Spatio-temporal Movement Data Research Articles

Facilitating insights with a user adaptable dashboard, illustrated by airport connectivity data

Abstract. Since the movement data exist, there have been approaches to collect and analyze them to get insights. This kind of data is often heterogeneous, multiscale and multi-temporal. Those interested in spatio-temporal patterns of movement data do not gain insights from textual descriptions. Therefore, visualization is required. As spatio-temporal movement data can be complex because size and characteristics, it is even challenging to create an overview of it. Plotting all the data on the screen will not be the solution as it likely will result into cluttered images where no data exploration is possible. To ensure that users will receive the information they are interested in, it is important to provide a graphical data representation environment where exploration to gain insights are possible not only in the overall level but at sub-levels as well. A dashboard would be a solution the representation of heterogeneous spatio- temporal data. It provides an overview and helps to unravel the complexity of data by splitting data in multiple data representation views. The adaptability of dashboard will help to reveal the information which cannot be seen in the overview.

Variable utilization of shelf break-associated habitats by chick-brooding rhinoceros auklets in the California Current System

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 590:211-226 (2018) - DOI: https://doi.org/10.3354/meps12500 Variable utilization of shelf break-associated habitats by chick-brooding rhinoceros auklets in the California Current System Bradley P. Wilkinson1,3,*, Jaime Jahncke2, Pete Warzybok2, Russell W. Bradley2, Scott A. Shaffer1 1Department of Biological Sciences, San José State University, San Jose, California 95192, USA 2Point Blue Conservation Science, Petaluma, California 94954, USA 3Present address: Department of Forestry and Environmental Conservation, and South Carolina Cooperative Fish and Wildlife Research Unit, Clemson University, Clemson, South Carolina 29634, USA *Corresponding author: bpwilki@g.clemson.edu ABSTRACT: Identifying frequently complex and dynamic spatial distributions of marine predators via the modeling of at-sea observations can be inherently challenging, especially when attempting to predict habitats of specific populations. Remotely-sensed tracking devices provide a viable alternative way to gather this information. We collected fine-scale spatiotemporal movement data for a small seabird, the rhinoceros auklet Cerorhinca monocerata, in the California Current System. Chick-brooding adults nesting on Southeast Farallon Island were outfitted with GPS loggers during the 2015 and 2016 breeding seasons (n = 15). Along with basic movement parameters, kernel density and residence time analyses were conducted to characterize at-sea distribution and identify areas of intensive use. Binomial generalized linear mixed modeling (GLMM) was used to assess foraging habitat. We found a greater utilization of areas beyond the shelf break than predicted by previous distributional models based on at-sea censuses. Interannual variation was evident, with relatively more outer break foraging in 2015 and relatively more shelf and inner break exploitation in 2016, although birds likely use the same habitat intrannually for self- and chick-provisioning. Prey availability and local oceanographic conditions may have influenced foraging decisions, with rockfish Sebastes spp. abundances and sub-mesoscale sea surface temperature (SST) fronts potentially acting as mediating factors. Results of mixed modeling highlighted a composite 3-way interaction between environmental variables facilitating auklet behavioral state switching, with an additional non-linear SST term being significant. Overall, foraging effort within the regional shelf break system appears to be variable at fine spatial scales depending on concomitant oceanographic conditions and related resource dynamics. KEY WORDS: Shelf break · Rhinoceros auklet · Cerorhinca monocerata · Spatial ecology · Foraging behavior · Residence time Full text in pdf format PreviousNextCite this article as: Wilkinson BP, Jahncke J, Warzybok P, Bradley RW, Shaffer SA (2018) Variable utilization of shelf break-associated habitats by chick-brooding rhinoceros auklets in the California Current System. Mar Ecol Prog Ser 590:211-226. https://doi.org/10.3354/meps12500 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 590. Online publication date: March 12, 2018 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2018 Inter-Research.

Individual movements and contact patterns in a Canadian long-term care facility.

Contact networks of individuals in healthcare facilities are poorly understood, largely due to the lack of spatio-temporal movement data. A better understanding of such networks of interactions can help improve disease control strategies for nosocomial outbreaks. We sought to determine the spatio-temporal patterns of interactions between individuals using movement data collected in the largest veterans long-term care facility in Canada. We processed close-range contact data generated by the exchange of ultra-low-power radio signals, in a prescribed proximity, between wireless sensors worn by the participants over a two-week period. Statistical analyses of contact and movement data were conducted. We found a clear dichotomy in the contact network and movement patterns between residents and healthcare workers (HCWs) in this facility. Overall, residents tend to have significantly more distinct contacts with the mean of 17.3 (s.d. 3.6) contacts, versus 3.5 (s.d. 2.3) for HCWs (p-value < 10−12), for a longer duration of time (with mean contact duration of 8 minutes for resident-resident pair versus 4.6 minutes for HCW-resident pair) while being less mobile than HCWs. Analysis of movement data and clustering coefficient of the hourly aggregated network indicates that the contact network is loosely connected (mean clustering coefficient: 0.25, interquartile range 0–0.40), while being highly structured. Our findings bring quantitative insights regarding the contact network and movements in a long-term care facility, which are highly relevant to infer direct human-to-human and indirect (i.e., via the environment) disease transmission processes. This data-driven quantification is essential for validating disease dynamic models, as well as decision analytic methods to inform control strategies for nosocomial infections.

