Overview | Urban Sprawl | Fringe Area Development | Urban Agglomeration | Emerging Technologies | Relevant Links
GIS As A Supporting Tool In The Establishment of Land Use – Road Density ModelThe dependent variable in Eq. (1), i.e.  , which represents road transportation variable can be substituted by other variable of interest, including road density. If other socio-economic and environment factors are considered, functional relationships as in Eq. (1) can be similarly developed that link each of these factors to the estimated future land uses  .On further examination, the estimated future land uses is itself sensitive to other variables like employment level, population size and the characteristics of the property market. The relationship between these variables is represented by: In fact, the functional relationships between the independent variables in Eq. (2) are not mutually exclusive. For example, the population size is dependent upon the availability of employment as well as on the availability of housing facilities. Similarly, the employment level is in turn dependent upon the availability of labour/manpower provided by the population. Also, for businesses/industries to operate (and, offer employment), there must be appropriate building facilities available. Invariably, the multicollinearity between these variables makes mathematical modelling increasingly complex. Fortunately, there is a practical solution to the issue of multicollinearity between the independent variables of Eq. (2). For the time being, if we substitute Eq. (2) into Eq. (1), the resulting functional relationship is as shown below:  In Eq. (3), it can be distinctively identified that there exist two separate functional relationships:
 Figure 3: Modeling sequence 2.2 Land Use – Transportation Models The last 20 years saw a surge in the development of land use-transportation models. This is partly due to the availability of cheap computing power not available previously. Some of these interaction models, e.g. DRAM/EMPAL, are now outdated as theoretical understanding of the interaction between land use and transportation matures. Most state-of-the art land use-transportation interaction models are currently grounded on the discrete choice theory. At present, there are three popular discrete-choice-based interaction models (Noth, et al., 2000) that are being implemented in various on-going projects around the world. These models are:
Miller (2003) pointed out that UrbanSim is by far the most representative of the current practices in the area of land use and transportation interaction model. The fact that UrbanSim has been operationally implemented at several cities further lends credibility to UrbanSim as the preferred land use-transportation interaction model. It is also important to note that all models, except UrbanSim, are based on proprietary licences. With UrbanSim, however, the users will have access to its source code making customisation feasible and inexpensive.
In UrbanSim, the interaction between land use and transportation is modeled upon the premise that spatial decision processes shape the physical form of urban area over time. These decision processes ultimately result in physical flows of people, goods and services within this area. These spatial decision processes include (Miller, 2003):
This study mainly involves the establishment of land use – road density model by regressing the observed data of eighteen independent variables representing the town of Johore Bahru and Kuantan. The simulation is performed using an open source software known as UrbanSim whereas the input data and display operation are carried out using ArcView GIS. 3.1 Data Modeling With UrbanSim By design, the modeling of the interaction between land use and transportation is destined to be data intensive. And, the method that these data are collected and prepared must naturally be vigorously identified and defined as the validity and reliability of the results depend on them. At this juncture, it is crucial to state the requirement in terms of data so that UrbanSim can function as it was designed to do – simulating urban growth. To do this, the understanding of the architecture of UrbanSim is mandatory. In general, the data required for running UrbanSim, based on Figure 4, can be divided into the following categories:
Thus, from Table 2, we see that GIS is an integral part of the model as it is used to provide the land use data required for UrbanSim to perform the simulation of urban growth. The next section provides detail explanation of the role of GIS in providing the input data required for predicting road density.  Figure 4: Data modeling in UrbanSim Source: Waddell (2002) |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||