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Sophisticated tools for spatio-temporal data exploration
The document discusses the stxplore R package, designed for exploring spatio-temporal data, highlighting its unique features such as initial analysis functions, various plotting options, and compatibility with dataframes and stars objects. It emphasizes the importance of understanding complex datasets with different temporal and spatial scales for effective modeling. Additionally, the document provides insights into practical applications, including an example of aerosol data analysis, and mentions job opportunities in related research fields.
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Sophisticated tools for spatio-temporal data exploration
- 1. stxplore: Tools forspatio- temporal data exploration Sevvandi Kandanaarachchi, Petra Kuhnert, Andrew Zammit-Mangion, Christopher Wikle MODSIM 2023
- 2. The story behindstxplore Spatio-Temporal Statistics with R by Christopher K. Wikle, Andrew Zammit- Mangion and Noel Cressie Short course: An Introduction to Statistics for Spatio-Temporal Data. (2017) Petra Kuhnert and Chris Wikle R package stxplore (2022/2023)
- 3. Spatio-temporal data is everywhere •Climate • Disease • Energy • Satellite data Vegetation Index (NASA) Land surface temperature anomaly (NASA)
- 4. Why is spatio-temporalexploration important? • Quantities can change differently in latitude and longitude • Temporal changes different to spatial changes • The difference in scales (temporal and spatial) • Different granularities • Data is complex and diverse • Generally, no two spatio-temporal datasets are the same
- 5. Before modelling weneed to understand the data better
- 6. R packages toexplore spatio-temporal data • cubble – Sherry Zhang et al • A spatio-temporal data object for mainly data wrangling • Many plotting packages to plot geospatial data • ggmap • leaflet • tmap • …
- 7. stxplore: what’s differentfrom other packages?
- 8. Features of stxplore •We provide initial simple analysis functions for spatio-temporal modelling • Simple explorations • Taking averages • Different types of plots for spatio-temporal data • Investigating covariances • Variograms • PCA like computations for spatio-temporal data
- 9. stxplore can use •Either dataframes • Or stars objects (stacked raster data) • R package stars
- 10. Example: explore aerosoldata • Aerosol data from December 2019 to December 2020 • Monthly data – from NASA • NASA Earth Observations (NEO).” https://neo.gsfc.nasa.gov/. • 2019/2020 bushfires
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- 12. 2020−12−01 2020−08−01 2020−09−01 2020−10−012020−11−01 2020−04−01 2020−05−01 2020−06−01 2020−07−01 2019−12−01 2020−01−01 2020−02−01 2020−03−01 120 140 160 180 120 140 160 180 120 140 160 180 120 140 160 180 −60 −40 −20 0 −60 −40 −20 0 −60 −40 −20 0 −60 −40 −20 0 x y 50 100 150 200 250 z Spatial Snapshots
- 13. Temporal variation 0 50 100 150 200 250 Jan 2020 Apr2020 Jul 2020 Oct 2020 Time Value Observed Average Temporal Empirical Means
- 14. Temporal variation –Ridgeline plots 2019−12−01 2020−02−01 2020−03−01 2020−04−01 2020−05−01 2020−06−01 2020−08−01 2020−09−01 2020−10−01 2020−11−01 0 100 200 Value Group Intervals 0 50 100 150 200 250 z Use ggridges under the hood
- 15. Jan 2020 Apr 2020 Jul2020 Oct 2020 Jan 2021 120 140 160 180 Longitude Day z 20 30 40 50 Jan 2020 Apr 2020 Jul 2020 Oct 2020 Jan 2021 −60 −40 −20 0 Latitude Day z 20 30 40 50 60 Hovmoller plots
- 16. Zooming in tothe high aerosol region
- 17. 2020−12−01 2020−08−01 2020−09−01 2020−10−012020−11−01 2020−04−01 2020−05−01 2020−06−01 2020−07−01 2019−12−01 2020−01−01 2020−02−01 2020−03−01 150 155 160 165 170 150 155 160 165 170150 155 160 165 170150 155 160 165 170 −40 −35 −30 −25 −20 −40 −35 −30 −25 −20 −40 −35 −30 −25 −20 −40 −35 −30 −25 −20 x y 50 100 150 200 250 z Spatial Snapshots
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- 19. Empirical Orthogonal Functions •A bit similar to PCA for Spatio-temporal data – 3D data cube • Used for dimension reduction • Input: A stack of rasters, components = 2 • Output: • 1st Spatial snapshot +1st time series • 2nd Spatial snapshot + 2nd time series • When you multiply the spatial snapshot by the time series it gives an approximation to the original data cube
- 20. −40 −35 −30 −25 −20 150 155 160165 170 Longitude° Latitude° −0.16 −0.12 −0.08 −0.04 EOF1 EOF_1 −2 −1 0 5 10 Time (t) Normalized Principal Component PC Time series for EOF_1 −40 −35 −30 −25 −20 150 155 160 165 170 Longitude° Latitude° −0.2 −0.1 0.0 0.1 0.2 EOF2 EOF_2 −2 −1 0 1 2 5 10 Time (t) Normalized Principal Component PC Time series for EOF_2 Empirical Orthogonal Functions
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- 22. About stxplore • Vignette:https://sevvandi.github.io/stxplore/index.html • R package is on CRAN • Thoughts and collaborations welcome!
- 23. We are hiring!– FairML Research • CSIRO Postdoctoral Fellowship in Fairness Research in Machine Learning • Salary Range: AU$92,624 to AU$101,459 pa • plus up to 15.4% superannuation • 3-year contract • https://jobs.csiro.au/go/CERC-Postdoctoral-and- Engineering-Fellowships/7829300/ • Job will be advertised in August
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