11 driving factors in combination with dynamic modeling. In contrast to most
empirical models, it is possible to simulate multiple land-use types simultaneously through the dynamic simulation of competition between land-use types.
2.1.2. Objective
The objective of this study is to forecast future land use changes in the Upstream Cisadane Watershed predicted by a land use change model CLUE-s.
2.2. Literature Review
2.2.1. Forecasting Land Use Change
Several methods have been developed for forecasting land use change, with varying degrees of sensitivity to the influence of transportation networks. The
simplest types of models for forecasting land use change are Markovian models Brown et al. 2000; Weng et al. 2002 such as Markov chain models, which tend to
treat land use change as a stochastic process. Assuming that rates of change between land use types are more or less constant from one period to the next, Markovian
models project land use transitions forward to any given future date, eventually reaching an equilibrium distribution of land uses. These models tend to have a
limited ability to incorporate transportation networks and other spatial features, except as states e.g., land use types in the model. More often, they are applied to
analyses of land use change. Cellular and agent-based models have recently gained greater acceptance as
tools for simulating land use change in urban areas. Advances in computational power and data storage have facilitated the development of models that
disaggregate urban space to a greater degree and can operate with individuals or land parcels as the units of analysis, rather than zones. These include micro-
simulation models of urban development, as well as models based on a cellular automata framework Jantz et al. 2005. Cellular automata models emphasize
neighbor effects and dynamic interactions between agents with land use cells as agents, while micro-simulation models treat individual households and firms as
12 agents and attempt to simulate their behavior in terms of location and travel choices.
Micro-simulation models of land use are often coupled with transportation models and are integrated into larger urban simulation models Waddell and Ulfarsson
2003. Despite these methodological advances, regression models continue to be a
popular method for modeling and simulating land use change. Indeed, many simulation models with a land use component use regression methods, either in the
form of discrete models of land use change Landis and Zhang 1998 or within hedonic or bid-rent frameworks for land prices Waddel 2003. Regression models
allow the identification of exogenous variables, which are thought to influence patterns of development. The variables can represent physical and social influences
on development Verburg 2004, neighborhood effects Verburg 2004; Zhou and Kockelman 2007, or the effects of transportation and accessibility. It is these latter
effects that are of the greatest interest in the current context. While regression techniques have been used previously to identify the correlates of highway network
growth in terms of land use and population characteristics Levinson 2007.
2.2.2. Modeling Land Use Change
The land-use change model, Conversion of Land-Use and its Effects at Small regional extent CLUE-S Verburg 1999, is used to simulate future land-use
change. The CLUE-S model is an empirical based model developed at the University of Wageningen in the Netherlands. The model attempts to identify
causes of land use changes driving forces, using a multivariate analysis on the possible contributors, to empirically derive rates of change Verburg et al. 1999.
The CLUE-S model has been chosen for the land-use modeling in this study based on the selection criteria developed by the US Environmental Protection
Agency US EPA, 2000. The most important reasons for choosing this model were: the flexibility on the input data driving forces, the possibility of linking the output
to another model e.g. Hydrological ModelHEC-HMS model, and free access to the model.
