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.

Publication opportunities

Supporting publication

Special Issue

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Conference topics

Topics Covered: Methods

Spatial & Space-Time Statistics

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

• Spatio/temporal modeling of points and objects

• Discrete spatial variation & change-of-support problems

• Spatial and spatio/temporal variability and dependence

• Stochastic geometry, tessellation, point processes, random sets

• Spatial non-stationarity, heterogeneity, and local modeling (e.g., GWR, MGWR)

• 3D/4D spatial statistics and dynamic voxel analytics

• Measurement error and spatial data quality

Spatial Sampling, Monitoring & Data Quality

• Spatial sampling design and survey optimization

• Long-term spatio-temporal monitoring and change detection

• Uncertainty quantification and error propagation in spatial data

• Multi-source spatial data fusion and downscaling

• Statistical methods for remote sensing inversion and data processing

Temporal, Spatial, and Spatio-Temporal Causal Inference

• Temporal, spatial, and spatio-temporal causal inference frameworks

• Spatial econometric causal evaluation & quasi-experimental designs

• Counterfactual spatio-temporal modeling

• Causal discovery in complex spatio-temporal systems

• Process-guided and physics-informed statistical inference

Predictive Modeling & Extremes

• Predictive modelling

• Modeling and predicting of extremes

• Digital twins and spatial analytics

• Hierarchical spatial models

Spatial Analytical Intelligence (GeoAI) & Machine Learning

• Geospatial Artificial Intelligence (GeoAI)

• Hybrid AI and statistical models

• Spatial deep learning & Spatial machine learning

• Spatial statistical learning

• Reinforcement learning in spatial systems

• Natural language processing for spatial challenges

• Neural networks in space

Decision Support & Explainable Systems

• Decision support systems

• Explainable AI for spatial decision-making (XAI)

• Spatial optimization and scenario simulation

Topics Covered: Applications

Environment, Hazards & Resilience

• Environmental risk assessment & eco-environmental toxicity

• Natural hazard assessment, disaster risk reduction, and resilience

• Climate extreme impacts and vulnerability modeling

Natural Resources, Energy & Carbon Dynamics

• Carbon source/sink spatial accounting and "Double Carbon" goals

• Mineral and geological resources estimation & 3D spatial modeling

• Renewable energy potential (wind, solar) & spatial site selection

• Natural resource accounting and ecosystem assets evaluation

Land Use, Agriculture & Cropland Protection

• Land-use and land-cover change (LUCC) modeling & simulation

• Cropland dynamic monitoring, quality evaluation, and yield estimation

• Food security and agricultural production spatial analytics

• Territorial space functional zoning and optimization

• Land degradation, desertification, and restoration analytics

Forest, Grassland & Ecological Systems

• Forest and grassland dynamics, biomass estimation, and canopy structure modeling

• Grassland degradation, overgrazing assessment, and pastoral ecosystem health

• Ecological restoration analytics (e.g., afforestation, returning farmland to forest/grassland)

• Integrated analytics of terrestrial-ecological systems (mountain-river-forest-farmland-lake-grass-sand)

• Ecological protection redlines and ecosystem service valuation

• Wildfire prediction, forest disease/pest monitoring, and ecosystem risk

Water Resources, Atmosphere & Marine Systems

• Eco-hydrology, watershed management, and water quality spatial modeling

• Atmosphere, air quality mapping, and climate change modelling

• Land-sea coordination and coastal zone spatial management

• Physical oceanography & marine spatio-temporal statistics

• Marine ecosystem health, fisheries, and polar ice sheet dynamics

Urban, Transport & Smart Cities

• Smart cities & Sustainable urban development

• Urban growth modelling & Urban heat island/microclimate

• Transportation and mobility analytics & Traffic forecasting

• Human mobility, spatio-temporal micro-behavior, and accessibility

Health & Environmental Epidemiology

• Spatial epidemiology & Disease mapping

• Infectious disease surveillance & Pandemic modelling

• Public health analytics & Environmental health

Spatial Economics, Rural & Social Dynamics

• Spatial econometrics and regional inequality

• Rural revitalization, regional interaction, and spatial transformation

• Demographic patterns, aging, and population mobility

Co-Organizers

• Commission on Geographical Modelling and Geographical Information Analysis, Geographical Society of China 中国地理学会 地理模型与地理信息分析专学会

• Commission on Cartography and Geographical Information System, Geographical Society of China 中国地理学会 地图学与地理信息系统专委会

• Key Laboratory of Remote Sensing of Agricultural Disasters, Ministry of Agriculture and Rural Affairs农业农村部农业灾害遥感重点实验室

Marketing Material

Spatial Statistics 2027 Conference A4 Flyer

Spatial Statistics 2027 Conference PPT Slides

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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)