Analysis and visualisation of movement: an interdisciplinary review

Movement Ecology

Robert Weibel³  Daniel Weiskopf²  Nico Van de Weghe⁷  Bettina Speckmann⁵  Kamran Safi⁴  Francesca Cagnacci¹  Kevin Buchin⁵  Urška Demšar⁶ 

[1]Biodiversity and Molecular Ecology Department, IASMA Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy

[2]Visualization Research Center, University of Stuttgart, Stuttgart, Germany

[3]Department of Geography, University of Zurich, Zurich, Switzerland

[4]Department of Biology, University of Konstanz, Konstanz, Germany

[5]Department of Mathematics and Computer Science, Technical University Eindhoven, Eindhoven, The Netherlands

[6]School of Geography & Geosciences, University of St Andrews, Irvine Building, North Street, St Andrews, Fife KY16 9AL, Scotland, UK

[7]Department of Geography, Ghent University, Ghent, Belgium

关键词: Interdisciplinary developments;    Visual analytics;    Visualisation;    Computational geometry;    Geographic information science;    Spatio-temporal visualisation;    Spatio-temporal analysis;    Trajectories;    Animal movement;    Movement ecology;   

Others  :  1171076
DOI  :  10.1186/s40462-015-0032-y

 received in 2014-11-17, accepted in 2015-02-02,  发布年份 2015

【 摘 要 】

The processes that cause and influence movement are one of the main points of enquiry in movement ecology. However, ecology is not the only discipline interested in movement: a number of information sciences are specialising in analysis and visualisation of movement data. The recent explosion in availability and complexity of movement data has resulted in a call in ecology for new appropriate methods that would be able to take full advantage of the increasingly complex and growing data volume. One way in which this could be done is to form interdisciplinary collaborations between ecologists and experts from information sciences that analyse movement. In this paper we present an overview of new movement analysis and visualisation methodologies resulting from such an interdisciplinary research network: the European COST Action “MOVE - Knowledge Discovery from Moving Objects” (http://www.move-cost.info webcite). This international network evolved over four years and brought together some 140 researchers from different disciplines: those that collect movement data (out of which the movement ecology was the largest represented group) and those that specialise in developing methods for analysis and visualisation of such data (represented in MOVE by computational geometry, geographic information science, visualisation and visual analytics). We present MOVE achievements and at the same time put them in ecological context by exploring relevant ecological themes to which MOVE studies do or potentially could contribute.

