Movement Beyond the Snapshot - Dynamic Analysis of Geospatial Lifelines

1. Movement Beyond the Snapshot - Dynamic Analysis of Geospatial Lifelines Patrick Laube1, Todd Dennis2, Mike Walker2 & Pip Forer1 1School of Geography and Environmental Science University of Auckland. Auckland, New Zealand Phone: +64 9 373-7599 # 88202 Fax: +64 9 373-7434 Email: [p.laube, p.forer]@auckland.ac.nz 2School of Biological Science University of Auckland. Auckland, New Zealand Email: [t.dennis, m.walker]@auckland.ac.nz

2. «The basic criticism of snapshots is that the ‘mutations’ do not all wait until the satellite flies over to make their change. Rather, the snapshot approach collapses many events, each of which occurred separately. There has not been enough discussion that connects the desired goal of continuous time with the reality of snapshot source material» Chrisman, N. R. (1998). Beyond the Snapshot: Changing the approach to change, error, and process. In Egenhofer, M., and Golledge, R., (eds.), Spatial and Temporal Reasoning in Geographical Information Systems, pages 85-93, Oxford University Press, Oxford, UK.

3. rationale 1. Rationale – spatio-temporal data mining 2. Dynamic analysis of (geospatial) lifelines 3. Lifeline context operators 4. Lifeline similarity 5. Discussion 6. Conclusions & Outlook Movement Beyond the Snapshot - Dynamic Analysis of Geospatial Lifelines

4. rationale Rationale

6. rationale analysing motion – a challenging imperative • Biosecurity • understand the diffusion of an infectious disease • understand, and potentially manage, the movement of invasive species • Traffic planning • understand dynamic emergence of traffic jams • Psychology • understand crowd behaviour e.g. diffusion of bird flu e.g. traffic jams around Auckland e.g. Notting Hill Carnival in London

7. rationale movement? – geospatial lifeline! • Focus on change of an objects’s position over time (Moving Point Objects = MPO) • «A geospatial lifeline is a continuous set of positions occupied in space over some time period.» (Mark 1998) • discrete space-time observations («fixes» ) • in a geographic space Lifeline of Caribou Lynetta for year 2002 Mark, D. M. (1998). Geospatial lifelines. In Integrating Spatial and Temporal Databases, Dagstuhl Seminars, No. 98471.

8. rationale 1999 2000 2001 the fetish of the static • Cartography • geospatial lifelines as static elements in a map • Limitation • legacy of static cartography: snapshot view instead of process view • «Move beyond the snapshot!» (Chrisman 1998) Lifelines of 13 individual Caribou, 1997 – 2001 Chrisman, N. R. (1998). Beyond the Snapshot: Changing the approach to change, error, and process. In Egenhofer, M., and Golledge, R., (eds.), Spatial and Temporal Reasoning in Geographical Information Systems, pages 85-93, Oxford University Press, Oxford, UK.

9. rationale limits of visualisation • Time geography • 3D with x, y, t • Limitation • visual exploration is difficult with increasing numbers of lifelines • «Although the aquarium is a valuable representation device, interpretation of patterns becomes difficult as the number of paths increases…» (Kwan 2000) Space-time paths of people moving in Portland (Kwan 2004) Kwan, M. P. (2000). Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with large dataset. Transportation Research Part C, 8 (1-6), 185-203.

10. rationale to sum up… • eclectic set of disciplines shows increasing interest in movement analysis: • geography, GIScience, • data base research, • animal behaviour research, • surveillance and security analysts, • transport analysts and • market researchers, so…. • unprecedented increase of detailed movement data • traditional (static) geographical analysis approaches not suited for movement • querying ≠ quantitative analysis

11. rationale research questions • How can we analyse movement data in a dynamic way, i.e. throughout the developing lifeline? • How can we derive movement descriptors such as speed or azimuth from detailed lifeline? • How can we quantify the similarity of lifeline in order to cluster them?

12. dynamic analysis Dynamic analysis of lifelines

13. dynamic analysis Dynamic analysis of lifelines • How can we analyse movement data in a dynamic way, i.e. throughout the developing lifeline?

14. dynamic analysis mean speed mean pop.density 15/km2 15 m/s a new era! Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland.

15. dynamic analysis ? ? ? ? a new era! home mean from vanishing bearing… … to δt = 1sec. Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland.

16. dynamic analysis avian navigation I: many strategies • internal reference • path integration (inverse vector) • internal clock • external references • landmarks • celestial (sun/stars) • magnetic compass • odours • Change in strategy withincreasing experience Wiltschko, R., & Wiltschko, W. (2003). Avian navigation: from historical to modern concepts. Animal Behaviour, 65, 257-272.

17. dynamic analysis Determination of the course of the goal step 1:map Compass course e.g.180°S Compass mechanism step 1:compass Direction of flight ‘this way’ avian navigation II: map & compass Kramer, G. (1961). Long-distance orientation. Biology and Comparative Physiology of Birds, London: Academic Press, pp. 341-371.

