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 de Ciencias Marinas, Mexico, 4Northwest Fisheries Science Center, USA, 5CEDO Intercultural, Mexico 14:45-15:00 [A3.2] A sandwich smoother [B3.2] Bivariate spatial [C3.2] Assessing the seismic for spatio-temporal functional dependence of apparent risk in Chile using a deep data electrical conductivity in learning approach J.P. French*1, P.S. Kokoszka2 agricultural soils and its O. Nicolis*1, F. Plaza1, 1University of Colorado, USA, relationship to univariate R. Salas1 2Colorado State University, measures of the moran 1Universidad Andres Bello, USA index Chile A. Darghan*, E. Grisales, J. Gutierrez, C. Rivera National University of Colombia, Colombia 15:00-15:15 [A3.3] Using complex [B3.3] Predicting global [C3.3] How likely is a correct covariance functions for human settlement extents in detection of a risk-region spatio-temporal vectorial the absence of urban based on area-level health data feature data` data? S. De Iaco*, C. Cappello, J.J. Nieves*1,4, C. Faes D. Posa, S. Maggio M. Bondarenko1,4, Hasselt University, Belgium University of Salento, Italy J.E. Steele1,4, C. Linard3,4, F.R. Stevens2,4, A.E. Gaughan2,4, A. Sorichetta1,4, D.J. Clarke1,4, A. Carioli1,4, D. Kerr1,4 et al 1University of Southampton, UK, 2University of Louisville, USA, 3Universite de Namur, Belgium, 4WorldPop, UK 15:15-15:30 [A3.4] Spatio-temporal [B3.4] Mapping the [C3.4] The performance of modelling of NO2 in urban air geogenic radon risk - how random forest and kriging in quality sensor networks does local characterization interpolating daily rainfall V.M. van Zoest*1, F.B. Osei1, uncertainty of radon B. Gräler*1, T. Hengl2, G. Hoek2, A. Stein1 concentration in soil gas M.P. Tadić3, A. de Wall1 1University of Twente, The and soil gas permeability 152°North Initiative for Netherlands, 2Utrecht affect the predictions? Geospatial Open Source University, The Netherlands E. Petermann*, P. Bossew Software GmbH, Germany, Federal Office for Radiation 2Envirometrix Ltd., The Protection, Germany Netherlands, 3Meteorological and Hydrological Service, Croatia 15:30-16:00 Refreshments | Room: Tramuntana 1 and Foyer Rooms Tramuntana 2+3 Garbi 1 Garbi 2 16:00-17:00 Session A4: Space/time Session B4: Predictive Session C4: Others Statistics Modelling Session chair: Sandra de Session chair: Marc Saez Session chair: Jeremiah J. Iaco Nieves 16:00-16:15 [A4.1] A multi-resolution [B4.1] Interpolating the [C4.1] Spatial statistics approximation via linear anomalies of satellite applied to map the impact projection for large spatial imagery for cloud-filling of drivers of change on datasets U. Pérez-Goya*1, ecosystem services supply in T. Hirano A.F. Militino1, M.D. Ugarte1, Lithuania Kanto Gakuin University, M. Genton2 P. Pereira*1, V. Acuna2, Japan 1Department of Statistics K. Miksa1, M. Kalinauskas1,

Spatial Statistics 2019: Towards Spatial Data Science | Oral Program and Operations Research, I. Misiune1, M. Inacio3 Public University of Navarre, 1Mykolas Romeris University, Spain, 2Computer, Electrical Lithuania, 2Catalan Institute and Mathematical Sciences for Water Research (ICRA), and Engineering (CEMSE), Spain, 3Leibniz-Institute for Saudi Arabia Baltic Sea Research, Germany 16:15-16:30 [A4.2] Data driven control of [B4.2] A knowledge based [C4.2] A divide and conquer Markov random fields with spatial model for utilising approach for large spatial unobserved connectivity point and nested areal dataset structure observations: A case study M. Moinuddin*1, C. Gaetan2 M. Garrod*, N.S. Jones of annual runoff predictions 1University of Padova, Italy, Imperial College London, UK in the Voss area 2Ca Foscari University of T. Roksvåg1, I. Steinsland*1, Venice, Italy K. Engeland2 1NTNU, Norway, 2NVE, Norway 