Keynote and plenary talks from renowned speakers
The program will include invited plenary lectures, contributed talks and poster sessions highlighting the latest latest research in Spatial Statistics.
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Spatial Statistics 2025: At the Dawn of AI
NH Leeuwenhoorst, Noordwijk, The Netherlands 15-18 July 2025
Welcome to the 7th Spatial Statistics conference, which will be held at NH Leeuwenhoorst, Noordwijk, The Netherlands, from 15-18 July 2025, under the theme At the Dawn of AI.
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During the Spatial Statistics 2025 conference in Noordwijk, the Netherlands, specific attention will be given to the opportunities, including challenges to be addressed, that Artificial Intelligence (AI) opens up and how spatial statistics can be developed further with AI.
The latest developments in spatial statistics will be presented, emphasising their contributions at the dawn of AI, now and in the future. The optimal use of collected data, predicting in space and time, object recognition and segmentation, and transferability in the presence of spatial and temporal correlations are typical, but not exhaustive examples.
Comments from delegates of the 6th Spatial Statistics Conference
“High quality of content and well thought out schedule and time for discussion through on-site lunches with poster sessions.”
“Great atmosphere, very collaborative and collegial.”
“I got the opportunity to meet and interact with some renowned experts in spatial statistics whom I have read and referenced during my Ph.D. study.”
Our society, atmosphere and environment are dynamic, and are continuously changing at many scales of time and space. A recent rapid development in society concerns the emergence of AI.
AI is supported by large numbers of available data, requiring statistical methods to handle these. Simultaneously, in the spatial domain, a large and important collection of spatial statistical methods and domain knowledge are being developed to help address the demands of society. The number and variety of data sources are increasing with the advent of more and more satellite and aircraft sensors, ground stations, surveys, mobile devices, and internet sources, recording human, climatic and environmental processes. These spatial and spatio-temporal data require to be transformed into meaningful information and both spatial statistics and AI are complementary in achieving this.
Artificial Intelligence has its roots in computer science. In the past, it has resulted in artificial neural networks including the multi-layer perceptron, while more recently, we recognize how deep learning has resulted in the development of convolutional neural networks and the transformer. Now, natural language processing has brought about a profound shift in thinking and opened great opportunities for research.
Spatial statistics, with its roots in probability theory and stochastic processes, has much to bring to the AI table. Moreover, the development of Statistical Learning in a spatial context has built a bridge towards spatial big data.
At the dawn of AI, therefore, spatial statistics should play a major role in the development and application of AI and, at the same time, AI can benefit the development of spatial statistics.
We are accepting oral and poster abstracts on the topics listed below. They should be submitted using the online abstract submission system opens in new tab/window.
Deadline: 24 January 2025
Methods
Spatial deep learning
Spatial statistical learning
Neural networks in space
Large language models in space
Natural language processing for spatial challenges
Spatio/temporal modeling of points and objects
Causality in space and time
Modeling and predicting of extremes
Space-time statistics: geostatistics, point patterns, estimation methods, large dimensions
Discrete spatial variation
Spatial and spatio/temporal variability and dependence
Stochastic geometry, tessellation, point processes, random sets
Applications
Environment: soil, water, atmosphere
Interface of neural computing and spatial/spatio-temporal statistics
Climate system modeling and observations
Health e.g. epidemiology, geohealth and global health
Spatially-explicit ecological models
Plant and animal epidemiology
Quantifying the spatial extent of hazards and risk
Crime, poverty, liveability mapping
Abstract submission deadline: 24 January 2025
Workshop proposal deadline: 24 January 2025
Author notification deadline: 21 March 2025
Author registration deadline: 2 May 2025
Early booking deadline: 2 May 2025
The program will include invited plenary lectures, contributed talks and poster sessions highlighting the latest latest research in Spatial Statistics.
Discover conference topics and learn about related events and how you can participate.
Choose from a variety of sponsorship and commercial options to raise your profile and position your company as a thought leader in the community.
A special conference issue of Spatial Statistics: At the Dawn of AI will be produced. Further information will be available soon.
