Yu Luo* Andrea Presotto Lan Mu University of Georgia

Outlines Introduction Study Area and Data Methodology Result Summary

Introduction People have always been interested in moving trajectories around us. e.g. Bird migration, Ants routing, Bees Waggle Dance Study animals movement helps us better understand their cognition, such as memory and navigation Bar-tailed Godwit Migratory Routes

Introduction Lab 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, three females, three infants and six juveniles

Data Collection Black 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

Data Some unique features of the Data Difficulty in data-collection The 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 exploration data quantification, query and representation

Objectives To 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

Methodology Descriptive 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 Battys 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 t1 t2 x y

Results Descriptive Statistics Home range: 4.6km 2 Average Travel Length: m Average Sinuosity: Average Elevation: m Ranging from 759 – 911 m

Comparison between April and May Coincided with Pre-knowledge: More food, more energy Longer length More random search pattern, higher sinuosity and lower mean vector length But not obvious

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 17 th 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 time Image processing techniques Resolution: time scale Resample, Interpolation Pattern recognition TT- X? Other attributes can also be explored 2D space

Summary Tracking 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