movement beyond the snapshot - dynamic analysis of geospatial lifelines patrick laube 1, todd dennis...

Movement Beyond the Snapshot - Dynamic Analysis of Geospatial Lifelines Patrick Laube1, Todd Dennis2, Mike Walker2 & Pip Forer1

2School of Biological Science

University of Auckland. Auckland, New Zealand

Email: [t.dennis, m.walker]@auckland.ac.nz

1School of Geography and Environmental Science

Phone: +64 9 373-7599 # 88202 Fax: +64 9 373-7434

Email: [p.laube, p.forer]@auckland.ac.nz

rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & 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.

rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & 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

Rationale

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. Notting Hill Carnival in London

e.g. traffic jams around Auckland

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

Mark, D. M. (1998). Geospatial lifelines. In Integrating Spatial and Temporal Databases, Dagstuhl Seminars, No. 98471.

Lifeline of Caribou Lynettafor year 2002

rationale

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 individualCaribou, 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

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)

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.

Space-time paths of people moving in Portland (Kwan 2004)

rationale

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

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

Dynamic analysis of lifelines

dynamic analysis

Dynamic analysis of lifelines

1. How can we analyse movement data in a dynamic way, i.e. throughout the developing lifeline?

dynamic analysis

a new era!

Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland.

mean pop.density

15/km2 mean speed

15 m/s

dynamic analysis

a new era!

home mean

from vanishing bearing…

… to δt = 1sec.

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.

dynamic analysis

avian navigation II: map & compass

Kramer, G. (1961). Long-distance orientation. Biology and Comparative Physiology of Birds, London: Academic Press, pp. 341-371.

Determination of thecourse of the goal

Compass coursee.g.180°S

step 1:map

Compassmechanism

Direction of flight‘this way’

step 1:compass

dynamic analysis

avian navigation III: grid navigation

• Two environmental gradients, that is, factors whose values continuously change in space

Determination of thecourse of the goal

Compass coursee.g.180°S

65430 21

I’m here

dynamic analysis

avian navigation IV: GISc agenda I

geoMagn?

landmarks?

• Biological HypothesesA. 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

Task: Relate movement descriptors to underlying geography / environment

longitude

dynamic analysis

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

dynamic analysis

Lifeline context operators

context operators

Lifeline context operators

2. How can we derive movement descriptors such as speed or azimuth from detailed lifelines?

context operators

“total”

lifeline context operators0.7 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

“interval”

“instantaneous” “episodal”

context operators

δt δt

movement azimuth

az’ P’

context operators

movement azimuth

context operators

movement azimuth

1weight

context operators

approaching rate

?δt δt

absolute approaching rate ra = da / 2δt [m/s]

relative approaching rate rr = da / δd [1-,…0, +1]

context operators

navigational displacement

directed d [-π, 0, +π]

undirected d [0, +π]

context operators

3 example pigeons - “drei weisse Tauben... ♫”

navigationaldisplacement low

highapproaching

rate low

sinuosity low

context operators

mapping trajectory descriptors

context operators

e1: sinuosity similar, variability in approaching rate

e2: approaching rate similar, variability sinuosity

rate of change

.7 .6 .4 .5 .8 .5

context operators

rate of change

context operators

aggregation

context operators

aggregation 1T

ReleaseSite Loft

-110 -65

pigeon 7

pigeon 22

pigeon 8

pigeon 1

pigeon 2

navigational displacement

episode 1

episode 2

ge even

context operators

aggregation 1D

context operators

aggregation 2D

averaged sinuosity gravityearth magnetic field

context operators

dominant axes for grid navigation?

high around200° and 20°

low around110° and 290°

longitude

context operators

Lifeline similarity – lifeline clustering

lifeline similarity

Lifeline similarity – lifeline clustering

3. How can quantify the similarity of trajectories in order to cluster them?

lifeline similarity

similarity

sinuosity low

s(t3)?

s(t2)?

s(t1)?

s(t1) s(t1) s(t1)

0.3 0.4 0.7

0.3 0.5 0.1

s(t1) s(t1) s(t1)

Sim{Pa,Pb}

1.0 0.8 0.22

Pa PcPb

- 0.2 0.1

0.2 - 0.8

0.1 0.8 -

lifeline similarity

Discussion

discussion

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)

az’ P’

discussion

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

fall winter spring summer

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.

discussion

Conclusions

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.

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

Outlook

…first results

nz west coast tourists

eat/drinkpetrolovernightcarbus

6 12 18 6

∑ edge similarity

∑ node similarity

Arthur’s Pass

Franz JosefFox

Pancake Rocks

spatialreference

temporalreference

…first results

acknowledgements

R O RE T W EB I B BL B B BE B M

B L LB P B BA B B WBB S A BB R I TI T A

U F B B D INA A R BC L

R I G B T BH L

V S G I I BA G

E H E N N BE Ö

B B SO S E KR O T RMH E S BB L B BL B W

B B TE E R OP B B BB BB T T CB V I PH E E

U B R DE I I SU I E

HT O L DU

B B IN K O SB B H LL E R BA M I

EH R M AR N NI B B BB A B BB P N

IM C H AB E LB B ET P H AS N I EF L D BM F M

RB Z B QB B CS B B BB I B BB B A

IB B T BB I BAM R C B B N

VR A N B K U BT B B BR B N

K R EV L D BE B R SS E L BA B B

RB B P XB B B

IB A B BB O F

PB T B ZH Q

Z B Q B C

B T B I B

B P X B B

A B B O F

Z B Q B C

B T B I B

B P X B B

A B B O F

PB T B ZH Q

B T B I B

B P X B B

A B B O F

BA GB P X B B

BA B P X B B

acknowledgements

R O RE T W EB I B BL B B BE B M

B L LB P B BA B B WBB S A BB R I TI T A

U F B B D INA A R TC L

R I G B T BH L

V S G I I BA G

E H E N N BE Ö

B S SO S E KR O T RMH E S BB E I BL B W

B U TE E R OP B B BB BB T T CB R S PH E E

U B R DE I I SU I E

HT O L DU

B L IN K O SB B H LL E R BA M I

EH R M AR N NI B B BB A B BB P N

IM C H AB E LB V ET P H AS N I EF L D BM F M

RB Z B QB B CS B B BB I B BB B A

IB B T BB I BAM R C B B N

VR A N B K U BT B B BR B N

K R EV L D BE B R SS E L BA B B

RB B P XB B B

IB A B BB O F

PB T B ZH Q

O D D B C

P F E I B

I O N B B

P R N O F

Z B Q B C

B T B I B

B O X B B

A ‘ B O F

VA I D ZD Q

PI T B ZH Q

B T B I B

B P X B B

A B B O F

BA GB P X B B

LA B P X B B

My current work is funded by the Swiss National Science Foundation, grant no. PBZH2-110315

contacts

Patrick Laube1, Todd Dennis2, Mike Walker2 & Pip Forer1

2School of Biological Science

Email: [t.dennis, m.walker]@auckland.ac.nz

1School of Geography and Environmental Science

Phone: +64 9 373-7599 # 88202 Fax: +64 9 373-7434

Email: [p.laube, p.forer]@auckland.ac.nz

movement beyond the snapshot - dynamic analysis of geospatial lifelines patrick laube 1, todd dennis...

snapshot approach

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oxford university press

temporal reasoning

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