16:30-16:45 [A4.3] Analysing conditional [B4.3] Accounting for [C4.3] Checking some independence in general conditional bias in digital soil characteristics of cross- multivariate spatial and mapping with proximal soil covariance functions for spatio-temporal data based sensing data` spatio-temporal data on partial marked point G.B.M. Heuvelink*1,2, S. De Iaco, M. Palma*, process characteristics L. Poggio2, D. Posa, C. Claudia M. Eckardt A.M.J.C. Wadoux1 University of Salento, Italy Humboldt-Universität zu Berlin, 1Wageningen University, The Germany Netherlands, 2ISRIC - World Soil Information, The Netherlands 16:45-17:00 [A4.4] Design and analysis of [B4.4] Spatial event hotspot [C4.4] Hard constrained a complex reaction system prediction using multivariate space-filling designs using a spatial-temporal Hawkes features E. Benková1, R. Harman2,1, variogram algorithm M.D. Porter W.G. Müller*1 C.S. Kim*1, C.H. You1, University of Virginia, USA 1Johannes Kepler University S.H. Moon2, H.J. Yoon3, Linz, Austria, 2Comenius H. Yang2, J.Y. Kim1, J.Y. Lee1, University Bratislava, Slovakia M.S. Park1 1Incheon National University, Republic of Korea, 2UTEC Co, Ltd., Republic of Korea, 3Yonsei University, Republic of Korea Room Tramuntana 2+3 17:05-17:45 [PLN03] Spatial data analysis: Information, complexity and risk Jose Miguel Angulo, University of Granada, Spain Session Chair: Jorge Mateu 17:45-18:45 Special session: The Spatial Statistical Society Friday, 12 July 2019 08:00-17:45 Registration | Room: Hall Tramuntana Room Tramuntana 2+3 08:30-09:10 [PLN04] Nonstationary spatial prediction of soil organic carbon Catherine Calder*1, M. Risser2, V. Berrocal3, C. Berrett4, 1The Ohio State University, USA, 2Lawrence Berkeley National Laboratory, USA, 3University of Michigan, USA, 4Brigham Young University, USA Session Chair: Alfred Stein

Spatial Statistics 2019: Towards Spatial Data Science | Oral Program Rooms Tramuntana 2+3 Garbi 1 Garbi 2 09:15-10:15 Session A5: Space/Time Session B5: Stochastic Session C5: Ecology Statistics Geometry Session chair: Catherine Session chair: Jorge Mateu Session chair: Inger Fabris- Calder Rotelli 09:15-09:30 [A5.1] A simulated annealing [B5.1] Modelling the [C5.1] On the relationship algorithm using the ABC evolution and stabilization of between normalized Shadow dynamics for wildfires using random difference vegetation index statistical inference of point spread process and land surface processes C. Díaz-Avalos*1, P. Juan2, temperature: MODIS-based R.S. Stoica*1, M. Deaconu2, L. Serra-Saurina3, P. Aragó analysis in a semi-arid to L. Hurtado-Gil3 Galindo1 arid environment 1Université de Lorraine, 1University Jaume I (UJI), S. Jaber et al France, 2INRIA, France, Spain, 2UNAM, Mexico, Hashemite University, Jordan 3Universidad CEU San Pablo, 3Pompeu Fabra, Spain Spain 09:30-09:45 [A5.2] Spatial modeling of [B5.2] Non-parametric [C5.2] Spectrum-based significant wave height using testing of independence monitoring model of leaf stochastic partial differential between marks and nitrogen content for equations covariates in the marked rapeseed A. Hildeman*, D. Bolin, point process setting H.X. Cao*1, W.T. Chen1,2, I. Rychlik J. Dvorák*1, T. Mrkvika2, B.J. Zhang1,2, W.Y. Zhang1, Chalmers University of J. Mateu3, J. Gonzáles3 W.X. Zhang1, D.K. Ge1, Technology, Sweden 1Charles University, Czech C.W. Song1, S.J. Ge1, Republic, 2University of South Q. Zhang1, J.A. Xia1 et al Bohemia, Czech Republic, 1Jiangsu Academy of 3Universitat Jaume I, Spain Agricultural Sciences/Engineering Research Center for Digital Agriculture, China, 2Northwest Agriculture and Forestry University, China 09:45-10:00 [A5.3] Max-infinitely divisible [B5.3] Testing goodness of fit [C5.3] Applying probability models and inference for for