CRAN Task View: 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
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
of observation are registered, and relevant for the analysis of
the data. The
task views shed light on spatial, and temporal data handling and
In long tables:
In some cases, spatio-temporal data can be held in tables
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
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
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
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
, where each observational
unit forms a record and each column an observation time.
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,
It has time series as different columns, each column representing
one location (weather station). The
function in package
accepts data in long,
time-wide or space-wide tables.
Formal classes for spatio-temporal data in R are provided by
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.
objects as slots
for the spatial and temporal components, and can deal with all
spatial classes (points, lines, polygons, grids) of
regular and irregular time series, and extend the powerful methods
(selection, aggregation, plotting coercion) from both packages.
dedicated classes are offered for:
offers an S3 class
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.
sets of rasters (called bricks, or stacks), and a
set may reflect a temporal sequence
on a brick or stack).
provides a class
sts, which holds a
slot for the areas, and
numeric slots to define a regular time series (no time objects,
provides a class
for a space-time point pattern.
provides a class
for a space-time cuboid, defining a (rectangular)
space-time window, and class
spatio-temporal point pattern (including window).
provides a class
deals spatial and temporal coordinate.
None of the point pattern classes mentioned support spatial or
explicit temporal reference systems.
offers a class
for trajectories, and methods for analyzing them; the packages
based classes for trajectories.
provides kriging, methods of moments
variogram estimation and model fitting for a limited
range of spatio-temporal models.
provides kriging, conditional simulation,
and covariance functions and maximum likelihood function fitting
for a very wide range of spatio-temporal covariance models.
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.
fits and predicts
a particular class of spatio-temporal models, described
in detail in its vignettes.
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.
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.
provides spatio-temporal modeling
of large data using a spectral SPDE approach.
is a package for the regulation, decomposition
and analysis of space-time series.
analyses spatio-temporal data, decomposing data in n-dimensional arrays and using the median polish technique.
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.
is a package for computing solar radiation and photovoltaic
to fit linear mixed models, and have facilities to model
spatial and/or temporal effects.
provides methods for spatial and
space-time point pattern analysis (khat, kernel3d,
is a package for spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process.
allows Space-Time Point Pattern simulation, visualization and analysis.
performs residual analysis on space-time point process models.
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.
(off-CRAN) provides methods and classes for spatio-temporal
("multi-dimensional") point process.
provides temporal and spatio-temporal
modeling and monitoring of epidemic phenomena.
fits linear panel models.
provides estimation and diagnostic
testing of econometric models for spatial panel data.
fit spatial models with heteroskedastic
to fit linear mixed models, and have facilities to model
spatial and/or temporal effects.
provides an R interface to the free (but non-open source) program SaTScan
Moving objects, trajectories
provides a collection of tools
for the analysis of animal movements, including biased random walk
simulation and home range estimation.
provides functions for accessing
and manipulating spatial data for animal tracking. Filter
for speed and create time spent plots from animal track
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.
provides utilities to represent,
visualize, filter, analyze, and summarize time-depth
recorder (TDR) data; miscellaneous functions for handling
location data are also provided.
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
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.
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.
is a package for analyzing animal
movement data; it contains functions to access movement data
as well as tools to visualize and statistically analyse
animal movement data.
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.
(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.
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.
package provides Animal movement models
including moving-resting process with embedded Brownian
motion, Brownian motion with measurement error.
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
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.
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.
package provides functions to compute and
visualize movement-based kernel density estimates (MKDEs) for animal
utilization distributions in 2 or 3 spatial dimensions.
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.)
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.
provides functions for identifying,
fitting, and applying continuous-space, continuous-time
stochastic movement models to animal tracking data.
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
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.
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
Electronic Tagging Geolocation Packages Repository
site provides a collection of statistical models (and R packages)
to estimate position errors, movement model parameters,
and most probable positions from tracking data.
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).
package allows fitting movement models to Argos data.
includes a variety of methods
that take advantage of the
slot of a
includes several examples, from the hovmoller plot and
horizon graph, to the density and histogram plots.
provides methods to convert
spatio-temporal data into KML files, which can be displayed
by external viewers, in particular Google Earth. It has a
provides an interface to show R data
(tables) in the
Google Chart Tools
animation and 3D interactive plots (using
for displaying spatio-temporal point patterns.
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
contains a data base with country boundaries,
varying over time.
contains the classic Irish wind data.
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
Packages for retrieving data are:
has tools to analyze, interpret and understand air pollution data, but also tools to download UK air quality data.
allow reading and writing
adds collective parallel read and write capability
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.
is a package for reading and writing Matlab MAT files from R.