13
Show the degree of access that people have to services and facilities, and
Assist in deciding appropriate investment for settlements on a hierarchical basis.
d Slope effect
Ordinarily, built-up area will grow where there are flatten areas. The first assumption of this method is that some areas are not developed because they
are too steep. The second assumption is that increasing slope implies a higher building cost. With this assumption follow, that there should be a
lower percentage of cells within higher slope intervals, compared to lower slope intervals. In this case, slope will constrain the urban form mainly
depends on the slope.
2 Constrained factors:
Constraints are limitations imposed by nature or by human beings that do not permit certain action to be taken Keeney, 1980. The specification of
constraint is typically based on available resources and regulations and involves value or professional judgment.
The constrained factors indicate those limitations posed on the urban devel- opment such as water body is not allowed to develop into urban use, high slope
terrain is not suitable for urban use, reserved area and public green area is not allowed to transfer into urban, etc.
2.2.3. Markov Changes Detection
Landuse and landcover changes are the result of the complex interaction between human and biophysical driving forces that act over wide range of
temporal and spatial scale. Change detection is a task to compare two sets of imagery to identify changes of Landuse and landcover. The use of satellite data
enables change detection to be done over broad spatial scales. Markov chains have been used to model changes in land use and land cover at a variety of spatial
scales. This type of change detection technique is one application of change detection that can be used to predict future changes based on the rates of past
change Wijanarto, 2007.
14 Land use studies using Markov chain models tend to focus on a much larger
spatial scale, and involve both urban and non-urban covers Drewett, 1969; Bourne, 1971; Bell, 1974; Bell and Hinojosa, 1977; Robinson, 1978; Jahan, 1986;
Muller and Middleton, 1994. The method is based on probability that a given piece of land will change from one mutually exclusive state to another Aavikson
1995. These probabilities are generated from past changes and then applied to predict future change. Brown et al. 2000 recently presented an approach to
estimating transition probabilities between two binary images in a study of land use and land cover relationship in the Upper Midwest, USA. This approach,
according to Huber 2001, needs to be improved and generalized in order to estimate properly Markov transitions from a pair of images.
The use of satellite imagery would create an opportunity for improved analysis. Moreover, the Markov models have been mostly employed for studies
around a city or a slightly larger area, with a regional concentration in North America. The application of stochastic models to simulate dynamic systems such
as land use and land cover changes in a developing nation is rare. Clearly, much work needs to be done in order to develop an operational procedure that integrates
the techniques of satellite remote sensing, GIS, and Markov modelling for monitoring and modelling land use and land cover changes.
2.2.4. Multi-criteria Evaluation for Built-up Area Suitability.
