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.
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2.3. Methodology
Changes in land use pattern are related to a large number of biophysical and socio-economic factors. The modeling of spatially explicit changes in land use
pattern requires, therefore, a large database of factors considered to be important in the case study. Therefore, the database is not similar for every application. To run
the model it is minimally needed to have spatially explicit data for at least 1 year. However, to allow calibration and validation model works, it is necessary to have
data of another different year. To meet this necessity, the research will employ data from 2 different years, with 6
years’ time difference, i.e. 1991 and 2009.
2.3.1. Data Preparation
2.3.1.1. Data Requirement for Land Use Change Model
For the simulation of dynamics of the spatial pattern of different land use types, data are needed for the land use distribution and a number of biophysical and
socio-economic parameters that are considered as important potential drivers of the land use pattern. These drivers are most commonly variables that describe the
demography, soil, geology, climate and infrastructural situation. This study only considers the biophysical aspects, while the socio-economic aspect is considered
constant Business as usual. The data required to analyze land use change process and build scenario
development were obtained from various sources. The data are derived from multispectral satellite data, extracted from digital topographic data, and from spatial
processing of statistical data. According to type, data are divided in three: remotely sensed data, digital topographic data and statistical data. How the data were
collected and used will be explained below.
Remotely Sensed Data
Remotely data used in this study comprises of Landsat images 1991 30 m, and ALOS-AVNIR image 2009 10 m. These data will be used for land use change
analysis and input for trend extrapolation to calculate land use requirements year 1991-2030.
