The sustainable and harmonious landscape in ancient Chinese philosophy and its parametrization with current GIS models. Characterization of Yin-Yang properties in Geographical Information Systems

摘要

The modeling of urban and natural environment is essential for both the landscape design experts and for the general public, as well. Since ancient times the landscape and its beauty has made a great influence on people and their philosophies. This resulted in such prominent examples as the ancient Chinese yin-yang theory which is still used in contemporary architecture and planning not only in China but world-wide as well. The current tendency is to incorporate public participation into the professional planning methodology. The traditional sense of harmony and beauty can be expressed with the modern technology. Since the GIS systems can work with different attributes and parameters of the landscape related entities, it is desired to find models that describe a harmonious or sustainable landscape and find parametrization that can be used to enhance and develop areas of having unfavourable conditions. The traditional GIS softwares were based on the discrete binary logic (yeso) whereas the yin-yang theory of harmony based on two dynamically changing opposite entities (yin and yang) and uses continuous values that are complementary to each other. The contemporary architecture (Meggyesi 2009) and science also has utilized the yin-yang model to enhance the spatial comfort. As our models get more realistic and interactive, sensor webs promise to animate and constantly update our models to provide a living and adaptive view of our built environments. Sensor inputs can in a sense act as a glue between the different tools because of their ability to inform living models. Sensors would include such inputs as weather, traffic movement, the way that wind affect the built environment, the movement and impact of pollution, as well as the many things that citizens as sensors can inform. The bringing in of these dynamic inputs inform our models, and the need for deep analysis to understand the complexity of these inputs calls for a whole new level of computing capabilities in order to better inform design and the management of our world.