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Abstract :
Model is an abstraction or simplification of real world. From early days of our childhood we learn (subconsciously) from models and play with models. Playing with guns, cars and dolls, making houses with sands, are some of the examples, which we never realize in our daily life. Models are being used as a very powerful learning tool. They are not only static like road maps but they can be dynamic like playing with the car or plane with remote control or in video games Modelling used in the present thesis comes under the category of ‘scenario models’.
Land use activity is a major issue and challenge for town and country planners (as well as environmentalists) to design the Eco friendly and sustainable economic growth. The human activity for development is forced against the environment, which results in consequences such as soil erosion, global warming, pollution etc. The causes for change in land use type activity may be due to socio-economic development or due to changes in the environment or may be due to both. For example, an increase in total export demand for a particular agriculture product will be translated into increase in demand for land for this particular product whereas increase in tourist influx will result in increased demand of land for constructions. Land can be gained by conversion of agricultural fields or by clearing the forest. In both cases the consequences will be different. Envisioning the consequent effects of land use changes, IGBP (International Geo-sphere and Biosphere Program) and IHDP (International Human Dimension Program) co-organized a working group to set up research agenda and promote research activity for land use and land cover changes (LUCC). The working group suggested three core subjects for LUCC research, such as: situation assessment, modeling and projecting, and conceptual scaling. The ultimate goal of global change study is to assess the impacts under each possible scenario and suggest preventive actions. The modeling and projecting of land use change is essential for scenario analysis and the assessment of LUCC. Consequently, issues related to data, information, and modeling have attracted many research interests ranging from local authorities to global organizations.
Adding the spatial component in the model was evolutionary concept in the Cartographic modeling in 1960s and 1970s. From the 1980s to current the focus has been on the spatial modeling. A cartographic model tends to be more 'static,' meaning it depicts spatial variation in quantitative data, but does not tell about the spatial influence on the variation. The cartographic data model uses points, lines and polygons (topologically encoded) with one, or only a few, attached attributes, such as a land use layer represented as polygons with associated land used code. Spatial models can be seen as the extension of the Cartographic models. It has all those geometric shapes mentioned in cartographic model with set of multiple associated attributes A spatial model depicts spatial processes, or the influence of spatial factors on spatial variation. Finally, spatial models can supply a more 'realistic' view of reality than a cartographic model - as spatial factors can have great influence on the variability of a statistic. A spatial model is the integration of spatial components into mathematical models. With spatial models, spatially dependent factors (e.g. distance or slope) can be incorporated with other statistical data (e.g. population or agricultural production) variables can help to refine model solution and yield better results.
Since the evolution of cellular automata, it is being used in many disciplines ranging from sciences to commercial fields. Because of its capabilities to address the complex patterns with the help of very simple transition rules it was accepted in every corner of the field of research.
In comparison with traditional approaches based on differential or difference equations the CA has advantages. CA can incorporate spatial component. And they address dynamism with simple rules, which increases computational efficiency. Since computational efficiency translates into better handling of dynamism CA becomes favorites to many modelers.
The advantages of CA are many. The construction of model is simple and easy. It has an ability to perform spatial dynamics, and time explicitly. After Analyzing the similarities and capabilities of CA it was proposed by Wagner that CA could be considered as analytical engine of GIS. Raster GIS with map algebra can be integrated with enhanced capabilities.
CA is considered to have a “natural affinity” with raster data. It has similarities with GIS, such as both represents attribute information in a layered fashion, and manipulate that information with operators (Overlay in GIS, Transitional rules in CA). The focalsum or focalmean functions of GIS has direct analogous with neighborhood functions. Having a natural affinity with the GIS it was obvious to have adopted by geographers as a tool for modeling spatial dynamics
An attempt was made to integrate non-spatial information with spatial information using GIS and Cellular automata concept integrated with Multi Criteria Technique. It was found that despite the limitations at this stage it could be used to generate the different scenario, which can address ‘what-if ‘ issues. This model can become the basis of further improved model.
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