CRAN Task View: Handling and Analyzing Spatio-Temporal Data
|Maintainer:||Edzer Pebesma, Roger Bivand|
|Contact:||edzer.pebesma at uni-muenster.de|
|Contributions:||Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide.|
|Citation:||Edzer Pebesma, Roger Bivand (2022). CRAN Task View: Handling and Analyzing Spatio-Temporal Data. Version 2022-10-01. URL https://CRAN.R-project.org/view=SpatioTemporal.|
|Installation:||The packages from this task view can be installed automatically using the ctv package. For example, |
ctv::install.views("SpatioTemporal", coreOnly = TRUE) installs all the core packages or
ctv::update.views("SpatioTemporal") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details.
This task view aims at presenting R packages that are useful for the analysis of spatio-temporal data.
If something is inaccurate or missing, please send an e-mail to the maintainer or submit an issue or pull request in the GitHub repository linked above.
The following people contributed to this task view: Roger Bivand, Achim Zeileis, Michael Sumner, Ping Yang.
Although one could argue that all data are spatio-temporal, as they must have been taken somewhere and at some point in time, in many cases the spatial locations or times of observation are not registered, and irrelevant to the purpose of the study. Here, we will address the cases where both location and time of observation are registered, and relevant for the analysis of the data. The Spatial and TimeSeries task views shed light on spatial, and temporal data handling and analysis, individually.
- In long tables: In some cases, spatio-temporal data can be held in tables (
data.frame objects), with longitude, latitude and time as three of the columns, or an identifier for a location or region and time as columns. For instance, data sets in package plm for linear panel models have repeated observations for observational units, where these units often refer to spatial areas (countries, states) by an index. This index (a name, or number) can be matched to the spatial coordinates (polygons) of the corresponding area, an example of this is given by Pebesma (2012, Journal of Statistical Software) . As these data sets usually contain more than one attribute, to hold the data in a two-dimensional table a long table form is chosen, where each record contains the index of the observational unit, observation time, and all attributes.
- In time-wide tables: When a single attribute is considered, another layout is that of the time-wide table , where each observational unit forms a record and each column an observation time. googleVis lets you analyze such data in a way similar to gapminder (see links).
- In space-wide tables: An example of a space-wide table is the Irish wind data set, obtained by
data(wind) in package gstat. It has time series as different columns, each column representing one location (weather station). The
stConstruct function in package spacetime accepts data in long, time-wide or space-wide tables.
- Generic classes: Formal classes for spatio-temporal data in R are provided by the spacetime package, which offers S4 classes for full space-time grids (every observational unit contains an observation for each observation time), sparse space-time grids (regular, but incomplete grids), irregular space-time data (each observational unit is observed at its own time), and has limited support for trajectory data. spacetime classes have sp and xts objects as slots for the spatial and temporal components, and can deal with all spatial classes (points, lines, polygons, grids) of sp, regular and irregular time series, and extend the powerful methods (selection, aggregation, plotting coercion) from both packages. Package stars replaces spacetime for full space-time grids; package sftime replaces its irregulat spacetime data, although
sf objects from package sf can also do that.
- Dedicated classes: dedicated classes are offered for:
- data cubes: package stars provides methods to create, analyse and visualise raster and vector data cubes, array data with arbitrary dimensionaly where some dimensions refer to space (raster, vector) and/or time
- Gridded/raster data: package raster and its successor terra deal with sets of rasters (called bricks, or stacks), and a set may reflect a temporal sequence; both package also handle vector data
- Lattice data: package surveillance provides a class
sts, which holds a
SpatialPolygonsDataFrame slot for the areas, and numeric slots to define a regular time series (no time objects, such as
- Point patterns: Package spatstat provides a class
ppx that deals spatial and temporal coordinate. None of the point pattern classes mentioned support spatial or explicit temporal reference systems.
- Trajectory data: see also the dedicated task view Tracking; Package adehabitatLT offers a class
ltraj for trajectories, and methods for analyzing them; the packages move and trip both extend sp based classes for trajectories. A blog post on tidy storm trajectories points out how nested dataframes, along with geometry list columns of the sf package, can be used to model sets of trajectories, and visualise properties at the set level and at the level of individual fixes.
