SpatioTemporal

Handling and Analyzing Spatio-Temporal Data

This task view aims at presenting R packages that are useful for the analysis of spatio-temporal data.

Please let the maintainer know if something is inaccurate or missing.

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.

Representing data

  • 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 acample 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.
  • Dedicated classes: dedicated classes are offered for:
    • Geostatistical data: Package SpatioTemporal offers an S3 class STdata which holds point observations and covariates that can vary in space, time, and space-time, with the aim of fitting and predicting a particular class of spatio-temporal models, described in its vignettes.
    • Gridded/raster data: package raster deals with sets of rasters (called bricks, or stacks), and a set may reflect a temporal sequence (use setZ on a brick or stack).
    • 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 POSIXct).
    • Point patterns: Package stppResid provides a class stwin for a space-time cuboid, defining a (rectangular) space-time window, and class stpp for a spatio-temporal point pattern (including window). 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: 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.
Analyzing data
  • Geostatistical data
    • gstat provides kriging, methods of moments variogram estimation and model fitting for a limited range of spatio-temporal models.
    • RandomFields provides kriging, conditional simulation, and covariance functions and maximum likelihood function fitting for a very wide range of spatio-temporal covariance models.
    • 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.
    • Package SpatioTemporal fits and predicts a particular class of spatio-temporal models, described in detail in its vignettes.
    • 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 provides 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.
    • spate provides spatio-temporal modeling of large data using a spectral SPDE approach.
    • 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.
  • Point patterns
    • splancs provides methods for spatial and space-time point pattern analysis (khat, kernel3d, visualizing).
    • lgcp is a package for spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process.
    • stppResid performs residual analysis on space-time point process models.
    • stam is an evolving package that target on the various methods to conduct Spatio-Temporal Analysis and Modelling,including Exploratory Spatio-Temporal Analysis and Inferred Spatio-Temporal Modelling, currently provides mostly kernel density estimation.
    • 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.
    • rsatscan provides an R interface to the free (but non-open source) program SaTScan.
    • 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.
  • Moving objects, trajectories
    • adehabitatLT provides a collection of tools for the analysis of animal movements, including biased random walk simulation and home range estimation.
    • trip provides functions for accessing and manipulating spatial data for animal tracking. Filter for speed and create time spent plots from animal track data.
    • tripEstimation provides a Metropolis sampler and supporting functions for estimating animal movement from archival tags and satellite fixes. It further provides 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 MCMC simulations of point data.
    • diveMove provides utilities to represent, visualize, filter, analyze, and summarize time-depth recorder (TDR) data; miscellaneous functions for handling location data are also provided.
    • argosfilter provides functions to filter animal satellite tracking data obtained from Argos. It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality.
    • GeoLight provides basic functions for global positioning based on light intensity measurements over time. Positioning process includes the determination of sun events, a discrimination of residency and movement periods, the calibration of period-specific data and, finally, the calculation of positions.
    • crawl: The (C)orrelated (RA)ndom (W)alk (L)ibrary of R functions was designed for fitting continuous-time correlated random walk (CTCRW) models with time indexed covariates. The model is fit using the Kalman-Filter on a state space version of the continuous-time stochastic movement process.
    • move is a package for analyzing animal movement data; it contains functions to access movement data stored in movebank as well as tools to visualize and statistically analyse animal movement data.
    • animalTrack provides animal track reconstruction for high frequency 2-dimensional (2D) or 3-dimensional (3D) movement data. 2D and 3D animal tracking data can be used to reconstruct tracks through time/space with correction based on known positions as well as 3D visualization of animal position and attitude.
    • The BBMM (Brownian bridge movement model) package provides an empirical estimate of a movement path using discrete location data obtained at relatively short time intervals. This is a continuous-time stochastic model of movement in which the probability of being in an area during the time of observation is conditioned on starting and ending locations. A BBMM is typically fit to animal location data obtained by a Global Positioning System (GPS) or Very High Frequency (VHF) device.
    • The bcpa package for 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 original paper on which it is based is: 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.
    • The smam package provides Animal movement models including moving-resting process with embedded Brownian motion, Brownian motion with measurement error.
    • The BayesianAnimalTracker package provides a Bayesian melding approach to combine the GPS observations and Dead-Reckoned path for an accurate animal's track, or equivalently, use the GPS observations to correct the Dead-Reckoned path. It can take the measurement errors in the GPS observations into account and provide uncertainty statement about the corrected path. The main calculation can be done by the BMAnimalTrack function.
    • The TrackReconstruction package 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.
    • The wildlifeDI package provides tools for calculating a suite of indices used for quantifying dynamic interaction with wildlife telemetry data. Dynamic interaction refers to spatial-temporal associations in the movements of two (or more) animals.
    • The mkde package provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.
    • The fishmove package provides functions to predict fish movement parameters based on multiple regression and plotting leptokurtic fish dispersal kernels (see Radinger and Wolter, 2013: Patterns and predictors of fish dispersal in rivers. Fish and Fisheries.)
    • The ctmcmove 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.
    • The ctmm provides functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data.
    • The moveHMM package provides animal movement modelling using hidden Markov models. Pre-processing of tracking data, fitting HMMs to movement data, visualization of data and fitted model.
    • The aim of the package trackeR is to provide infrastructure for handling running and cycling data from GPS-enabled tracking devices. After extraction and appropriate manipulation of the training or competition attributes, the data are placed into session-based and unit-aware data objects of class trackeRdata (S3 class). The information in the resultant data objects can then be visualised, summarised, and analysed through corresponding flexible and extensible methods.
    • The VTrack package is designed to facilitate the assimilation, analysis and synthesis of animal location and movement data collected by the VEMCO suite of acoustic transmitters and receivers. As well as database and geographic information capabilities the principal feature of VTrack is the qualification and identification of ecologically relevant events from the acoustic detection and sensor data. This procedure condenses the acoustic detection database by orders of magnitude, greatly enhancing the synthesis of acoustic detection data.
    • The SimilarityMeasures package computes 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.
    • the sigloc package provides a collection of tools for estimating the location of a transmitter signal from radio telemetry studies using the maximum likelihood estimation (MLE) approach described in Lenth (1981).
    • The Electronic Tagging Geolocation Packages Repository (off-CRAN) site provides a collection of statistical models (and R packages) to estimate position errors, movement model parameters, and most probable positions from tracking data.
    • The bsam (off-CRAN) package fits Bayesian state-space models to Argos satellite tracking data. Currently, models provided are DCRW (for location filtering), DCRWS (for location filtering and behavioural state estimation), and hDCRWS (a hierarchical model for location filtering and behavioural state estimation across multiple animals).
    • The argosTrack (off-CRAN) package allows fitting movement models to Argos data.

Visualization

  • rasterVis includes a variety of methods that take advantage of the z slot of a RasterStack or RasterBrick object. Its webpage includes several examples, from the hovmoller plot and horizon graph, to the density and histogram plots.
  • package plotKML provides methods to convert spatio-temporal data into KML files, which can be displayed by external viewers, in particular Google Earth. It has a gallery too.
  • 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).

Data sets

  • 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.

Retrieving data

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; pbdNCDF4 adds collective parallel read and write capability to ncdf4.
  • M3 contains functions to read in and manipulate air quality model output from Models3-formatted files. This format is used by the Community Multiscale Air Quaility (CMAQ) model.
  • rmatio is a package for reading and writing Matlab MAT files from R.

View on CRAN

a month ago

Edzer Pebesma

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