【 授权许可】

   
2015 Demšar et al.; licensee BioMed Central.

【 图 表 】

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【 参考文献 】

• [1]Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, et al.: A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci 2008, 105(49):19052-9.
• [2]Sutherland WJ, Freckleton RP, Godfray HCJ, Beissinger SR, Benton T, Cameron DD, et al.: Identification of 100 fundamental ecological questions. J Ecol 2013, 2013(101):58-67.
• [3]Jeltsch F, Bonte D, Pe'er G, Reineking B, Leimgruber P, Balkenhol N, et al.: Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics. Movement Ecology 2013, 2013:1-6.
• [4]Holden C: Inching Toward Movement Ecology. Science 2006, 313:779-82.
• [5]Cagnacci F, Boitani L, Powell RA, Boyce MS: Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Phil Trans Biol Sci 2010, 365:2157-62.
• [6]Mueller T, Fagan WF: Search and navigation in dynamic environments from individual behaviors to population distributions. Oikos 2008, 117:654-64.
• [7]Wikelski M, Kays RW, Kasdin NJ, Thorup K, Smith JA, Swenson GW Jr: Going wild: what a global small-animal tracking system could do for experimental biologists. J Exp Biol 2007, 210:181-6.
• [8]Tomkiewicz SM, Fuller MR, Kie JG, Bates KK: Global positioning system and associated technologies in animal behaviour and ecological research. Phil Trans Biol Sci 2010, 365:2163-76.
• [9]Bridge ES, Thorup K, Bowlin MS, Chilson PB, Diehl RH, Flacron RW, et al.: Technology on the Move: Recent and Forthcoming Innovations for Tracking Migratory Birds. Bioscience 2011, 61:689-98.
• [10]Kranstauber B, Cameron A, Weinzerl R, Fountain T, Tilak S, Wikelski M, et al.: The Movebank data model for animal tracking. Environ Model Software 2011, 26:834-5.
• [11]Rodriguez A, Negro JJ, Mulero M, Rodriguez C, Hernandez-Pliego J, Bustamante J: The Eye in the Sky: Combined Use of Unmanned Aerial Systems and GPS Data Loggers for Ecological Research and Conservation of Small Birds. PLoS One 2012, 7(12):e50336.
• [12]Fedak MA: Marine animals as platforms for oceanographic sampling: a “win/win” situation for biology and operational oceanography. Memoir Natl Inst Polar Res 2004, 58:133-47.
• [13]Calenge C, Draya S, Royer-Carenzia M: The concept of animals’ trajectories from a data analysis perspective. Ecol Informat 2009, 4:34-41.
• [14]Wikelski M, Rienks F: Global Satellite Tracking of (Small) Animals Will Revolutionize Insight Into Migration, Human Health, and Conservation. ICARUS white paper. 2008.
• [15]Long JA, Nelson TA: A review of quantitative methods for movement data. Int J Geogr Inform Sci 2012, 27(2):292-318.
• [16]Shamoun-Baranes J, van Loon EE, Purves RS, Speckmann B, Weiskopf D, Camphuysen CJ: Analysis and visualization of animal movement. Biol Lett 2011, 8(1):6-9.
• [17]Sack J, Speckmann B, Van Loon EE and Weibel R (Eds.) 2010. Representation, Analysis and Visualization of Moving Objects, Dagstuhl Seminar Proceedings 10491, 5–10 Dec 2010, http://drops.dagstuhl.de/portals/index.php?semnr=10491.
• [18]Gudmundsson J, Laube P, Van Loon, EE, 2012. Representation, Analysis and Visualization of Moving Objects, Dagstuhl Seminar Proceedings 12512, 16–21 Dec 2012. http://drops.dagstuhl.de/opus/volltexte/2013/3996/.
• [19]Slingsby A, Dykes J: Experiences in involving analysts in visualisation design. Proceedings of BELIV ‘12: Beyond Time and Errors - Novel Evaluation Methods for Visualization, 14–15 Oct 2012. USA, Seattle; 2012.
• [20]Seton ET: Life-histories of northern animals: an account of the mammals of Manitoba. Charles Scribner, New York, NY; 1909.
• [21]Burt WH: Territoriality and Home Range Concepts as Applied to Mammals. J Mammal 1943, 24:346-52.
• [22]Powell RA, Mitchell MS: What is a home range? J Mammal 2012, 93:948-58.
• [23]Worton BJ: Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies. Ecology 1989, 70(1):164-8.
• [24]Kie JG, Matthiopoulos J, Fieberg J, Powell RA, Cagnacci F, Mitchell MS, et al.: The home-range concept: are traditional estimators still relevant with modern telemetry technology? Phil Trans Biol Sci 2010, 365:2221-31.
• [25]Getz WM, Fortmann-Roe S, Cross PC, Lyons AJ, Ryan SJ, Wilmers CC: LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions. PLoS One 2007, 2(2):e207.
• [26]Worton BJ: A review of models of home range for animal movement. Ecol Model 1987, 38:277-98.
• [27]Laver PN, Kelly MJ: A Critical Review of Home Range Studies. J Wildl Manage 2008, 72(1):290-8.
• [28]Börger L, Dalziel BD, Fryxell JM: Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecol Lett 2008, 11(6):637-50.
• [29]Moorcroft PR: Mechanistic approaches to understanding and predicting mammalian space use: recent advances, future directions. J Mammal 2012, 93(4):903-16.
• [30]Silverman BW: Density estimation for statistics and data analysis. Chapman and Hall, New York; 1986.
• [31]Seaman DE, Powell RA: An Evaluation of the Accuracy of Kernel Density Estimators for Home Range Analysis. Ecology 1996, 77(7):2075-85.
• [32]Calenge C: The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals. Ecol Model 2006, 198:516-9.
• [33]Fieberg J: Kernel density estimators of home range: smoothing and the autocorrelation red herring. Ecology 2007, 88:1059-66.
• [34]Brito JC: Seasonal Variation in Movements, Home Range, and Habitat Use by Male Vipera latastei in Northern Portugal. J Herpetol 2003, 37(1):155-60.
• [35]Fieberg J, Börger L: Could you please phrase “home range” as a question? J Mammal 2012, 93:890-902.
• [36]Walter DW, Beringer J, Hansen LP, Fischer JW, Millspaugh JJ, Vercauteren KC: Factors affecting space use overlap by white-tailed deer in an urban landscape. Int J Geogr Inform Sci 2011, 25(3):379-92.
