Towards an integrated science of movement: converging research on animal movement ecology and human mobility science

Miller, Harvey J.; Dodge, Somayeh; Miller, Jennifer A.; Bohrer, Gil

doi:10.1080/13658816.2018.1564317

Cited by 84 publications

(80 citation statements)

References 127 publications

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“…As a crucial platform for human dynamics and activities, social media content can be mined in multiple approaches to determine how individuals connect and share information as well as purposefully move across scales and resolutions (Croitoru et al 2013;Miller et al 2019). When social media activities are attached with locational information, these online human activities can generate tremendous electronic footprints on Digital Earth.…”

Section: Resultsmentioning

confidence: 99%

Social Media and Social Awareness

Zhao

Nguyen

et al. 2019

Manual of Digital Earth

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The human behaviors and interactions on social media have maintained themselves as highly dynamic real-time social systems representing individual social awareness at fine spatial, temporal, and digital resolutions. In this chapter, we introduce the opportunities and challenges that human dynamics-centered social media bring to Digital Earth. We review the information diffusion of social media, the multi-faced implications of social media, and some real-world cases. Social media, on one hand, has facilitated the prediction of human dynamics in a wide spectrum of aspects, including public health, emergency response, decision making, and social equity promotion, and will also bring unintended challenges for Digital Earth, such as rumors and location spoofing on the other. Considering the multifaceted implications, this chapter calls for GIScientists to raise their awareness of the complex impacts of social media, to model the geographies of social media, and to understand ourselves as a unique species living both on the Earth and in Digital Earth. Keywords Social media • Human dynamics • Social awareness • Location spoofing 12.1 Introduction: Electronic Footprints on Digital Earth Geo-positioning system-enabled instruments can record and reveal personal awareness at fine spatial, temporal, and digital resolutions (Siła-Nowicka et al. 2016; Li et al. 2017; Ye and Liu 2019). With an exponential growth, human dynamics data are retrieved from location-aware devices, leading to a revolutionary research agenda regarding what happens where and when in the everyday lives of people in both real and virtual worlds (Batty 2013; Yao et al. 2019). Many location-based social media

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Section: Resultsmentioning

confidence: 99%

Social Media and Social Awareness

Zhao

Nguyen

et al. 2019

Manual of Digital Earth

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“…Importantly, these measures are not limited solely to comparing spatial positions but can also utilise parameters such as speed and environmental conditions. Thus, these similarity measures represent both a useful tool for ecologists in an area of growing interest, and an introduction into the wider world of movement analysis beyond ecology (Demšar et al 2015;Miller et al 2019). As new technology and analysis techniques proliferate across ecology and the information sciences, closer ties between these fields promises further innovative analysis of movement data.…”

Section: Discussionmentioning

confidence: 99%

Using time-series similarity measures to compare animal movement trajectories in ecology

Cleasby

Wakefield

Morrissey

et al. 2019

Behav Ecol Sociobiol

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Identifying and understanding patterns in movement data are amongst the principal aims of movement ecology. By quantifying the similarity of movement trajectories, inferences can be made about diverse processes, ranging from individual specialisation to the ontogeny of foraging strategies. Movement analysis is not unique to ecology however, and methods for estimating the similarity of movement trajectories have been developed in other fields but are currently under-utilised by ecologists. Here, we introduce five commonly used measures of trajectory similarity: dynamic time warping (DTW), longest common subsequence (LCSS), edit distance for real sequences (EDR), Fréchet distance and nearest neighbour distance (NND), of which only NND is routinely used by ecologists. We investigate the performance of each of these measures by simulating movement trajectories using an Ornstein-Uhlenbeck (OU) model in which we varied the following parameters: (1) the point of attraction, (2) the strength of attraction to this point and (3) the noise or volatility added to the movement process in order to determine which measures were most responsive to such changes. In addition, we demonstrate how these measures can be applied using movement trajectories of breeding northern gannets (Morus bassanus) by performing trajectory clustering on a large ecological dataset. Simulations showed that DTW and Fréchet distance were most responsive to changes in movement parameters and were able to distinguish between all the different parameter combinations we trialled. In contrast, NND was the least sensitive measure trialled. When applied to our gannet dataset, the five similarity measures were highly correlated despite differences in their underlying calculation. Clustering of trajectories within and across individuals allowed us to easily visualise and compare patterns of space use over time across a large dataset. Trajectory clusters reflected the bearing on which birds departed the colony and highlighted the use of well-known bathymetric features. As both the volume of movement data and the need to quantify similarity amongst animal trajectories grow, the measures described here and the bridge they provide to other fields of research will become increasingly useful in ecology. Significance statement As the use of tracking technology increases, there is a need to develop analytical techniques to process such large volumes of data. One area in which this would be useful is the comparison of individual movement trajectories. In response, a variety of measures Communicated by L. Z. Garamszegi

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“…The work presented in this special section responds to previously identified gaps in the CMA literature (Dodge et al 2016, Long et al 2018, Dodge 2019, Miller et al 2019 on computationally intensive movement data analytics and visualization (Graser et al, in this issue), representation of collective movement and interactions in groups of trajectories (Buchin et al, in this issue), sensor fusion and data integration to contextualize movement (Li et al and Ma et al, in this issue), as well as movement pattern analysis using crowdsourced data (Xin and MacEachren; Qiang and Xu, in this issue). These studies and the research presented in a preceding IJGIS special section on 'Big Spatiotemporal Data Analytics' (Yang et al 2020) highlight important achievements in data-driven approaches to modeling, representation and analytics of movement using 'big' mobility and crowdsourced data, forming one of the pillars of the 'data science framework for movement' (Dodge 2019) to advance the knowledge and understanding of movement processes and individuals' behavior.…”

Section: Conclusion: Towards a Movement Data Sciencementioning

confidence: 92%

“…Today more than ever we recognize the significance of movement data and movement analytics in urban planning, crisis mitigation, public health (Wang and Taylor 2016, Li et al 2019, Kraemer et al 2020. While our community has played a major role in the progress of movement data analytics and advancing its methods and applications over the past two decades (Demšar et al 2015, Dodge et al 2016, Long et al 2018, Dodge 2019, Miller et al 2019, we still have to further advance developing methods that enable the large-scale characterization of mobility patterns and knowledge discovery in large mobility data (Scherrer et al 2018). With ever-increasing access to large repositories of raw trajectory data contributed voluntarily or often involuntarily through the widespread use of cellphones (Huang et al 2019), location-aware apps and mobile services, the technology industry has gained an unprecedented data advantage to develop new computational movement analysis methods.…”

Section: Introductionmentioning

confidence: 99%