point processes via forests to spatiotemporally spatial extremes topological data analysis structured data in the R. Huser*1, T. Opitz2, C.A.N. Biscio1, investigation of Red Wood E. Thibaud3 N. Chenavier1,2, C. Hirsch*1, Ants' habitat associations 1King Abdullah University of A.M. Svane1 B.R. Fitzpatrick*1, Science and Technology, 1Aalborg University, A. Baltensweiler1, Saudi Arabia, 2INRA, France, Denmark, 2Université du C. Düggelin1, M. Fraefel1, 3Ecole Polytechnique Littoral Côte d'Opale, A. Freitag2, Fédérale de Lausanne, France M.L. Vandegehuchte3, Switzerland B. Wermelinger1, A.C. Risch1 1The Swiss National Forest Inventory, Switzerland, 2Cantonal Museum of Zoology, Switzerland, 3Ghent University, Belgium 10:00-10:15 [A5.4] A diagonally weighted [B5.4] Predicting the intensity [C5.4] Use of statis multi-way matrix norm between two function of point processes technique to assess the covariance matrices inside unobserved windows influence of water quality C. Hardouin*1, N. Cressie1 F.J. Rodríguez-Cortés*1, and land use on population 1University Paris Nanterre, E. Gabriel2, J. Coville3, characteristics of mangrove France J. Mateu4, J. Chadœuf1 clam Crassostrea 1Universidad Nacional de columbiensis Colombia, Colombia, I. Nolivos*1, M. Gonzalez1, 2Avignon University, France, A. Rosado1, J. Ramos1,2, 3Biostatistic and Spatial R. Parra1, O. Ruiz1, M. Pozo2, Processes Unit – INRA, L. Dominguez-Granda1 France, 4University Jaume I, 1Escuela Superior Politécnica Spain del Litoral (ESPOL), Ecuador,

Spatial Statistics 2019: Towards Spatial Data Science | Oral Program 2Universidad de Guayaquil, Ecuador 10:15-10:45 Refreshments | Room: Tramuntana 1 and Foyer Rooms Tramuntana 2+3 Garbi 1 Garbi 2 10:45-11:45 Session A6: Space/time Session B6: Image Analysis Session C6: Ecology Statistics Session chair: Peter Atkinson Session chair: Benjamin Session chair: Victor de Fitzpatrick Oliveira 10:45-11:00 [A6.1] Spatially varying [B6.1] Bathymetry and [C6.1] Mapping biodiversity coefficients models for large seabed classification in using opportunistic samples: data using maximum three marine protected should we trust our likelihood estimation areas using remote sensing inferences? J.A. Dambon*1,2, F. Sigrist2, D. Petatán-Ramírez*1, T. Neyens*1, P.J. Diggle2, R. Furrer1 H. Reyes-Bonilla1, C. Faes1, N. Beenaerts1, 1University of Zurich, M.A. Ojeda-Ruiz1, T. Artois1, E. Giorgi2 Switzerland, 2Lucerne L.G. Hernández-Moreno1, 1Hasselt University, Belgium, University of Applied Sciences D. Olivier2, 1Universidad 2Lancaster University, UK and Arts, Switzerland Autónoma de Baja California Sur, Mexico, 2CONACYT- Universidad Autónoma de Baja California Sur, Mexico 11:00-11:15 [A6.2] A precompression [B6.2] Spatial downscaling of [C6.2] Geostatistical approach for fast spatial soil moisture product from simulation of space-time mixed effects modeling microwave data assimilation stochastic rainfall fields for D. Murakami*1, D.A. Griffith1 with machine learning- uncertainty propagation in 1Institute of Statistical based geostatistical method rainfall-runoff and urban Mathematics, Japan considering land cover drainage system modelling information J.A. Torres-Matallana*1, Y. Jin*1, Y. Ge2, Y.J. Liu3 U. Leopold2 1Nanjing University of Posts 1Wageningen University, The and Telecommunications, Netherlands, 2Luxembourg China, 2Chinese Academy Institute of Science and of Sciences, China, 3Nanjing Technology (LIST), University, China Luxembourg 11:15-11:30 [A6.3] Spatial modeling of [B6.3] Mechanisms to model [C6.3] Bayesian estimation of repeated events with an the stochastic anisotropy of modified ETAS model for application to disease DPT point patterns in a big invasive species mapping data context E. Balderama*1, B. Deng2 S. Balamchi*, M. Torabi