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equiticity

Project details

Programme
Cluster RC14
Year 1

In this saturated world of instant gratification and dopamine, there has been an increase in anxiety and depression, detaching people from reality. Social media's ocular-centric culture exacerbates this isolation. Hence, citizens must embrace reality through urban spaces. Our thesis addresses mental well-being by shifting the focus from the figure to the ground, particularly in London's perplexing transition from residential to commercial areas. We explore the concept of perception of space from the inside out and vice versa, by designing with environmental, visual, material, and colour factors that affect users, fostering rootedness and well-being. This humane solution, aided by physiological, psychological, and social data in Vauxhall, South London, would act as a process model to counter the detachment caused by visual dominance, promoting a grounded user experience.

Research Question

Research Question

01

Data Collection

Data Collection

Data Collection

The data collection was based on physiological, psychological, and social factors of London. The space syntax method was employed to analyse the impact of the city's spatial configuration.

02

Data Analytics

Data Stacking and Clustering

Data Stacking and Clustering

The collected data was divided into two groups, and then, using unsupervised machine learning methods, it was compared, differentiated, and clustered to identify the area of focus.

Social Media Data

Social Media Data

On the selected sited of Vauxhall, the data extracted from image-centred social media was utilised for conducting sentimental and area perception analysis, and Open Street Maps helped scrutinise pedestrian behaviour.

Environmental Analysis

Environmental Analysis

In visibility graph analysis, inter visibility connections within urban networks, and sun exposure throughout the year were analysed.

03

Site Simulation

The Pix2pix technique used generative adversarial networks (GANs) to transform input data into desired output representations, which was employed for site selection by process.

Pix2pix

The Pix2pix technique used generative adversarial networks (GANs) to transform input data into desired output representations, which was employed for site selection by process.

Liminal Space

Liminal Space

The route from residential to commercial area, also known as liminal space, is the most challenging space in the city. Pix2pix identified data-driven spaces, leading to the selection of a route through these areas as the intervention site.

Urban Green Analysis

Urban Green Analysis

The urban green analysis on the extracted google street data helped us identify nature-deprived locations.

City within a City

City within a City

Considering the ‘city within a city’ concept, which entails creating a self-contained and holistic urban environment catering to all needs, a microsite was selected where data from Vauxhall was employed to train and generate desirable design outputs.

04

Design Strategy

Form Generation

Form Generation

The forms for the design were inspired by the English landscape, in line with the ‘city within a city"’ concept, aiming to create contextual transitional spaces.

Design Iterations

Design Iterations

The generated forms were iterated on-site using computational techniques.

Design Optimisation

Design Optimisation

The extreme weather conditions in London, which affect the haptic senses of individuals, prompted iterations of the building blocks in various climatic conditions, ultimately leading to an optimised outcome.

Design Overview

Design Overview

Sensorial Design Details

Sensorial Design Details

Perceptual quality was improved by considering sensorial factors such as colours from pix2pix. Windows were optimised for various viewpoints, and textures were generated from mid-journey, taking into account activity, colour, and emotional needs.

05

Design Visualisations

Sensorial View

Sensorial View

Corner Street

Corner Street

View from Interior

View from Interior

Central Spine

Central Spine

Night View

Night View

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