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spacetime: Spatio-Temporal Data in R

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  • Pebesma, Edzer

Abstract

This document describes classes and methods designed to deal with different types of spatio-temporal data in R implemented in the R package spacetime, and provides examples for analyzing them. It builds upon the classes and methods for spatial data from package sp, and for time series data from package xts. The goal is to cover a number of useful representations for spatio-temporal sensor data, and results from predicting (spatial and/or temporal interpolation or smoothing), aggregating, or subsetting them, and to represent trajectories. The goals of this paper is to explore how spatio-temporal data can be sensibly represented in classes, and to find out which analysis and visualisation methods are useful and feasible. We discuss the time series convention of representing time intervals by their starting time only. This document is the main reference for the R package spacetime, and is available (in updated form) as a vignette in this package.

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  • Pebesma, Edzer, 2012. "spacetime: Spatio-Temporal Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i07).
    • Handle: RePEc:jss:jstsof:v:051:i07
    DOI: http://hdl.handle.net/10.18637/jss.v051.i07
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    References listed on IDEAS

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    1. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    2. Zeileis, Achim & Grothendieck, Gabor, 2005. "zoo: S3 Infrastructure for Regular and Irregular Time Series," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i06).
    3. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    4. Grolemund, Garrett & Wickham, Hadley, 2011. "Dates and Times Made Easy with lubridate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i03).
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    Cited by:

    1. Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
    2. Hengl, Tomislav & Roudier, Pierre & Beaudette, Dylan & Pebesma, Edzer, 2015. "plotKML: Scientific Visualization of Spatio-Temporal Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i05).
    3. Hutniczak, Barbara & Münch, Angela, 2018. "Fishermen's location choice under spatio-temporal update of expectations," Journal of choice modelling, Elsevier, vol. 28(C), pages 124-136.
    4. Gabriel, Edith & Rowlingson, Barry S. & Diggle, Peter J., 2013. "stpp: An R Package for Plotting, Simulating and Analyzing Spatio-Temporal Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i02).
    5. Joseph Acquah & Francis Benyah & Jerry S. Y. Kuma, 2019. "Regularisation Technique for a Distributed Parameter Identification Problem," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 11(1), pages 64-75, February.
    6. Bakar, Khandoker Shuvo & Sahu, Sujit K., 2015. "spTimer: Spatio-Temporal Bayesian Modeling Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i15).
    7. Lixin Li & Xiaolu Zhou & Marc Kalo & Reinhard Piltner, 2016. "Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application," IJERPH, MDPI, vol. 13(8), pages 1-20, July.
    8. Martínez-López, Javier & Martínez-Fernández, Julia & Naimi, Babak & Carreño, María F. & Esteve, Miguel A., 2015. "An open-source spatio-dynamic wetland model of plant community responses to hydrological pressures," Ecological Modelling, Elsevier, vol. 306(C), pages 326-333.
    9. Feras Saad & Jacob Burnim & Colin Carroll & Brian Patton & Urs Köster & Rif A. Saurous & Matthew Hoffman, 2024. "Scalable spatiotemporal prediction with Bayesian neural fields," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    10. Meyer, Sebastian & Held, Leonhard & Höhle, Michael, 2017. "Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i11).
    11. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).
    12. Roger S. Bivand, 2021. "Progress in the R ecosystem for representing and handling spatial data," Journal of Geographical Systems, Springer, vol. 23(4), pages 515-546, October.
    13. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2013. "lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i04).
    14. de Iaco, Sandra, 2017. "The cgeostat Software for Analyzing Complex-Valued Random Fields," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i05).

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