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Oral Program Oral Program Wednesday, 10 July 2019 08:30-09:30 Workshop Registration | Room: Llevant Foyer Room Llevant 1 Llevant 2 09:00-17:30 Workshop 1 Workshop 2 09:00-12:30 How to model skewed and heavy tailed Quantitative integration of spatial data for distributions: An introduction to copula predicting modeling and mapping Benedikt Gräler B. Daneshfar 52°North Initiative for Geospatial Open Earth Observation Service, Science and Source Software GmbH, Germany Technology Branch, Agriculture and Agri- Food, Canada 12:30-13:30 Finger Lunch | Room: Hall Auditorium 13:30-17:30 Workshop 3 Workshop 2 (contd.) 13:30-15:30 Introduction to modeling and analysis of Quantitative integration of spatial data for regionalised compositions predicting modeling and mapping V. Pawlowsky-Glahn*1, J.A. Martín- B. Daneshfar Fernández1, J.J. Egozcue2, R. Tolosana- Earth Observation Service, Science and Delgado3 Technology Branch, Agriculture and Agri- 1University of Girona, Spain, 2Technical Food, Canada University of Catalonia, Spain, 3Helmholtz Institute Freiberg for Resource Technology, Spain 15:30-16:00 Coffee Break | Room: Hall Auditorium 16:00-17:30 Workshop 3 (contd.) Workshop 2 (contd.) 16:00-17:30 Introduction to modeling and analysis of Quantitative integration of spatial data for regionalised compositions predicting modeling and mapping V. Pawlowsky-Glahn*1, J.A. Martín- B. Daneshfar, Earth Observation Service, Fernández1, J.J. Egozcue2, R. Tolosana- Science and Technology Branch, Agriculture Delgado3, 1University of Girona, Spain, and Agri-Food, Canada 2Technical University of Catalonia, Spain, 3Helmholtz Institute Freiberg for Resource Technology, Spain 17:30-18:30 Registration and Welcome Reception | Room: Hall Auditorium Thursday, 11 July 2019 08:00-17:45 Registration | Room: Hall Tramuntana Room Tramuntana 2+3 08:15-08:30 Opening Remarks 08:30-09:10 [PLN01] Model diagnostics for spatial point patterns Adrian Baddeley*1, E. Rubak2, R. Turner3, 1Curtin University, Australia, 2Aalborg University, Denmark, 3University of Auckland, New Zealand Session Chair: Jorge Mateu Rooms Tramuntana 2+3 Garbi 1 Garbi 2 09:15-10:15 Session A1: Space/time Session B1: New Spatial Data Session C1: Health Statistics Session Chair: Ben Graeler Session Chair: Frank Osei Session Chair: Matthias Eckardt 09:15-09:30 [A1.1] Bayesian analysis of [B1.1] A spatially varying [C1.1] Spatial small area streamflow networks change points model for smoothing models for J. Fuglstad*1, I. Steinsland1, monitoring glaucoma handling survey data with G.A. Fuglstad1, E.A. Steel2, progression using visual field nonresponse A.H. Fullerton3, 1Norwegian data K. Watjou*1, C. Faes1, University of Science and J.L. Warren*1, S.I. Berchuck2, A. Lawson2, R.S. Kirby3, Technology, Norway, 2FAO, J.C. Mwanza3, 1Yale M. Aregay4, R. Carroll5, Italy, 3NOAA, USA University, USA, 2Duke Y. Vandendijck6, 1Hasselt University, USA, 3University of University, Belgium, 2Medical Spatial Statistics 2019: Towards Spatial Data Science | Oral Program North Carolina at Chapel University of South Carolina, Hill, USA USA, 3University of South Florida, USA, 4Novartis Pharmaceutical Corporation, USA, 5National Institute of Environmental Health Sciences, USA, 6The Janssen Pharmaceutical Companies of Johnson & Johnson, Belgium 09:30-09:45 [A1.2] Nonparametric [B1.2] Opportunities for [C1.2] Bivariate borrowing covariate-based first-order turning smartphone zombies strength for diabetes risk intensity estimation for point into sustainable mapping in London with patterns on networks development data sensors misaligned data: a multiple M.I. Borrajo*1, C. Comas2, M. Hasan membership approach J. Mateu1 University of Flensburg, M. Gramatica*, P. Congdon, 1Lancaster University, UK, Germany S. Liverani 2Universitat de Lleida, Spain, Queen Mary University of 3Universitat Jaume I, Spain London, UK 09:45-10:00 [A1.3] Bayesian spatial [B1.3] Bayesian hierarchical [C1.3] Spatial index cancer prediction with nonparametric modeling of citizen science tissue protein biomarkers modeling of a spectral density data in biodiversity using I. Chervoneva*1, H. Rui2 Y.B. Jun*, C.Y. Lim INLA: A case study with 1Thomas Jefferson University, Seoul National University, citizen science data in USA, 2Medical College of Republic of Korea Norway Wisconsin, USA J. Sicacha Parada*, I. Steinsland, B. Cretois Norwegian University of Science and Technology, Norway 10:00-10:15 [A1.4] Estimating extremely [B1.4] Crowd sourcing [C1.4] A spatially discrete large amounts of missing space-time food price maps approximation to log- precipitation data in poor areas: A comparison gaussian Cox processes for H. Aguilera*1, C. Guardiola- with traditional price surveys modelling aggregated Albert1, C. Serrano-Hidalgo1,2 in Indonesia disease count data 