# Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility
## 6 Discussion  
We showed Modalflow to domain experts and potential users working in Santiago through an asynchronous qualitative evaluation process lasting a month. Each week, a new view of the system would be uploaded to the demo page and iterated according to modifications that were suggested by the freely commenting participants in the online discussion room. In some occasions, the participants would even mock-up a new solution and present it to the group of their own will. In total, eight people (four visualization/urban studies students and four urban planners from the private sector) voluntarily participated throughout the whole process. After this, a one-session workshop was arranged with governmental transport authorities, where five experts were shown the final system. Here, we report on the results. Early feedback was incorporated into the design, like the use of black background, only three modes of transportation and the size of the visual elements, which can be switched between fixed and variable (dependant on encoded flow magnitude). The sinusoidal encoding of flows was deemed to be beautiful, but a bit confusing and using the more familiar arc encoding was suggested. We explain this comment based on familiarity but we are aware that a formal evaluation is needed. Interestingly, the sinusoidal encoding was more readable when dealing with XDR, rather than survey data, arguably due to the artificial modal clustering computed for the former. Admittedly, the domain experts were deterred at a later stage by the modal distribution of the mobile phone network data, which, being still undergoing research, did not meet their expectations. It seemed hard for them to abstract the visualization tool from the data being visualized, expecting to see results rather than a tool to evaluate results by themselves. Conversely, the group of experts from the private sector received the tool with much more interest and excitement. Even though both domain expert profiles work on similar problems, experts working in the public sector seemed to be more focused on strict results that could be readily applied, whereas experts in the private sector were more open to new methodologies and how these visualizations \"create\" a new reality for the data that can be used to strongly communicate and innovate. This difference has implications in how we define and apply methodologies to enable collaborative work between transportation and data science, using visualization as intermediary between disciplines ([PERSON] and [PERSON], 2018). The distribution-aware selector tool in the scatterplot was received as an interesting feature and its implications could be further developed, attending to the idea of an \"equivalence principle\" between flows of different datasets. Such an endeavour could be an interesting area of research for modelling and mixed data analysis. This also poses new questions regarding the layout of the system. Modalflow was designed with linked, juxtaposed visualizations, where the selector tool provided a way to link data semantics. However, there exist other ways of composing visualizations in layouts, such as superimposition, overloading and nesting ([PERSON] and [PERSON], 2018), which would imply a different implementation of the selection, while maintaining the data-semantic approach. These layouts will be studied in future work. Because of the notebook layout, they are not exclusive--for instance, the OD matrices may be juxtaposed, whereas the flow maps may be partitioned or nested, enabling an advanced comparison and interaction with cross-origin datasets.