• [37]Lyons AJ, Turner WC, Getz WM: Home range plus: a space-time characterization of movement over real landscapes. Movement Ecology 2013, 2013:1-2.
• [38]Long JA, Nelson TA: Time Geography and Wildlife Home Range Delineation. J Wildl Manage 2011, 76(2):407-13.
• [39]Bullard F: Estimating the Home Range of an Animal: a Brownian Bridge Approach. MSc thesis, University of North Carolina at Chapel Hill; 1999.
• [40]Horne JS, Garton EO, Krone SM, Lewis JS: Analyzing Animal Movements Using Brownian Bridges. Ecology 2007, 88(9):2354-63.
• [41]Benhamou S: Dynamic Approach to Space and Habitat Use Based on Biased Random Bridges. PLoS One 2011, 6(1):e14592.
• [42]Kranstauber B, Kays R, LaPoint SD, Wikelski M, Safi K: A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. J Anim Ecol 2012, 81:738-46.
• [43]Benhamou S, Cornélis D: Incorporating Movement Behavior and Barriers to Improve Kernel Home Range Space Use Estimates. J Wildl Manage 2010, 74(6):1353-60.
• [44]Benhamou S, Riotte-Lambert L: Beyond the Utilization Distribution: Identifying home range areas that are intensively exploited or repeatedly visited. Ecol Model 2012, 227(2012):112-6.
• [45]Sinclair ARE: The function of distance movements in vertebrates. In The ecology of animal movement. Edited by Swingland IR, Greenwood PJ. Claredon Press, Oxford, UK; 1983:248-58.
• [46]Bunnefeld N, Börger L, Van Moorter B, Rolandsen CM, Dettki H, Solberg EJ: A model‐driven approach to quantify migration patterns: individual, regional and yearly differences. J Anim Ecol 2011, 80:466-76.
• [47]Cagnacci F, Focardi S, Heurich M, Stache A, Hewison AJM, Morellet N, et al.: Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors. Oikos 2011, 120:1790-802.
• [48]Alerstam T: Conflicting Evidence About Long-Distance Animal Navigation. Science 2006, 313:791-4.
• [49]Freeman R, Mann R, Guilford T, Biro D: Group decisions and individual differences: route fidelity predicts flight leadership in homing pigeons (Columba livia). Biol Lett 2010, 7:63-6.
• [50]Guildford R, Freeman R, Boyle D, Dean B, Kirk H, Phillips R, et al.: A Dispersive Migration in the Atlantic Puffin and Its Implications for Migratory Navigation. PLoS One 2011, 6(7):e21336.
• [51]Hansen IJ, Johnson CH, Cluff HD: Synchronicity of movement paths of barren-ground caribou and tundra wolves. Polar Biology 2013, 36:1363-71.
• [52]Pettit B, Perna A, Biro D, Sumpter DJT: Interaction rules underlying group decisions in homing pigeons. J R Soc Interface 2013, 10:20130529.
• [53]Dias MP, Granadeiro JP, Catry P: Individual variability in the migratory path and stopovers of a long-distance pelagic migrant. Anim Behav 2013, 86:359-64.
• [54]Long JA, Nelson TA: Measuring Dynamic Interaction in Movement Data. Trans GIS 2013, 17(1):62-77.
• [55]Williams DM, Dechen Quinn AC, Porter WF: Informing Disease Models with Temporal and Spatial Contact Structure among GPS-Collared Individuals in Wild Populations. PLoS One 2014, 9(1):e84368.
• [56]Raisman EA, Barner Rasmussen H, King LE, Ihwagi FW, Douglas-Hamilton I: Feasibility study on the spatial and temporal movement of Samburu’s cattle and wildlife in Kenya using GPS radio-tracking, remote sensing and GIS. Prev Vet Med 2013, 111:76-80.
• [57]Harris KJ, Blackwell PG: Flexible continuous-time modelling for heterogeneous animal movement. Ecol Model 2013, 255:29-37.
• [58]Reimers E, Tsegaye D, Colman JE, Efetstøl S: Activity patterns in reindeer with domestic vs. wild ancestry. Appl Anim Behav Sci 2014, 150:74-84.
• [59]Riotte-Lambert L, Benhamou S, Chamaillé-Jammes S: Periodicity analysis of movement recursions. Journal of Theoretical Biology 2013, 317:238-43.
• [60]Fryxell JM, Hazell M, Börger L, Dalziel BD, Haydon DT, Morales JM: Multiple movement modes by large herbivores at multiple spatiotemporal scales. Proc Natl Acad Sci 2008, 105(49):19114-9.
• [61]Benhamou S: How to reliably estimate the tortuosity of an animal’s path: straightness, sinuosity, or fractal dimension? J Theor Biol 2004, 229:209-20.
• [62]Long JA, Nelson TA, Nathoob FS: Toward a kinetic-based probabilistic time geography. Int J Geogr Inform Sci 2014, 28(5):855-74.
• [63]Miller JA: Using Spatially Explicit Simulated Data to Analyze Animal Interactions: A Case Study with Brown Hyenas in Northern Botswana. Trans GIS 2012, 16(3):271-91.
• [64]Levitis DA, Lidicker WZ Jr, Freund G: Behavioural biologists do not agree on what constitutes behaviour. Anim Behav 2009, 78(1):103-10.
• [65]Dingle H, Drake VA: What is migration? Bioscience 2007, 57:113-21.
• [66]Ropert-Coudert Y, Wilson RP: Trends and perspectives in animal-attached remote sensing. Front Ecol Environ 2005, 3(8):437-44.
• [67]Brown DD, Kays R, Wikelski M, Wilson R, Klimley AP: Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelemetry 2013, 2013:1-20.
• [68]Beyer HL, Morales JM, Murray DL, Fortin M-J: Estimating behavioural states from movement paths using Bayesian state-space models: a proof of concept. Methods in Ecology and Evolution 2013, 4:433-41.
• [69]Gurarie E, Andrews RD, Laidre KL: A novel method for identifying behavioural changes in animal movement data. Ecol Lett 2009, 12:395-408.
• [70]Gautestad AO, Mysterud A: The Lévy flight foraging hypothesis: forgetting about memory may lead to false verification of Brownian motion. Movement Ecology 2013, 2013:1-9.
• [71]Morales JM, Haydon DT, Frair J, Holsinger KE, Fryxell JM: Extracting More from Relocation Data: Building Movement Models as Mixtures of Random Walks. Ecology 2004, 85(9):2436-45.
