# Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility
## 2 Related Work  
Mobility visualization has been mostly related to the visualization subfields of Infovis and Visual Analytics [7]. Here, we review the most common visualization techniques that are used for depicting flows in geospatial contexts and classify them according to the kind of data they are meant to be used with. Origin-Destination MatrixOrigin-Destination (OD) matrices are a common representation used in transportation planning for low granularity data (such as municipal aggregation level). From a network visualization perspective, the OD matrix is a weighted adjacency matrix, where a continuous color scale is used in order to denote the flow magnitude between each node pair. Developments in this type of visualization have focused on improving readability and spatial mapping: MapTrix [8] enhances the traditional OD Matrix with interactive features and a linked geographical representation; OD Map [9] reorganizes the cells of the OD matrix to improve spatial pattern recognition; and, the OD Map can be combined with a tile map [10] to perform in more realistic scenarios. In spite of these improvements, OD matrices have not been used for visualizing networks of more than around a hundred nodes. GraphGraph-based flow visualization literature is greatly centered around reducing the hairball problem, i.e., visual clutter. We can divide them into those that operate some kind of simplification over the graph and then render the result with a linear technique [11; 12; 13], and those that make use of non-linear or iterative techniques for path calculation and rendering [14; 15]. The techniques that were developed for this purpose rely on heavily distorting flow paths and edge appearance in order to improve readability. However, these developments have generally taken place without taking into account the particular characteristics of datasets and how they might behave on different data scenarios (e.g., do small changes in the data correspond to small changes in the result?) as it has not been studied how distribution can affect their performance at specific tasks (e.g., are scale-invariant as readable as random networks using this particular technique)? Edge BundlingEdge Bundling methods aim to visually simplify and aggregate trajectories, trail paths, or graph in dense visualizations. Recent surveys [16; 17] provide a global overview of existing technique assets and usages. The first bundling algorithms were based on hierarchical data structure [18]. Recent techniques capture the statistical properties of the bundling [19] and enable combining multiple aggregation techniques [20]. GlyphsGlyphs allow for encoding abstract flow information, confining it to a limited icon-sized space, and benefit from being visually independent from one another [21]. Following this line of research, [PERSON] et al. [22] used the sunburst diagram as a model for a glyph, which encodes destination direction. In [PERSON] et al. [23], radial diagrams are used to encode flow magnitude by direction and distance range. [PERSON] and [PERSON] expanded this idea to include mode of transportation [24]. Other Visual TechniquesOther kinds of transport mode-specific techniques have been developed for scenarios with constrained mobility, e.g., for metro use [25; 26]. We are explicitly excluding heatmaps, which have been extensively used, as they are not able to retain network data. Quantitative Data Source ComparisonThere are studies reporting the quantitative differences between cross-origin sources: travel surveys and mobile phone data [27]/GPS data [28; 29]. Additionally, survey inaccuracies from misreporting [30] suggest the need for a mixed approach [27; 29]. This entails that, although flows, as data, may look like and be treated as the same matter when extracted from travel surveys or mobile phone network data, they are not. They are shaped by assumptions and hegemonic views that frame their methodological workflows [31; 32].