Spatial Statistics

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 without methodological development are not acceptable for publication.

Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties.

Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science.

Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.

Application fields include The physical domains, e.g. agriculture, geology, soil science, hydrology, ecology, mining, oceanography, forestry, air quality, remote sensing The social/economic domains, e.g. spatial econometrics, epidemiology and disease mapping.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.5
2024	2.10

SJR (SCImago Journal Rank)

Year	Value
2024	0.874

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	42

Journal Rank

Year	Value
2024	5953

Journal Citation Indicator

Year	Value
2024	670

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Earth and Planetary Sciences, Environmental Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Assuring the quality of volunteered geographic information

  Citation: 559

  Authors: Michael F., Linna

• A review of spatial sampling

  Citation: 274

  Authors: Jin-Feng, A., Bin-Bo, Yong

• Correcting for spatial heterogeneity in plant breeding experiments with P-splines

  Citation: 266

  Authors: María Xosé, Martin P., Fred A., Paul H.C.

• Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania

  Citation: 172

  Authors: Tim, Ephraim, Douglas R., Andreas, Thomas

• Evaluation of empirical Bayesian kriging

  Citation: 163

  Authors: Konstantin, Alexander

• Spatial econometric panel data model specification: A Bayesian approach

  Citation: 130

  Authors: James P.

• Determining the spatial effects of COVID-19 using the spatial panel data model

  Citation: 126

  Authors: Hasraddin

• Limitations on low rank approximations for covariance matrices of spatial data

  Citation: 125

  Authors: Michael L.

• Spatial statistics and Gaussian processes: A beautiful marriage

  Citation: 109

  Authors: Alan E., Erin M.

• Scaling intrinsic Gaussian Markov random field priors in spatial modelling

  Citation: 106

  Authors: Sigrunn Holbek, HÃ¥vard

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