I. Fabris-Rotelli*1, A. Stein1 1California State University, University of Manitoba, 1University Of Pretoria, South USA, 2Loyola University Canada Africa, 2University of Twente, Chicago, USA The Netherlands 11:30-11:45 [A6.4] ELSA: A new entropy- [C6.4] National-scale based statistic to measure modelling of connectivity local and global spatial and stressor interactions on structure aquatic biodiversity B. Naimi1, N.A.S. Hamm*2, C. Wilkie*, C. Miller, M. Scott, T. Groen3, A.K. Skidmore3,4, J.B. Osuna A.G. Toxopeus3, S. Alibakhshi5 University of Glasgow, UK 1University of Amsterdam, The Netherlands, 2University of Nottingham, China, 3University of Twente, The Netherlands, 4Macquarie University, Australia, 5Aalto University, Finland 11:45-12:45 Poster Session 2 | Room: Tramuntana 1 and Hall 12:45-13:45 Lunch | Room: Noray Restaurant

Spatial Statistics 2019: Towards Spatial Data Science | Oral Program 13:45-14:25 [PLN05] Data integration and change of support for spatial data science Peter M. Atkinson1, 2, 1Lancaster University, UK, 2Queen's University Belfast, UK Session Chair: Jorge Mateu Rooms Tramuntana 2+3 Garbi 1 Garbi 2 14:30-15:30 Session A7: Space/Time Session B7: Traffic and Session C7: Health Statistics Trajectories Session chair: Thomas Opitz Session chair: Edzer Pebesma Session chair: Michael Cork 14:30-14:45 [A7.1] Business re-opening [B7.1] Mapping road traffic [C7.1] Identifying after Katrina: Survival crash hotspots using GIS- opportunities to improve the modelling compared to based methods: A case effectiveness in rabies modelling re-opening within 3, study of Muscat control in Thailand using 6 or 12 months with spatial Governorate in the Sultanate geographical analysis extensions of Oman K. Kanankege*1, R. Bivand*1, V. Gómez-Rubio2 A. Al Aamri*1, S. Padmadas2, A. Wiratsudakul2, 1Norwegian School of L. Zhang2, A. AL Maniri3 O. Prasarnphanich1, Economics, Norway, 1Ministry of Higher P. Wongnak2, 2University of Castilla-La Education, Oman, 2University C. Yoopatthanawong2, Mancha, Spain of Southampton, UK, 3Oman J. Alvarez3, K. Myhre- Medical Specialty Board, Errecaborde1, A. Perez1 Oman 1University of Minnesota, USA, 2Mahidol University, Thailand, 3Centro de Vigilancia Sanitaria Veterinaria (VISAVET), Spain 14:45-15:00 [A7.2] Intensity estimation of [B7.2] Application of a [C7.2] Comparing two non-homogeneous Poisson random forest model models for disease mapping processes with transportation approach to predict the data not varying of probability measures choice of using the bicycle systematically in space T.L.J. Ng*, A. Zammit-Mangion as mode of transport H. Baptista*1, J.M. Mendes1, University of Wollongong, S. Nieland*, M. Hardinghaus P. Congdon2 Australia German Aerospace Center 1Universidade Nova de (DLR) - Institute of Transport Lisboa, Portugal, 2University Research, Germany of London, UK 15:00-15:15 [A7.3] Use of microsensor data [B7.3] Modelling air-mass [C7.3] A statistical modelling for urban-scale air quality movements via contact framework for mapping the modelling and mapping networks: An application to seasonality in malaria A. Gressent*, L. Malherbe, the dissemination of air- M. Nguyen*1, D.J. Weiss1, A. Colette borne pathogens and R.E. Howes1 et al INERIS, France perspectives for epidemio- 1University of Oxford, UK, surveillance 2National Malaria Control M. Choufany*, Programme, Madagascar S. Soubeyrand, D. Martinetti, C. Morris INRA, France 15:15-15:30 [A7.4] Minimum temperature [B7.4] Photonic impulse [C7.4] Discrete and mapping with spatial copula transfer measurements using continuous domain models interpolation predictive real-time image for disease mapping and P. Bostan*1, A. Stein2, processing algorithms applications on childhood F. Alidoost1, F. Osei1 M. Barbuta1, A. Marcu*2, cancers 1Van Yuzuncu Yil University, R. Ungureanu2, F. Stokker2, G. Konstantinoudis*1, Turkey, 2Twente University, The F. Dumitrache2, C. Fleaca2, D. Schuhmacher3, H. Rue2, Netherlands A. Achim2, M. Serbanescu2 B. Spycher1 1“Politehnica” University, 1Institute of Social and Romania, 2National Institute Preventive Medicine, for Laser, Plasma and Switzerland, 2King Abdullah Radiation Physics (INFLPR), University of Science and Romania Technology, Saudi Arabia, 3Institute for Mathematical Stochastics, Germany 15:30-16:00 Refreshments | Room: Tramuntana 1 and Foyer

