Spatial Statistics
ISSN:2211-6753

Spatial Statistics

SPAT STAT-NETH
学科领域:数学
是否预警:不在预警名单内
是否OA:
录用周期:-
新锐分区:数学3区
年发文量:67
影响因子:2.5
JCR分区:Q1

基本信息

《空间统计》发表关于空间和时空统计的理论和应用的文章。它喜欢手稿,目前的理论产生的新应用,或其中新的理论是应用于一个重要的实际情况。纯粹的理论研究很少会被接受。不接受未经方法学发展的纯案例研究的出版。空间统计涉及空间和时空数据的定量分析,包括其统计依赖性、准确性和不确定性。空间统计的方法学通常见于概率论、随机建模和数理统计以及信息科学。空间统计用于制图、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进行有效推断。
2211-6753SCIE/Scopus收录
2.5
2.5
2026年3月发布
点击查看历史分区趋势    >
大类学科小类学科Top期刊综述期刊
数学3区
GEOSCIENCES, MULTIDISCIPLINARY 地球科学:综合
3区
MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 数学跨学科应用
3区
REMOTE SENSING 遥感
4区
STATISTICS & PROBABILITY 统计学与概率论
3区
N/A
WOS期刊SCI分区  2024-2025最新升级版
按JIF指标学科分区收集子录JIF分区JIF排名百分位
学科:GEOSCIENCES, MULTIDISCIPLINARY
SCIE
Q2
108/258
学科:MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
SCIE
Q1
31/136
学科:REMOTE SENSING
SCIE
Q3
34/65
学科:STATISTICS & PROBABILITY
SCIE
Q1
21/169
按JCR指标学科分区收集子录JCR分区JCR排名百分位
学科:GEOSCIENCES, MULTIDISCIPLINARY
SCIE
Q2
78/258
学科:MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
SCIE
Q2
39/136
学科:REMOTE SENSING
SCIE
Q2
25/65
学科:STATISTICS & PROBABILITY
SCIE
Q1
34/169
17
67
4%---GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
8%
时间预警情况
2026年03月发布的新锐学术版不在预警名单中
2025年03月发布的2025版不在预警名单中
2024年02月发布的2024版不在预警名单中
2023年01月发布的2023版不在预警名单中
2021年12月发布的2021版不在预警名单中
2020年12月发布的2020版不在预警名单中
100.00%27.2%-
CiteScore:4.90
SJR:0.874
SNIP:1.121
学科类别分区排名百分位
大类:Mathematics
小类:Statistics and Probability
Q1
37 / 293
大类:Mathematics
小类:Computers in Earth Sciences
Q2
24 / 76
大类:Mathematics
小类:Management, Monitoring, Policy and Law
Q2
147 / 406

期刊高被引文献

Evaluation of empirical Bayesian kriging
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.100368
Bayesian modelling for spatially misaligned health and air pollution data through the INLA-SPDE approach
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.04.001
Stochastic investigation of long-term persistence in two-dimensional images of rocks
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.11.002
BWM-ARAS: A new hybrid MCDM method for Cu prospectivity mapping in the Abhar area, NW Iran
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.100382
Bayesian analysis of areal data with unknown adjacencies using the stochastic edge mixed effects model
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.100357
Isotropy, symmetry, separability and strict positive definiteness for covariance functions: A critical review
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.09.003
Handling missing data in self-exciting point process models
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.12.004
ELSA: Entropy-based local indicator of spatial association
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.10.001
Social Network Spatial Model.
来源期刊:Spatial statisticsDOI:10.1016/J.SPASTA.2018.11.001
A multivariate nonparametric scan statistic for spatial data
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.10.002
Functional SAR models: With application to spatial econometrics
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.12.002
Analysis of variance for spatially correlated functional data: Application to brain data
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.100381
A Bayesian multivariate functional model with spatially varying coefficient approach for modeling hurricane track data
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.12.006
A spatially varying change points model for monitoring glaucoma progression using visual field data.
来源期刊:Spatial statisticsDOI:10.1016/j.spasta.2019.02.001
Leverage and influence diagnostics for Gibbs spatial point processes
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.09.004
Using a spatial point process framework to characterize lung computed tomography scans.
来源期刊:Spatial statisticsDOI:10.1016/J.SPASTA.2018.12.003
Velocities for spatio-temporal point patterns
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.12.007
GMM estimation of partially linear single-index spatial autoregressive model
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.04.002
Some observations on a recently proposed cross-correlation model
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.03.003
Global multivariate point pattern models for rain type occurrence.
来源期刊:Spatial statisticsDOI:10.1016/J.SPASTA.2019.04.003
Design-based estimation of mark variograms in forest ecosystem surveys
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.02.002
Exploring geometric anisotropy for point-referenced spatial data
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.100370
A new centered spatio-temporal autologistic regression model with an application to local spread of plant diseases
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.100361
A diagonally weighted matrix norm between two covariance matrices
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.01.001
Parametric spatial covariance models in the ensemble Kalman filter
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.12.005
Simulation of decorrelated factors in presence of secondary data
来源期刊:spatial statisticsDOI:10.1016/j.spasta.2019.100385
On the correlation structure between point patterns and linear networks
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.12.001
A variational method for parameter estimation in a logistic spatial regression
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.100365
Multivariate control charts to monitor the monthly frequency of vehicle robberies in São Paulo city
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2018.09.002
Efficient Bayesian modeling of large lattice data using spectral properties of Laplacian matrix
来源期刊:spatial statisticsDOI:10.1016/J.SPASTA.2019.01.003

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