lulcc: land use change modelling in R

Description

The lulcc package is an open and extensible framework for land use change modelling in R.

Details

The aims of the package are as follows:

1. to improve the reproducibility of scientific results and encourage reuse of code within the land use change modelling community

2. to make it easy to directly compare and combine different model structures

3. to allow users to perform several aspects of the modelling process within the same environment

To achieve these aims the package utilises an object-oriented approach based on the S4 system, which provides a formal structure for the modelling framework. Generic methods implemented for the lulcc classes include summary, show, and plot.

Land use change models are represented by objects inheriting from the superclass Model. This class is designed to represent general information required by all models while specific models are represented by its subclasses. Currently the package includes two inductive land use change models: the first is an implementation of the Change in Land Use and its Effects at Small Regional extent (CLUE-S) model (Verburg et al., 2002) (class CluesModel), while the second is an ordered procedure based on the algorithm described by Fuchs et al. (2013) but modified to allow stochastic transitions (class OrderedModel).

The main input to inductive land use change models is a set of predictive models relating observed land use or land use change to spatially explicit explanatory variables. A predictive model is usually obtained for each category or transition. In lulcc these models are represented by the class PredictiveModelList. Currently lulcc supports binary logistic regression, provided by base R (glm), recursive partitioning and regression trees, provided by package rpart and random forest, provided by package randomForest. To a large extent the success of the allocation routine depends on the strength of the predictive models: this is one reason why an R package for land use change modelling is attractive.

To validate model output lulcc includes a method developed by Pontius et al. (2011) that simultaneously compares a reference map for time 1, a reference map for time 2 and a simulated map for time 2 at multiple resolutions. In lulcc the results of the comparison are represented by the class ThreeMapComparison. From objects of this class it is straightforward to extract information about different sources of agreement and disagreement, represented by the class AgreementBudget, which can then be plotted. The results of the comparison are conveniently summarised by the figure of merit, represented by the classFigureOfMerit.

In addition to the core functionality described above, lulcc inludes several utility functions to assist with the model building process. Two example datasets are also included.

Author(s)

Simon Moulds

References

Fuchs, R., Herold, M., Verburg, P.H., and Clevers, J.G.P.W. (2013). A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe, Biogeosciences, 10:1543-1559.

Pontius Jr, R.G., Peethambaram, S., Castella, J.C. (2011). Comparison of three maps at multiple resol utions: a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers 101(1): 45-62.

Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S.S. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30(3):391-405.

Examples

## Not run: 

## Plum Island Ecosystems

## load data
data(pie)

## observed maps
obs <- ObsLulcRasterStack(x=pie,
                          pattern="lu", 
                          categories=c(1,2,3), 
                          labels=c("Forest","Built","Other"), 
                          t=c(0,6,14))
obs

plot(obs)

crossTabulate(obs, times=c(0,14))

## explanatory variables
ef <- ExpVarRasterList(x=pie, pattern="ef")
ef

part <- partition(x=obs[[1]], size=0.1, spatial=TRUE)
train.data <- getPredictiveModelInputData(obs=obs, ef=ef, cells=part[["train"]])

forms <- list(Built ~ ef_001+ef_002+ef_003,
              Forest ~ ef_001+ef_002,
              Other ~ ef_001+ef_002)

glm.models <- glmModels(formula=forms, family=binomial, data=train.data, obs=obs)
rpart.models <- rpartModels(formula=forms, data=train.data, obs=obs)
rf.models <- randomForestModels(formula=forms, data=train.data, obs=obs)

## test ability of models to predict allocation of forest, built and other
## land uses in testing partition
test.data <- getPredictiveModelInputData(obs=obs, ef=ef, cells=part[["test"]])

glm.pred <- PredictionList(models=glm.models, newdata=test.data)
glm.perf <- PerformanceList(pred=glm.pred, measure="rch")

rpart.pred <- PredictionList(models=rpart.models, newdata=test.data)
rpart.perf <- PerformanceList(pred=rpart.pred, measure="rch")

rf.pred <- PredictionList(models=rf.models, newdata=test.data)
rf.perf <- PerformanceList(pred=rf.pred, measure="rch")

plot(list(glm=glm.perf, rpart=rpart.perf, rf=rf.perf))

## test ability of models to predict location of urban gain 1985 to 1991
part <- rasterToPoints(obs[[1]], fun=function(x) x != 2, spatial=TRUE)
test.data <- getPredictiveModelInputData(obs=obs, ef=ef, cells=part, t=6)

glm.pred <- PredictionList(models=glm.models[[2]], newdata=test.data)
glm.perf <- PerformanceList(pred=glm.pred, measure="rch")

plot(list(glm=glm.perf))

## obtain demand scenario
dmd <- approxExtrapDemand(obs=obs, tout=0:14)
matplot(dmd, type="l", ylab="Demand (no. of cells)", xlab="Time point",
        lty=1, col=c("Green","Red","Blue"))
legend("topleft", legend=obs@labels, col=c("Green","Red","Blue"), lty=1)

## get neighbourhood values
w <- matrix(data=1, nrow=3, ncol=3)
nb <- NeighbRasterStack(x=obs[[1]], weights=w, categories=2)

## create CLUE-S model object
clues.rules <- matrix(data=1, nrow=3, ncol=3, byrow=TRUE) 

clues.parms <- list(jitter.f=0.0002,
                    scale.f=0.000001,
                    max.iter=1000,
                    max.diff=50, 
                    ave.diff=50) 

clues.model <- CluesModel(obs=obs,
                          ef=ef,
                          models=glm.models,
                          time=0:14,
                          demand=dmd,
                          elas=c(0.2,0.2,0.2),
                          rules=clues.rules,
                          params=clues.parms)

## Create Ordered model
ordered.model <- OrderedModel(obs=obs,
                              ef=ef,
                              models=glm.models,
                              time=0:14,
                              demand=dmd,
                              order=c(2,1,3)) 

## perform allocation
clues.model <- allocate(clues.model)
ordered.model <- allocate(ordered.model, stochastic=TRUE)

## pattern validation

## CLUE-S
clues.tabs <- ThreeMapComparison(x=clues.model,
                                 factors=2^(1:8),
                                 timestep=14)
plot(clues.tabs)
plot(clues.tabs, category=1, factors=2^(1:8)[c(1,3,5,7)])