• [72]Schwager M, Anderson DM, Butler Z, Rus D: Robust classification of animal tracking data. Comput Electron Agr 2007, 56:46-59.
• [73]Wilmers CC, Wang Y, Nickel B, Houghtaling P, Shakeri Y, Allen ML, et al.: Scale Dependent Behavioral Responses to Human Development by a Large Predator, the Puma. PLoS One 2013, 8(4):e60590.
• [74]Shamoun-Baranes J, Bouten W, Camphuysen CJ, Baaij E: Riding the tide: intriguing observations of gulls resting at sea during breeding. Ibis 2011, 153(2):411-5.
• [75]Nathan R, Spiegel O, Fortmann-Roe S, Harel R, Wikelski M, Getz WM: Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures. J Exp Biol 2012, 215:986-96.
• [76]Shamoun-Baranes J, Bom R, van Loon EE, Ens BJ, Oosterbeek K, Bouten W: From Sensor Data to Animal Behaviour: An Oystercatcher Example. PLoS One 2012, 7(5):e37997.
• [77]Kays R, Jansen PA, Knecht EMH, Vohwinkel R, Wikelski M: The effect of feeding time on dispersal of Virola seeds by toucans determined from GPS tracking and accelerometers. Acta Oecol 2011, 37:625-31.
• [78]Guo Y, Poulton G, Corke P, Bishop-Hurley GJ, Wark T, Swain DL: Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model. Ecol Model 2009, 220:2068-75.
• [79]Gerencser L, Vasarhely G, Nagy M, Viscek T, Miklosi A: Identification of Behaviour in Freely Moving Dogs (Canis familiaris) Using Inertial Sensors. PLoS One 2013, 8(10):e77814.
• [80]Rutz C, Bluff LA, Weir AAS, Kacelnik A: Video cameras on wild birds. Science 2007, 318(5851):765-765.
• [81]Cachat J, Stewart A, Utterback E, Hart P, Gaikwad S, Wong K, et al.: Three-Dimensional Neurophenotyping of Adult Zebrafish Behavior. PLoS One 2011, 6(3):e17597.
• [82]Dodge S, Bohrer G, Weinzierl R, Davidson SC, Kays R, Douglas D, et al.: The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data. Movement Ecology 2013, 2013:1-3.
• [83]Coyne MS, Godley BJ: Satellite Tracking and Analysis Tool (STAT): an integrated system for archiving, analyzing and mapping animal tracking data. Mar Ecol Prog Ser 2005, 301:1-7.
• [84]Dodge S, Bohrer G, Bildstein K, Davidson SC, Weinzierl R, Bechard MJ, et al.: Environmental drivers of variability in the movement ecology of turkey vultures (Cathartes aura) in North and South America. Phil Trans Biol Sci 2014, 369:20130195.
• [85]Morellet N, Bonenfant C, Börger L, Ossi F, Cagnacci F, Heurich M, et al.: Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within Europe. J Anim Ecol 2013, 82(6):1326-39.
• [86]Safi K, Kranstauber B, Weinzierl R, Griffin L, Rees EC, Cabot D, et al.: Flying with the wind: scale dependency of speed and direction measurements in modelling wind support in avian flight. Movement Ecology 2013, 2013:1-4.
• [87]Shamoun-Baranes J, van Loon EE, van Gasteren H, van Belle J, Bouten W, Buurma L: A comparative analysis of the influence of weather on the flight altitudes of birds. Bull Am Meteorol Soc 2006, 87:47-61.
• [88]Robinson DW, Bowlin MS, Bisson I, Shamoun-Baranes J, Thorup K, Diehl RH, et al.: Intergating concepts and technologies to advance the study of bird migration. Front Ecol Environ 2010, 8(7):354-61.
• [89]Willems EP, Barton RA, Hill RA: Remotely sensed productivity, regional home range selection, and local range use by an omnivorous primate. Behav Ecol 2009.
• [90]Hastie GD, Gillespie DM, Gordon JCD, MacAulay JDJ, McConnell BJ, Sparling CE: Tracking technologies for quantifying marine mammal interactions with tidal turbines: pitfalls and possibilities. In Marine Renewable Energy Technology and Environmental Interactions. Edited by Shields M, Payne A. Springer Science and Business Media, Humanity and the Sea. Dordrecht; 2014.
• [91]Cooke SJ, Hinch SG, Wikelski M, Andrews RD, Kuchel LJ, Wolcott TG, et al.: Biotelemetry: a mechanistic approach to ecology. Trends Ecol Evol 2004, 19(6):334-43.
• [92]Cook TR, Hamann M, Pichegru L, Bonadonna F, Gremillet D, Ryan PG: GPS and time-depth loggers reveal underwater foraging plasticity in a flying diver, the Cape Cormorant. Mar Biol 2012, 159:373-87.
• [93]Fedak MA: The impact of animal platforms on polar ocean observation. Deep Sea Research Part II: Topical Studies in Oceanography 2013, 88–89(1):7-13.
• [94]Roquet F, Wunsch C, Forget G, Heimbach P, Guinet C, Reverdin G, et al.: Estimates of the Southern Ocean general circulation improved by animal-borne instruments. Geophys Res Lett 2013, 40:1-5.
• [95]Chevaillier D, Karpytchev M, McConnell BJ, Moss S, Vincent C: Can gray seals maintain heading within areas of high tidal current? Preliminary results from numerical modeling and GPS observations. Mar Mamm Sci 2014, 30(1):374-80.
• [96]Urbano F, Cagnacci F, Calenge C, Dettki H, Cameron A, Neteler M: Wildlife tracking data management: a new vision. Phil Trans Biol Sci 2010, 365(1550):2177-85.
• [97]De Berg M, Cheong O, van Kreveld M, Overmars M: Computational Geometry. 3rd edition. Springer Verlag, Berlin-Heidelberg; 2008.
• [98]Wilson JP, Fotheringham AS: The Handbook of Geographic Information Science. Blackwell Publishing, Oxford; 2008.
• [99]Buchin M, Kruckenberg H, Kölzsch A: Segmenting Trajectories by Movement States. In Advances in Spatial Data Handling, Advances in Geographic Information Science. Edited by Timpf S, Laube P. Springer-Verlag, Berlin Heidelberg; 2013:15-25.
• [100]Buchin M, Driemel A, van Kreveld M, Sacristan V: Segmenting Trajectories: A framework and algorithms using spatiotemporal criteria. Journal of Spatial Information Science 2011, 3:33-63.
• [101]Alewijnse SPA: A framework for trajectory segmentation by stable criteria and Brownian bridge movement model. Eindhoven University of Technology, Master’s thesis; 2013.
