PART I - Data mining

Introduction to data minning (datasets, repositories, territorial resources), importing urban data.

Scope I: First steps with GH and advantages of using visual programming for urban data workflows.

Plan (vectorized map), from .shp files. Information sources. What kind of data is relevant? Data classification and selection



Generating urban models on 3d and vectorial mapping. Translating data into geometric parameters.

Scope II: Combining 3D's and axial maps from urban environments.


PART III - Introduction to urban variables I

Application of analytical methodologies to vectorial models.

Space Syntax: Analysis of Integration, entropy, control and election. Local integration and difference factor. Diagramming and visualization of resulting data.


PART IV - Introduction to urban variablesII

Application of analytical methodologies to vectorial models.

SmartSpace: Variables of visibility and bidimensional vision field from a single point, global visibility in an area, walkability and connectivity. Diagramming and visualizing data.


PART V - Analytical methods

Space Syntax. Graph theory, connectivity and topology of nodal networks on different scales. Data visualization.



Elisa Pozo Menéndez