Sanjay Somanath
Sanjay Somanath is a PhD student in the Department of Architecture and Civil engineering. The focus of his research is Social sustainability in neighbourhoods, specialising in computational design methods and GIS. The goal of the research is to create a framework to analyse social sustainability in the built environment through digital tools.
Social sustainability is often considered and underdeveloped and under-theorised subject. One of the main reasons cited for this to be the case is a lack of tools and methods to extract and compare data regarding the social sphere. This research currently deals with data management and processing techniques to allow architects, planners and policymakers to explore and make meaningful inferences from this data within a digital design environment and integrate it into their existing workflows.
Sanjay earned his bachelor’s degree in Architecture from BMSCE Bangalore, India and a master’s degree in Digital Architecture and Tectonics from the University of Nottingham, Nottingham, UK. He has previously worked in Qatar as an architect and in the UK as a computational designer, optimising digital design and manufacturing workflows through an Innovate UK KTP. Sanjay is also the recipient of a Santander fellowship project that explored reducing thermal heat gain in greenhouses. Sanjay has also conducted workshops on parametric design in the UK, Egypt and India.
Showing 17 publications
Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data
An activity-based synthetic population of Gothenburg, Sweden: Dataset of residents in neighbourhoods
Sentiment and semantic analysis: Urban quality inference using machine learning algorithms
Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data
Evaluation of social facilities coverage: A case study of Sofia city
Procedural digital twin generation for co-creating in VR focusing on vegetation
Combining Open Source and Commercial Tools in Digital Twin for Cities Generation
AI-baserad segementering av fasader för att optimera renovering i en större skala
Towards digitalisation of socially sustainable neighbourhood design
MiljöVis: Effektiv representation av miljödata i digitala modeller
A user interaction model for digital tools to support socially sustainable neighbourhood planning
Towards digitisation of socially sustainable neighbourhood design
The relation between social life cycle assessment and green building certification systems
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Showing 4 research projects
Extracting amenity demand and visitation profiles from mobile phone location data
Urban modelling and planning tool for energy and invisible environmental factors using AI methods
Climate adapted equal Gothenburg: The digital twin as a meeting place for discussion and co-creation