Analysing

The Analysing category defines how structured datasets are evaluated, measured, and transformed into quantitative descriptors inside the computational environment.

It operates on data introduced during the Gathering phase and converts it into metrics, indicators, and relational information that can inform subsequent generative or visual processes.

Analysing is divided into two main groups:

Unlike Gathering, this category does not include Supporting Tools. All nodes in this category perform direct computational evaluation.

Analysing Design Tools

This group focuses on spatial and environmental computation applied to geographically referenced data and urban systems.

It supports the analysis of:

  • Terrain morphology (elevation-derived attributes such as slope, aspect, curvature)
  • Street and transport networks (centrality, shortest paths, connectivity)
  • Visibility fields (isovists and spatial exposure)
  • GIS-based spatial operations and raster processing
  • Image-based spatial feature extraction

These operations compute measurable properties of space, infrastructure, and landscape conditions.

Analysing Data Tools

This group focuses on structured, attribute-based computation and statistical evaluation.

It supports:

  • Correlation analysis
  • Feature extraction and indexing
  • DataFrame transformations
  • Statistical operations
  • Machine learning workflows (model selection, fitting, prediction, evaluation)

These tools transform structured datasets into numerical indicators, relational patterns, and predictive outputs.

Structural Role

The Analysing category ensures:

  • Separation between raw data ingestion and metric computation
  • Explicit transformation of datasets into measurable descriptors
  • Compatibility between spatial analysis and statistical evaluation
  • Modular evaluation following Sverchok’s tree logic

It establishes the computational core of the workflow, producing quantitative outputs that can drive generative systems or visualisation processes.