Advances in Spatial Statistics and Spatial Data Science: Theory, Methods and Applications

Dear Colleagues,

Spatial data are becoming increasingly important across science, engineering, environmental research, social sciences, and many other disciplines. The rapid growth of Earth observation systems, sensor networks, geospatial technologies, and large-scale spatial databases has created unprecedented opportunities for extracting knowledge from spatially structured information. However, this progress also raises fundamental scientific challenges related to spatial dependence, uncertainty quantification, explainability, and the integration of modern artificial intelligence with statistical methodologies.

This Special Issue focuses on recent advances in spatial statistics and spatial data science, with particular emphasis on the development of statistically sound, interpretable, and uncertainty-aware methods for analyzing complex spatial data. The scope includes geostatistics, spatial and spatio-temporal point processes, statistical learning, uncertainty quantification, and modern artificial intelligence methods. These approaches provide complementary frameworks for modeling spatial dependence, event occurrence, and complex spatial phenomena across multiple application domains. Our aim is to provide a forum where modern statistical modeling and artificial intelligence complement each other to support the next generation of interpretable, uncertainty-aware, and reliable spatial analytics.

A special point of interest is the role of these methods in computational social science. These approaches can help us better understand cities, mobility, demographic processes, economic activity, public health, education, social networks, and public policy. For this reason, we particularly encourage papers that combine statistical rigor with real-world relevance in social, urban, economic, and institutional contexts.

We also welcome research that supports human-centered and evidence-based decision-making through interpretable, transparent, uncertainty-aware, and robust spatial methods. Applications in science, business, healthcare, environmental management, urban planning, remote sensing, planetary science, astronomy, cosmology, and related fields are particularly encouraged.

We welcome theoretical, methodological, and applied research contributions on topics such as the following:

• Spatial statistics and spatial dependence modeling;
• Spatial and spatio-temporal models;
• Spatial and spatio-temporal point processes and point pattern analysis;
• Statistical inference and forecasting for spatio-temporal data;
• Geostatistics and spatial interpolation;
• Bayesian spatial statistics and uncertainty quantification;
• Spatial data science and statistical learning;
• Spatial clustering and pattern analysis;
• Spatial machine learning, GeoAI, and explainable artificial intelligence;
• Spatial big data and multi-source data integration;
• Computational social science and spatial analytics;
• Human-centered and evidence-based decision support;
• Applications in social sciences, business, environmental sciences, engineering, remote sensing, planetary science, astronomy, and cosmology.

Dr. Gabriel Marín Díaz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• spatial statistics
• spatial data science
• geostatistics
• spatio-temporal modeling
• spatial and spatio-temporal point processes
• point pattern analysis
• statistical inference and forecasting
• spatial machine learning
• GeoAI
• explainable artificial intelligence
• Bayesian spatial models
• uncertainty quantification
• computational social science
• spatial big data