Spatial Statistics 2027: At the core of analytical intelligence

During the Spatial Statistics 2027 conference in Beijing (China), specific attention is given to the important and unprecented bridge between spatial statistics and analytical intelligence. It embeds predictive modelling, artificial intelligence, real-time decision support systems and many more.

We live in an era often described as the age of data. Every day, billions of observations are recorded from satellites, environmental sensors, mobile devices, social networks, transportation systems, healthcare infrastructures, and digital platforms. Much information from these data is inherently spatial, while every decision ultimately affects places, communities, and environments. Spatial statistics provides the scientific foundation and means for our understanding and interpretation. It equips us with rigorous methods to model spatial dependence, quantify uncertainty, detect patterns, predict future scenarios, and distinguish genuine signals from random variation. In the end, it transforms geographic information into analytical intelligence—the capacity to support decisions that are both data-driven, context-aware and scientifically sound.

Analytical intelligence encompasses predictive modelling, machine learning, artificial intelligence, digital twins, and real-time decision support systems. These technologies reach their full potential when grounded in robust statistical principles. Spatial statistics bridges between sophisticated algorithms and reliable inference, ensuring that increasingly powerful computational methods remain interpretable, reproducible, and trustworthy. The integration of spatial statistics with artificial intelligence represents one of the most exciting frontiers in contemporary analytical intelligence. Machine learning excels at discovering complex patterns, while spatial statistical models provide principled approaches for incorporating spatial dependence, quantifying uncertainty, and producing interpretable results. Together, they enable a new generation of intelligent analytical systems capable of addressing challenges that are simultaneously scientific, societal, and strategic.

Deadlines

Abstract Submission Deadline: 29 January 2027

Author Notification Deadline: 25 March 2027

Author Registration Deadline: 23 April 2027

Early bird Registration Deadline: 23 April 2027

Publication opportunities

Supporting publication

Special Issue

More information coming soon.

Conference topics

Methods

Predictive modelling

• Space-time statistics: geostatistics, point patterns, estimation methods, large dimensions

• Causality in space and time

• Modeling and predicting of extremes

• Digital twins and spatial analytics

• Hierarchical spatial models

Artificial intelligence

• Hybrid AI and statistical models

• Spatial deep learning

• Spatial statistical learning

• Spatial machine learning

• Reinforcement learning in spatial systems

• Natural language processing for spatial challenges

• Neural networks in space

• Geospatial Artificial Intelligence (GeoAI)

Decision support systems

• Explainable AI for spatial decision-making

And more…

• Spatio/temporal modeling of points and objects

• Discrete spatial variation

• Spatial and spatio/temporal variability and dependence

• Stochastic geometry, tessellation, point processes, random sets

• Change-of-support problems

• Measurement error in spatial data

• Functional spatial data analysis

• Spatial compositional data analysis

• High-performance computing for large spatial datasets

Applications

Environment

• Environmental and climate applications

• Climate change modelling

• Environmental risk assessment

• Air pollution mapping

• Water resources management

• Forest monitoring

• Biodiversity and ecological modelling

• Wildfire prediction

• Coastal and marine spatial analysis

• Natural hazard assessment

• Disaster risk reduction and resilience

Health

• Spatial epidemiology

• Disease mapping

• Infectious disease surveillance

• Public health analytics

• Environmental health

• Pandemic modelling

Urban and Transport

• Smart cities

• Urban growth modelling

• Sustainable urban development

• Transportation and mobility analytics

• Traffic forecasting

And more…

• Land use and land cover modelling

• Housing market analysis

• Energy systems modelling

Organizers

Co-Organizers

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Previous Events

Spatial Statistics 2025: At the Dawn of AI 15-18 July 2025 | NH Leeuwenhoorst, Noordwijk, The Netherlands

Conference topics

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

Spatial Statistics 2023: Climate and the Environment 18 - 21 July 2023 | University of Colorado, Boulder, USA

Conference topics

Methods

• 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

Applications

• 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

Spatial Statistics 2019: Towards Spatial Data Science 10 – 13 July 2019 | Sitges, Spain

Conference Topics

Methods

• 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:

Applications

• 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

Spatial Statistics: One world, one health 4-7 July 2017 | Lancaster University, UK

Conference Topics

• 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

Spatial Statistics Avignon: Emerging Patterns 9-12 June 2015 | Avignon, France

Conference Topics

• 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)