Mobility and spatial interaction data have become increasingly available due to the wide adoption of location-aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article we focus on a special type of mobility data, i.e. origin-destination pairs, and present a new approach to the discovery and understanding of spatio-temporal patterns in the movements. Specifically, to extract information from complex connections among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is two-fold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high-level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi-resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.

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.

Flexible dielectric materials with high electrical energy densities are of crucial importance in advanced electronics and electric power systems. The conventional methods for fabricating flexible dielectric materials with high electrical energy densities are introducing zero-, one-, and three-dimensional high-k inorganic nanofiller into a dielectric polymer matrix while less twodimensional high-k nanofillers were included. Herein, two-dimensional (2D) high-k titanium dioxide nanosheets prepared by a one-step hydrothermal reaction were utilized to boost the energy storage performance of dielectric polymer nanocomposites. It was found that compared with the polymer matrix the nanocomposites not only exhibit an enhanced dielectric constant but also show suppressed dielectric loss, which is desirable for energy storage applications. The nanocomposite with 5 wt % 2D nanosheets exhibits a superhigh discharged energy density of 13.0 J/cm 3 at 570 MV/m, which is nearly four times greater than that of commercialized biaxially oriented polypropylene (BOPP) (3.6 J/cm 3 at 600 MV/m). In addition, nanocomposites with 5 wt % zero-and one-dimensional (0D and 1D) nanofiller are also fabricated for comparison. Results reveal that discharged energy densities of nanocomposites with 5 wt % 2D nanosheets are 236% and 382% higher than those of nanocomposites with 5 wt % 1D (5.5 J/cm 3 at 400 MV/m) and 0D (3.4 J/cm 3 at 300 MV/m) nanofiller, respectively. Finite element simulation was conducted to study the electric field distribution in nanocomposites with different shapes of nanofillers. Furthermore, the comparison of the current nanocomposites and previous reported nanocomposites with 0D, 1D, and 3D nanofillers shows that the 2D high-k nanofiller exhibited superior potential in advancing the energy storage nature of polymer nanocomposites. This remarkable exhibition of energy storage capability provides new insights into the development of high performance dielectric materials.

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