Adaptation of architectural object with contemporary urban information taxonomy through neuron patterning model

How complex urban information can be classified using neuron patterning and speculates about the adaptation of architectural object in urban environment through this method? In order to achieve this, three main steps might be followed, which are, grasping the neuron patterning activities and location based urban information data, relating two mechanisms to classify urban information, and focus on the adaptation of architectural object with its form and program in order to create the mutual relationship between the user and the built environment.

Human brain has the ability to store various kinds of data taken from outsources by creating unique
patterns for every single situation. Memory is formed as a consequence of this process, which is also a very indeterminate one. Every recall of a memory in the present, is also affected by the current
stimulations, and therefore perception of the current stimulations are also affected by the past events.

Cities, considered as the playground of miscellaneous activities, are endless data generators. People, as being users of the city, are adding value with every activity they are involved in, where they establish the ‘perceived program’. Even though the general assigned program of the urban environment is quite yet obvious, the perceived program is in a constant change. Enhancements in information technologies, even more importantly, bring more complex data. Location, traffic, transit, social messages, podcasts and likewise make urban information even harder to classify. Users start to create exact, measurable data, about their locations and activities, along with their personalities and interests. This brings up a problem of taxonomy, whereas these data, as much, doesn’t rely on a common ground. Based on these emerging properties, a deterministic way of urban information taxonomy is unable to create solutions for the necessities of people and their relation with built environment. Therefore, the way human brain handles memory generation process, can be considered as a model for urban information taxonomy, since it relies
on the constant regeneration of the information with past and present events.

These events are taking place in certain ‘locations’ in urban environments. ‘Locations’ may vary from a tight personal boundary to a cafe or giant ballpark, which are defined by the activity data generated by users that also exists in virtual, with the help of information technologies. This activity data may occur, dissolve or transform through time, thus the same applies to these ‘locations’. However, even in its vagueness, as data accumulate, it starts to form a memory of the very location.

As these memories are formed in various indeterminate locations, some network formations will start to emerge in between. Location, and memory, networks generate patterns of information throughout the city. At this point it can be grasped that a virtual information overlay will cover the urban environment, in mutual relationship with the structure of the physical city.

Urban information taxonomy is crucial to understand architectural object’s role in city. Physicality of the cities are directly related with its architectural capability. As mentioned above since the physical structure is in mutual relationship with the information patterns, architecture situates itself in a position where it affects and gets affected with/by the new taxonomy of urban information. The accumulation of information in a certain location, in which an architectural entity is positioned, forces that entity to deform the boundaries as a consequence of its structure. The virtual information overlay mentioned above will start forcing the physical city to keep up with its dynamic form. Thus, the architectural object needs to adapt itself according to information pattern generated by city happenings to create a mutual base between the persons and the built environment.


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