• [102]Aronov B, Driemel A, van Kreveld M, Löffler M, Staals F: Segmentation of Trajectories on Non-Monotone Criteria. Proceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, 6-8 January 2013, New Orleans, Louisiana, USA; 2013.
• [103]Alewijnse SPA, Buchin K, Buchin M, Sijben S, Westenberg MA: Model-based segmentation and classification of trajectories. Proceedings of the 30th European Workshop on Computational Geometry March 3-5, 2014, Dead Sea, Israel; 2014.
• [104]Sester M, Feuerhake U, Kuntzsch C, Zhang L: Revealing Underlying Structure and Behaviour from Movement Data. KI - Künstliche Intelligenz 2012, 2012:1-9.
• [105]Panagiotakis C, Pelekis N, Kopanakis I, Ramasso E, Theodoridis Y: Segmentation and Sampling of Moving Object Trajectories based on Representativeness. IEEE Transactions on Knowledge and Data Engineering 2011, 2011:1-17.
• [106]Buchin K, Buchin M, van Kreveld M, Löffler M, Silveira RI, Wenk C, et al.: Median Trajectories. Algorithmica 2013, 66(3):595-614.
• [107]Brundson C: Path Estimation from GPS Tracks. Proceedings of Geocomputation 2007, Maynooth: Ireland; 2007.
• [108]Goren-Bar T, Greenfeld J: A Method for Constructing 3D Traveling Routes from GPS Navigation Data. Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming, November 2012, Redondo Beach, California, USA; 2012.
• [109]Etienne L, Devogele T, Bouju A: Spatio-temporal trajectory analysis of mobile objects following the same itinerary. Advances in Geo-Spatial Information Science 2012, 10:47-57.
• [110]Pelekis N, Kopanakis I, Kotsifakos EE, Frentzos E, Theodoridis Y: Clustering Uncertain Trajectories. Knowledge and Information Systems 2011, 28(1):117-47.
• [111]Fieberg J, Rieger RH, Zicus MC, Schildcrout JS: Regression modelling of correlated data in ecology: subject specific and population averaged response patterns. J Appl Ecol 2009, 46:1018-25.
• [112]Goodchild MF: Scale in GIS: An overview. Geomorphology 2011, 130:5-9.
• [113]Laube P, Purves RP: How fast is a cow? Cross-Scale Analysis of Movement Data. Trans GIS 2011, 15(3):401-18.
• [114]Soleymani A, Cachat J, Robinson K, Dodge S, Kalueff AV, Weibel R: Integrating cross-scale analysis in the spatial and temporal domains for classification of behavioral movement. Journal of Spatial Information Science 2014, 8:1-25.
• [115]Soleymani A, van Loon E, Weibel R: Capability of movement features extracted from GPS trajectories for the classification of fine-grained behaviors. Proceedings of the AGILE 2014 International Conference on Geographic Information Science, 3–6 June 2014, Castellón, Spain; 2014.
• [116]Dodge S, Weibel R, Lautenschütz K: Towards a taxonomy of movement patterns. Information Visualization 2008, 2008:1-13.
• [117]Orellana D, Wachowicz M, De Knegt HJ, Ligtenberg A, Bregt AK: Uncovering Patterns of Suspension in Movement. Proceedings of GIScience, 4-7 September 2010, Zurich, Switzerland; 2010.
• [118]Jain AK, Murty MN, Flynn PJ: Data Clustering: A Review. ACM Computing Surveys 1999, 31(3):264-323.
• [119]Xu R, Wunsch D: Survey of Clustering Algorithms. IEEE Trans Neural Netw 2005, 16(3):645-78.
• [120]Buchin K, Buchin M, Gudmundsson J, Löffler M, Luo J: Detecting Commuting Patterns by Clustering Subtrajectories. International Journal of Computational Geometry and Applications 2011, 21(3):253-82.
• [121]Buchin K, Buchin M, van Kreveld MJ, Luo J: Finding long and similar parts of trajectories. Computational Geometry 2011, 44(9):465-76.
• [122]Buchin K, Buchin M, Gudmundsson J: Constrained free space diagrams: A tool for trajectory analysis. Int J Geogr Inform Sci 2010, 24(7):1101-25.
• [123]Pelekis N, Andrienko G, Andrienko N, Kopanakis I, Marketos G, Theodoridis Y: Visually Exploring Movement Data via Similarity-based Analysis. Journal of Intelligent Information Systems 2012, 38(2):343-91.
• [124]Merki M, Laube P: Detecting reaction movement patterns in trajectory data. Proceedings of the AGILE 2012 International Conference on Geographic Information Science, 25-27 April 2012, Avignon, France; 2012.
• [125]Rinzivillo S, Pedreschi D, Nanni M, Giannotti F, Andrienko N, Andrienko G: Visually Driven Analysis of Movement Data by Progressive Clustering. Information Visualization 2008, 7:225-39.
• [126]Nanni M, Pedreschi D: Time-focused clustering of trajectories of moving objects. Journal of Intelligent Information Systems 2006, 27:267-89.
• [127]Buchin K, Buchin M, van Kreveld M, Speckmann B, Staals F: Trajectory Grouping Structure. Proceedings of 2013 Algorithms and Data Structures Symposium (WADS), August 12-14, 2013, London, Ontario, Canada; 2013.
• [128]Yanagisawa Y, Satoh T: Clustering Multidimensional Trajectories based on Shape and Velocity. Proceedings of the 22nd IEEE International Conference on Data Engineering Workshops (ICDEW'06), 3-6 April 2006, Atlanta, Georgia, USA; 2006.
• [129]Dodge S, Weibel R, Forootan E: Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Comput Environ Urban Syst 2009, 33:419-34.
• [130]Hanks EM, Hooten MB, Johnson DS, Sterling JT: Velocity-Based Movement Modeling for Individual and Population Level Inference. PLoS One 2011, 6(8):e22795.
• [131]Dodge S, Weibel R, Laube P: Trajectory Similarity Analysis in Movement Parameter Space. Proceedings of GISRUK, 27-29 April 2011, Plymouth, UK; 2011.
• [132]Dodge S, Laube P, Weibel R: Movement similarity assessment using symbolic representation of trajectories. Int J Geogr Inform Sci 2012, 26(9):1563-88.
