Spatial Statistics SCIEScopus

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 includeThe physical domains, e.g. agriculture, geology, soil science, hydrology, ecology, mining, oceanography, forestry, air quality, remote sensingThe social/economic domains, e.g. spatial econometrics, epidemiology and disease mapping.Spatial Statistics aims to publish reproducible science. Authors are encouraged to submit and publish procedures and data, along with the manuscript.

空间统计发表关于空间和时空统计的理论和应用的文章。它倾向于那些由新应用产生的理论或将新理论应用于重要实际案例的手稿。纯理论研究很少被接受。没有方法发展的纯案例研究是不能发表的。空间统计是对空间和时空数据进行定量分析，包括其统计相关性、准确性和不确定性。空间统计方法在概率论、随机建模、数理统计以及信息科学中都有广泛的应用。空间统计用于映射、评估空间数据质量、抽样设计优化、依赖结构建模以及从有限的时空数据集绘制有效推理。应用领域包括物理领域，如农业、地质学、土壤科学、水文学、生态学、采矿、海洋学、林业、空气质量、遥感社会/经济领域，例如空间计量经济学、流行病学和疾病地图。空间统计的目的是发表可重复的科学。作者被鼓励提交和出版程序和数据，连同手稿。

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