- Geostatistical data
- gstat provides kriging, methods of moments variogram estimation and model fitting for a limited range of spatio-temporal models.
- IDE provides functionality for modelling spatio-temporal data using the integro-difference equation.
- the spTimer package is able to fit, spatially predict and temporally forecast large amounts of space-time data using Bayesian Gaussian Process (GP) Models, Bayesian Auto-Regressive (AR) Models, and Bayesian Gaussian Predictive Processes (GPP) based AR Models.
- spBayes provides functions for fitting Bayesian dynamic space-time regression models for settings where space is viewed as continuous but time is taken to be discrete.
Stem provided estimation of the parameters of a spatio-temporal model using the EM algorithm, estimation of the parameter standard errors using a spatio-temporal parametric bootstrap, spatial mapping.
- pastecs is a package for the regulation, decomposition and analysis of space-time series.
- STMedianPolish analyses spatio-temporal data, decomposing data in n-dimensional arrays and using the median polish technique.
- R-Forge package spcopula provides a framework to analyze via copulas spatial and spatio-temporal data provided in the format of the spacetime package. Additionally, support for calculating different multivariate return periods is implemented.
- solaR is a package for computing solar radiation and photovoltaic systems performance.
- nlme and lme4 contain functions to fit linear mixed models, and have facilities to model spatial and/or temporal effects.
- mlr3spatiotempcv provides spatiotemporal resampling methods; it extends the mlr3 ML framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation in predictor variables.
- sdmTMB Implements spatial and spatiotemporal predictive-process generalized linear mixed effect models using ‘TMB’, ‘INLA’, and the SPDE approximation to Gaussian random fields.
- Point patterns
- splancs provides methods for spatial and space-time point pattern analysis (khat, kernel3d, visualizing).
- ptproc (off-CRAN) provides methods and classes for spatio-temporal (“multi-dimensional”) point process.
- Lattice data
- surveillance provides temporal and spatio-temporal modeling and monitoring of epidemic phenomena.
- plm fits linear panel models.
- splm provides estimation and diagnostic testing of econometric models for spatial panel data.
- sphet fit spatial models with heteroskedastic innovations.
- nlme and lme4 contain functions to fit linear mixed models, and have facilities to model spatial and/or temporal effects.
- CARBayesST implements a class of spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (McMC) simulation.
- gapfill provides tools to fill missing values in satellite data and to develop new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. The package is illustrated with MODIS NDVI data.
- Moving objects, trajectories see the dedicated task view Tracking
- There is a large (74+) and growing number of tracking, trajectory, movement and related packages on CRAN. The review paper by Loo et al (2018) provides a guide to summarize many available packages and their functionality.
- adehabitatLT A collection of tools for the analysis of animal movements.
- animalTrack 2D and 3D animal tracking data can be used to reconstruct tracks through time/space with correction based on known positions. 3D visualization of animal position and attitude.
- anipaths Animation of observed trajectories using spline-based interpolation. Intended to be used exploratory data analysis, and perhaps for preparation of presentations.
- bcpa The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408.
- bsam Tools to fit Bayesian state-space models to animal tracking data. Models are provided for location filtering, location filtering and behavioural state estimation, and their hierarchical versions. The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit to other tracking data types. For Global Positioning System data, consider the ‘moveHMM’ package. Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged to explore tools available in packages such as ‘coda’ and ‘boa’.
- caribou This is a package for estimating the population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990).
- crawl Fit continuous-time correlated random walk models with time indexed covariates to animal telemetry data. The model is fit using the Kalman-filter on a state space version of the continuous-time stochastic movement process.
- ctmcmove Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015), this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
- ctmm Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data.
- EMbC Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, (“Expectation-Maximization Binary Clustering for Behavioural Annotation”).
- eyelinker Eyelink eye trackers output a horrible mess, typically under the form of a ‘.asc’ file. The file in question is an assorted collection of messages, events and raw data. This R package will attempt to make sense of it.
