The generated illustration shows that the differences between the edge and node based scores are very small, resulting in pretty much the same resulting overall shape, which would not cause any difference for the model and the resulting outcomes for the model. This allows me to focus on node based centralties.
Approximation uses simple linear regression to determine whether
the _location information_ is significant enough (through the
calculated steepness `beta`) which can be used to determine in a
faster and more efficient way whether the calculation of the
model is necessary and helpful in the first place.
The final API will be derived from these scripts into a different
repository, which then only holds the corresponding functions that
provide the corresponding functionalities described in the associated
master thesis.
Model assumes that top 10% of centrality values are not effected by the boundary.
This means that they form the basis for the constant part of the two-part function:
- linear function determined through simple linear regression for the remaining
points that are below the calculated threshold
- constant consistenting of the threshold
The threshold describes the median of the centrality values of the top 10% of the
values.
yves-biener