## Ordered
ordered.tabs <- ThreeMapComparison(x=ordered.model,
                                 factors=2^(1:8),
                                 timestep=14)
plot(ordered.tabs)
plot(ordered.tabs, category=1, factors=2^(1:8)[c(1,3,5,7)])

## calculate agreement budget and plot

## CLUE-S
clues.agr <- AgreementBudget(x=clues.tabs)
plot(clues.agr, from=1, to=2)

## Ordered
ordered.agr <- AgreementBudget(x=ordered.tabs)
plot(ordered.agr, from=1, to=2)

## calculate Figure of Merit and plot

## CLUE-S
clues.fom <- FigureOfMerit(x=clues.tabs)
p1 <- plot(clues.fom, from=1, to=2)

## Ordered
ordered.fom <- FigureOfMerit(x=ordered.tabs)
p2 <- plot(ordered.fom, from=1, to=2)


## End(Not run)

Create an AgreementBudget object

Description

This function quantifies sources of agreement and disagreement between a reference map for time 1, a reference map for time 2 and a simulated map for time 2 to provide meaningful information about the performance of land use change simulations.

Usage

AgreementBudget(x, ...)

## S4 method for signature 'ThreeMapComparison'
AgreementBudget(x, ...)

## S4 method for signature 'RasterLayer'
AgreementBudget(x, ...)

Arguments

Details

The types of agreement and disagreement considered are those descibed in Pontius et al. (2011):

1. Persistence simulated correctly (agreement)

2. Persistence simulated as change (disagreement)

3. Change simulated incorrectly (disagreement)

4. Change simulated correctly (agreement)

5. Change simulated as persistence (disagreement)

Value

An AgreementBudget object.

References

Pontius Jr, R.G., Peethambaram, S., Castella, J.C. (2011). Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers 101(1): 45-62.

See Also

AgreementBudget-class, plot.AgreementBudget, ThreeMapComparison, FigureOfMerit

Examples

## see lulcc-package examples

Class AgreementBudget

Description

An S4 class for information about sources of agreement and disagreement between three categorical raster maps.

Slots

tables

list of data.frames that depict the three dimensional table described by Pontius et al. (2011) at different resolutions

factors

numeric vector of aggregation factors

maps

list of RasterStack objects containing land use maps at different resolutions

categories

numeric vector of land use categories

labels

character vector corresponding to categories

overall

data.frame containing the overall agreement budget

category

list of data.frames showing the agreement budget for each category

transition

list of data.frames showing the agreement budget for all possible transitions

Virtual class CategoryLabel

Description

A virtual S4 class to represent information about categorical Raster* objects.

Slots

categories

numeric vector of land use categories

labels

character vector corresponding to categories

Create a CluesModel object

Description

Methods to create a CluesModel object to supply to allocate.

Usage

CluesModel(obs, ef, models, ...)


  ## S4 method for signature 
## 'ObsLulcRasterStack,ExpVarRasterList,PredictiveModelList'
CluesModel(obs,
  ef, models, time, demand, hist, mask, neighb = NULL, elas, rules = NULL,
  nb.rules = NULL, params, output = NULL, ...)

Arguments

Details

The params argument is a list of parameter values which should contain the following components:

jitter.f

Parameter controlling the amount of perturbation applied to the probability surface prior to running the CLUE-S iterative algorithm. Higher values result in more perturbation. Default is 0.0001

scale.f

Scale factor which controls the amount by which suitability is increased if demand is not met. Default is 0.0005

max.iter

The maximum number of iterations in the simulation

max.diff

The maximum allowed difference between allocated and demanded area of any land use type. Default is 5

ave.diff

The average allowed difference between allocated and demanded area. Default is 5

Note that, in order to achieve convergence, it is likely that some adjustment of these parameters will be required.

Value

A CluesModel object.

References

Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S.S. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30(3):391-405.

See Also

CluesModel-class, allocate

Examples

## see lulcc-package examples

Class CluesModel

Description

An S4 class to represent inputs to the CLUE-S land use change model.

Slots

obs

an ObsLulcRasterStack object

ef

an ExpVarRasterList object

models

a PredictiveModelList object

time

numeric vector of timesteps over which simulation will occur

demand

matrix containing demand scenario

hist

RasterLayer showing land use history or NULL

mask

RasterLayer showing masked areas or NULL

neighb

NeighbRasterStack object or NULL

categories

numeric vector of land use categories

labels

character vector corresponding to categories

rules

matrix with land use change decision rules

nb.rules

numeric with neighbourhood decision rules

elas

numeric indicating elasticity to change (only required for

params

list with model parameters

output

RasterStack containing simulated land use maps or NULL

Create an ExpVarRasterList object

Description

Methods to load maps of explanatory variables, which may be created from file, an existing Raster* object or a list of Raster* objects.

Usage

ExpVarRasterList(x, ...)

## S4 method for signature 'missing'
ExpVarRasterList(x, pattern = NULL, ...)

## S4 method for signature 'character'
ExpVarRasterList(x, pattern = NULL, ...)

## S4 method for signature 'RasterStack'
ExpVarRasterList(x, pattern = NULL, ...)

## S4 method for signature 'list'
ExpVarRasterList(x, pattern = NULL, ...)

Arguments

Details

Explanatory variables should follow a naming convention to identify them as static (one map provided for the study period) or dynamic (one map provided for each year of the study period). The name should consist of two (static) or three (dynamic) parts: firstly, the prefix should differentiate explanatory variables from other maps in the directory, list or RasterStack. This should be followed by a unique number to differentiate the explanatory variables (note that the order of variables in the ExpVarRasterList object is determined by this value) If the variable is dynamic this number should be followed by a second number representing the timestep to which the map applies. Dynamic variables should include a map for time 0 (corresponding to the initial observed map) and every subsequent timestep in the simulation. The different parts should be separated by a period or underscore.

Maps of different explanatory variables should have the same coordinate reference system but do not have to have the same extent and resolution as long as the minimum extent is that of the study region defined by an ObsLulcRasterStack object. However, maps for different timesteps of the same dynamic variable should have the same extent and resolution because these are stored as RasterStack objects.

Value

An ExpVarRasterList object.

See Also

raster::stack

Examples

## Plum Island Ecosystems
ef <- ExpVarRasterList(x=pie, pattern="ef")

## Sibuyan
ef <- ExpVarRasterList(x=sibuyan$maps, pattern="ef")

Class ExpVarRasterList

Description

An S4 class for explanatory variables.

