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Movement Beyond the Snapshot - Dynamic Analysis of Geospatial Lifelines Patrick Laube 1, Todd Dennis 2, Mike Walker 2 & Pip Forer 1 2 School of Biological.

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Presentation on theme: "Movement Beyond the Snapshot - Dynamic Analysis of Geospatial Lifelines Patrick Laube 1, Todd Dennis 2, Mike Walker 2 & Pip Forer 1 2 School of Biological."— Presentation transcript:

1 Movement Beyond the Snapshot - Dynamic Analysis of Geospatial Lifelines Patrick Laube 1, Todd Dennis 2, Mike Walker 2 & Pip Forer 1 2 School of Biological Science University of Auckland. Auckland, New Zealand [t.dennis, 1 School of Geography and Environmental Science University of Auckland. Auckland, New Zealand Phone: # Fax: [p.laube,

2 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook «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 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlookrationale 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 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Rationale rationale

5 dynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook

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

7 dynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook 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) n discrete space-time observations («fixes» ) n in a geographic space Mark, D. M. (1998). Geospatial lifelines. In Integrating Spatial and Temporal Databases, Dagstuhl Seminars, No Lifeline of Caribou Lynetta for year 2002 rationale

8 dynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook the fetish of the static Cartography n geospatial lifelines as static elements in a map Limitation n legacy of static cartography: snapshot view instead of process view è «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 rationale

9 dynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook limits of visualisation Time geography n 3D with x, y, t Limitation n visual exploration is difficult with increasing numbers of lifelines è «Although the aquarium is a valuable representation device, interpretation of patterns becomes difficult as the number of paths increases…» (Kwan 2000) 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), Space-time paths of people moving in Portland (Kwan 2004) rationale

10 dynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook to sum up… 1.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…. 2.unprecedented increase of detailed movement data 3.traditional (static) geographical analysis approaches not suited for movement 4.querying ≠ quantitative analysis rationale

11 dynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook research questions 1. How can we analyse movement data in a dynamic way, i.e. throughout the developing lifeline? 2. How can we derive movement descriptors such as speed or azimuth from detailed lifeline? 3. How can we quantify the similarity of lifeline in order to cluster them? rationale

12 dynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Dynamic analysis of lifelines dynamic analysis

13 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Dynamic analysis of lifelines 1. How can we analyse movement data in a dynamic way, i.e. throughout the developing lifeline? dynamic analysis

14 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook a new era! Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland. mean pop.density 15/km 2 mean speed 15 m/s dynamic analysis

15 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook a new era! homemean from vanishing bearing… … to δt = 1sec. ? ? ? ? Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland. dynamic analysis

16 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook avian navigation I: many strategies internal reference n path integration (inverse vector) n internal clock external references n landmarks n celestial (sun/stars) n magnetic compass n odours Change in strategy with increasing experience Wiltschko, R., & Wiltschko, W. (2003). Avian navigation: from historical to modern concepts. Animal Behaviour, 65, dynamic analysis

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

18 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook avian navigation III: grid navigation Two environmental gradients, that is, factors whose values continuously change in space Determination of the course of the goal Compass course e.g.180 °S home I’m here dynamic analysis

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

20 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook avian navigation IV: GISc agenda II Avian navigation experiments: n cut olfactory nerves of racing pigeons n can we quantitatively distinguish the resulting trajectories from the test pigeons and the untreated control group? è Task: Lifeline clustering PaPa PbPb PcPc dynamic analysis

21 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Lifeline context operators context operators

22 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Lifeline context operators 2. How can we derive movement descriptors such as speed or azimuth from detailed lifelines? context operators

23 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook “total” lifeline context operators 0.7 localzonalglobal “interval” focal “instantaneous”“episodal” context operators

24 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook δtδt δtδt movement azimuth ? az’ az az’ P’ P az context operators

25 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook movement azimuth ? az context operators

26 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook movement azimuth az ? 0 1 weight context operators

27 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook approaching rate ? δtδt δtδt δdδd dada absolute approaching rate r a = d a / 2δt [m/s] relative approaching rate r r = d a / δd [1-,…0, +1] context operators

28 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook navigational displacement ? d a(t q ) directed d [-π, 0, +π] undirected d [0, +π] context operators

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

30 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook mapping trajectory descriptors Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland. context operators e 1 : sinuosity similar, variability in approaching rate e 2 : approaching rate similar, variability sinuosity

31 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook rate of change context operators

32 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook rate of change s s context operators

33 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook aggregation context operators

34 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook aggregation 1T Release Site Loft navigational displacement episode 1 episode 2 change event context operators

35 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook aggregation 1D context operators

36 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook aggregation 2D averaged sinuositygravityearth magnetic field context operators

