Modeling Methodology

Now that the structure of the Key Categories as the structural tools have been decided, the main challenge is to find the numerical patterns that relate the morphological attributes inherent in them to the energy indicators as the evaluation tools. For this, this research takes one of the Key Categories and carries out a scientific inquiry into the possible patterns of relationship between this certain KC and the list of indicators selected by Integrated Modification Methodology. 
The Key Category selected for this purpose is Proximity which indicates how the distribution of different types of uses can affect the non-motorized traffic in the urban context. Its Operational Facet is Urban Current built by Links and Voids, therefore, its morphological values are those which affect the choice between non-motorized modes and motorized ones and the relevant indicators would be those that could size car/bike usage, fuel consumption, and the potential of walking flow.
There will be three steps in undertaking this research:
  1. To conclude a certain number of morphological attributes that rule Proximity. This attributes should be expressed as normalized numerical values containing explicit morphological meanings (e.g. the ratio of window shops in the street level to the opaque surface, the ratio of the length of the non-motorized links to the total length of links, etc.). There should be a minimum level of numerical correlation between the selected attributes. 
  2.  To suggest an automized manner of calculation of the morphological attributes.
  3. To suggest a theoretical platform for a set of supervised learning algorithms able to address the patterns of relationship between the morphological attributes and the evaluation indicator.