The computational algorithm finds negative datasets to identify the macro scales most affected by negative conditions. The above images show the original values for analysing.

The result of Principal Component Analysis. It is important to associate the analysis with its actual geolocations. The brighter colours indicate such cells are more influenced by our selected datasets.

The site we chose for the next step contains six clusters, which indicates that the choen areas have the potential as they are applied with multiple dimensions from our negative urban conditions.

We used layers of fundamental datasets we have gathered, and the results of our data analytics of principal component values and other algorithms.

Custom Vector based algorithm was created for the agents to move weighed by vectors that deviate them from the light to the dark zones to determine the destinations.

The points from the vector dominated simulation reach the most agminated parts where shadow is located. The point cloud generates the results.

The initial design formed by the voxels, is read as the traces of floating platforms travelling through time, redefining the connection as the bridge with our shadow-oriented definition.