Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies and algorithms without methodological development are not acceptable for publication.

Spatial Statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, heterogeneity, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information and computer science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of spatial structures, and drawing of valid inference and causality from a limited set of spatio-temporal data. Application fields include:

• The physical domains, e.g. environment, climate, agriculture, ecology, geosciences oceanography and remote sensing.
• The social/economic domains, e.g. epidemiology, population characteristics, and disease mapping.

Spatial Statistics encourages the submission of short communications and case studies in spatial statistics (i.e. manuscripts up to 3000 words presenting novel spatial statistical applications).

Spatial Statistics aims to publish reproducible science. Authors are encouraged to submit and publish data, procedures, models and methods that support your research publication. It provides facilities to interlink those with your published articles.

Spatial Statistics has an open attitude towards the latest developments in data science, deep learning and geoAI, as long as a substantial statistical component is present.