- foieGras (archived) Fits continuous-time random walk and correlated random walk state-space models to filter Argos satellite location data. Template Model Builder (‘TMB’) is used for fast estimation. The Argos data can be: (older) least squares-based locations; (newer) Kalman filter-based locations with error ellipse information; or a mixture of both. Separate measurement models are used for these two data types. The models estimate two sets of location states corresponding to: 1) each observation, which are (usually) irregularly timed; and 2) user-specified time intervals (regular or irregular).
- gazepath Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality.
- marcher A set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017). Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment. Tests assess, for example, whether the shift was “significant”, and whether a two-shift migration was a true return migration.
- mdftracks ‘MTrackJ’ is an ‘ImageJ’ plugin for motion tracking and analysis. This package reads and writes ‘MTrackJ Data Files’ (‘.mdf’). It supports 2D data and read/writes cluster, point, and channel information. If desired, generates track identifiers that are unique over the clusters. See the project page for more information and examples.
- momentuHMM Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum’’) include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018).
- mousetrap Mouse-tracking, the analysis of mouse movements in computerized experiments, is a method that is becoming increasingly popular in the cognitive sciences. The mousetrap package offers functions for importing, preprocessing, analyzing, aggregating, and visualizing mouse-tracking data.
- move Contains functions to access movement data stored in ‘movebank.org’ as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions.
- movecost Provides the facility to calculate non-isotropic accumulated cost surface and least-cost paths using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain Model, a start location and (optionally) destination locations.
- moveHMM Provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process.
- moveVis Tools to visualize movement data (e.g. from GPS tracking) and temporal changes of environmental data (e.g. from remote sensing) by creating video animations.
- moveWindSpeed Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach.
- oce Supports the analysis of Oceanographic data, including ‘ADCP’ measurements, measurements made with ‘argo’ floats, ‘CTD’ measurements, sectional data, sea-level time series, coastline and topographic data, etc. Provides specialized functions for calculating seawater properties such as potential temperature in either the ‘UNESCO’ or ‘TEOS-10’ equation of state. Produces graphical displays that conform to the conventions of the Oceanographic literature. This package is discussed extensively in Dan Kelley’s book Oceanographic Analysis with R, published in 2018 by ‘Springer-Verlag’ with ISBN 978-1-4939-8842-6.
- rerddapXtracto Contains three functions that access environmental data from any ‘ERDDAP’ data web service. The rxtracto() function extracts data along a trajectory for a given “radius” around the point. The rxtracto_3D() function extracts data in a box. The rxtractogon() function extracts data in a polygon. All of those three function use the ‘rerddap’ package to extract the data, and should work with any ‘ERDDAP’ server. There are also two functions, plotBBox() and plotTrack() that use the ‘plotdap’ package to simplify the creation of maps of the data.
- riverdist Reads river network shape files and computes network distances. Also included are a variety of computation and graphical tools designed for fisheries telemetry research, such as minimum home range, kernel density estimation, and clustering analysis using empirical k-functions with a bootstrap envelope. Tools are also provided for editing the river networks, meaning there is no reliance on external software.
- SimilarityMeasures Functions to run and assist four different similarity measures. The similarity measures included are: longest common subsequence (LCSS), Frechet distance, edit distance and dynamic time warping (DTW). Each of these similarity measures can be calculated from two n-dimensional trajectories, both in matrix form.
- SiMRiv Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014) to test a variety of ‘Movement Ecology’ hypotheses (Nathan et al. 2008), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics.
- smam Animal movement models including moving-resting process with embedded Brownian motion according to Yan et al. (2014), Pozdnyakov et al. (2017), Brownian motion with measurement error according to Pozdnyakov et al. (2014), and moving-resting-handling process with embedded Brownian motion, Pozdnyakov et al. (2018).
- stampr Perform spatial temporal analysis of moving polygons; a longstanding analysis problem in Geographic Information Systems. Facilitates directional analysis, shape analysis, and some other simple functionality for examining spatial-temporal patterns of moving polygons.
- surveillance Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Höhle and Paul (2008).
- trackdem Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in evolutionary and ecological studies.
- trackdf Data frame class for storing collective movement data (e.g. fish schools, ungulate herds, baboon troops) collected from GPS trackers or computer vision tracking software.
- trackeR Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class ‘trackeRdata’ (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) >, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.
- trackeRapp Provides an integrated user interface and workflow for the analysis of running, cycling and swimming data from GPS-enabled tracking devices.
