. In P. van Oosterom, S. Zlatanova, D. Penninga, & E. M. Fendel (Eds.), Advances in 3D geoinformation systems, lecture notes in geoinformation and cartography Berlin: Springer. doi:10.1007/978-3-540-72135-2_27
   Google Scholar
• Wheatley, L. M., & Togias, A. (2015). Allergic Rhinitis. The New England Journal of Medicine, 372(5), 456–463. doi:10.1056/NEJMcp1412282
   PubMed Web of Science ®Google Scholar
• Whelan, B. M., McBratney, A. B., & Minasny, B. (2002). Vesper 1.5–Spatial prediction software for precision agriculture. Precision Agriculture, Proc. 6th Int. Conf. on Precision doi:10.1044/1059-0889(2002/er01)
   Google Scholar
• Wikle, C. K., & Royle, J. A. (1999). Space: Time dynamic design of environmental monitoring networks. Journal of Agricultural, Biological, and Environmental Statistics, 4, 489–507. doi:10.2307/1400504
   Web of Science ®Google Scholar
• Wilkinson, B., & Allen, M. (2005). Parallel programming: techniques and applications using networked workstations and parallel computers. Upper Saddle River, NJ: Pearson/Prentice Hall.
   Google Scholar
• Yamada, I., & Rogerson, P. A. (2003). An empirical comparison of edge effect correction methods applied to K‐function analysis. Geographical Analysis, 35(2), 97–109. doi:10.1111/j.1538-4632.2003.tb01103.x
   Web of Science ®Google Scholar
• Zhou, Y., Dao, T. H. D., Thill, J. C., & Delmelle, E. (2015). Enhanced 3D visualization techniques in support of indoor location planning. Computers, Environment and Urban Systems, 50, 15–29. doi:10.1016/j.compenvurbsys.2014.10.003
   Google Scholar