Spatial Statistics: Climate and the Environment (2024) https://www.sciencedirect.com/special-issue/10Z1BLBW7ZD opens in new tab/window
Spatial Statistics: Towards Spatial Data Science (2021) https://www.sciencedirect.com/journal/spatial-statistics/vol/42/suppl/C opens in new tab/window
Spatial Statistics: One world, one health (2017) www.sciencedirect.com/journal/spatial-statistics/vol/28 opens in new tab/window
Spatial Statistics Avignon: Emerging Patterns (2015) www.sciencedirect.com/journal/spatial-statistics/vol/18/part/PA opens in new tab/window
Revealing Intricacies in Spatial and Spatio-Temporal Data: Papers from the Spatial Statistics 2013 Conference (2013) www.sciencedirect.com/journal/spatial-statistics/vol/9/suppl/C opens in new tab/window
Spatial Statistics for Mapping the Environment (2011) www.sciencedirect.com/journal/international-journal-of-applied-earth-observation-and-geoinformation/vol/22/suppl/C opens in new tab/window
The Spatial Statistics Society aims to create a community and network of scientists who are interested in the theory and application of spatial statistics in the widest sense, including all physical and social/economic domains. An ad-hoc committee consisting of the following members Alfred Stein, Edzer Pebesma, Kate Calder, Renato Assuncao, Benedikt Graler, and Denis Allard has been formed and has taken some preliminary first steps.
Do you want to be kept up to date on this new Spatial Statistics Society? If so, please visit our website opens in new tab/window to sign up to be a member
Space-time statistics, e.g. geostatistics, point patterns, estimation methods, large dimensions
Spatial deep learning
Inverse modeling
Modeling of extremes
Stochastic geometry, tesselation, point processes, random sets
Causal statistical modeling
Trajectory/movement modeling
Climate system modeling and observations
Spatially-Explicit Ecological Models
Health e.g. epidemiology, geohealth and global health
Air, Water and Soil spatio-temporal variability
Plant and animal epidemiology
Quantifying the spatial extent of hazards and risk
Crime and poverty mapping
Space/time econometrics
Interface of Neural Computing and Spatial/Spatio-Temporal Statistics
Inferring Movement and Behavior from Telemetry
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
Space-time statistics
Models for point processes
Lattice models
Geostatistics
Copulas in space and time
Spatial extremes
Change-point analysis
Estimation methods
Issues of scale: upscaling and downscaling methodology
Stochastic geometry, random sets and stereology
Causal statistical modeling
Image analysis (e.g. satellite sensor image time-series, DNA data, brain imaging)
Predictive modelling
Spatial data quality and uncertainty
New spatial data sources (e.g. social media, Google, citizen science, crowd sourced data)
Large dimensional big spatial data
With these methods being applied in a range of relevant domains. For the theme of the conference, we particularly invite contributions in:
Statistical aspects of epidemiology
Geo-Health and One Health
Plant and animal diseases
Health and Global change
Zoonotic and vector-borne diseases (e.g. emerging epidemics)
Hazards, disasters and risks (e.g. outbreaks, risk mapping)
Ecology (e.g. dispersion, migration, colonisation and invasion of species)
Spatial econometrics
Space-time statistics (e.g. point patterns models, estimation methods, large dimensions, scale issues)
Spatial data quality and uncertainty
Parameter estimation in PDEs
Stochastic geometry, tesselation, point processes, random sets
Spatial econometrics
New spatial data sources (e.g. big, data, social media, Google, citizen science, crowd source maps)
Image analyses (e.g. satellite images time series, DNA data, nano particles, nervous systems)
Predictive modelling
Tipping points (e.g. sea-level rise, socio-economic shifts)
Hazards, disasters and risks (e.g. tsunamis, earthquakes, landslides, air pollution levels)
Global change (e.g. stochastic weather generators)
Health, medical and epidemiology
Plant and animal epidemiology (emerging epidemics)
Ecology (e.g. dispersion, migration, colonisation and invasion of species)
Spatial Statistics 2013 Conference
Spatial Statistics for Mapping the Environment (2011)