lulcc-package: lulcc: land use change modelling in R
In lulcc: Land Use Change Modelling in R

Description Details Author(s) References Examples

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

The aims of the package are as follows:

to improve the reproducibility of scientific results and encourage reuse of code within the land use change modelling community
to make it easy to directly compare and combine different model structures
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.

Simon Moulds

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.

## 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)