# Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility
## 1 Introduction  
Urban planning has been around since ancient times, but only at the beginning of the 20 th century was it developed into an academic field, where it had its boom in the need of accommodating old and new cities to the needs of the industry. Thus, urban planning, along with transportation planning, are modern disciplines based on models developed in pre-digital industrial times. Today, urban planning is turning towards sustainable mobility, a new paradigm that changes how the relation of the city to the environment and people is understood. In this scenario, urbanism is faced with a major challenge: incorporating new data sources and data-driven methodologies into its framework. We aim at easing this transition by using visualization in order to give domain experts a contrasting view of traditional and new data sources against each other, also filling a gap between domains that has already been noted by the visualization community [1]. Owing to its complexity and massiveness, no data source alone can account for the whole phenomenon of mobility within a city. Different ways of measuring and approximating this phenomenon have been developed in order to understand and shape the city. Travel surveys have been a key tool, relying on a small, but carefully orchestrated, sample, access to census data and statistical craftsmanship. They are expensive and time consuming to produce [2] and they show heavy under-reporting [3]. This traditional source is contrasted with the information that can be extractedfrom available and emerging pervasive data sources, such as mobile phone [4], Wi-Fi [5], and social network data [6]. This type of data are inexpensive and could potentially be used for real-time analysis, but, as a secondary data source for urban planning, it has its different methodological origin, which makes a comparison between datasets a challenge for both domain experts and data scientists. For example, as network data does not rely on self-report, trips, and mode of transport need to be inferred from waypoint traces, which is not an easy task, because of their relatively inaccurate temporal and spatial sampling, which introduces an uncertainty that was not present in this form in survey data (or at least, it does not have the same origin). We call datasets stemming from different methodologies \"cross-origin\". Especially at the pilot stage, where a new dataset is being evaluated for its possible adoption and use, urbanists need to qualitatively assess the new data source. From what we have collected in interviews with experts, there are no protocols for this kind of assessment in use yet. Because official information on mobility is scarce, the travel survey is the main benchmark to which compare a dataset, which suffers from its own structural biases, and it is updated in the lapse of years. In this unprecedented scenario, we developed Modalflow as a system for visualizing and comparing cross-origin datasets of urban mobility. Its name is derived from the two concepts that define its data workflow: flows and modality (as in modal split). Using juxtaposed and linked views in a notebook-style layout, it incorporates and adapts different visualization techniques for flow data including scatterplots, origin-destination matrices, edge bundling, flow maps (Figure 1), and ternary plots. Modalflow allows users to look into the phenomena of transport and, more importantly, to compare different sources on these phenomena, identifying each source by its methodological features. The main contributions of our work are:  
* A visualization system with coordinated views derived from a set of considerations about the nature of pervasive and traditional data sources,
* The distribution-aware selection tooltip: an enhancement for selection techniques. * The sinusoidal flow encoding: a new encoding for bidirectional and multivariate flows as edges of a graph. * A validation of our approach with a case study using real-world cross-origin datasets.