Origin-Destination Flow Data Smoothing and Mapping

Abstract

This paper presents a new approach to flow mapping that extracts inherent patterns from massive geographic mobility data and constructs effective visual representations of the data for the understanding of complex flow trends. This approach involves a new method for origin-destination flow density estimation and a new method for flow map generalization, which together can remove spurious data variance, normalize flows with control population, and detect high-level patterns that are not discernable with existing approaches. The approach achieves three main objectives in addressing the challenges for analyzing and mapping massive flow data. First, it removes the effect of size differences among spatial units via kernel-based density estimation, which produces a measurement of flow volume between each pair of origin and destination. Second, it extracts major flow patterns in massive flow data through a new flow sampling method, which filters out duplicate information in the smoothed flows. Third, it enables effective flow mapping and allows intuitive perception of flow patterns among origins and destinations without bundling or altering flow paths. The approach can work with both point-based flow data (such as taxi trips with GPS locations) and area-based flow data (such as county-to-county migration). Moreover, the approach can be used to detect and compare flow patterns at different scales or in relatively sparse flow datasets, such as migration for each age group. We evaluate and demonstrate the new approach with case studies of U.S. migration data and experiments with synthetic data.

A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data

“…For example, Guo et al presented an approach to group spatial points into clusters, derive statistical summaries, and visualize spatiotemporal mobility patterns [9]. In addition, they presented a flow-based density estimation method and a flow selection method to normalize and smooth flows with a controlled neighborhood size and detect high-level patterns in the data [2,10]. Mao et al presented another novel approach for the spatial clustering of OD pairs based on traffic grid partitioning to discover spatiotemporal patterns in urban commuting and the job-housing balance.…”

“…Recent improvements in Big Data and tracking facilities have made it possible to collect a large amount of travel data for moving objects. However, previous studies of OD matrices, which have been based on point statistics over administrative or traffic spatial units, quickly become illegible as the data size increases due to the massive intersections and overlapping of OD flows [2]. Automatic algorithms that are able to extract useful information from these sources have consequently acquired great interest [3].…”

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

“…Flow maps show the spatial context of flow data in an undistorted way but often suffer from numerous intersections, occlusions, and visual clutter. The problems of flow mapping are comprehensively discussed by Guo and Zhou [28]. Moreover, map animation may be ineffective [31,44], because the user cannot memorize and mentally compare multiple spatial situations.…”

“…Guo [26,27] applies spatially constrained hierarchic graph partitioning techniques to group places into larger units and then visualizes aggregated flows between these larger units. Guo and Zhu [28] point to the modifiable areal unit problem (MAUP) [38]. They propose kernel-based density estimation, which also normalizes the flow magnitudes.…”

Revisiting flow maps: a classification and a 3D alternative to visual clutter

“…Aggregation combines several elements into, one therefore reduce the number of items in flow maps. Aggregation of nodes and regions can represent a hierarchy pattern of movement (Demšar & Virrantaus, 2010;Guo, 2009;Guo & Zhu, 2014;Hu, Zhang, & Li, 2013;Rinzivillo et al, 2008), and aggregation of flows allows users to distinguish frequent flows from less frequent and occasional ones (N. Andrienko & Andrienko, 2007). The aggregation provides users with visually scalable representation (Elmqvist & Fekete, 2010).…”