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英国
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Spatial Statistics 中科院2区 JCR:Q1 JCR:Q3 JCR:Q2 SCIE PubMed JST
发文量 851
被引量 13,103
影响因子(2024) 2.270

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. New! The journal 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 procedures and data, along with the manuscript. The journal highly encourages you to share the data, software code, models and methods that support your research publication and it provides facilities to interlink those with your published articles.

  • 主办单位： ELSEVIER SCI LTD
  • 出版地区： LONDON
  • 出版周期： 季刊
  • 别名： SPAT STAT-NETH;Spat. Stat.;空间统计;SPATIAL STATISTICS
  • 创刊时间： 2012年
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Spatial StatisticsSpatial DependenceSpatial DataSpatial AutoregressivePoint ProcessSpatial Point ProcessesPoint ProcessesCovariance FunctionGeographically Weighted RegressionMarkov Chain Monte CarloSpatial AutocorrelationSpatial ProcessPoint Process ModelsSpatial PredictionGaussian Markov Random FieldBayesian InferenceMarkov Random FieldsGaussian Random FieldDisease MappingSpatial Point Process

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