Aims & Scope

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.

Special Issue on Dynamic Spatial Statistics for Transport Modelling and Human Mobility
Submission Date: 2026-08-18

Spatial statistical models are helpful tools for showing geographic structures, patterns, and dynamics of transportation systems and urban mobility. Current big transport data requires spatial statistics, mathematical and AI models to help planners understand individual travel behavior, spatial accessibility, transport equity, transport infrastructure design, and policy impacts.

Guest editors:

Dr. Zehua Zhang
School of Design and the Built Environment, Curtin University, Bentley, Australia
zehua.zhang@curtin.edu.au

Dr. Frank Osei
ITC, University of Twente, Enschede, the Netherlands

Dr. Yongze Song
School of Design and the Built Environment,, Curtin University, Bentley, Australia

Dr. Elisa Fusco​
Department of Statistics, Computer Science, Applications "G. Parenti",, University of Florence, Florence, Italy

Manuscript submission information:

We are pleased to host a special issue, at Spatial Statistics, which is dedicated to the innovation and application of spatial statistical models for handling challenges from spatial big data in transport fields. Submitted manuscripts should include innovative spatial statistical models, indication of spatial structures, associations, or patterns through quantitative analysis, or empirical findings that contribute to transport planning. 

Topics of interest include, but are not limited to:

    Spatial dependence effects in transport geography and travel behavior studies
    Spatio-temporal analysis and space-time interaction models for urban mobility
    Spatial decision-making for evidence-based transport planning
    Spatial planning and statistical evaluation for active and public transport policy
    Spatial reasoning for traffic crash and travel behavior analysis
    Spatial accessibility analysis and spatial inequity assessment for transport system and infrastructure
    Spatial estimations and predictions of travel demand, traffic volumes, and individual travel behavior
    Spatial statistics and AI in agent-based model for transport systems
    Spatial statistical models with efficiency analysis for the performance evaluation of transport systems
    Spatial / mathematical / AI models for interpreting multi-dimensional transport and human behavior data
    Mathematical models for transport network, traffic flow and geographic accessibility
    Indicators for assessing transport efficiency, accessibility, and equality across spatial and temporal scales
    Spatial sampling and survey design in transport behavior research
    Innovative geospatial software, open-source programming packages, or GIS toolkits for transport planning / transport data analysis

Final Manuscript Submission Deadline: 23/08/2026
Editorial Acceptance Deadline: 18/11/2026

We invite researchers from the fields of statistics, data sciences, mathematics, machine learning, artificial intelligence, operation research, computer science, political science, geography, and related disciplines to submit original research papers that apply spatial statistical techniques to electoral studies. Manuscripts should present rigorous spatial analyses, innovative methodologies, or empirical findings that contribute to the understanding of electoral phenomena from a spatial perspective.

General information and instructions for submitting papers to SPASTA can be found at the journal website: https://www.editorialmanager.com/spasta. Please see the “Guide for Authors” and “Submit Your Paper” links. When submitting a paper to this special issue, please make sure to select the “VSI:SPASTA_Dynamic Spatial Transport” option when prompted for “Select Article Type” during the submission process.

Submissions must follow the journal’s formatting guidelines. All submissions will undergo a double-blind peer-review process.

Keywords:

Spatial data science, Spatial planning, Transport and traffic, Human behavior, Transport infrastructure and system