Slots

maps

list of RasterStack objects. The length of the list corresponds to the number of explanatory variables and the number of layers in each RasterStack represents time

names

character vector with the name of each variable in maps

dynamic

logical indicating whether dynamic variables are present

Extract by index

Description

object[[i]] can be used to extract individual objects from container classes such as ExpVarRasterList, PredictiveModelList, PredictionList and PerformanceList.

Usage

## S4 method for signature 'ExpVarRasterList,ANY,ANY'
x[[i, j, ...]]

## S4 method for signature 'CategoryLabel,ANY,ANY'
x[[i, j, ...]]

Arguments

Examples

## Plum Island Ecosystems

## Load observed land use maps
obs <- ObsLulcRasterStack(x=pie,
                   pattern="lu",
                   categories=c(1,2,3),
                   labels=c("forest","built","other"),
                   t=c(0,6,14))

summary(obs[[1]])
summary(obs[[1:2]])

Create a FigureOfMerit object

Description

Calculate the figure of merit at different levels and at different resolutions for a reference map at time 1, a reference map at time 2 and a simulated map at time 2.

Usage

FigureOfMerit(x, ...)

## S4 method for signature 'RasterLayer'
FigureOfMerit(x, ...)

## S4 method for signature 'ThreeMapComparison'
FigureOfMerit(x, ...)

Arguments

Details

In land use change modelling the figure of merit is the intersection of observed change and simulated change divided by the union of these, with a range of 0 (perfect disagreement) to 1 (perfect agreement). It is useful to calculate the figure of merit at three levels: (1) considering all possible transitions from all land use categories, (2) considering all transitions from specific land use categories and (3) considering a specific transition from one land use category to another.

Value

A FigureOfMerit object.

References

Pontius Jr, R.G., Peethambaram, S., Castella, J.C. (2011). Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers 101(1): 45-62.

See Also

plot.FigureOfMerit, ThreeMapComparison

Examples

## see lulcc-package examples

Class FigureOfMerit

Description

An S4 class for different figure of merit scores.

Slots

tables

list of data.frames that depict the three dimensional table described by Pontius et al. (2011) at different resolutions

factors

numeric vector of aggregation factors

maps

list of RasterStack objects containing land use maps at different resolutions

categories

numeric vector of land use categories

labels

character vector corresponding to categories

overall

list containing the overall figure of merit score for each aggregation factor

category

list of numeric vectors containing category specific scores

transition

list of matrices containing transition specific scores

Fit predictive models

Description

These functions fit parametric and non-parametric models to data.

Usage

glmModels(formula, family = binomial, model = FALSE, ..., obs,
  categories = NA, labels = NA)

randomForestModels(formula, ..., obs, categories = NA, labels = NA)

rpartModels(formula, ..., obs, categories = NA, labels = NA)

Arguments

Value

A PredictiveModelList object.

See Also

glm, rpart::rpart, randomForest::randomForest

Examples

## see lulcc-package examples

Virtual class Model

Description

A virtual S4 class to represent land use change models.

Slots

output

RasterStack containing simulated land use maps or NULL

Create a NeighbRasterStack object

Description

Methods to calculate neighbourhood values for cells in raster maps using raster::focal. By default the fraction of non-NA cells within the moving window (i.e. the size of the weights matrix) devoted to each land use category is calculated. This behaviour can be changed by altering the weights matrix or providing an alternative function. The resulting object can be used as the basis of neighbourhood decision rules.

Usage

NeighbRasterStack(x, weights, neighb, ...)

## S4 method for signature 'RasterLayer,list,ANY'
NeighbRasterStack(x, weights, neighb,
  categories, fun = mean, ...)

## S4 method for signature 'RasterLayer,matrix,ANY'
NeighbRasterStack(x, weights, neighb,
  categories, fun = mean, ...)

## S4 method for signature 'RasterLayer,ANY,NeighbRasterStack'
NeighbRasterStack(x, weights,
  neighb)

Arguments

Value

A NeighbRasterStack object.

See Also

NeighbRasterStack-class, allowNeighb, raster::focal

Examples

## Plum Island Ecosystems

## observed data
obs <- ObsLulcRasterStack(x=pie,
                    pattern="lu",
                    categories=c(1,2,3),
                    labels=c("forest","built","other"),
                    t=c(0,6,14))

## create a NeighbRasterStack object for 1985 land use map
w1 <- matrix(data=1, nrow=3, ncol=3, byrow=TRUE)
w2 <- w1
w3 <- w1

nb1 <- NeighbRasterStack(x=obs[[1]],
                 categories=c(1,2,3),
                 weights=list(w1,w2,w3))

## update nb2 for 1991
nb2 <- NeighbRasterStack(x=obs[[2]],
                  neighb=nb1)

## plot neighbourhood map for forest
plot(nb2[[1]])

Class NeighbRasterStack

Description

An S4 class for neighbourhood maps.

Slots

filename

see raster::Raster-class

layers

see raster::Raster-class

title

see raster::Raster-class

extent

see raster::Raster-class

rotated

see raster::Raster-class

rotation

see raster::Raster-class

ncols

see raster::Raster-class

nrows

see raster::Raster-class

crs

see raster::Raster-class

history

see raster::Raster-class

z

see raster::Raster-class

calls

list containing each call to raster::focal

categories

numeric vector of land use categories for which neighbourhood maps exist

Create an ObsLulcRasterStack object

Description

Methods to create an ObsLulcRasterStack object, which may be created from file, an existing Raster* object or a list of Raster* objects.

Usage

ObsLulcRasterStack(x, pattern, ...)

## S4 method for signature 'missing,character'
ObsLulcRasterStack(x, pattern, ...)

## S4 method for signature 'character,character'
ObsLulcRasterStack(x, pattern, ...)

## S4 method for signature 'list,character'
ObsLulcRasterStack(x, pattern, ...)

## S4 method for signature 'RasterLayer,ANY'
ObsLulcRasterStack(x, pattern, ...)

## S4 method for signature 'RasterStack,ANY'
ObsLulcRasterStack(x, pattern, categories, labels,
  t)

Arguments

Details

Observed land use maps should have the same extent and resolution. The location of non-NA cells in ObsLulcRasterStack objects defines the region for subsequent analysis.

Value

An ObsLulcRasterStack object.