- TrackReconstruction Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data. Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
- trajectories Classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.
- trajr A toolbox to assist with statistical analysis of 2-dimensional animal trajectories. It provides simple access to algorithms for calculating and assessing a variety of characteristics such as speed and acceleration, as well as multiple measures of straightness or tortuosity. McLean & Skowron Volponi (2018).
- trip Functions for accessing and manipulating spatial data for animal tracking, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from animal track data. There are coercion methods to convert between ‘trip’ and ‘ltraj’ from ‘adehabitatLT’, and between ‘trip’ and ‘psp’ and ‘ppp’ from ‘spatstat’. Trip objects can be created from raw or grouped data frames, and from types in the ‘sp’, ‘sf’, ‘amt’, ‘trackeR’, ‘mousetrap’, and other packages.
- tripEstimation Data handling and estimation functions for animal movement estimation from archival or satellite tags. Helper functions are included for making image summaries binned by time interval from Markov Chain Monte Carlo simulations.
- wildlifeDI Dynamic interaction refers to spatial-temporal associations in the movements of two (or more) animals. This package provides tools for calculating a suite of indices used for quantifying dynamic interaction with wildlife telemetry data. For more information on each of the methods employed see the references within. The package (as of version 0.3) also has new tools for automating contact analysis in large tracking datasets. The package draws heavily on the classes and methods developed in the ‘adehabitat’ packages.
- rasterVis includes a variety of methods that take advantage of the
z slot of a
RasterBrick object. Its webpage includes several examples, from the hovmoller plot and horizon graph, to the density and histogram plots.
- package googleVis provides an interface to show R data (tables) in the Google Chart Tools . spacetime has a vignette demonstrating its use for spatio-temporal data.
- Package splancs provides animation and 3D interactive plots (using rgl) for displaying spatio-temporal point patterns.
- mvtsplot provides multivariate time series plots, with examples on spatio-temporal data, published by Peng (2008, Journal of Statistical Software) .
- Table data for fitting linear panel models are found in plm.
- Package cshapes contains a data base with country boundaries, varying over time.
- gstat contains the classic Irish wind data.
- spacetime contains rural PM10 air quality measurements over Germany.
- Some parts of the Cressie and Wikle (2011) book “Statistics for spatio-temporal data” can be reproduced by
demo(CressieWikle) in spacetime.
Packages for retrieving data are:
- Package openair has tools to analyze, interpret and understand air pollution data, but also tools to download UK air quality data.
- ncdf4 and RNetCDF allow reading and writing netcdf files.
- rmatio is a package for reading and writing Matlab MAT files from R.
|Core:||adehabitatLT, gstat, raster, sp, spacetime, stars, surveillance, terra, trip, xts.|
|Regular:||animalTrack, anipaths, bcpa, bsam, CARBayesST, caribou, crawl, cshapes, ctmcmove, ctmm, EMbC, eyelinker, gapfill, gazepath, googleVis, IDE, lme4, marcher, mdftracks, mlr3spatiotempcv, momentuHMM, mousetrap, move, movecost, moveHMM, moveVis, moveWindSpeed, mvtsplot, ncdf4, nlme, oce, openair, pastecs, plm, rasterVis, rerddapXtracto, rgl, riverdist, rmatio, RNetCDF, sdmTMB, sf, SimilarityMeasures, SiMRiv, smam, solaR, spatstat, spBayes, sphet, splancs, splm, spTimer, stampr, STMedianPolish, trackdem, trackdf, trackeR, trackeRapp, TrackReconstruction, trajectories, trajr, tripEstimation, wildlifeDI.|
- Pebesma (2012). spacetime: Spatio-Temporal Data in R. Journal of Statistical Software, 51(7).
- Peng (2008). A Method for Visualizing Multivariate Time Series Data. Journal of Statistical Software, Code Snippets, 25(1).
- Gapminder world: A tool for visualization of economic and health indicators per country (including access to the underlying tables in time-wide form).
- Wikle, C. K., Zammit-Mangion, A., and Cressie, N. (2019), Spatio-Temporal Statistics with R, Boca Raton, FL: Chapman & Hall/CRC (free to download).