18. dynamic analysis Determination of the course of the goal Compass course e.g.180°S 0 1 2 3 4 5 6 0 1 2 3 4 5 6 avian navigation III: grid navigation • Two environmental gradients, that is, factors whose values continuously change in space I’m here home

19. dynamic analysis longitude latitude avian navigation IV: GISc agenda I • Biological Hypotheses • Birds use different strategies along a single trajectory • Movement descriptors (speed, azimuth, sinuosity) mirror navigational strategy • e.g. sinuosity mirrors navigational confidence • Navigational displacement is smallest moving perpendicular strongest gradient • Task: Relate movement descriptors to underlying geography / environment sun? geoMagn? landmarks?

20. dynamic analysis Pa Pb Pc avian navigation IV: GISc agenda II • Avian navigation experiments: • cut olfactory nerves of racing pigeons • can we quantitatively distinguish the resulting trajectories from the test pigeons and the untreated control group? • Task: Lifeline clustering

21. context operators Lifeline context operators

22. context operators Lifeline context operators • How can we derive movement descriptors such as speed or azimuth from detailed lifelines?

23. context operators “total” “instantaneous” “interval” “episodal” lifeline context operators 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 local zonal global focal

24. context operators az P’ az’ az’ az P δt δt ? movement azimuth

25. context operators az ? movement azimuth

26. context operators az ? movement azimuth 0 weight 1

27. context operators δt δt δd ? da approaching rate absolute approaching rate ra = da / 2δt [m/s] relative approaching rate rr = da /δd [1-,…0, +1]

28. context operators directed d [-π, 0, +π] undirected d [0, +π] a(tq) d ? navigational displacement

29. context operators high high high navigational displacement approaching rate sinuosity low low low 3 example pigeons - “drei weisse Tauben... ♫” Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland.

30. context operators mapping trajectory descriptors e1: sinuosity similar, variability in approaching rate e2: approaching rate similar, variability sinuosity Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland.

31. context operators .7 .6 .4 .5 .8 .5 rate of change

32. context operators s s rate of change

33. context operators aggregation

34. context operators episode 2 episode 1 aggregation 1T navigational displacement change event Release Site Loft

35. context operators aggregation 1D

36. context operators aggregation 2D averaged sinuosity earth magnetic field gravity

37. context operators longitude high around 200° and 20° low around 110° and 290° latitude dominant axes for grid navigation?

38. lifeline similarity Lifeline similarity – lifeline clustering

39. lifeline similarity Lifeline similarity – lifeline clustering • How can quantify the similarity of trajectories in order to cluster them?

40. lifeline similarity high s(t1) s(t1) s(t1) sinuosity 1 low Pa 0.3 0.4 0.7 Pb 0.3 0.5 0.1 s(t3)? s(t1)? s(t1)? s(t2)? s(t2)? s(t3)? 2 s(t1) s(t1) s(t1) 1.0 0.8 0.2 Sim{Pa,Pb} … Pa Pb Pc 3 Pa - 0.2 0.1 Pb 0.2 - 0.8 Pc 0.1 0.8 - Pa 4 Pb Pc similarity

42. discussion Discussion

43. discussion P’ az’ az P context operators I • There is not just a single way to compute trajectory descriptors, such as speed, azimuth or sinuosity • Algorithms influences results (summary vector vs. mean) • Parameterisation influences results (e.g. smoothing effects with wider interval widths)

44. discussion fall winter spring summer context operators II • The interplay of the lifeline data and the applied context operator algorithms may produce artefacts • e.g. coarser sampling rate  underestimation of path and speed • e.g. directional change is very sensitive to variable sampling rates along a trajectory • e.g. flying birds slow down in curves  finer sampling rate

45. discussion lifeline similarity • There has been done a lot on similarity of (life)lines, there almost certainly are lots of adoptable methods out there! • However, lifelines are special lines. They are typically very variable, and thus difficult to compare quantitatively • unequal length • varying sampling rates • uncertain, error-prone • Need for specific similarity approaches for lifelines • That are spatially and temporally implicit • That do not solely rely on geometry but also semantics • Wrapping or shifting to equalise the start and end times offers an alternative way to address the problem of unequal lifelines without excluding the dynamic view.

46. conclusions & outlook Conclusions

47. conclusions & outlook conclusions In this talk I have • …adopted the concept of spatial context operators associated with Tomlin’s map algebra to create a framework for the computation of descriptive measures of lifeline data. • …proposed instantaneous, interval, episodal, and total context operators applicable to a continuous stream of movement descriptors along a trajectory. • …illustrated this conceptual framework by applying it to some well known movement properties such as speed, movement azimuth, sinuosity and additionally propose some new movement descriptors which we believe show value. • …proposed a set of standardisations to harmonise lifelines of differing length or chronology so as to allow consecutive statistical analysis. • …proposed a conceptual framework to cluster lifelines, adopting a temporal or spatial sampling schema.

48. conclusions & outlook conclusions • Summary/collapsing lifeline descriptors are of limited use with respect to detailed lifeline data. • Need for methods that can quantitatively compare and categorise lifelines • …dynamically as the lifelines develops • ... consider the lifelines’ extents and positions in (geographic) space and time • The quantitative analysis of movement is very sensitive • to the used data capture procedures, • the data models representing the moving object, and • the algorithms which derive descriptive measures from the lifelines. • In order to increase the transparency and the repeatability of analysis of movement trajectories, I suggest that researchers report more detail about how their lifeline descriptors are computed

49. conclusions & outlook Outlook

50. conclusions & outlook …first results