37 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook dominant axes for grid navigation? high around 200° and 20° low around 110° and 290° latitude longitude context operators

38 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Lifeline similarity – lifeline clustering lifeline similarity

39 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Lifeline similarity – lifeline clustering 3. How can quantify the similarity of trajectories in order to cluster them? lifeline similarity

40 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook similarity sinuosity low high s(t 3 )? s(t 2 )? s(t 1 )? PaPa PbPb s(t 1 ) s(t 1 ) Sim{P a,P b } … PaPa PbPb PcPc PaPa PcPc PbPb PaPa PbPb PcPc 4 lifeline similarity

41 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook

42 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Discussion discussion

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

44 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook context operators II The interplay of the lifeline data and the applied context operator algorithms may produce artefacts n e.g. coarser sampling rate  underestimation of path and speed n e.g. directional change is very sensitive to variable sampling rates along a trajectory s e.g. flying birds slow down in curves  finer sampling rate fallwinterspringsummer discussion

45 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook 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 n unequal length n varying sampling rates n uncertain, error-prone Need for specific similarity approaches for lifelines n That are spatially and temporally implicit n 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. discussion

46 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Conclusions conclusions & outlook

47 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & 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. conclusions & outlook

48 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook conclusions Summary/collapsing lifeline descriptors are of limited use with respect to detailed lifeline data. èNeed for methods that can quantitatively compare and categorise lifelines n …dynamically as the lifelines develops n... consider the lifelines’ extents and positions in (geographic) space and time The quantitative analysis of movement is very sensitive n to the used data capture procedures, n the data models representing the moving object, and n the algorithms which derive descriptive measures from the lifelines. èIn 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 conclusions & outlook

49 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook Outlook conclusions & outlook

50 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook …first results conclusions & outlook

51 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook nz west coast tourists eat/drink petrol overnight car bus ∑ edge similarity ∑ node similarity Haast Arthur’s Pass Franz Josef Fox Pancake Rocks spatial reference temporal reference conclusions & outlook

52 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook …first results conclusions & outlook

53 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook acknowledgements RORETWEBIBBLBBBEBM BLLBPBBABBWBBSABBRITITA UFBBDINAARBCL RIGBTBHL VSGIIBAG EHENNBEÖ BBSOSEKROTRMHESBBLBBLBW BBTEEROPBBBBBBTTCBVIPHEE UBRDEIISUIE HTOLDU R BBINKOSBBHLLERBAMI EHRMARNNIBBBBABBBPN IMCHABELBBETPHASNIEFLDBMFM RBZBQBBCSBBBBIBBBBA IBBTBBIBAMRCBBN VRANBKUBTBBBRBN KREVLDBEBRSSELBABB A W I R Z B EL RBBPXBBB IBABBBOF NN FA EA IX PN BZ PBTBZHQ ZBQBC BTBIB BPXBB ABBOF ZBQBC BTBIB BPXBB ABBOF PBTBZHQ PBTBZHQ QBC BIB I C H A E L H I C H A E L H I C H A E L H I C H A E L H BL CV PT BTBIB BPXBB ABBOF BAGBPXBB BAGBPXBB BABPXBB conclusions & outlook

54 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook acknowledgements RORETWEBIBBLBBBEBM BLLBPBBABBWBBSABBRITITA UFBBDINAARTCL RIGBTBHL VSGIIBAG EHENNBEÖ BSSOSEKROTRMHESBBEIBLBW BUTEEROPBBBBBBTTCBRSPHEE UBRDEIISUIE HTOLDU R BLINKOSBBHLLERBAMI EHRMARNNIBBBBABBBPN IMCHABELBVETPHASNIEFLDBMFM RBZBQBBCSBBBBIBBBBA IBBTBBIBAMRCBBN VRANBKUBTBBBRBN KREVLDBEBRSSELBABB A W I R Z B EL RBBPXBBB IBABBBOF NN FA EA IX PN BZ PBTBZHQ ODDBC PFEIB IONBB PRNOF ZBQBC BTBIB BOXBB A‘BOF VAIDZDQ PITBZHQ QNC BIB I C H A E L H I C H A E L H I C H A E L H I C H A E L H BL CV PT BTBIB BPXBB ABBOF BAGBPXBB BAGBPXBB LABPXBB My current work is funded by the Swiss National Science Foundation, grant no. PBZH conclusions & outlook

55 rationaledynamic analysisdiscussioncontext operatorslifeline similarityconclusions & outlook contacts conclusions & outlook Patrick Laube 1, Todd Dennis 2, Mike Walker 2 & Pip Forer 1 2 School of Biological Science University of Auckland. Auckland, New Zealand [t.dennis, 1 School of Geography and Environmental Science University of Auckland. Auckland, New Zealand Phone: # Fax: [p.laube,


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