Spatial Statistics 2019: Towards Spatial Data Science | Oral Program Rooms Tramuntana 2+3 Garbi 1 Garbi 2 16:00-17:00 Session A8: Space/Time Session B8: Others Session C8: Spatial Statistics Econometrics Session chair: Jakob Dambon Session chair: Gerard Session chair: Roger Bivand Heuvelink 16:00-16:15 [A8.1] A non-stationary non- [B8.1] The distribution of [C8.1] Spatio-temporal gaussian hedonic spatial facility births at sub-national modeling of second home model for house selling prices levels: An application of dynamics in Corsica V. De Oliveira*1, M. Ecker2 small area estimation to Y. Ling*, P. Dominique, 1The University of Texas at San measure spatial inequities in D. Claudio Antonio, USA, 2University of Cambodia Université de Corse-Pascal- Northern Iowa, USA K. Nilsen*, A. Luna, Paoli, France A. Channon, N. Tzavidis University of Southampton, UK 16:15-16:30 [A8.2] Revisiting the random [B8.2] On the impact of [C8.2] Irrigation access in shift approach for testing in residential history in the northern Peru: an application spatial statistics spatial analysis of diseases of global and local T. Mrkvicka*1, J. Dvorak2, with a long latency period: A geographic models J. Gonzales3, J. Mateu3 study of mesothelioma in E. Zegarra 1University of South Bohemia, Belgium Group for the Analysis of Czech Republic, 2Charles O. Petrof*1, T. Neyens1, Development, Peru University, Czech Republic, V. Nuyts2, K. Nackaerts3, 3University of Jaume I, Spain B. Nemery2, C. Faes1 1Hasselt University, Belgium, 2University of Leuven, Belgium, 3University Hospital Leuven, KU Leuven, Belgium

16:30-16:45 [A8.3] Non-Gaussian [B8.3] Bayesian spatio- [C8.3] A Bayesian spatial simulation by including temporal model for data clustering method for the multiple types of information integration with application analysis of the Brazilian at non-colocated locations to multiple air pollutants energy distribution service B. Xiao*1, C. Haslauer1, C. Forlani*1, M. Cameletti2, operators’ cost efficiencies G. Bohling2, A. Bárdossy3 E. Krainski3, S. Bhatt1, M.A. Costa, L.B. Mineti*, 1University of Tübingen, M. Blangiardo1 V.D. Mayrink, A.M. Lopes Germany, 2Kansas Geological 1Imperial College London, Universidade Federal de Survey, USA, 3University of UK, 2Università degli Studi di Minas Gerais, Brazil Stuttgart, Germany Bergamo, Italy, 3Universidade Federal do Paranà, Brazil 16:45-17:00 [A8.4] Dimple effect for [B8.4] A weighted [C8.4] Spatial-temporal space-time covariance multivariate spatial analysis of poverty:A case functions coming from clustering model to study in Yunyang county of transport effect models determine agricultural China A. Alegría*1, E. Porcu2 management zones M.X. Liu*, Y. Ge, Z.P. Ren, 1Universidad Técnica Federico N. Ohana-Levi*1, I. Bahat1,2, S. Hu Santa María, Chile, A. Peeters3, A. Shtein4, Institute of Geographic 2Newcastle University, UK Y. Cohen1, Y. Netzer5,6, Sciences and Natural A. Ben-Gal1 Resources Research, China 1Agricultural Research Organization, Israel, 2The Hebrew University of Jerusalem, Israel, 3TerraVision Lab, Israel, 4Ben Gurion University of The Negev, Israel, 5Eastern R&D Center, Israel, 6Ariel University, Israel