See Also

ObsLulcRasterStack-class, raster::stack

Examples

## Plum Island Ecosystems
obs <- ObsLulcRasterStack(x=pie,
                   pattern="lu",
                   categories=c(1,2,3),
                   labels=c("forest","built","other"),
                   t=c(0,6,14))

## Sibuyan Island
obs <- ObsLulcRasterStack(x=sibuyan$maps,
                   pattern="lu",
                   categories=c(1,2,3,4,5),
                   labels=c("forest","coconut","grass","rice","other"),
                   t=c(0,14))

Class ObsLulcRasterStack

Description

An S4 class for observed land use maps.

Slots

filename

see raster::Raster-class

layers

see raster::Raster-class

title

see raster::Raster-class

extent

see raster::Raster-class

rotated

see raster::Raster-class

rotation

see raster::Raster-class

ncols

see raster::Raster-class

nrows

see raster::Raster-class

crs

see raster::Raster-class

history

see raster::Raster-class

z

see raster::Raster-class

t

numeric vector with timesteps corresponding to each observed map

categories

numeric vector of land use categories

labels

character vector corresponding to categories

Create an OrderedModel object

Description

Methods to create a OrderedModel object to supply to allocate.

Usage

OrderedModel(obs, ef, models, ...)


  ## S4 method for signature 
## 'ObsLulcRasterStack,ExpVarRasterList,PredictiveModelList'
OrderedModel(obs,
  ef, models, time, demand, hist, mask, neighb = NULL, rules = NULL,
  nb.rules = NULL, order, params, output = NULL, ...)

Arguments

Details

The params argument is a list of parameter values which should contain the following components:

max.diff

The maximum allowed difference between allocated and demanded area of any land use type. Default is 5

Value

An OrderedModel object.

References

Fuchs, R., Herold, M., Verburg, P.H., and Clevers, J.G.P.W. (2013). A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe, Biogeosciences, 10:1543-1559.

See Also

OrderedModel-class, allocate

Examples

## see lulcc-package examples

Class OrderedModel

Description

An S4 class to represent inputs to the Ordered allocation procedure

Slots

obs

an ObsLulcRasterStack object

ef

an ExpVarRasterList object

models

a PredictiveModelList object

time

numeric vector of timesteps over which simulation will occur

demand

matrix containing demand scenario

hist

RasterLayer showing land use history or NULL

mask

RasterLayer showing masked areas or NULL

neighb

NeighbRasterStack object or NULL

categories

numeric vector of land use categories

labels

character vector corresponding to categories

rules

matrix with land use change decision rules

nb.rules

numeric with neighbourhood decision rules

order

numeric vector of land use categories in the order that change should be allocated

params

list with model parameters

output

RasterStack containing simulated land use maps or NULL

Create a PerformanceList object

Description

This function uses different measures to evaluate multiple ROCR::prediction objects stored in a PredictionList object.

Usage

PerformanceList(pred, measure, x.measure = "cutoff", ...)

Arguments

Value

A PerformanceList object.

References

Sing, T., Sander, O., Beerenwinkel, N., Lengauer, T. (2005). ROCR: visualizing classifier performance in R. Bioinformatics 21(20):3940-3941.

See Also

performance, PredictionList

Examples

## see lulcc-package examples

Class PerformanceList

Description

An S4 class that extends ROCR::performance-class to hold the results of multiple model evaluations.

Slots

performance

list of ROCR performance objects. Each object is calculated for the corresponding ROCR prediction object held in the PredictionList object supplied to the constructor function

auc

numeric vector containing the area under the curve for each performance object

categories

numeric vector of land use categories for which performance objects were created

labels

character vector with labels corresponding to categories

Create a PredictionList object

Description

This function creates a ROCR::prediction object for each predictive model in a PredictiveModelList object. It should be used with PerformanceList to evaluate multiple models with exactly the same criteria while keeping track of which model corresponds to which land use category.

Usage

PredictionList(models, newdata, ...)

Arguments

Value

A PredictionList object.

References

Sing, T., Sander, O., Beerenwinkel, N., Lengauer, T. (2005). ROCR: visualizing classifier performance in R. Bioinformatics 21(20):3940-3941.

See Also

link{PerformanceList}, ROCR::prediction

Examples

## see lulcc-package examples

Class PredictionList

Description

An S4 class that extends ROCR::prediction-class to hold the results of multiple model predictions.

Slots

prediction

a list of ROCR::prediction-class objects. These objects are calculated for each statistical model in the PredictiveModelList object supplied to the constructor function

categories

numeric vector of land use categories for which prediction objects were created

labels

character vector with labels corresponding to categories

Class PredictiveModelList

Description

An S4 class to hold multiple mathematical models for different land use categories belonging to the same map.

Slots

models

list of predictive models

categories

numeric vector of land use categories

labels

character vector with labels corresponding to categories

Evaluate allocation performance with three maps

Description

An implementation of the method described by Pontius et al. (2011), which compares a reference map at time 1, a reference map at time 2 and a simulated map at time 2 to evaluate allocation performance at multiple resolutions while taking into account persistence. The method quantifies disagreement within coarse squares (minor allocation disagreement), disagreement between coarse squares (major allocation disagreement), disagreement about the quantity of land use change and agreement.

Usage

ThreeMapComparison(x, x1, y1, ...)

## S4 method for signature 'Model,ANY,ANY'
ThreeMapComparison(x, x1, y1, factors, timestep, ...)

## S4 method for signature 'RasterLayer,RasterLayer,RasterLayer'
ThreeMapComparison(x, x1, y1,
  factors, categories, labels, ...)

Arguments

Value

A ThreeMapComparison object.

References

Pontius Jr, R.G., Peethambaram, S., Castella, J.C. (2011). Comparison of three maps at multiple resol utions: a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers 101(1): 45-62.

See Also

AgreementBudget, FigureOfMerit, raster::aggregate

Examples

## see lulcc-package examples

Class ThreeMapComparison

Description

An S4 class to hold results of a comparison between a reference map for time 1, a reference map for time 2 and a simulation map for time 2 using the the method described by Pontius et al. (2011).

Slots

tables

list of data.frames that depict the three dimensional table described by Pontius et al. (2011) at different resolutions

factors

numeric vector of aggregation factors

maps

list of RasterStack objects containing land use maps at different resolutions

categories

numeric vector of land use categories

labels

character vector corresponding to categories

References

Pontius Jr, R.G., Peethambaram, S., Castella, J.C. (2011). Comparison of three maps at multiple resol utions: a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers 101(1): 45-62.