• [133]McArdle G, Demšar U, van der Spek S, McLoone S: Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data. In 12th International Symposium on Web and Wireless Geographical Information Systems W2GIS 2013. 4–5 April 2013. Edited by Liang S, Wang X, Claramunt C. Lecture Notes in Computer Science, Springer, Banff, Alberta, Canada; 2013:64-81.
• [134]McArdle G, Demšar U, van der Spek S, McLoone S: Classifying Pedestrian Movement Behaviour From GPS Trajectories using Visualistion and Clustering. Annals of GIS 2014, 20(2):85-98.
• [135]Çöltekin A, Demšar U, Brychtova A, Vandrol J: Eye-hand coordination during visual search on geographic displays. Spatial Eye-tracking Workshop at GIScience 2014. 23-26 Sept 2014, Vienna, Austria; 2014.
• [136]Ranacher P, Tzavella K: How to compare movement? A review of physical movement similarity measures in geographic information science and beyond. Cartography and Geographic Information Science 2014, 41(3):286-307.
• [137]Buchin M, Dodge S, Speckmann B: Context-Aware Similarity of Trajectories. Proc. 7th International Conference on Geographic Information Science (GIScience). Lecture Notes in Computer Science 2012, 7478:43-56.
• [138]De Groeve J, Van de Weghe N, Neutens T, Cagnacci F. Roe deer (Capreolus capreolus) spatio-temporal sequential habitat use. An application of Sequence Alignment Methods. Vienna, Austria: MOVE Final conference, 30 Sept – 1 Oct 2013; 2013.
• [139]Sankoff D, Kruskal J: Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison. Addison Wesley, Reading; 1983.
• [140]Ware C: Information Visualization: Perception for Design. 3rd edition. Kaufmann, Elsevier/Morgan; 2013.
• [141]Tory M, Möller T: Rethinking visualization: A high-level taxonomy. Proceedings of the IEEE Symposium on Information Visualization 10-12 October 2004, Austin, Texas, USA; 2004.
• [142]Thomas JJ, Cook KA: Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society, Los Alamitos, CA; 2005.
• [143]Keim D, Andrienko G, Fekete J-D, Görg C, Kohlhammer J, Melançon G: Visual analytics: definition, process, and challenges. In Information visualization – human-centered issues and perspectives. LNCS state-of the-art survey. Edited by Kerren A, Stasko JT, Fekete J-D, North C, Kerren A, Stasko JT, Fekete J-D, North C. Springer, Berlin-Heidelberg; 2008:154-75.
• [144]Andrienko G, Andrienko N, Demšar U, Dransch D, Dykes J, Fabrikant SI, et al.: Space, time and visual analytics. Int J Geogr Inform Sci 2010, 24(10):1577-600.
• [145]Andrienko G, Andrienko N: A general framework for using aggregation in visual exploration of movement data. The Cartographic Journal 2010, 47(1):22-40.
• [146]Andrienko N, Andrienko G: Visual Analytics of Movement: an Overview of Methods, Tools, and Procedures. Information Visualization 2013, 12(1):3-24.
• [147]Andrienko G, Andrienko N, Bak P, Keim DA, Wrobel S: Visual Analytics of Movement. Springer Verlag, Berlin-Heidelberg; 2013.
• [148]Grundy E, Jones MW, Laramee RS, Wilson RP, Shephard ELC: Visualisation of Sensor Data from Animal Movement. Eurographics 2009, 28(3):815-22.
• [149]Spretke D, Janetzko H, Mansmann F, Bak P, Kranstauber B, Davidson S: Exploration through Enrichment: A Visual Analytics Approach for Animal Movement. Proceedings of ACM SIGSPATIAL GIS 2011. 1-4 Nov 2011, Chicago, IL, USA; 2011.
• [150]Janetzko H, Jäckle D, Deussen O, Keim DA: Visual Abstraction of Complex Motion Patterns. In Visualization and Data Analysis 2014. Edited by Wong PC, Kao DL, Hao MC, Chen C. Proceedings of SPIE-IS&T Electronic Imaging, 2 - 6 Feb 2014, San Francisco, California, USA; 2014.
• [151]Pequet D: Representations of Space and Time. The Guilford Press, New York, USA; 2002.
• [152]Kwan M-P, Neutens T: Space-time research in GIScience. Int J Geogr Inform Sci 2014, 28(5):851-4.
• [153]Hägerstrand T: What about people in regional science? Papers of the Regional Science Association 1970, 24:7-21.
• [154]Kwan M-P: Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transportation Research Part C 2000, 8:185-203.
• [155]Kapler T, Wright W: GeoTime information visualization. Information Visualization 2005, 4:136-46.
• [156]Kraak M-J: Geovisualization and time – new opportunities for space-time cube. In Geographic visualization: concepts, tools and applications. Edited by Dodge M, McDerby M, Turner M. John Wiley & sons, Chichester, UK; 2008:293-306.
• [157]Bach B, Dragicevic P, Archambault D, Hurger C, Carpendale S: A Review of Temporal Data Visualizations based on Space-Time Cube Operations. STAR – State of the Art Report. Eurographics Conference on Visualization (EuroVis) 2014, 9-13 June 2014, Swansea, UK; 2014.
• [158]Demšar U, Çöltekin A: Quantifying the interactions between eye and mouse movements on spatial visual interfaces through trajectory visualisations. Workshop on Analysis of Movement Data at GIScience 2014. 23-26 Sept 2014, Vienna, Austria; 2014.
• [159]Kristensson PO, Dahlbäck N, Anundi D, Björnsad M, Gillberg H, Haraldsson J, et al.: An Evaluation of Space Time Cube Representation of Spatio Temporal Patterns. IEEE Trans Vis Comput Graph 2009, 15(4):696-702.
• [160]Kveladze I, Kraak M-J: What do we know about the space-time cube from cartographic and usability perspective?. Proceedings of Autocarto 2012. 16–18 Sept 2012, Columbus, Ohio, USA; 2012.
• [161]Kveladze I, Kraak M-J, van Elzakker CPJM: A Methodological Framework for Researching the Usability of the Space-Time Cube. The Cartographic Journal 2013, 50(3):201-10.
• [162]Aigner W, Miksch S, Müller W, Schumann H, Tominski C: Visualizing time-oriented data – a systematic view. Computers & Graphics 2007, 31:401-9.
• [163]Aigner W, Miksch S, Müller W, Schumann H, Tominski C: Visual Methods for Analyzing Time-Oriented Data. IEEE Trans Vis Comput Graph 2008, 14(1):47-60.
