PRAKRITI SRIMAL
URBAN DATA SCIENTIST | AEC TECH AUTOMATION SPECIALIST | COMPUTATIONAL DESIGNER | URBAN DESIGNER | ARCHITECT | LEED® GREEN ASSOCIATE™ | TEACHING ENTHUSIAST
IsoChronic City
IsoChronic City is a master's thesis developed at the Bartlett School of Architecture, University College London, as part of the B-Pro program. The project was conceived by a team of four: Prakriti Srimal, Sonali Bordia, Shuyao Li, and Siyang Zheng.
IsoChronic City is an urban response to the recent predicament of what the process of reurbanization in cities will entail in the post-pandemic era. It is based on the 15-minute neighbourhood concept which is derived from historical ideas about proximity and walkability.
CORE COMPETENCIES
Revit API
Automated BIM scripts, model data extraction, and intelligent workflows using Python, Grasshopper & Dynamo.
Geospatial Intelligence
Urban analysis using satellite imagery, spatial metrics, and clustering models.
Urban Data Visulaisation
Interactive dashboards, maps, and visual storytelling for spatial data insights
ABOUT
Trained as an architect and specializing in computational urbanism, with experience spanning large-scale infrastructure, spatial intelligence, and BIM automation.
With experience across architectural practice and computational design, current work focuses on geospatial intelligence, Revit API development, and urban intelligence metrics using Big Data and Machine Learning.
At Dar-al Handasah, this involves building Python-based workflows for spatial analysis, clustering, and modeling urban systems — with an emphasis on turning complex datasets into clear, actionable insights.
Previous experience includes developing Revit plugins and automation scripts using the Revit API and pyRevit, resulting in significant improvements in efficiency and precision across BIM workflows.
Academic grounding from The Bartlett, UCL, where the master’s thesis IsoChronic City explored scalable frameworks for 15-minute cities through accessibility analysis and spatial clustering.
Core Areas:
• Python-based geospatial workflows (geopandas, shapely, rasterio, scikit-learn)
• Machine learning for urban form clustering and performance metrics
• Revit API scripting, pyRevit automation, WPF forms
• Spatial computation to support strategic planning and design
Combining architecture, computation, and data to shape more adaptive and informed urban systems.