Allocate land use change spatially

Description

Perform spatially explicit allocation of land use change using different models. Currently the function provides an implementation of the Change in Land Use and its Effects at Small regional extent (CLUE-S) model (Verburg et al., 2002) and an ordered procedure based on the algorithm described by Fuchs et al., (2013), modified to allow stochastic transitions.

Usage

allocate(model, ...)

## S4 method for signature 'CluesModel'
allocate(model, ...)

## S4 method for signature 'OrderedModel'
allocate(model, stochastic = TRUE, ...)

Arguments

Value

An updated Model object.

References

Fuchs, R., Herold, M., Verburg, P.H., and Clevers, J.G.P.W. (2013). A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe, Biogeosciences, 10:1543-1559.

Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S.S. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30(3):391-405.

See Also

CluesModel, OrderedModel

Examples

## see lulcc-package examples

Implement decision rules for land use change

Description

Identify legitimate transitions based on land use history and specific transition rules.

Usage

allow(x, categories, cd, rules, hist = NULL, ...)

Arguments

Details

Decision rules are based on those described by Verburg et al. (2002). The rules input argument is a square matrix with dimensions equal to the number of land use categories in the study region where rows represent the current land use and columns represent future transitions. The value of each element should represent a rule from the following list:

1. rule == 0 | rule == 1: this rule concerns specific land use transitions that are allowed (1) or not (0)

2. rule > 100 & rule < 1000: this rule imposes a time limit (rule - 100) on land use transitions, after which land use change is not allowed. Time is taken from hist

3. rule > 1000: this rule imposes a minimum period of time (rule-1000) before land use is allowed to change

allow should be called from allocate methods. The output is a matrix with the same dimensions as the matrix used internally by allocation functions to store land use suitability. Thus, by multiplying the two matrices together, disallowed transitions are removed from the allocation procedure.

Value

A matrix.

References

Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S.S. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental management, 30(3):391-405.

See Also

allowNeighb

Examples

## Plum Island Ecosystems

## load observed land use data
obs <- ObsLulcRasterStack(x=pie,
                   pattern="lu",
                   categories=c(1,2,3),
                   labels=c("forest","built","other"),
                   t=c(0,6,14))

## get land use values
x <- getValues(obs[[1]])
x <- x[!is.na(x)]

## create vector of arbitrary land use history values
hist <- sample(1:10, length(x), replace=TRUE)

## calculate demand and get change direction for first timestep
dmd <- approxExtrapDemand(obs=obs, tout=0:14)
cd <- dmd[2,] - dmd[1,]
 
## create rules matrix, only allowing forest to change if the cell has
## belonged to forest for more than 8 years
rules <- matrix(data=c(1,1008,1008,
                        1,1,1,
                        1,1,1), nrow=3, ncol=3, byrow=TRUE)

allow <- allow(x=x,
               hist=hist,
               categories=obs@categories,
               cd=cd,
               rules=rules)

## create raster showing cells that are allowed to change from forest to built
r <- obs[[1]]
r[!is.na(r)] <- allow[,2]
r[obs[[1]] != 1] <- NA
plot(r)

## NB output is only useful when used within allocation routine

Implement neighbourhood decision rules

Description

Identify legitimate transitions for each cell according to neighbourhood decision rules.

Usage

allowNeighb(neighb, x, categories, rules, ...)

Arguments

Value

A matrix.

See Also

allow, NeighbRasterStack

Examples

## Plum Island Ecosystems

## load observed land use data
obs <- ObsLulcRasterStack(x=pie,
                     pattern="lu",
                     categories=c(1,2,3),
                     labels=c("forest","built","other"),
                     t=c(0,6,14))

## create a NeighbRasterStack object for forest only
w <- matrix(data=1, nrow=3, ncol=3)
nb <- NeighbRasterStack(x=obs[[1]], weights=w, categories=1)

## only allow change to forest within neighbourhood of current forest cells
## note that rules can be any value between zero (less restrictive) and one
## (more restrictive)
nb.allow <- allowNeighb(neighb=nb,
                        x=obs[[1]],
                        categories=obs@categories,
                        rules=0.5)

## create raster showing cells allowed to change to forest
r <- obs[[1]]
r[!is.na(r)] <- nb.allow[,1]
plot(r)

## NB output is only useful when used within an allocation routine

Extrapolate land use area in time

Description

Extrapolate land use area from two or more observed land use maps to provide a valid (although not necessarily realistic) demand scenario.

Usage

approxExtrapDemand(obs, tout, ...)

Arguments

Details

Many allocation routines, including the two included with lulcc, require non-spatial estimates of land use demand for every timestep in the study period. Some routines are coupled to complex economic models that predict future or past land use demand based on economic considerations; however, linear extrapolation of trends remains a useful technique.

Value

A matrix.

See Also

Hmisc::approxExtrap

Examples

## Plum Island Ecosystems

## load observed land use maps
obs <- ObsLulcRasterStack(x=pie,
                   pattern="lu",
                   categories=c(1,2,3),
                   labels=c("forest","built","other"),
                   t=c(0,6,14))

## obtain demand scenario by interpolating between observed maps
dmd <- approxExtrapDemand(obs=obs, tout=c(0:14))

## plot
matplot(dmd, type="l", ylab="Demand (no. of cells)", xlab="Time point",
        lty=1, col=c("Green","Red","Blue"))
legend("topleft", legend=obs@labels, col=c("Green","Red","Blue"), lty=1)

## linear extrapolation is also possible
dmd <- approxExtrapDemand(obs=obs, tout=c(0:50))

## plot
matplot(dmd, type="l", ylab="Demand (no. of cells)", xlab="Time point",
        lty=1, col=c("Green","Red","Blue"))
legend("topleft", legend=obs@labels, col=c("Green","Red","Blue"), lty=1)

Coerce objects to data.frame

Description

This function extracts data from all raster objects in ObsLulcRasterStack or ExpVarRasterList objects for a specified timestep.

Usage

## S3 method for class 'ExpVarRasterList'
as.data.frame(x, row.names = NULL,
  optional = FALSE, cells, t = 0, ...)

## S3 method for class 'ObsLulcRasterStack'
as.data.frame(x, row.names = NULL,
  optional = FALSE, cells, t = 0, ...)

## S4 method for signature 'ExpVarRasterList'
as.data.frame(x, row.names = NULL,
  optional = FALSE, cells, t = 0, ...)

## S4 method for signature 'ObsLulcRasterStack'
as.data.frame(x, row.names = NULL,
  optional = FALSE, cells, t = 0, ...)