• [164]Kölzsch A, Slingsby A, Wood J, Nolet BA and Dykes J. Visualisation design for representing bird migration tracks in time and space. Workshop on Visualisation in Environmental Sciences (EnvirVis), Jul 2013, Leipzig, Germany: 2013. http://openaccess.city.ac.uk/2384/
• [165]Slingsby A and van Loon EE. Visual Analytics for Exploring Changes in Biodiversity. Workshop on Visualisation in Environmental Sciences (EnvirVis), Jul 2013. Leipzig, Germany: 2013. http://openaccess.city.ac.uk/2385/.
• [166]Zhang QC, Slingsby A, Dykes J, Wood J, Kraak MJ, Blok CA, et al.: Visual analysis design to support research into movement and use of space in Tallinn: A case study. Information Visualization 2014, 13(3):213-31.
• [167]Wang Z, Yuan X: Urban Trajectory Timeline Visualization. Proceedings of IEEE BigComp 2014, Bangkok, Thailand; 2014.
• [168]Netzel R, Burch M, Weiskopf D: Comparative eye tracking study on node-link visualizations of trajectories. IEEE Trans Vis Comput Graph 2014, 12(20):2221-30.
• [169]Holten D, van Wijk JJ: Force‐Directed Edge Bundling for Graph Visualization. Computer Graphics Forum 2009, 28(3):983-90.
• [170]Höferlin M, Höferlin B, Heidemann G, Weiskopf D: Interactive schematic summaries for faceted exploration. IEEE Transactions on Multimedia 2013, 15(4):908-20.
• [171]Verbeek K, Buchin K, Speckmann B: Flow map layout via spiral trees. IEEE Trans Vis Comput Graph 2011, 17(12):2536-44.
• [172]Hurter C, Ersoy O, Fabrikant SI, Telea A: Bundled Visualization of Dynamic Graph and Trail Data. IEEE Trans Vis Comput Graph 2014, 20(8):1141-57.
• [173]Tominski C, Schumann H, Andrienko G, Andrienko N: Stacking-Based Visualization of Trajectory Attribute Data. IEEE Trans Vis Comput Graph 2012, 18(12):2565-74.
• [174]Miller HJ: Modelling accessibility using space-time prism concepts within geographical information systems. Int J Geogr Inform Sci 1991, 5(3):287-301.
• [175]Downs JA, Horner MW, Tucker AD: Time-geographic density estimation for home range analysis. Annals of GIS 2011, 17(3):163-71.
• [176]Downs JA, Horner MW: Analysing infrequently sampled animal tracking data by incorporating generalized movement trajectories with kernel density estimation. Comput Environ Urban Syst 2012, 36:302-10.
• [177]Willems N, van de Wetering H, van Wijk JJ: Visualization of vessel movements. Eurographics/IEEE-VGTC Symposium on Visualization 2009, 2009:28(3).
• [178]Willems N. Visualization of Vessel Traffic. PhD thesis. The Netherlands: Eindhoven University of Technology; 2011.
• [179]Scheepens R, Willems N, van de Wetering H, Andrienko G, Andrienko N, van Wijk JJ: Composite Density Maps for Multivariate Trajectories. IEEE Trans Vis Comput Graph 2011, 17(12):2518-27.
• [180]Scheepens R, van de Wetering H, van Wijk JJ: Contour based visualization of vessel movement predictions. Int J Geogr Inform Sci 2014, 28(5):891-909.
• [181]Krisp JM, Peters S, Burkert F: Visualizing Crowd Movement Patterns Using a Directed Kernel Density Estimation. Earth Observation of Global Changes (EOGC). Lecture Notes in Geoinformation and Cartography 2013, 2013:255-68.
• [182]Buchin K, Sijben S, Arseneau TJ, Willems EP: Detecting Movement Patterns using Brownian Bridges. Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS). 6-9 Nov 2012, Redondo Beach, California, USA 2012.
• [183]Kurzhals K, Weiskopf D: Space-Time Visual Analytics of Eye-Tracking Data for Dynamic Stimuli. IEEE Trans Vis Comput Graph 2013, 19(12):2129-38.
• [184]Nakaya T, Yano K: Visualising crime clusters in a space-time cube: and exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Trans GIS 2010, 14(3):223-39.
• [185]Delmelle E, Dony C, Casas I, Jia M, Tang W: Visualizing the impact of space-time uncertainties on dengue fever patterns. Int J Geogr Inform Sci 2014, 28(5):1107-27.
• [186]Downs JA, Horner MW, Hyzera G, Lamba D, Loraamm R: Voxel-based probabilistic space-time prisms for analysing animal movements and habitat use. Int J Geogr Inform Sci 2014, 28(5):875-90.
• [187]Tracey JA, Sheppard J, Zhu J, Wei F, Swaisgood RS, Fisher RN: Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation. PLoS One 2014, 9(7):e101205.
• [188]Demšar U, Virrantaus K: Space-time density of trajectories: exploring spatiotemporal patterns in movement data. Int J Geogr Inform Sci 2010, 24(10):1527-42.
• [189]Demšar U, van Loon EE: Visualising movement: The seagull. Significance 2013, 10(5):40-2.
• [190]Demšar U, Buchin K, van Loon EE, Shamoun-Baranes J: Stacked space-time densities: a geovisualisation approach to explore dynamics of space use over time. GeoInformatica 2015, 19(1):85-115.
• [191]Roberts J: State of the art: Coordinated & multiple views in exploratory visualization. Proceedings of the International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV ‘07). 2 July 2007, Zurich, Switzerland. 2007 2007, 61-71.
• [192]Wills G: Linked Data Views. In Handbook of Data Visualization. Edited by Chen C-H, Härdle W, Unwin A. Springer Verlag, Berlin-Heidelberg; 2008.
• [193]Andrienko G, Andrienko N, Heurich M: An eventbased conceptual model for context-aware movement analysis. Int J Geogr Inform Sci 2011, 25(9):1347-70.
• [194]Andrienko N, Andrienko G, Barrett L, Dostie M, Henzi SP: Space Transformation for Understanding Group Movement. IEEE Trans Vis Comput Graph 2013, 19(12):2169-78.
• [195]Andrienko G, Andrienko N, Burch M, Weiskopf D: Visual Analytics Methodology for Eye Movement Studies. IEEE Trans Vis Comput Graph 2012, 18(12):2889-98.
