Delhi: AI and Urban Identity
The unprecedented pace of urbanization has transformed cities into complex and dynamic ecosystems where physical form, cultural practices, and human perception intersect to create evolving urban identities. Delhi, as India’s national capital, reflects these challenges acutely with its spatial fragmentation, diverse cultural layers, and rapid urban expansion. This research investigates Delhi’s urban identity by integrating Kevin Lynch’s theoretical framework of five elements—paths, edges, districts, nodes, and landmarks—with advanced data-driven and artificial intelligence (AI) techniques.
Using a hybrid methodological framework, the study collects and processes multi-source datasets, including crowdsourced Google reviews, TripAdvisor ratings, Wikipedia narratives, TomTom traffic data, NASA nightlight imagery, and OpenStreetMap layers. These heterogeneous datasets are analyzed through Natural Language Processing (NLP), sentiment analysis, and image recognition models. An AI-based Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) model, trained on data from over 80 global cities, enables predictive scoring of Delhi’s urban identity at multiple spatial scales.
Aditya Maheshwari
Batch of 2023-25
Department of Urban Planning
SPA/NS/UP/2023/1609
The KNIME platform facilitates end-to-end data processing, model training, and scoring automation, ensuring replicability and scalability of the framework. The model outputs include zonal identity scores, landmark and node evaluations, pathway coherence ratings, and spatial heatmaps. Primary surveys and field validations were incorporated to cross-verify and fine-tune model predictions.
The findings reveal clear spatial variations across Delhi, identifying high-identity zones such as Central Delhi and the Walled City, while peripheral regions exhibit weaker identity markers. The study culminates in a set of data-driven urban structure proposals, area-based revitalization strategies, policy frameworks, and a real-time AI-powered identity monitoring dashboard for adaptive planning.
This research offers a novel, scalable model for assessing urban identity in megacities, demonstrating how AI and urban planning can converge to guide future sustainable, inclusive, and identity-conscious urban development.