The following project aims to develop a web app which provides an interactive workflow to geometry exploration and beside a brief introduction to store data from user interaction with the intention to improve the interface based on click tracking.

Main Workflow

First the application started with hops to prototype the inputs and outputs from gh. Then we use rhino compute and interact inside the local server. After checking that everything works fine and satisfied with the functionality, the application was uploaded to heroku (cloud application platform). Finally use firebase to store data from web application interaction.

GH Workflow
The application uses a default geometry to interact with. Then geometry is processed by a k-means clustering component which brings 3 outputs beside the final geometry. Including cluster indexes for data visualization.


  • Random reduce
  • X, Y, Z grid growth
  • Seed for different variations
  • Clusters


  • Final geometry
  • Colors
  • Total geometry panels
  • Total area m2
  • Cluster indexes and quantity from each interaction based on total geometry panels

During the interaction, users are able to explore different variations from a basic aggregation simulation and data extracted from final geometry (outputs). The main geometry is colored by the corresponding cluster.



– Data visualization:
– Line Chart based on outputs from gh definition.
– Clicks counter:
– Buttons are able to track and count clicks.

Data Storage
The data is stored in firebase. Firebase works as a container for this app and shares features like real-time database and analytics. This means that the application is able to store additional
data (in this case non sensible data) that could be used to improve performance and user experience.
In this case tracking clicks and sending a message that could be customized in further development or could be written as json strings with more relevant information from final geometry and user interaction.


K-Means module – Digital Tools for Cloud based Data Management.– is a project of IAAC, Institute for Advanced Architecture of Catalonia, developed at Master in Advanced Computation for Architecture & Design in 2021/22 by:

Student: Salvador Calgua

Lead faculty: David Andres Leon

Faculty assistant: Hesham Shawqy