Computational Movement Analysis

This SpringerBrief discusses the characteristics of spatiotemporal movement data, including uncertainty and scale. It investigates three core aspects of Computational Movement Analysis: Conceptual modeling of movement and movement spaces, spatiotemporal analysis methods aiming at a better understanding of movement processes (with a focus on data mining for movement patterns), and using decentralized spatial computing methods in movement analysis. The author presents Computational Movement Analysis as an interdisciplinary umbrella for analyzing movement processes with methods from a range of fields including GIScience, spatiotemporal databases and data mining. Key challenges in Computational Movement Analysis include bridging the semantic gap, privacy issues when movement data involves people, incorporating big and open data, and opportunities for decentralized movement analysis arising from the internet of things. The interdisciplinary concepts of Computational Movement Analysis make this an important book for professionals and students in computer science, geographic information science and its application areas, especially movement ecology and transportation research.

Movement Pattern Analysis Based on Sequence Signatures

Increased affordability and deployment of advanced tracking technologies have led researchers from various domains to analyze the resulting spatio-temporal movement data sets for the purpose of knowledge discovery. Two different approaches can be considered in the analysis of moving objects: quantitative analysis and qualitative analysis. This research focuses on the latter and uses the qualitative trajectory calculus (QTC), a type of calculus that represents qualitative data on moving point objects (MPOs), and establishes a framework to analyze the relative movement of multiple MPOs. A visualization technique called sequence signature (SESI) is used, which enables to map QTC patterns in a 2D indexed rasterized space in order to evaluate the similarity of relative movement patterns of multiple MPOs. The applicability of the proposed methodology is illustrated by means of two practical examples of interacting MPOs: cars on a highway and body parts of a samba dancer. The results show that the proposed method can be effectively used to analyze interactions of multiple MPOs in different domains.

VectorLens: Angular Selection of Curves within 2D Dense Visualizations.

We investigate the selection of curves within a 2D visualization by specifying their angle or slope. Such angular selection has applications in parallel coordinates, time series visualizations, spatio-temporal movement data, etc. Our interaction technique specifies a region of interest in the visualization (with a position and diameter), a direction, and an angular tolerance, all with a single drag. We experimentally compared this angular selection technique with other techniques for selecting curves, and found that angular selection resulted in a higher number of trials that were successful on the first attempt and fewer incorrectly selected curves, and was also subjectively preferred by participants. We then present the design of a popup lens widget, called the VectorLens, that allows for easy angular selection and also allows the user to perform additional filtering operations based on type of curve. Multiple VectorLens widgets can also be instantiated to combine the results of their filtering operations with boolean operators.

The Impact of Interactivity on Comprehending 2D and 3D Visualizations of Movement Data.

GPS, RFID, and other technologies have made it increasingly common to track the positions of people and objects over time as they move through two-dimensional spaces. Visualizing such spatio-temporal movement data is challenging because each person or object involves three variables (two spatial variables as a function of the time variable), and simply plotting the data on a 2D geographic map can result in overplotting and occlusion that hides details. This also makes it difficult to understand correlations between space and time. Software such as GeoTime can display such data with a three-dimensional visualization, where the 3rd dimension is used for time. This allows for the disambiguation of spatially overlapping trajectories, and in theory, should make the data clearer. However, previous experimental comparisons of 2D and 3D visualizations have so far found little advantage in 3D visualizations, possibly due to the increased complexity of navigating and understanding a 3D view. We present a new controlled experimental comparison of 2D and 3D visualizations, involving commonly performed tasks that have not been tested before, and find advantages in 3D visualizations for more complex tasks. In particular, we tease out the effects of various basic interactions and find that the 2D view relies significantly on "scrubbing" the timeline, whereas the 3D view relies mainly on 3D camera navigation. Our work helps to improve understanding of 2D and 3D visualizations of spatio-temporal data, particularly with respect to interactivity.

Scalable Detection of Spatiotemporal Encounters in Historical Movement Data

AbstractThe widespread adoption of location‐aware devices is resulting in the generation of large amounts of spatiotemporal movement data, collected and stored in digital repositories. This forms a fertile ground for domain experts and scientists to analyze such historical data and discover interesting movement behavioral patterns. Experts in many domains, such as transportation, logistics and retail, are interested in detecting and understanding movement patterns and behavior of objects in relation to each other. Their insights can point to optimization potential and reveal deviations from planned behavior. In this paper, we focus on the detection of the encounter patterns as one possible type in movement behavior. These patterns refer to objects being close to one another in terms of space and time. We define scalability as a core requirement when dealing with historical movement data, in order to allow the domain expert to set parameters of the encounter detection algorithm. Our approach leverages a designated data structure and requires only a single pass over chronological data, thus resulting in highly scalable and fast technique to detect encounters. Consequently, users are able to explore their data by interactively specifying the spatial and temporal windows that define encounters. We evaluate our proposed method as a function of its input parameters and data size. We instantiate the proposed method on urban public transportation data, where we found a large number of encounters. We show that single encounters emerge into higher level patterns that are of particular interest and value to the domain.