Spatial Time Series Analysis
Spatial Time Series Analysis is a computational framework within spatial data analytics that integrates temporal dynamics with geographic dependencies to model and analyze sequences of spatially referenced observations over time, distinguishing itself from conventional time series analysis by explicitly accounting for spatial heterogeneity and autocorrelation.
342
Publications
17.7K
Citations
929
Authors
476
Institutions
Publications per year
2017–2026
106
342
The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field
Christopher S. Bretherton, Martin Widmann, Valentin Dymnikov et al. · Journal of Climate · 1999 · 1.6K citations · Full text
Slocum Gliders: Robust and ready
Oscar Schofield, Josh Kohut, David Aragon et al. · Journal of Field Robotics · 2007 · 280 citations
1–5 of 342
929
5
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4
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University of California, Santa Barbara, United States
3 publications · 128 citations
3
h-index
3
h-index
3
h-index
1–5 of 929
476
University of Chicago
Chicago, United States
7 h-index
10 publications
337 citations

Peking University
Beijing, China
7 h-index
14 publications
785 citations
Wuhan University
Wuhan, China
6 h-index
19 publications
706 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
18 publications
444 citations
Indiana University Bloomington
Bloomington, United States
6 h-index
6 publications
468 citations

Centre National de la Recherche Scientifique
Paris, France
6 h-index
19 publications
325 citations

Zhejiang University
Hangzhou, China
5 h-index
21 publications
1.1K citations

Beijing Normal University
Beijing, China
5 h-index
19 publications
796 citations
1–8 of 476
15 publications · 955 citations
12
h-index
5 publications · 154 citations
5
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4 publications · 101 citations
4
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4 publications · 237 citations
4
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IRSR · 4 publications · 222 citations
4
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1–5