• [196]Ooms K, Andrienko G, Andrienko N, De Maeyer P, Fack V: Analysing the spatial dimension of eye movement data using a visual analytic approach. Expert Systems with Applications 2012, 39(1):1324-32.
• [197]Kurzhals K, Heimerl F, Weiskopf D: ISeeCube: Visual analysis of gaze data for video. Proceedings of the ACM Conference on Eye Tracking Research and Applications (ETRA 2014), 26 – 28 March, 2014, Safety Harbor, Florida, USA; 2014.
• [198]Kurzhals K, Bopp CF, Bässler J, Ebinger F, Weiskopf D: Benchmark data for evaluating visualization and analysis techniques for eye tracking for video stimuli. Proceedings of the 2014 BELIV Workshop: Beyond Time and Errors - Novel Evaluation Methods for Visualization. 10 Nov 2014, Paris, France; 2014.
• [199]Levenshtein V: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics-Doklady 1966, 10(8):707-10.
• [200]Technitis G, Weibel R: An Algorithm for Random Trajectory Generation Between Two Endpoints, Honoring Time and Speed Constraints. Proceedings of GIScience. 23-26 Sept 2014, Vienna, Austria; 2014.
• [201]Fieberg J, Matthiopoulos J, Hebblewhite M, Boyce MS, Frair JL: Correlation and studies of habitat selection: problem, red herring or opportunity? Phil Trans Biol Sci 2010, 365(1550):2233-44.
• [202]Tobler WA: A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 1970, 46(2):234-40.
• [203]Fotheringham AS, Brunsdon C, Charlton M: Geographically Weighted Regression: the Analysis of Spatially Varying Relationships. Wiley, Chichester; 2002.
• [204]Demšar U, Harris P, Brunsdon C, Fotheringham AS, McLoone S: Principal Component Analysis on Spatial Data: a Review. Annals of Association of American Geographers 2013, 103(1):106-28.
• [205]Fleming CH, Calabrese JM, Mueller T, Olson KA, Leimgruber P, Fagan WF: From Fine-Scale Foraging to Home Ranges: A Semivariance Approach to Identifying Movement Modes across Spatiotemporal Scales. Am Nat 2014, 183:E154-67.
• [206]Sarkar D, Chapman CA, Griffin L, Sengupta R: Analyzing Animal Movement Characteristics From Location Data. Trans GIS: Advanced Online Publication. 2014.
• [207]Ye Y, Zheng Y, Chen Y. Mining individual life pattern based on location history. In: Mobile data management: systems, services and middleware. Taipei, Taiwan: IEEE MDM’09, 18–20 May 2009; 2009. p. 1–10.
• [208]Rodrigues A, Damásio C, Cunha JE: Using GPS Logs to Identify Agronomical Activities, Connecting a Digital Europe Through Location and Place. Lecture Notes in Geoinformation and Cartography 2014, 2014:105-21.
• [209]Umair M, Kim WS, Choi BC, Jung SY: Discovering personal places from location traces. Proceedings of the 16th International Conference on Advanced Communication Technology (ICACT 2014), 16–19 Feb. 2014, Bongpyong-myun, Pyeongchang, South Korea; 2014.
• [210]Zhou C, Frankowski D, Ludford PJ, Shekhar S, Terveen LG: Discovering personally meaningful places: An interactive clustering approach. ACM Transactions on Information Systems 2007, 25:3.
• [211]Huang W, Li M, Hu W, Song G, Xing X, Xie K: Cost sensitive GPS-based activity recognition. Proceedings of the 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) 23-25 July 2013, Shenyang, China; 2013.
• [212]Siła-Nowicka K, Oshan T, Vandrol J, Fotheringham S: The Use of Volunteered Geographic Information for Spatial Interaction Modelling. Proceedings of GIScience. 23-26 Sept 2014, Vienna, Austria; 2014.
• [213]Delafontaine M, Versichele M, Neutens T, Van de Weghe N: Analysing spatiotemporal sequences in Bluetooth tracking data. Applied Geography 2012, 34:659-68.
• [214]Versichele M, Neutens T, Delafontaine M, Van de Weghe N: The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the Ghent Festivities. Applied Geography 2012, 32(2):208-20.
• [215]Versichele M, Neutens T, Goudeseune S, Van Bossche F, Van de Weghe N: Mobile Mapping of Sporting Event Spectators Using Bluetooth Sensors. Sensors 2012, 12(10):14196-213.
• [216]Versichele M, De Groote L, Claeys Bouuaert M, Neutens T, Moerman I, Van de Weghe N: Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium. Tour Manage 2014, 44:67-81.
• [217]Bonné B, Barzan A, Quax P, Lamotte W: WiFiPi: Involuntary tracking of visitors at mass events. 14th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), 4-7 June 2013, Madrid, Spain; 2013.
• [218]Ji W, White PCL, Clout MN: Contact rates between possums revealed by proximity data loggers. J Appl Ecol 2005, 42:595-604.
• [219]Prange S, Jordan T, Hunter C, Gehrt SD: New radiocollars for the detection of proximity among individuals. Wildl Soc Bull 2006, 34(5):1333-44.
• [220]Rutz C, Burns ZT, James R, Ismar SM, Burt J, Otis B, et al.: Automated mapping of social networks in wild birds. Curr Biol 2012, 22(17):R669-71.
• [221]Krause J, Krause S, Arlinghaus R, Psorakis I, Roberts S, Rutz C: Reality mining of animal social systems. Trends Ecol Evol 2013, 28(9):541-51.
• [222]Demšar U, Fotheringham AS, Charlton M: Exploring the spatio-temporal dynamics of geographical processes with Geographically Weighted Regression and Geovisual Analytics. Information Visualization 2008, 7:181-97.
• [223]Foley P, Demšar U: Using geovisual analytics to compare the performance of Geographically Weighted Discriminant Analysis versus its global counterpart, Linear Discriminant Analysis. Int J Geogr Inform Sci 2013, 27(4):633-61.
• [224]Purves R, Laube P, Buchin M, Speckmann B: Moving beyond the point: An agenda for research in movement analysis with real data. Comput Environ Urban Syst 2014, 47:1-4.
• [225]Laube P: Computational Movement Analysis. Springer Briefs in Computer Science. Springer-Verlag, Berlin-Heidelberg; 2014.