Channebeta - Speciale - De _Gridding Chandigarh

This study wants to investigate methodologies of spatial organization alternative to the orthogonal grid. In order to do so I've decided to work on Chandigarh, a clear example of the role of the orthogonal grid in urban and architectural design in the 20th century. From a recent demographic studies done on Chandigarh, it merged that there's a trend that could, if persisting, bring to a 33% population growth in the turn of 15 year: this constituted my operational pretext.

Today's urban and economic structures of Chandigarh would not be able to deal with such an expansion, thus it is necessary to formulate a strategy for change. The first problem to be tackled then is to choose were to intervene.

This project, moving from these issues, focuses on the individuation of active areas, those areas, that is, that have the potential for change. These areas could then inform strategic decisions and characterize the early stages of the design process.

The paradigm adopted by this research has been as close as possible to a non deterministic and acausal model. The approach has been characterized by a mix of top-down/arbitrary (push) and bottom-up/data-driven (pull). Software tools have been used unconventionally through rigorous operational methodologies, defining operation-tools whose application was consistent through out the work.

The project developed three phases: Information, Activation, Individuation.

In the first phase systems of data have been chosen in order to inform the three basic elements in particle animation: the emitter, the attractors and the rejectors.

The use of 3D animation software allowed, in the second phase, the objectification of the complex systems of relations that was set up during phase one.

In the last part area of potential intervention have been identified, they've been characterized based on boolean operations (unite and intersect), whose operational meaning is substantiated by the process that generated them.

Image captions.

01.0.0 Information

Definition of the elements within the particle animation: emitter, attractors, rejectors.

01.1.0 Emitter.

01.1.1 Pre-existing villages: the area that Chandigarh occupies today as it was before the city was built, with the indication of the location and size of villages and the grid of the modern city. The influence of previous systems of spatial organization can effectively undermine the rigidity imposed by the orthogonal grid to the evolution of the city.

01.1.2 Information on pre-existing villages is extracted from the initial plan. Starting from this data a new spatial organization will be generated which will inform the movement of the emitter and the quantity of particles in the animation. Villages included within the boundaries of Chandigarh are shown in black, external ones are shown in gray.

01.1.3 Through the use of the gaussian blur in Photoshop, interaction among the elements of the previous diagram is shown Different shades of color indicate higher or lower degrees of interaction among the parts. The gray fill indicates the shared area of interaction (continuos isochromatic area).

01.1.4 By tracing the contours of the isochromatic areas an interaction levels map is obtained. The gaussian blur is now applied to the shared interaction area in order to show the areas of higher or lower intensity. The edge of the area in color is what generates the emitter's trajectory in the particle animation.

01.1.5 In this last diagram the definition of the emitter is finally complete. The colored area represents the emitter: it's axis corresponds to the path followed during the animation, changes in width, instead, represent different intensities in the emission of particles. These values were obtained using the isochromatic curves (intensity index) to increase the width of the colored strip by a factor multiplied by the isochromatic level in that point.

01.2.0 Attractors.

01.2.1 Informal markets (rehris). The area that Chandigarh occupies today as it was before the city was built is shown, with the indication of the location and size of informal markets. Markets are at the base of daily life: areas around markets would attract incoming population.

01.2.2 Information on informal markets is extracted from the initial plan. Starting from this data a new spatial organization will be generated which will inform the location and strength of the attractors in the particle animation

01.2.3 Through the use of the gaussian blur in Photoshop, interaction among the elements of the previous diagram is shown Different shades of color indicate higher or lower degrees of interaction among the parts. The gray fill indicates the shared area of interaction (continuos isochromatic area).

01.2.4 By tracing the contours of the isochromatic areas an interaction levels map is obtained. The gaussian blur is now applied to the shared interaction area in order to show the areas of higher or lower intensity. The color fill generate the organization of the attractors in the field.

01.2.5 In this last diagram the definition of the attractors is finally complete. The small crosses indicate the position of the attractors, while changes in the width of the colored strip represent different intensities. These values were obtained using the isochromatic curves (intensity index) to increase the width of the colored strip by a factor multiplied by the isochromatic level in that point.

01.3.0 Rejectors.

01.3.1 Rents. This diagram shows rent costs for each sector: the higher the rent the less people would be likely to move to that area.

01.3.2 Information on rents is extracted from the initial plan. Starting from this data a new spatial organization will be generated which will inform the location and strength of the rejectors in the particle animation

01.3.3 Through the use of the gaussian blur in Photoshop, interaction among the elements of the previous diagram is shown Different shades of color indicate higher or lower degrees of interaction among the parts. The gray fill indicates the shared area of interaction (continuos isochromatic area).

01.3.4 By tracing the contours of the isochromatic areas an interaction levels map is obtained. The gaussian blur is now applied to the shared interaction area in order to show the areas of higher or lower intensity. The color fill generate the organization of the rejectors in the field.

01.3.5 In this last diagram the definition of the rejectors is finally complete. The small crosses indicate the position of the rejectors, while changes in the width of the colored strip represent different intensities. These values were obtained using the isochromatic curves (intensity index) to increase the width of the colored strip by a factor multiplied by the isochromatic level in that point.

02.0.0 activation.

The particle animation is run The particles' movement in the field objectifies the complex system of relationships that was set up in section 1.

02.1.0 First frame.

02.1.1 Particle distribution in the chosen frame.

02.1.2 Gaussian blur shows areas of higher or lower particle density.

02.1.3 different levels of density are identified through the tracing of isochromatic areas.

02.1.4 The area shown in color identifies the largest area in which particles can be found (area of diffusion).

02.1.5 The area shown in color identifies the area of highest density (area of concentration).

02.2.0 Second frame.

02.2.1 Particle distribution in the chosen frame.

02.2.2 Gaussian blur shows areas of higher or lower particle density.

02.2.3 different levels of density are identified through the tracing of isochromatic areas.

02.2.4 The area shown in color identifies the largest area in which particles can be found (area of diffusion).

02.2.5 The area shown in color identifies the area of highest density (area of concentration).

02.3.0 Third frame.

02.3.1 Particle distribution in the chosen frame.

02.3.2 Gaussian blur shows areas of higher or lower particle density.

02.3.3 different levels of density are identified through the tracing of isochromatic areas.

02.3.4 The area shown in color identifies the largest area in which particles can be found (area of diffusion).

The area shown in color identifies the area of highest density (area of concentration).

03.0.0 Individuation.

Framing of the areas in which changes in Chandigarh's urban and economical structures could occur.

03.1.0 Active Areas

03.1.1 Superimposition of largest areas in which particles can be found from the selected frames (areas of diffusion).

03.1.2 Areas are colored and made translucid to show where they overlap. The intersection of all the areas of diffusion identifies a specific zone (area of permanence) relevant to develop a strategy for intervention, the area, that is, in which, in any given moment in the animation, particles can be found. They're concentration can vary, but we have absolute certainty of particle activity in this area.

Superimposition of areas of highest density from the selected frames (areas of concentration).

03.1.4 The union of all the areas of concentration identifies a specific zone (area of variation) relevant to develop a strategy for intervention, the area, that is, in which, in any given moment in the animation, the highest particle density can be found, thus we have absolute certainty of highest concentrations of particles will float within these boundaries.

03.1.5 The area of permanence and the area of variation allow to develop a substantiated strategy. The architect is now able to interact with the physical site through an abstract construct distant from the orthogonal grid.

Enrico G. Botta

QuArch 2001

Quantumarchitecture