Apparently quite some interesting insights!
Using a simple clustering method, transport data such as passenger counts can be used to identify the main activity centers. Activity clusters are then classified based on their time-dependent flow profile including magnitude, directness and the distribution of incoming and outgoing flows. It is postulated that urban structure and the spatial distribution of activities are manifested through time-dependent flow profile because activity centers with distinguished functions will yield distinctive travel patterns throughout the day. The method developed in this paper is directly transferable to different data sources, networks and scales.
The method can be used by policy makers and planners to provide insights on the discrepancy between the planning policy and the prevailing urban structure. This technique might be most valuable for anlayzing urban forms in mega-cities in emerging economies which undergo rapid changes.
Together with Qian Wang and Yu Zhao, we applied this method to the Stockholm metropolitan area. Stockholm is famous for its long-term monocentric planning with a dominant central core. Since the turn of the century there has been a noticeable shift towards developing sub-centres but to what extent has it been realized insofar?
See the results of our analysis in the full paper – available here.