Exploratory analysis of Spatio-temporal movement patterns of

Black Capuchin Monkeys in Brazil

Yu Luo*Andrea Presotto

Lan MuUniversity of Georgia

OutlinesIntroductionStudy Area and DataMethodologyResultSummary

IntroductionPeople have always been interested in moving

trajectories around us. e.g. Bird migration, Ant’s routing, Bee’s Waggle Dance

Study animals’ movement helps us better understand their cognition, such as memory and navigation

Bar-tailed Godwit Migratory Routes

IntroductionLab Constraint

The recent development of location-aware devices provides great opportunities:track the animals’ movement over large

spatial extent with great accuracy

But also challenges: the high-resolution GPS tracking produces

mass data Large data volume: short recording intervals Complex data structure: space, time, attributes

Any rules? Or any moving strategy?

This project…

Cebus nigritus:

Widely lived in Atlantic Forest in south-eastern Brazil and far north-eastern Argentina

The study group had 14 individuals, including one dominant male, one adult male, threefemales, three infants and six juveniles

Data CollectionBlack Capuchin movement data (2007)

Follow the objective group of monkeys and record the geographic coordinates at five-minute interval

Food patches along the routes

Environment Data: DEM, RS (CBERS),Hydrology

DataSome unique features of the Data

Difficulty in data-collectionThe study area is a deep forest, the low

visibility greatly increases the uncertainties of the monkeys’ movement

We got only one group of monkeys’ motion, we should be careful before making any conclusive statement

At this stage, this study focuses on data explorationdata quantification, query and representation

ObjectivesTo analyze the movement pattern of the black

capuchin monkey in Brazil based on the GPS-collected data

To develop better techniques to explore the mass data, with a focus on the temporal perspective

Integrate all the functions into a toolbox for primatologist or cognition scientist to explore the data

MethodologyDescriptive Statistics:

to get a general view of the monkeys’ movement

Exploratory Data Analysis:Explore the in-path attribute dynamics

Space-time Aquarium x and y for space, and z for time

Attribute Clock inspired by Michael Batty’s Rank Clock (Nature,2006) project temporal changes in the clock angle: time; radius: value data in this project suitable for this visualization

TT-plot

Transform 3d motion data to 2d representation by converting the spatial component to an inter-event distance matrix and adding a second time axis (Imfeld,2000)

For example, the TT- δ plot The x and y are both time, the value at the point

(t1 ,t2) is the distance δ between two locations Pt1 and Pt2.

If there is a zero value point, it implies that the moving object revisit the same location. Indicator of memory

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ResultsDescriptive Statistics

Home range: 4.6km2

Average Travel Length: 2042.379 mAverage Sinuosity: 4.846Average Elevation: 816.846m

Ranging from 759 – 911 m

Comparison between April and May

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Daily Movement Length (m)

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Sinuosity (length/beeline)

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Mean Vector Length (0,1)

Coincided with Pre-knowledge:

•More food, more energy• Longer length• More random search pattern,

higher sinuosity and lower mean vector length

•But not obvious

Null Hypothesis p-value Result

April Length < May Length 0.0001 reject

April Sinuosity < May Sinuosity 0.533 non-reject

April MVL > May MVL 0.094 non-reject

April TAMS < May TAMS 0.382 non-reject

Welch Two Sample t-test

Hypothesis test shows the activity pattern is not

obviously different between April and May.

The analysis of the in-path dynamics is necessary.

Exploratory data analysis

Space-time Aquarium

Attribute Clock

1.Attibute dynamics in April 17th

e.g.: elevation min: 781m max: 852m

2.Activity dynamics green: eating red : non-eating

3.Aggreated level 3 days paths overlay

Because monkeys stop frequently, some attributes are not continuous over space-time, such as velocity. If we still use line to connect the points:

Instead, use “transparent pies” to represent the time sequence and emphasize the stop period

We can overlap the data

The transparency shows how often the monkeys stop during that period

Lower Transparency, More Stops

TT- δ plot

Random Search Path

Oriented Path

Space and timeImage processing techniques

Resolution: time scale Resample, Interpolation

Pattern recognition

TT- X? Other attributes can also be explored

2D space

SummaryTracking the animals’ movement is a promising

way to study the animals’ behavior and cognition. But challenges such as complex data structure, temporal analysis need to addressed

The exploratory data analysis techniques presented in this project help us better understand the monkeys’ behavior pattern

Future work need to be done to model and simulate the cognition effects

Thank youAny Question?