Arguments

Details

If x is an ObsLulcRasterStack object the raster corresponding to t is first transformed to a RasterBrick with a boolean layer for each class with raster::layerize.

Value

A data.frame.

See Also

as.data.frame, ObsLulcRasterStack, ExpVarRasterList, partition

Examples

## Not run: 

## Plum Island Ecosystems

## observed maps
obs <- ObsLulcRasterStack(x=pie,
                          pattern="lu", 
                          categories=c(1,2,3), 
                          labels=c("Forest","Built","Other"), 
                          t=c(0,6,14))

## explanatory variables
ef <- ExpVarRasterList(x=pie, pattern="ef")

## separate data into training and testing partitions
part <- partition(x=obs[[1]], size=0.1, spatial=TRUE)
df1 <- as.data.frame(x=obs, cells=part[["all"]], t=0)
df2 <- as.data.frame(x=ef, cells=part[["all"]], t=0)


## End(Not run)

Merge PredictiveModelList objects

Description

Combine different PredictiveModelList objects into one

Usage

## S3 method for class 'PredictiveModelList'
c(..., recursive = FALSE)

Arguments

Value

a PredictiveModelList object

Examples

## Not run: 

## Plum Island Ecosystems

## load data
data(pie)

## observed maps
obs <- ObsLulcRasterStack(x=pie,
                   pattern="lu", 
                   categories=c(1,2,3), 
                   labels=c("Forest","Built","Other"), 
                   t=c(0,6,14))

## explanatory variables
ef <- ExpVarRasterList(x=pie, pattern="ef")

part <- partition(x=obs[[1]], size=0.1, spatial=TRUE)
train.data <- getPredictiveModelInputData(obs=obs, ef=ef, cells=part[["train"]], t=0)

forms <- list(Built ~ ef_001+ef_002+ef_003,
              Forest ~ 1,
              Other ~ ef_001+ef_002)

glm.models <- glmModels(formula=forms, family=binomial, data=train.data, obs=obs)
glm.models

## separate glm.models into two PredictiveModelList objects
mod1 <- glm.models[[1]]
mod2 <- glm.models[[2:3]]

## put them back together again
glm.models <- c(mod1, mod2)
glm.models


## End(Not run)

Calculate the area under the ROC curve (AUC)

Description

Estimate the AUC for each ROCR::prediction object in a PredictionList object.

Usage

compareAUC(pred, ...)

## S4 method for signature 'PredictionList'
compareAUC(pred, digits = 4, ...)

## S4 method for signature 'list'
compareAUC(pred, digits = 4, ...)

Arguments

Details

The user can compare the performance of different statistical models by providing a list of PredictionList objects. Note that compareAUC should be used in conjunction with other comparison methods because the AUC does not contain as much information as, for instance, the ROC curve itself (Pontius and Parmentier, 2014).

Value

A data.frame.

References

Sing, T., Sander, O., Beerenwinkel, N., Lengauer, T. (2005). ROCR: visualizing classifier performance in R. Bioinformatics 21(20):3940-3941.

Pontius Jr, R. G., & Parmentier, B. (2014). Recommendations for using the relative operating characteristic (ROC). Landscape ecology, 29(3), 367-382.

See Also

PredictionList, ROCR::performance

Examples

## see PredictiveModelList examples

Cross tabulate land use transitions

Description

Cross tabulate land use transitions using raster::crosstab. This step should form the basis of further research into the processes driving the most important transitions in the study region (Pontius et al., 2004).

Usage

crossTabulate(x, y, ...)

## S4 method for signature 'RasterLayer,RasterLayer'
crossTabulate(x, y, categories,
  labels = as.character(categories), ...)

## S4 method for signature 'ObsLulcRasterStack,ANY'
crossTabulate(x, y, times, ...)

Arguments

Value

A data.frame.

References

Pontius Jr, R.G., Shusas, E., McEachern, M. (2004). Detecting important categorical land changes while accounting for persistence. Agriculture, Ecosystems & Environment 101(2):251-268.

See Also

ObsLulcRasterStack, raster::crosstab

Examples

## Plum Island Ecosystems 

## Load observed land use maps
obs <- ObsLulcRasterStack(x=pie,
                   pattern="lu",
                   categories=c(1,2,3),
                   labels=c("forest","built","other"),
                   t=c(0,6,14))

crossTabulate(x=obs, times=c(0,14))

## RasterLayer input
crossTabulate(x=obs[[1]],
              y=obs[[3]],
              categories=c(1,2,3),
              labels=c("forest","built","other"))

Extract data to fit predictive models

Description

Extract a data.frame containing variables required for fitting predictive models.

Usage

getPredictiveModelInputData(obs, ef, cells, ...)

Arguments

Value

A data.frame.

See Also

as.data.frame, ObsLulcRasterStack, ExpVarRasterList, partition

Examples

## TODO

Partition raster data

Description

Divide a categorical raster map into training and testing partitions. A wrapper function for
caret::createDataPartition (Kuhn, 2008) to divide a categorical raster map into training and testing partitions.

Usage

partition(x, size = 0.5, spatial = TRUE, ...)

Arguments

Value

A list containing the following components:

train

a SpatialPoints object or numeric vector indicating the cells in the training partition

test

a SpatialPoints object or numeric vector indicating the cells in the testing partition

all

a SpatialPoints object or numeric vector indicating all non-NA cells in the study region

References

Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1-26.

See Also

caret::createDataPartition

Examples

## Not run: 

## Plum Island Ecosystems

## Load observed land use maps
obs <- ObsLulcRasterStack(x=pie,
                   pattern="lu",
                   categories=c(1,2,3),
                   labels=c("forest","built","other"),
                   t=c(0,6,14))

## create equally sized training and testing partitions
part <- partition(x=obs[[1]], size=0.1, spatial=FALSE)
names(part)


## End(Not run)

Create ROCR performance objects

Description

A wrapper function for ROCR::performance (Sing et al, 2005) to create performance objects from a list of prediction objects.

Usage

performance(prediction.obj, ...)

