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Movement Beyond the Snapshot - Dynamic Analysis of Geospatial Lifelines Patrick Laube 1, Todd Dennis...
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Transcript of 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
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
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 dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Rationale
rationale
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & 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)
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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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.
1999
2000
2001
rationale
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & 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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & 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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Dynamic analysis of lifelines
dynamic analysis
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Dynamic analysis of lifelines
1. How can we analyse movement data in a dynamic way, i.e. throughout the developing lifeline?
dynamic analysis
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
a new era!
home mean
from vanishing bearing…
… to δt = 1sec.
??
?
?
Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland.
dynamic analysis
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
65
43
02
1home
I’m here
dynamic analysis
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
avian navigation IV: GISc agenda I
geoMagn?
sun?
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
lati
tud
e
longitude
dynamic analysis
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
Pa
Pb
Pc
dynamic analysis
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Lifeline context operators
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Lifeline context operators
2. How can we derive movement descriptors such as speed or azimuth from detailed lifelines?
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
“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
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”
focal
“instantaneous” “episodal”
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
δt δt
movement azimuth
?
az’
az
az’ P’
Paz
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
movement azimuth
?
az
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
movement azimuth
az
?0
1weight
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
approaching rate
?δt δt
δd
da
absolute approaching rate ra = da / 2δt [m/s]
relative approaching rate rr = da / δd [1-,…0, +1]
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
navigational displacement
?
d
a(tq)
directed d [-π, 0, +π]
undirected d [0, +π]
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
3 example pigeons - “drei weisse Tauben... ♫”
navigationaldisplacement low
highapproaching
rate low
high
Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland.
sinuosity low
high
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
mapping trajectory descriptors
Data: Todd Dennis and Mike Walker, School of Biological Science, University of Auckland.
context operators
e1: sinuosity similar, variability in approaching rate
e2: approaching rate similar, variability sinuosity
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
rate of change
.7 .6 .4 .5 .8 .5
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
rate of change
s
s
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
aggregation
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
aggregation 1T
ReleaseSite Loft
0
20
40
60
80
100
120
140
160
180
-965
-920
-875
-830
-785
-740
-695
-650
-605
-560
-515
-470
-425
-380
-335
-290
-245
-200
-155
-110 -65
-20
time
nav
igat
ion
al e
rro
r
pigeon 7
pigeon 22
pigeon 8
pigeon 1
pigeon 2
navigational displacement
episode 1
episode 2
chan
ge even
t
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
aggregation 1D
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
aggregation 2D
averaged sinuosity gravityearth magnetic field
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
dominant axes for grid navigation?
high around200° and 20°
low around110° and 290°
lati
tud
e
longitude
context operators
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Lifeline similarity – lifeline clustering
lifeline similarity
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Lifeline similarity – lifeline clustering
3. How can quantify the similarity of trajectories in order to cluster them?
lifeline similarity
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
similarity
sinuosity low
high
s(t3)?
s(t3)?
s(t2)?
s(t2)?
s(t1)?
s(t1)?
Pa
Pb
s(t1) s(t1) s(t1)
0.3 0.4 0.7
0.3 0.5 0.1
1
s(t1) s(t1) s(t1)
Sim{Pa,Pb}
…
1.0 0.8 0.22
Pa
Pb
Pc
Pa PcPb
- 0.2 0.1
0.2 - 0.8
0.1 0.8 -
3
Pa
Pb
Pc
4
lifeline similarity
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Discussion
discussion
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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’
Paz
discussion
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & 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
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
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Conclusions
conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity 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 & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity 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
conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
Outlook
conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
…first results
conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
nz west coast tourists
eat/drinkpetrolovernightcarbus
6 12 18 6
∑ edge similarity
∑ node similarity
Haast
Arthur’s Pass
Franz JosefFox
Pancake Rocks
spatialreference
temporalreference
conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
…first results
conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
R
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
A
W
I
R
Z
B
EL
RB B P XB B B
IB A B BB O F
N N
F A
E A
I X
P N
B Z
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
PB T B ZH Q
Q B C
B I B
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
B L
C V
P T
B T B I B
B P X B B
A B B O F
BA GB P X B B
BA GB P X B B
BA B P X B B
conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
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
R
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
A
W
I
R
Z
B
EL
RB B P XB B B
IB A B BB O F
N N
F A
E A
I X
P N
B Z
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
Q N C
B I B
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
B L
C V
P T
B T B I B
B P X B B
A B B O F
BA GB P X B B
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
conclusions & outlook
rationale dynamic analysis discussioncontext operators lifeline similarity conclusions & outlook
contacts
conclusions & outlook
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
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