1Geological Survey of Spain, P. Alivianur, S. Olivia, O.O. Johnson*, P.J. Diggle, Spain, 2Technical University of J. Gibson* E. Giorgi Madrid, Spain University of Waikato, New Lancaster University, UK Zealand 10:15-10:45 Refreshments | Room: Tramuntana 1 and Foyer Rooms Tramuntana 2+3 Garbi 1 Garbi 2 10:45-11:45 Session A2: Space/Time Session B2: Global Change Session C2: Health Statistics Session chair: TBC Session chair: Olatunji Session chair: Sandra de Iaco Olugoke Johnson 10:45-11:00 [A2.1] Co-regionalization of [B2.1] Spatio-temporal [C2.1] A geostatistical Markov Cube Kriging. Dependence Analysis and framework for combining Application to the modelling Comparison on Flow indices spatially referenced disease of the variation of air pollution related to Watershed prevalence data from and in the construction of risk Characteristics based on multiple diagnostics maps of crimes and offenses RCP Scenario dataset B. Amoah*, E. Giorgi, M. Saez*1,2, M.A. Barceló1,2, J. Lee*, S. Ha P.J. Diggle P. Juan3, A. Tobías4, J. Mateu3 Chungbuk National Lancaster University, UK 1University of Girona, Spain, University, Republic of Korea 2CIBER of Epidemiology and Public Health (CIBERESP), Spain, 3Universitat Jaume I, Spain, 4Spanish Council for Scientific Research (CSIC), Spain Spatial Statistics 2019: Towards Spatial Data Science | Oral Program 11:00-11:15 [A2.2] Spatiotemporal [B2.2] Determining gaps and [C2.2] Spatial-temporal reconstruction of forest redundancy of the climate distribution and influencing inventory data using remote monitoring station network factors of reproductive tract sensing observations of British Columbia, Canada infections among women in M.K.H. Khan*1, A. S. Deschenes*1, E. Weick2, Bangladesh：A Chakraborty1, G. Petris1, V. Foord1, A. Faron3 geostatistical analysis of the B.T. Wilson2 1Natural Resource demographic and health 1University of Arkansas, USA, Operations and Rural survey data 2Northern Research Station, Development, Canada, C. Feng*, R. Li, Y. Lai, Y. Hao USDA Forest Service, USA 2B.C. Ministry of Environment Sun Yat-sen University, China and Climate Change Strategy, Canada, 3University of Victoria, Canada 11:15-11:30 [A2.3] Geostatistical space- [B2.3] Monthly temperature [C2.3] Bayesian time prediction of air quality and precipitation maps and spatiotemporal modeling of pollutants in Israel derived climate monitoring male circumcision patterns J.A. Torres-Matallana1, products for HIV transmission U. Leopold*2, B. Fishbain3 M.P. Tadic*, I. Nimac prevention in sub-Saharan 1Wageningen University, The Meteorological and Africa Netherlands, 2Luxembourg Hydrological Service, M.A. Cork*, K. Wilson, Institute of Science and Croatia L. Dwyer-Lindgren, S. Hay Technology (LIST), University of Washington, USA Luxembourg, 3Israel Institute of Technology, Israel 11:30-11:45 [A2.4] Spatialfd: An R [B2.4] Variability of [C2.4] Bivariate spatial package for functional temperature trend patterns clustering of the differential kriging, functional cokriging in the Iberian Peninsula time trends of tropical and optimal spatial sampling M.P. Iglesias*, X. Pons, diseases: Application to of functional data M. Ninyerola, P. Serra diarrheal and intestinal A. Villamil*1, M. Bohorquez1, Universitat Autònoma de parasites morbidities in R. Giraldo1, J. Mateu2 Barcelona, Spain Ghana 1National University of F. Osei*, A. Stein Colombia, Colombia, University of Twente, The 2Universitat Jaume I, Spain Netherlands 11:45-12:45 Poster Session 1 | Room: Tramuntana 1 and Hall 12:45-13:45 Lunch | Room: Noray Restaurant Room Tramuntana 2+3 13:45-14:25 [PLN02] In a world of many processes - what's the point? Janine Illian, University of St Andrews, Scotland Session chair: Alfred Stein Rooms Tramuntana 2+3 Garbi 1 Garbi 2 14:30-15:30 Session A3: Space/time Session B3: Predictive Session C3: Hazards Statistics Modelling Session chair: Simon Nieland Session chair: Jose Angulo Session chair: Konstantin Krivoruchko 14:30-14:45 [A3.1] Modelling the space- [B3.1] Modelling the habitat [C3.1] A cellular automaton time dynamics of suitability and fisheries in model for pedestrians' interprovincial migration flows Mexico under climate movements influenced by in China: A spatial dynamic change scenarios gaseous hazardous material panel approach D. Petatán-Ramírez*1, spreading Y. Pu*1, X. Zhao1, G. Chi2 M.A. Ojeda-Ruiz1, J. Makmul 1Nanjing University, China, C. Salvadeo2, L. Sánchez- Kasetsart University, Thailand 2Pennsylvania State University, Velasco3, H. Reyes-Bonilla1, USA H.N. Morzaria-Luna4,5, G. Cruz-Piñón1 1Universidad Autónoma de Baja California Sur, Mexico, 2CONACYT- Universidad Autónoma de Baja Spatial Statistics 2019: Towards Spatial Data Science | Oral Program California Sur, Mexico, 3Instituto Politécnico Nacional-Centro Interdisciplinario
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