# Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility
## 5 Results  
Here, we describe the insights that we, as researchers and users of the system, learned by comparing pervasive data to survey data, in a general sense, and about the particular inference model behind the XDR dataset. The scatterplot reveals that pervasive data capture shorter and longer flows better than the survey, which has a slightly narrower distance range and is also biased towards mid-distance flows, as shown on Figure 5. This difference can have a rather important effect on the interpretation of data: a steeper, long-tailed (powerlaw) distribution of flows suggests a scale-invariant network phenomenon, while the flatter survey distribution does not, and thus surveys do not capture the full complexity of mobility. XDR has almost total municipal origin-destination pair sampling coverage for a given period, whereas survey data do not, as shown through the OD Matrix in Figure 6. Additionally, there is an observable pattern of sampling in survey data: short active flows are unintentionally oversampled (showing in the bright diagonal). However, when flows are expanded, these flows are downplayed and overshadowed by expanded public and private mode longer-distance flows. The geographic view of the flows is quite dissimilar for both datasets, exhibiting rather different features, as can be observed in Figure 7. Pervasive data show notorious change in behaviour during the day: in the morning, peripheral flows are directed towards the center and northeastern part of the city (the small peaks appearing on these nodes); flows in the afternoon show roughly the opposite pattern, but also an emergence of active flows around certain parts of the city; at night, short flows become totally predominant across the city. On the other hand, survey data, apart from density changes, do not exhibit a coherent structure in each period or in time, as its most prominent features are chaotic mid- and long-distance flows. The high density and large flows inside neighbors suggests that network-based analysis (e.g., community analysis) could be possibleusing XDR data, while, with the travel survey, a network structure does not appear so clearly, as shown by the OD matrix in Figure 8. Also, the fact that pervasive data resembles more a powerlaw mean that it could be a much better fitted by a model, e.g., the gravitational model [50], and so it would be a better input choice for a trained model, and for network-based analysis. Figure 5: Pervasive (**a**) and survey (**b**) flows at night time. Each circle is an origin-destination pair, its area the flow magnitude, with color coded mode split. Highlighted are the different overall shapes present in their distribution. Figure 6: XDR (**a**) and survey (**b**) flows at morning peak 1 at municipal aggregation level. Even at a time period where they show a similar total flow magnitude, the sampling density is different. Regarding modal distribution, the results are less general and more specific to the particular mode of transportation inference model. This is due to the fact that the visualization reveals many features that point to an over-fitted model, which is explained by the extended context in which the model was applied (and not prepared for). A quick observation of bundled flows on Figure 9 a particularly harsh contrast between private mode distributions, with flows in the northeastern corner of the city heavily charged towards private car usage and almost none in the rest of the city. Public transport is known to be less predominant in the northeastern area of the city, which leads to the hypothesis that the current inference model may be drifting toward extreme border conditions that are based on a small spatial trend. By looking through different periods, as in Figure 7 we observe that this spatial partitioning of the city is stable, but it has  
Figure 8: Zone-level OD matrix of XDR (**a**) and survey (**b**) flows for the night valley period. Sparser measures and an over representation of intramuipical flows can be appreciated in the survey.