## S4 method for signature 'list'
performance(prediction.obj, measure, x.measure = "cutoff",
  ...)

Arguments

Value

A list of performance objects.

References

Sing, T., Sander, O., Beerenwinkel, N., Lengauer, T. (2005). ROCR: visualizing classifier performance in R. Bioinformatics 21(20):3940-3941.

See Also

ROCR::prediction, ROCR::performance

Land use change dataset for Plum Island Ecosystem

Description

Dataset containing land use maps for 1985, 1991 and 1999 and several explanatory variables derived from Pontius and Parmentier (2014).

Usage

pie

Format

A list containing the following elements:

lu_pie_1985

RasterLayer showing land use in 1985 (forest, built, other)

lu_pie_1991

RasterLayer showing land use in 1991

lu_pie_1999

RasterLayer showing land use in 1999

ef_001

RasterLayer showing elevation

ef_002

RasterLayer showing slope

ef_003

RasterLayer showing distance to built land in 1985

References

Pontius Jr, R. G., & Parmentier, B. (2014). Recommendations for using the relative operating characteristic (ROC). Landscape ecology, 29(3), 367-382.

Examples

data(pie)

Plot method for objects based on Raster* data

Description

Plot lulcc objects based on Raster* data

Usage

## S3 method for class 'ObsLulcRasterStack'
plot(x, y, ...)

## S3 method for class 'Model'
plot(x, y, ...)

## S3 method for class 'ThreeMapComparison'
plot(x, y, category, factors, ...)

## S4 method for signature 'ObsLulcRasterStack,ANY'
plot(x, y, ...)

## S4 method for signature 'Model,ANY'
plot(x, y, ...)

## S4 method for signature 'ThreeMapComparison,ANY'
plot(x, y, category, factors, ...)

Arguments

Value

A trellis object.

See Also

rasterVis::levelplot

Examples

## see lulcc-package examples

Plot method for AgreementBudget objects

Description

Plot an AgreementBudget object.

Usage

## S3 method for class 'AgreementBudget'
plot(x, y, from, to,
  col = RColorBrewer::brewer.pal(5, "Set2"), key, scales, xlab, ylab, ...)

## S4 method for signature 'AgreementBudget,ANY'
plot(x, y, from, to,
  col = RColorBrewer::brewer.pal(5, "Set2"), key, scales, xlab, ylab, ...)

Arguments

Details

The plot layout is based on work presented in Pontius et al. (2011)

Value

A trellis object.

References

Pontius Jr, R.G., Peethambaram, S., Castella, J.C. (2011). Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers 101(1): 45-62.

See Also

AgreementBudget, lattice::xyplot

Examples

## see lulcc-package examples

Plot method for FigureOfMerit objects

Description

Plot the overall, category-specific or transition-specific figure of merit at different resolutions.

Usage

## S3 method for class 'FigureOfMerit'
plot(x, y, ..., from, to,
  col = RColorBrewer::brewer.pal(8, "Set2"), type = "b", key, scales, xlab,
  ylab)

## S4 method for signature 'FigureOfMerit,ANY'
plot(x, y, ..., from, to,
  col = RColorBrewer::brewer.pal(8, "Set2"), type = "b", key, scales, xlab,
  ylab)

Arguments

Value

A trellis object.

See Also

FigureOfMerit, lattice::xyplot, lattice::panel.xyplot

Examples

## see lulcc-package examples

Plot method for PerformanceList objects

Description

Plot the the ROC curve for each performance object in a PerformanceList object. If more than one PerformanceList objects are provided ROC curves for the same land use category from different objects are included on the same plot for model comparison.

Usage

## S3 method for class 'PerformanceList'
plot(x, y, multipanel = TRUE, type = "l",
  abline = list(c(0, 1), col = "grey"), col = RColorBrewer::brewer.pal(9,
  "Set1"), key.args = NULL, ...)

## S4 method for signature 'list,ANY'
plot(x, y, multipanel = TRUE, type = "l",
  abline = list(c(0, 1), col = "grey"), col = RColorBrewer::brewer.pal(9,
  "Set1"), key.args = NULL, ...)

Arguments

Value

A trellis object.

See Also

PerformanceList, lattice::xyplot

Examples

## see lulcc-package examples

Predict location suitability

Description

Estimate location suitability with predictive models.

Usage

## S3 method for class 'PredictiveModelList'
predict(object, newdata, data.frame = FALSE,
  ...)

## S4 method for signature 'PredictiveModelList'
predict(object, newdata, data.frame = FALSE,
  ...)

Arguments

Details

This function is usually called from allocate to calculate land use suitability at each timestep. However, it may also be used to produce suitability maps (see examples).

Value

A matrix.

See Also

Model fitting, allocate

Examples

## Not run: 

## Sibuyan Island

## load observed land use data
obs <- ObsLulcRasterStack(x=sibuyan$maps,
                    pattern="lu",
                    categories=c(1,2,3,4,5),
                    labels=c("Forest","Coconut","Grass","Rice","Other"),
                    t=c(0,14))

## load explanatory variables
ef <- ExpVarRasterList(x=sibuyan$maps, pattern="ef")

## separate data into training and testing partitions
part <- partition(x=obs[[1]], size=0.1, spatial=TRUE)
train.data <- getPredictiveModelInputData(obs=obs, ef=ef, cells=part[["train"]])
all.data <- getPredictiveModelInputData(obs=obs, ef=ef, cells=part[["all"]])

## get glm.models from data
forms <- list(Forest ~ ef_001+ef_002+ef_003+ef_004+ef_005+ef_006+ef_007+ef_008+ef_010+ef_012,
              Coconut ~ ef_001+ef_002+ef_005+ef_007+ef_008+ef_009+ef_010+ef_011+ef_012,
              Grass~ef_001+ef_002+ef_004+ef_005+ef_007+ef_008+ef_009+ef_010+ef_011+ef_012+ef_013,
              Rice~ef_009+ef_010+ef_011,
              Other~1)

glm.models <- glmModels(formula=forms, family=binomial, data=train.data, obs=obs)

## create suitability maps
suitability.maps <- predict(object=glm.models, newdata=all.data, data.frame=TRUE)
points <- rasterToPoints(obs[[1]], spatial=TRUE)
suitability.maps <- SpatialPointsDataFrame(coords=points, data=suitability.maps)
r <- stack(rasterize(x=suitability.maps, y=obs[[1]], field=names(suitability.maps)))
plot(r)

## library(rasterVis)
## levelplot(r)


## End(Not run)

Resample maps in ExpVarRasterList object or list

Description

A wrapper function for raster::resample to resample raster objects in an ExpVarRasterList object or list.