Spatial Statistics 2019: Towards Spatial Data Science | Oral Program Room Tramuntana 2&3 17:05-17:45 [PLN06] Towards spatial data science Eder Pebesma, University of Münster, Germany Session Chair: Jorge Mateu 18:30-22:00 Conference Gala dinner Location: Can Laury Saturday, 13 July 2019 08:00-17:45 Registration | Room: Hall Tramuntana Room Tramuntana 2&3 08:30-09:10 [PLN07] Can one geostatistical model practice many arts with success? K. Krivoruchko, USA Session Chair: Alfred Stein 09:15-10:15 Session A9: Space/time Statistics Session chair: Denis Allard 09:15-09:30 [A9.1] Novel statistical machine learning methods for mapping health and development metrics using big data C.E. Utazi, University of Southampton, UK 09:30-09:45 [A9.2] Bayesian spatiotemporal modelling of automated vehicle location data R. Ingebrigtsen, Institute of Transport Economics, Norway 09:45-10:00 [A9.3] Likelihood approximation and prediction for large spatial datasets using hierarchical matrices` A. Gorshechnikova*1, C. Gaetan2, 1University of Padova, Italy, 2Ca’ Foscari University of Venice, Italy 10:00-10:15 [A9.4] Fast and robust spatial statistics with transformed Gaussian scale mixtures G. Mazo, T. Opitz*, INRA, France 10:15-10:45 Refreshments | Room: Tramuntana 1 and Foyer Rooms Tramuntana 2&3 10:45-11:45 Session A10: Space/Time Statistics Session chair: Adrian Baddely 10:45-11:00 [A10.1] Comparison of different software implementations for spatial disease mapping M. Vranckx*1,2, T. Neyens1,2, C. Faes1,2 1University of Hasselt, Belgium, 2The Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Belgium 11:00-11:15 [A10.2] Resampling-based simulation with extremes D. Allard*1, T. Opitz1, G. Mariethoz2 1INRA, France, 2UNIL, Switzerland 11:15-11:30 [A10.3] Nonparametric estimation of circular spatial trends A. Meilán-Vila*1, M. Francisco-Fernández1, R.M. Crujeiras2, A. Panzera3 1Universidade da Coruña, Spain, 2Universidade de Santiago de Compostela, Spain, 3Università degli Studi di Firenze, Spain 11:30-11:45 [A10.4] Regularization techniques for inhomogeneous gibbs point process models with a diverging number of covariates I. Ba*, J-F. Coeurjolly UQÀM, Canada 11:45-12:25 [PLN08] Global envelope tests for spatial processes and beyond Mari Myllymäki, Natural Resources Institute Finland (Luke), Finland Session Chair: Alfred Stein 12:25-13:25 Lunch | Room: Noray Restaurant Rooms Tramuntana 2&3 13:30-14:45 Session A11: Crime and Spatial Data Quality Session chair: Jorge Mateu 13:30-13:45 [A11.1] Hurricanes and property crime: a spatio-temporal analysis of crime rates after Hurricane Maria in Puerto Rico L. Hernández-Fradera, I. da Luz* University of Puerto Rico-Medical Sciences Campus, Puerto Rico

Spatial Statistics 2019: Towards Spatial Data Science | Oral Program 13:45-14:00 [A11.2] Statistical measures for space-time network-based point patterns M. Moradi*, J. Mateu University of Jaume I, Spain 14:00-14:15 [A11.3] Investigating the spatially varied effects of environmental and socioeconomical factors on village level poverty, Yongxin, China Z.P. Ren*, Y.W. Luo, Y. Ge Chinese Academy of Sciences, China 14:15-14:30 [A11.4] Log-Gaussian Cox process models versus Gaussian process models for modelling multivariate spatial and spatio-temporal point patterns S. Ishida, London School of Economics and Political Science, UK 14:30-14:45 [A11.5] Imputed spatial data: Response and covariate measurement error implications D. Griffith*, Y-T. Liau University. of Texas at Dallas, USA 14:45-15:25 [PLN09] Geostatistical modelling of spatially structured zero-inflation with applications to tropical disease mapping Emanuele Giorgi, Lancaster University, UK Chair: Jorge Mateu 15:25-15:40 Closing Ceremony | Room: Tramuntana 2&3 End of conference

Spatial Statistics 2019: Towards Spatial Data Science | Oral Program