Spatial Statistics 2019: Towards Spatial Data Science
10 – 13 July 2019 | Sitges, Spain
Welcome to the 5th Spatial Statistics conference, which will be held in Sitges (near to Barcelona), Spain, from 10 – 13 July 2019 under the theme Towards Spatial Data Science.
The conference will bring together leading scientists in the field of spatial statistics to debate and discuss how to make statistically sound decisions and judgments in the domain of spatial data science.
Issues of statistical learning, but also common methods for spatial and spatio-temporal interpolation, stochastic geometry, classifications, tessellation, networks, point processes, random sets, including Bayesian modelling will be presented in the new context of spatial data science.
The Workshop and Welcome Reception will be held on 10 July and the actual conference will start on 11 July 2019.
Professor Jorge Mateu
Universitat Jaume I, Castellon, Spain
Jorge Mateu graduated in Mathematical Sciences at University of Valencia (Spain) and fulfilled the PhD at the University of Valencia with long visiting periods to University of Lancaster (UK), with Prof. Peter Diggle.
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He is currently a full professor of Statistics at the Department of Mathematics of University Jaume I of Castellon, where he has worked for the past 20 years. His main fields of interest are stochastic processes in their wide sense with a particular focus on spatial and spatio-temporal point processes and geostatistics. He has published more than 200 papers in peer-reviewed international journals, and he is co-author of several proceedings and research books. He has organised several international conferences with a focus on modelling space-time processes, and leads the organising committee of a series of biannual conferences (called METMA, ten by now) co-sponsorised by TIES, for which he was also Secretary.
He currently sits on the editorial boards of Spatial Statistics, Journal of the Royal Statistical Society (series C), Journal of Environmental Statistics, Stochastic Environmental Research and Risk Assessment, Environmetrics, and Journal of Agricultural, Biological, and Environmental Statistics.
Prof. Mateu is also director of the Unit "Statistical Modelling of Crime Data", based in the Department of Mathematics, University Jaume I of Castellon, and he is co-director of the Erasmus Mundus Master in Geospatial Technologies, funded by the European Commission. Prof. Mateu has held several large grants, and has supervised over 20 PhD students. The research experience covers topics such as latent variables, spatial and spatio-temporal modelling, and also networks and trajectory-based data.
Professor Alfred Stein
University of Twente, The Netherlands
More information coming soon
We are at the start of a large and unprecedented expansion of our information provision and use. Both the volume and the complexity of available data will increase, creating a great potential and demand for scientific and technological innovations.
In addition to “traditional” data, unverified spatial data is now available from volunteers, dynamic data from moving sources, streams of remote sensing images and distributed data from various types of sensors, including gadgets monitoring the function of human bodies, high-tech engine monitors and low-cost, distributed environmental sensors.
Of particular scientific interest and societal importance are spatially referenced data with temporal dependence, i.e. space-time data. The increasing volume, complexity, heterogeneity, uncertainty and interdependence of space-time data are rapidly outrunning current methods of analysis.
This big-data era is creating research challenges for many fields of science and engineering. Emerging questions have a different focus, depending on the scientific discipline in which they are born and reflecting a significant variability in the nature of big-data problems, in spite of the many common elements.
Crucial developments in this context are new scalable methods, design algorithms, and implement software for big space-time data that will:
- Capitalize on local models and sparse matrix structures
- Allow for fast simulation of space-time processes
- Include new data types (including streams of spatial data, space-time point processes, point patterns on networks, and trajectories)
This results in the domain of spatial data science. The conference will provide a platform to debate and discuss how to make statistically sound decisions and judgments in the domain of spatial data science
We are still accepting poster submissions on the topics below. They should be submitted using the online abstract submission system.
- Space-time statistics, e.g. geostatistics, point patterns, estimation methods, large dimensions
- New spatial data sources, e.g. social media, Google, citizen science, crowd source maps
- Stochastic geometry, tesselation, point processes, random sets
- Causal statistical modeling
- Trajectory/movement modeling
- Predictive modelling
- Spatial data quality and uncertainty
With these methods being applied in a range of relevant domains. For the theme of the conference, we particularly invite contributions in:
- Image analyses, e.g. satellite images
- Traffic and transport
- Global change
- Ecology, e.g. dispersion, migration, colonisation and invasion of species
- Plant and animal epidemiology, e.g. emerging epidemics
- Hazards, disasters and risks, e.g. outbreaks, risk mapping
- Crime and poverty mapping
- Health e.g. epidemiology, geohealth and global health
- Spatial econometrics