Usage

## S4 method for signature 'ExpVarRasterList,Raster'
resample(x, y, method = "ngb", ...)

## S4 method for signature 'list,Raster'
resample(x, y, method = "ngb", ...)

Arguments

Value

An ExpVarRasterList object or list, depending on x.

See Also

ExpVarRasterList, raster::resample

Examples

## Not run: 

## Plum Island Ecosystems

## observed data
obs <- ObsLulcRasterStack(x=pie,
                    pattern="lu",
                    categories=c(1,2,3),
                    labels=c("forest","built","other"),
                    t=c(0,6,14))

## explanatory variables
ef <- ExpVarRasterList(x=pie, pattern="ef")

## resample to ensure maps have same characteristics as observed maps
ef <- resample(x=ef, y=obs, method="ngb")


## End(Not run)

Round elements in matrix or data.frame rows

Description

Round all numbers in a matrix or data.frame while ensuring that all rows sum to the same value.

Usage

roundSum(x, ncell, ...)

Arguments

Details

The main application of roundSum is to ensure that each row in the demand matrix specifies exactly the number of cells to be allocated to each land use category for the respective timestep. It may also be used to convert the units of demand to number of cells.

Value

A matrix.

Examples

## Sibuyan Island

## load observed land use data and create demand scenario
obs <- ObsLulcRasterStack(x=sibuyan$maps,
                    pattern="lu",
                    categories=c(1,2,3,4,5),
                    labels=c("Forest","Coconut","Grass","Rice","Other"),
                    t=c(0,14))

dmd <- approxExtrapDemand(obs, tout=0:14)
apply(dmd, 1, sum)

## artificially perturb for illustration purposes
dmd <- dmd * runif(1)
apply(dmd, 1, sum)

## use roundSum to correct demand scenario
ncell <- length(which(!is.na(getValues(sibuyan$maps$lu_sib_1997))))
ncell
dmd <- roundSum(dmd, ncell=ncell)
apply(dmd, 1, sum)

Show

Description

Show objects

Usage

## S4 method for signature 'ExpVarRasterList'
show(object)

## S4 method for signature 'PredictiveModelList'
show(object)

## S4 method for signature 'PredictionList'
show(object)

## S4 method for signature 'PerformanceList'
show(object)

## S4 method for signature 'Model'
show(object)

## S4 method for signature 'ThreeMapComparison'
show(object)

Arguments

Land use change dataset for Sibuyan Island

Description

Dataset containing land use map for 1997 and several explanatory variables for Sibuyan Island derived from Verburg et al. (2002). Data are modified by Peter Verburg to demonstrate the CLUE-s model; as such the dataset should not be used for purposes other than demonstration.

Usage

sibuyan

Format

A list containing the following components:

maps

list containing the following RasterLayers:

lu_sib_1997

RasterLayer with land use in 1997 (forest, coconut, grassland, rice, other)

ef_001

RasterLayer showing distance to sea

ef_002

RasterLayer showing mean population density

ef_003

RasterLayer showing occurrence of diorite rock

ef_004

RasterLayer showing occurrence of ultramafic rock

ef_005

RasterLayer showing occurrence of sediments

ef_006

RasterLayer showing areas with no erosion

ef_007

RasterLayer showing areas with moderate erosion

ef_008

RasterLayer showing elevation

ef_009

RasterLayer showing slope

ef_010

RasterLayer showing aspect

ef_011

RasterLayer showing distance to roads in 1997

ef_012

RasterLayer showing distance to urban areas in 1997

ef_013

RasterLayer showing distance to streams

restr1

RasterLayer showing location of current national park

restr2

RasterLayer showing location of proposed national park

demand

list of matrices with different demand scenarios:

demand1

data.frame with demand scenario representing slow growth scenario

demand2

data.frame with demand scenario representing fast growth scenario

demand3

data.frame with demand scenario representing land use change primarily for food production

References

Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S.S (2002). Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management 30(3): 391-405.

Examples

data(sibuyan)

Subset

Description

Extract a subset of objects from container classes such as ExpVarRasterList, PredictiveModelList, PredictionList and PerformanceList.

Usage

## S4 method for signature 'ExpVarRasterList'
subset(x, subset, ...)

## S4 method for signature 'PredictiveModelList'
subset(x, subset, ...)

## S4 method for signature 'PerformanceList'
subset(x, subset, ...)

## S4 method for signature 'PredictionList'
subset(x, subset, ...)

Arguments

Examples

## Sibuyan Island

## load observed land use data
obs <- ObsLulcRasterStack(x=sibuyan$maps,
                    pattern="lu",
                    categories=c(1,2,3,4,5),
                    labels=c("Forest","Coconut","Grass","Rice","Other"),
                    t=c(0,14))

summary(obs)
obs <- subset(obs, subset=names(obs)[1])
summary(obs)

## load explanatory variables
ef <- ExpVarRasterList(x=sibuyan$maps, pattern="ef")

summary(ef)
ef <- subset(ef, subset=1:5)
summary(ef)

Summary

Description

Summarise lulcc objects containing Raster* data or predictive models

Usage

summary(object, ...)

## S4 method for signature 'ObsLulcRasterStack'
summary(object, ...)

## S4 method for signature 'ExpVarRasterList'
summary(object, ...)

## S4 method for signature 'NeighbRasterStack'
summary(object, ...)

## S4 method for signature 'PredictiveModelList'
summary(object, ...)

## S4 method for signature 'Model'
summary(object, ...)

Arguments

Value

A matrix, data.frame or list

Total number of cells in a categorical Raster* object

Description

Count the number of cells belonging to each category in a Raster* object.

Usage

total(x, categories)

Arguments

Value

A list containing the following components:

total

a matrix containing the total number of cells belonging to each category. Rows represent layers in the input Raster* object

categories

the categories included in the calculation

Examples

## Sibuyan Island

## load observed land use data
obs <- ObsLulcRasterStack(x=sibuyan$maps,
                    pattern="lu",
                    categories=c(1,2,3,4,5),
                    labels=c("Forest","Coconut","Grass","Rice","Other"),
                    t=c(0,14))

total(x=obs)
total(x=obs[[1]])
total(x=obs[[2]])